case study supply chain uncertainty

Future-proofing the supply chain

Supply chains matter. The plumbing of global commerce has rarely been a topic of much discussion in newsrooms or boardrooms, but the past two years have pushed the subject to the top of the agenda. The COVID-19 crisis , postpandemic economic effects , and the ongoing conflict in Ukraine  have exposed the vulnerabilities of today’s global supply chains. They have also made heroes of the teams that keep products flowing in a complex, uncertain, and fast-changing environment . Supply chain leaders now find themselves in an unfamiliar position: they have the attention of top management and a mandate to make real change.

Forward-thinking chief supply chain officers (CSCOs) now have a once-in-a-generation opportunity to future-proof their supply chains. And they can do that by recognizing the three new priorities alongside the function’s traditional objectives of cost/capital, quality, and service 1 Employee safety, food safety, and employee retention are considered operational preconditions, not supply chain objectives. and redesigning their supply chains accordingly.

The first of these new priorities, resilience, addresses the challenges that have made supply chain a widespread topic of conversation. The second, agility, will equip companies with the ability to meet rapidly evolving, and increasingly volatile, customer and consumer needs. The third, sustainability, recognizes the key role that supply chains will play in the transition to a clean and socially just economy (Exhibit 1).

Boosting supply chain resilience

Supply chains have always been vulnerable to disruption . Prepandemic research by the McKinsey Global Institute found that, on average, companies experience a disruption of one to two months in duration every 3.7 years . In the consumer goods sector, for example, the financial fallout of these disruptions over a decade is likely to equal 30 percent of one year’s EBITDA.

Historical data also show that these costs are not inevitable. In 2011, Toyota suffered six months of reduced production following the devastating Tohoku earthquake and tsunami. But the carmaker revamped its production strategy, regionalized supply chains, and addressed supplier vulnerabilities. When another major earthquake hit Japan in April 2016, Toyota was able to resume production after only two weeks.

During the pandemic’s early stages, sportswear maker Nike accelerated a supply chain technology program that used radio frequency identification (RFID) technology to track products flowing through outsourced manufacturing operations. The company also used predictive-demand analytics to minimize the impact of store closures across China. By rerouting inventory from in-store to digital-sales channels and acting early to minimize excess inventory buildup across its network, the company was able to limit sales declines in the region to just 5 percent. Over the same period, major competitors suffered much more significant drops in sales.

Supply chain risk manifests at the intersection of vulnerability and exposure to unforeseen events (Exhibit 2). The first step in mitigating that risk is a clear understanding of the organization’s supply chain vulnerabilities. Which suppliers, processes, or facilities present potential single points of failure in the supply chain? Which critical inputs are at risk from shortages or price volatility?

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In 2021, for example, many companies in North America were affected by labor shortages  across their supply chain operations. Tackling those shortages has forced companies to be creative in their hiring and staffing strategy. One food distributor created regional floating labor pools of drivers, warehouse workers, and supervisors, recruiting staff in areas where they were more available and deploying them wherever they were most needed. Other companies are building their labor pipeline-management capabilities—from recruiting through retention—and sharpening their labor planning as well.

Today, most organizations lack effective systems to measure and monitor those vulnerabilities, and few have such visibility beyond their direct suppliers. In a 2021 McKinsey survey  of senior supply chain executives, just under half said they understood the location of their tier-one suppliers and the key risks those suppliers face. But only 2 percent could make the same claim about suppliers in the third tier and beyond. That matters because most disruptions originate in these deeper supply chain tiers.

Closing the industry’s current knowledge gaps will require it to increase its surveillance of supply chain participants and its understanding of the physical, financial, political, and social risks they may face (Exhibit 3). The complexity and diversity of supply chain risks require smart management tools, and leading companies are applying a range of new techniques, from digital alerting systems to track potential disruptive events to risk “heat maps” that help them focus their attention on high-risk regions and suppliers.

Companies will also need a management infrastructure to steer a proactive response to these risks. Such an infrastructure would include a dedicated team, headed by a senior leader, with the remit to identify, prioritize, and respond to vulnerabilities. Those responses might include structural changes to the supply chain, as well as the development of detailed contingency plans for disruptive events. Introducing resilience metrics into supply chain KPIs helps the whole organization to ensure supply chain design and execution decisions are made in a way that balances efficiency and vulnerability. And because supply chain risk is a continually moving target, the organization should conduct regular stress tests and reviews to ensure its resilience measures remain appropriate.

Increasing supply chain agility

Customer loyalty is no longer a given. During the COVID-19 pandemic, for example, 77 percent of US consumers changed stores, brands, or the way they shop . Much of that change was driven by necessity. People went online when they couldn’t access their regular stores, and two-thirds said that lack of availability was the primary reason for switching brands. The big winners of the crisis were companies, often the largest players, that could keep products flowing to their customers in a difficult operating environment.

In the postpandemic economy, established brands will face new challenges. As consumer-generated content replaces traditional brand marketing campaigns, companies have less control over the peaks or troughs of demand. Where a business might have once spent months preparing its supply chains for a carefully targeted promotional campaign, now a single viral video can bring attention from millions of consumers overnight. One consumer goods manufacturer experienced a surge in demand in 2020 after a video of a customer enjoying its product on a skateboard ride received millions of views and spawned dozens of imitators.

New players are disrupting retail channels too, widening available choices and creating space for smaller, independent manufacturers. While consumers opted for the security of big brands during the COVID-19 pandemic, a preference for smaller producers is rising, especially among younger cohorts. And the growth of comanufacturing businesses and third-party logistics (3PLs) organizations means new entrants can compete in consumer markets with fewer expensive manufacturing and supply chain assets.

For incumbents, the lesson is clear: move at the same speed as consumers. That means creating innovative products and brands that meet the changing needs of different consumer groups as those needs emerge. And it means greater skill in managing complex portfolios of brands with different market characteristics and delivering their products through multiple channels. These same pressures increasingly hold true for B2B businesses as well, as increased consumer product complexity and demand volatility trickle down the supply chain.

This fast-moving, fragmented, consumer-centric world will require a different sort of supply chain. Traditional supply chains sought to achieve stability and minimize costs. Future supply chains will need to be much more dynamic—and be able to predict, prepare, and respond to rapidly evolving demand and a continually changing product and channel mix. In short, supply chains will need to become agile .

The good news for CSCOs is that agility and resilience are highly complementary: an agile supply chain is inherently more resilient. To be truly effective, however, this agility would need to extend into R&D, procurement, planning, manufacturing, and logistics (Exhibit 4).

At the planning stage, for example, supply chain teams will need to work in a much more proactive way. As potential market opportunities are identified, the supply chain function can begin creating scenarios that are ready for implementation alongside the development of the new product or market offering. After launch, the use of advanced techniques for demand sensing and dynamic forecasting, aided by machine learning technologies, is set to become an essential part of day-to-day supply chain operations.

In supply chain execution, agility requires new capabilities and tools. Agile operations make extensive use of digital technologies in manufacturing, for example, and maximize the use of smart automation in both production and logistics settings. Unlike the rigid supply chain automation systems of the past, technologies such as collaborative robots and smart packaging machines are capable of faster changeovers and can handle a much wider range of products and shipment types.

The drive for agility may require companies to reassess make-versus-buy decisions. In manufacturing, for example, big players typically keep the production of their stable, high-volume products in-house, using comanufacturers for niche and special projects. Leading companies appear likely to invert this trend, investing in flexible core assets and skills that allow their own manufacturing to respond quickly to rapidly changing demands—and, in some cases, outsourcing stable, high-volume products to cost-advantaged external providers. In downstream logistics, meanwhile, greater use of 3PLs may become the most cost-effective way to increase asset flexibility and proximity to customers.

Agile supply chains will also need skilled, flexible people. An agile supply chain workforce is comfortable working with and alongside advanced technologies, and personnel may need a wider range of skills so they can move between tasks as business needs change. Accordingly, agile supply chains make use of agile teams and working methods, borrowing elements of the approach that have transformed flexibility, productivity, and quality in the software industry and beyond. Agile organizational principles are well-described elsewhere , but key elements of the approach include the use of tight-knit, cross-functional teams that work together to implement new concepts and solve difficult problems in short, incremental sprints. These principles are already gaining traction across a range of industries: one major consumer products manufacturer is using “flow to work” pools in its global support functions to dynamically allocate staff to projects, for example.

Digital twins: The art of the possible in product development and beyond

Achieving supply chain sustainability.

Post-COVID-19 consumers have become even more likely to prefer brands that offer robust sustainability credentials and a strong purpose, but industry surveys conducted in mid-2020 suggested that environmental, social, and governance (ESG) topics slipped down companies’ list of priorities during the pandemic. Big players are now making up for lost time. In 2021, 29 percent of companies included ESG metrics in their staff incentive plans, for example, a seven percentage-point uptick over the previous year.

Companies looking to avoid the increasing reputational, regulatory, and financial risks of poor ESG performance are being pressed to act. And as companies such as Henkel have shown, strong environmental actions are also delivering real operational results: a digital twin connects and benchmarks 30 factories and prescribes real-time sustainability actions, which over ten years have reduced energy consumption by almost 40 percent and waste by 20 percent.

The supply chain has a central role to play in the enterprise sustainability transformation. Of nine ESG initiatives highlighted by senior executives in a 2020 industry survey, most either involve the supply chain directly, or have significant implications for supply chain setups (Exhibit 5).

The foundation for an ESG-focused transformation is a clear understanding of the organization’s baseline impact. That would include, for example, quantification of the resources consumed and emissions generated by the company’s direct activities (Scopes 1 and 2) and by participants in its wider supply chain (Scope 3). This baseline allows an organization to identify the largest opportunities for improvement, helping it set challenging but realistic goals and timescales that can be communicated to external stakeholders. Capturing those improvements requires rigorous sustainability KPIs and changes from the shop floor to the boardroom, including optimized operating practices, an ESG focus in procurement decisions , and the adoption of more sustainable technologies in existing and planned manufacturing or logistics projects.

These new priorities of resilience, agility, and sustainability can’t be tacked on to existing supply chain setups. Realistically, they will need to be built in from the foundation and considered in every element of supply chain design, organization, and operation. For many companies, that will likely require a change in mindset from the top, with risk, agility, and sustainability KPIs considered alongside traditional ones focused on cost, capital usage, service, and quality. To excel in these six supply chain dimensions, workforce management and digital capabilities will be essential.

Jan Henrich is a senior partner in McKinsey’s Chicago office; Jason D. Li is an associate partner in the Toronto office; and Carolina Mazuera is an associate partner in the Miami office, where Fernando Perez is a partner.

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Managing supply chains in an uncertain world: challenges and solutions

Modern Supply Chain Research and Applications

Welcome to the virtual special issue of the  Modern Supply Chain Research and Applications !

The businesses and their supply chains were still struggling with the uncertainties that stemmed from the Trade War between the U.S. and China, the world’s largest economies, when another storm hit them in the form of COVID-19 pandemic. The pandemic emerged as a healthcare crisis, but soon it was identified as a cause of supply chain crises at a scale and scope that is unprecedented. Its impact has been felt not only on the supply of healthcare products but also of tourism, food, cleaning products, and countless other products and services (Laksito & Yudiarta, 2021; Mahmoudi et al., 2021).

Consequently, many businesses were facing existential threats and scrambling to survive in the short term (Wuest et al., 2020). This mega-crisis would be gone sooner or later, but it would leave behind a new world. It would be a world where not only the fear of new exogenous shocks would be real, but the brawl between globalization and trade protectionism is likely to give rise to new forms of business relationships and supply chain disruptions. For example, when the U.S. sanctions on Huawei prevented it from using American chips, the company accelerated the development of its own chips and emerged stronger by improving relations with local suppliers and partners. Last year, as the European Union imposed a ban on Pakistan International Airlines, the company announced a ‘historic reduction in fares’ to offset the damage through increased domestic operations. When the U.S. sanctioned the Nord Stream 2 pipeline project, a multinational gas pipeline to connect Russian supplies to European consumers via Germany, it was enough to scare away Swiss pipelaying firm Allseas Group SA and promoting speculations about project delays. It is evident that different companies respond to supply chain disruptions and uncertainty in different ways in light of their own strengths and limitations. Considering the uncertainty and unpredictability surrounding the business environment of today, studying the factors, both natural and human-made, which can contribute to supply chain uncertainty and disruptions, is an important endeavour with both short-term and long-term implications for suppliers and consumers. 

It was in this background the Call for Papers for released. The aim was to seek submissions that could highlight the need to include uncertainty in the way we manage our supply chains to make them more resilient to future uncertainties and risk. In response to our call, we received some submissions. From them only five manuscripts were accepted after a double-blind peer review. Also, the diversity that this special issue holds within itself is interesting. We hope readers would enjoy reading it.

Michael Wang and colleagues attempted to conceptualise reverse logistics uncertainty using supply chain uncertainty literature and presented the types of reverse logistics uncertainty in a triadic model (Wang et al., 2021). Prabal Barua and colleagues attempted to assess the sustainable value chain approaches for marketing channel development opportunities for agricultural products in coastal Bangladesh to combat climate change through an approach of community-based adaptation options (Barua et al., 2021). Thakshila Samarakkody attempted to optimize the business process of a green tea dealer, who is a key supply chain partner of the Sri Lankan tea industry. Most appropriate trips for each vehicle in multiple trip routing system was identified to minimize the total cost with respect to travelling distance (Samarakkody & Alagalla , 2021). Amir Rahimzadeh Dehaghani and colleagues developed a mathematical model for optimizing the blood supply chain network (Dehaghani et al., 2021). Dhruman Gohil and Shivangi Thakker showed how blockchain technology can enhance flexibility and agility in supply chain operations (Gohil & Thakker, 2021).

Guest Editors:

Saad Ahmed Javed,

School of Business, Nanjing University of Information Science and Technology, P. R. China.

Amin Mahmoudi,

School of Civil Engineering, Southeast University, P. R. China.

Mostafa Salari,

Department of Civil Engineering, University of Calgary, Canada

Seyed Farid Ghannadpour,

Department of Industrial Engineering, Iran University of Science and Technology, Iran.

References:

• Barua, P., Rahman, S.H. and Barua, M. (2021), "Sustainable management of agriculture products value chain in responses to climate change for South-Eastern coast of Bangladesh",  Modern Supply Chain Research and Applications , Vol. 3 No. 2, pp. 98-126.  https://doi.org/10.1108/MSCRA-07-2020-0020
• Dehaghani, A.R., Nawaz, M., Sultanie, R. and Quartey-Papafio, T.K. (2021), "Mathematical modeling for optimizing the blood supply chain network",  Modern Supply Chain Research and Applications , Vol. 3 No. 3, pp. 174-190.  https://doi.org/10.1108/MSCRA-09-2020-0024
• Gohil, D. and Thakker, S.V. (2021), "Blockchain-integrated technologies for solving supply chain challenges",  Modern Supply Chain Research and Applications , Vol. 3 No. 2, pp. 78-97.  https://doi.org/10.1108/MSCRA-10-2020-0028
• Laksito, I. Y., and Yudiarta, I.G.A. (2021). “Grey Forecasting of Inbound Tourism to Bali and Financial Loses from the COVID-19”, International Journal of Grey Systems, Vol. 1, No. 1, https://doi.org/10.52812/ijgs.17
• Mahmoudi, A., Javed, S.A., & Mardani, A. (2021). “Gresilient supplier selection through Fuzzy Ordinal Priority Approach: decision‑making in post‑COVID era”, Operations Management Research, https://doi.org/10.1007/s12063-021-00178-z
• Samarakkody, T. and Alagalla, H. (2021), "Optimizing the multiple trip vehicle routing plan for a licensee green tea dealer in Sri Lanka",  Modern Supply Chain Research and Applications , Vol. 3 No. 4, pp. 246-261.  https://doi.org/10.1108/MSCRA-10-2020-0027
• Wang, M., Wang, B. and Chan, R. (2021), "Reverse logistics uncertainty in a courier industry: a triadic model", Modern Supply Chain Research and Applications, Vol. 3 No. 1, pp. 56-73. https://doi.org/10.1108/MSCRA-10-2020-0026
• Wuest, T., Kusiak, A., Dai, T., and Tayur, S. R. (2020). “Impact of COVID-19 on Manufacturing and Supply Networks—The Case for AI-Inspired Digital Transformation.” SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.3593540

A comprehensive list of papers in the special issue “Managing Supply Chains in an Uncertain World: Challenges and Solutions” guest edited by Saad Ahmed Javed, Amin Mahmoudi, Mostafa Salari and Seyed Farid Ghannadpour  are as follows.

Table of Contents

Reverse logistics uncertainty in a courier industry: a triadic model Michael Wang, Bill Wang, Ricky Chan 

Blockchain-integrated technologies for solving supply chain challenges Dhruman Gohil, Shivangi Viral Thakker

Sustainable management of agriculture products value chain in responses to climate change for South-Eastern coast of Bangladesh Prabal Barua, Syed Hafizur Rahman, Maitri Barua

Mathematical modeling for optimizing the blood supply chain network Amir Rahimzadeh Dehaghani, Muhammad Nawaz, Rohullah Sultanie, Tawiah Kwatekwei Quartey-Papafio 

Optimizing the multiple trip vehicle routing plan for a licensee green tea dealer in Sri Lanka Thakshila Samarakkody, Heshan Alagalla

The publisher would like to take this opportunity to thank the guest editors and journal editors for their time and effort put into all papers.

Case Study: Managing Uncertainties

Andre Agassi, a legend in men’s tennis, lost his first three matches to Boris Becker in 1988-1989 . Agassi later won 10 of the remaining 11 contests against Becker in his career. Amazon.com, founded in 1994 by Jeff Bezos as an online bookstore, initially struggled to raise investor capital. Amazon eventually became one of the world’s most-valued companies. A Dutch sugar refinery, Cosun Beet Company, improved its yield management practices by offering sugar beet growers a low-code platform to manage their crop development . This helped the firm achieve sustainable growth.

In all these cases, corporations or people achieved a competitive edge by successfully managing the uncertainties facing them. They rationalized others’ behavior, acted preventively to reduce negative aspects of uncertainty, or invested in the jaws of it. However, many organizations still lack capabilities to manage uncertainties, struggling to deal with them. To address these issues, I investigate uncertainty through the lens of economics and link its findings to organizational strategies.

Uncertainty is a barrier between our knowledge and truth. When it widens, reality becomes less clear given the current state of knowledge. Uncertainty takes three different forms. The first is where decision-makers recognize the uncertain elements and their patterns very well. Yet, some variations may persist under some uncontrollable factors. Such known unknowns are referred to as truth uncertainty by economists. A second type features distorted knowns where uncertainty is deliberately created by decision-makers to induce some stakeholders to behave in a selected way, known in economics as epistemological uncertainty . The third case is unknown unknowns such that truth is not known by anybody, which is referred to as o ntological uncertainty . Organizations wisely strive to eliminate truth uncertainty, keep epistemological uncertainty at a low level to improve short-term profitability, and invest in ontological uncertainty to sustain long-term profitability.

Truth Uncertainty: “Known” Unknowns

Organizations create value through certain operational activities they have excelled over the years. While they can control such operations along several dimensions, some variations may still exist. Organizational knowledge might correctly identify the truth about an object or a working system. However, a high level of knowledge may not suffice to eliminate process variations, thus leading to truth uncertainty. Elimination of truth uncertainty helps organizations boost profits and reduce quality problems. If total elimination proves impossible, firms can still effectively manage truth uncertainty by employing traditional predictive and prescriptive analytics methods. For example, FMCG companies, such as Pepsi and Colgate, have used a combination of predictive and prescriptive methods to manage truth uncertainty .

There is no strategic value in carrying truth uncertainty. Firms can build organizational capabilities through digital transformation and advanced analytics to absorb uncertainties internally. Otherwise, they would consider paying others to bear it. For example, manufacturers use analytical models to exploit advance demand information and to predict customer demand accurately , helping them absorb demand uncertainty. If absorbing uncertainties internally is not viable, firms may exchange them with other supply chain parties. For instance, producers of commodity products exposed to large, protracted price volatility may eliminate this uncertainty by selling their products in advance via forward contracts. To convince buyers to accept these contracts, producers forego the upside potential of uncertainty when commodity prices rise. Here, the upside of price volatility comprises the cost of transferring the uncertainty to another. If both prediction and transfer are infeasible, organizations suffer from uncertainties and lose profits. In the end, companies have three options in dealing with truth uncertainty: (1) control it internally, (2) transfer it to other supply chain parties, or (3) suffer.

Epistemological Uncertainty: Distorted Unknowns

One of the biggest rivalries in men’s tennis featured Andre Agassi versus Boris Becker. These two stars faced each other 14 times. After losing the first three matches in 1988 and 1989, Agassi won the next eight in a row. He lost only once more to Becker after 1989. Andre’s dominance on the court was due to Becker’s facial ‘tell’ that Agassi described after retiring. In a conversation with journalists in 2017, Agassi disclosed: “if … he put his tongue in the middle of his lip, he was either serving up the middle or the body. But if he put it to the side, he was going to serve out wide.” When he eventually admitted this to Becker, Boris nearly fell off his chair: “I used to go home all the time and just tell my wife: it is like he reads my mind.” In sports, players create uncertainties to trick their foes, and no one expects, for example, Becker to signal where he intends to serve! Those working to resolve such competitive uncertainties attain more career titles and reputation, just as Andre Agassi won eight grand slams versus only six for Boris Becker.

If decision makers’ knowledge suffers distortion because of information inaccuracy or agency conflict, organizations become exposed to epistemological uncertainty. In the case of a tennis match, each player faces epistemological uncertainty created by his opponent. To manage epistemological uncertainty, each player must first rationalize the behavior of his opponent and then optimize his decision. This is exactly what Agassi did in his matches against Becker.

Organizations have incentives to create epistemological uncertainty. Nevertheless, it is not a sustainable strategy to keep epistemological uncertainty at a high level. Imagine a farmer aiming to sell 10 watermelons in the farmers’ market where he always offers the best price. However, less than half of his watermelons are expected to be ripe. Suppose a restauranteur has learned over weeks that only 40% of the farmer’s watermelons are ripe. Whenever the restauranteur attempts to buy watermelons from the farmer, he first asks which ones are ripe. If the farmer does not disclose any information, then the buyer opts out. If the farmer designates five watermelons of the ten, then the odds of selecting a ripe one improves. Here, the restauranteur would likely buy all five. In practice, organizations are free to determine how much to disclose (or not disclose) of their proprietary information with outside parties. They may create epistemological uncertainty that would, in turn, yield increasing profits in the short term. However, too much epistemological uncertainty might repel customers or cause retaliatory actions. Thus, epistemological uncertainty must be kept at low or moderate levels in practice.

To manage epistemological uncertainty with farmers, for example, crop processors have often used crop management systems . This makes the crop development and agriculture supply chain fully transparent for both processors and farmers, reducing epistemological uncertainty and avoiding retaliatory actions. However, processors do not necessarily have any incentive to share their market price trajectories with growers. Indeed, information of a potential price rise is best undisclosed with farmers in negotiating the optimal price for buying crops.

Ontological Uncertainty: “Unknown” Unknowns

The strategic importance of ontological uncertainty for organizations may be aptly expressed in a Chinese proverb: “There is no fish in clear water.” For example, the Amazon.com project was born into ontological uncertainty. When the online retail giant was founded, Jeff Bezos contacted investors to raise capital. However, investors asked him what the Internet was, and they were very sceptical about the future of Amazon . At that time, the future of online retail was highly exposed to ontological uncertainty. Amazon would not have achieved high growth over the years if the future of e-commerce had been well-predicted in the 1990s. In that case big players, such as Walmart, could have invested in developing a better online platform and prevented Amazon’s evolution from an online bookstore into a retail giant in the 2010s.

Ontological uncertainty makes it problematic for wealthy investors to identify and invest in key technologies that customers will value in the future. Here, smaller firms and start-ups fill this gap and achieve sustainable growth. Thus, organizations wisely invest amid ontological uncertainty to foster entrepreneurship and sustainable growth.

Case Questions

What would be potential outcomes of elimination of uncertainties for each uncertainty type?

How can analytical approaches be designed to deal with each type of uncertainty?

What are the threats of epistemological uncertainty in social media?

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Please note you do not have access to teaching notes, managing supply chain uncertainty with emerging ethical issues.

International Journal of Operations & Production Management

ISSN : 0144-3577

Article publication date: 3 October 2016

The purpose of this paper is to empirically investigate effective management strategies for 14 sources of supply chain uncertainty, with a particular emphasis on uncertainties or strategies that involve ethical issues.

Design/methodology/approach

Manufacturing strategy theory, underpinned by alignment and contingency theory, is used as the theoretical foundation. Multi-case study data are collected from 12 companies in the Indonesian food industry, including four focal manufacturers, four first-tier suppliers, and four first-tier customers (retailers).

Within the context of appropriately aligned management strategies to address 14 sources of uncertainty, three ethical issues are empirically identified: first, collusion amongst suppliers to ration supplies and increase prices; second, unethical influences on government policy; and third, “abuse” of power by large retailers at the expense of smaller competitors. Joint purchasing is argued to be a key strategy for combatting the first of these ethical issues.

Research limitations/implications

The study is limited to the Indonesian food industry, and so further research is needed in other cultures/contexts.

Practical implications

Management strategies that aim to reduce an uncertainty at its source lead to better overall supply chain performance than strategies that merely cope with uncertainty, which only have an impact on firm-level performance.

Social implications

The ethical issues identified have implications for fair negotiations between customers and suppliers.

Originality/value

This study is unique in its in-depth case study-based empirical investigation of the management of multiple supply chain uncertainties; and in its discussion of ethical issues in this context.

• Supply chain uncertainty
• Ethical purchasing
• Multi-case study
• Parallel interaction
• Supplier collusion
• Supply chain ethics

Simangunsong, E. , Hendry, L.C. and Stevenson, M. (2016), "Managing supply chain uncertainty with emerging ethical issues", International Journal of Operations & Production Management , Vol. 36 No. 10, pp. 1272-1307. https://doi.org/10.1108/IJOPM-12-2014-0599

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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Open Access

Peer-reviewed

Research Article

A multi-level multi-product supply chain network design of vegetables products considering costs of quality: A case study

Roles Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, IR

Roles Supervision

Affiliation Agricultural Garden, Yaman Avenue, Shahid Chamran Highway, Tehran, IR

Roles Conceptualization

• Sareh Khazaeli, 
• Ramazan Kalvandi, 
• Hadi Sahebi

• Published: September 3, 2024
• https://doi.org/10.1371/journal.pone.0303054
• Peer Review
• Reader Comments

Effective logistics management is crucial for the distribution of perishable agricultural products to ensure they reach customers in high-quality condition. This research examines an integrated, multi-echelon supply chain for perishable agricultural goods. The supply chain consists of four stages: supply, processing, storage, and customers. This study investigates the quality-related costs associated with product perishability to maximize supply chain profitability. Key factors considered include the network design, location of processing and distribution centers, the ability to process raw products to minimize post-harvest quality degradation, the option to sell the excess produce to a secondary market due to unpredictable yields, and the decision not to fulfill demand from distant customers where significant quality loss and price drops would be involved, instead diverting those products to the aforementioned secondary market. Quantitative methods and linear mathematical programming are employed to model and validate the proposed supply chain using actual data from a real-world case study on vegetable supply chains. The main contribution of this research is the incorporation of quality costs into the objective function, which allows the supply chain to prioritize meeting nearby customers’ demands with minimal quality loss over serving distant customers where high quality loss is unavoidable. Additionally, deploying a faster transportation fleet can significantly improve the overall profitability of the perishable product supply chain.

Citation: Khazaeli S, Kalvandi R, Sahebi H (2024) A multi-level multi-product supply chain network design of vegetables products considering costs of quality: A case study. PLoS ONE 19(9): e0303054. https://doi.org/10.1371/journal.pone.0303054

Editor: Md. Monirul Islam, Bangladesh Agricultural University, BANGLADESH

Received: September 8, 2023; Accepted: April 18, 2024; Published: September 3, 2024

Copyright: © 2024 Khazaeli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The most critical data are presented in Supporting Information files. All are not presented due to the high space they need. If there is no space limitation in the paper, it can be published.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Vegetables are perishable, edible, agricultural products that deteriorate during a limited shelf life [ 1 ]. Quality of perishable products is essential to the customer because such products deteriorate fast and endanger the consumer’s health [ 2 ]. There is a consensus in the literature on the reasons why people buy organic food; however, there is also a gap between the consumers’ generally positive attitude toward organic food and their relatively low level of actual purchases [ 3 ]. Quality of vegetables is one of the important measures to its customers due to the quality deterioration rate of products which relates to the health of consumers [ 2 ]. Time decay and shortages are common phenomena in products with short life cycles, and financial volatility necessitates a more accurate characterization of inventory costs based on time-adjusted value [ 4 ]. The supply chain management concept evolved when manufacturers experienced a strategic partnership with their direct suppliers. Then the logistics and transportation experts improved it one step forward and involved the distribution and transportation operations. Next, the concept of integrated logistics was recognized as the supply chain management [ 5 ]. Product quality is another novel concept in the supply chain management [ 6 ]. Moreover the quality deterioration often happens in traditional supply chains which, for the most part, are poorly planned [ 7 ]. From a product quality perspective, when processed products decay at a faster rate than raw materials, storing raw materials is favored [ 8 ]. Alternatively, when processing decreases the quality decay rate, a short time until processing is favored [ 9 ]. The supply chain (SC) of vegetables consists of four echelons: 1) purchasing raw materials, 2) processing, 3) distribution, and 4) customers to which products are delivered [ 10 ]. Since perishable products (agri-foods) have limited shelf life, logistic-related topics are important in business [ 11 ]. Transportation share in supply chain costs reached about 92% in the distribution sector in some traditional chains [ 12 ]. The post-harvest pre-customer-sent product loss [ 13 ] accounts for more than 40% of the supply chain costs even in industrialized and developed countries [ 14 ]. It occurs in terms of both the product quantity and agri-food quality loss throughout the chain and imposes quality costs on the chain [ 15 ]. Although considering the shelf life losses is in relation to an increase in transportation costs, it worth investing on transportation infrastructure due to less quality loss. Moreover, from a system’s point of view, integrating warehousing and transportation in the supply chain can highly affect the total cost, customer satisfaction and inventory level. Integrated models of providing and storing perishable products help to maximize meeting demands [ 11 ]. Integration of storing and distributing decisions leads to more efficiency than other operational integration [ 16 , 17 ]. Integration of strategic decision making and operational processes appears relevant, especially for such perishable products as agri-foods [ 18 ]. Recently some strategies were studied in supply chain management of perishable products to control the perishability of products which are inventory management [ 19 ], reverse logistic management [ 20 ], pricing [ 7 ], and robust optimization [ 21 ].

Notably, product quality is characterized by the product’s remaining shelf life and thus is time-dependent [ 22 ]. Taguchi described the deviation in performance using the quality loss function that measures the product’s quality loss in terms of the total loss to society due to functional variation and harmful side effects [ 23 ]. For perishable foods, product quality degradation must be identified because it significantly affects consumers’ decisions and retailer profitability [ 22 ]. On the other hand, computing the cost of quality loss for an integrated supply chain allows for exploring the interrelationships among business entities. It enables the supply chain to achieve a minimum total cost by investing in quality and, hence, increasing the overall benefit [ 24 ]. Today, lateral marketing is the most effective way of competing in mature/immature markets, where micro-segmentation and plenty of brands don’t leave any space for new opportunities [ 25 ]. One of problems in the perishable agricultural products’ supply chain is a high quality loss post-harvest, which leads to different quality costs and the customer dissatisfaction. A brief review of the literature reveals that rarely is there any established advanced multi-echelon vegetable supply chain wherein the profit is maximized by considering such features as product quality degradation, quality loss-related costs, and settling lateral markets. Due to this research gap, current study is aimed to maximize the profit of perishable products supply chain considering their related quality costs. The question in this research is how considering both the cost of qualities and the second market in the supply chain network design (SCND) of perishable products can affect the benefits of stakeholders, such as farmers and customers in the supply chain.

The research objective is to formulate a SCND of perishable products by considering different costs of qualities in the supply chain and settling a lateral market and processing the part of perishable products that have not entered the supply chain due to its high level of perishability and enters to the second market be used in specific form satisfying customers, in the mathematical mixed integer linear programming. The current study intends to make affecting decisions in different levels of decision making as: 1) strategic level; locating different centers in the supply chain, 2) tactical level; determining the processing type, and quantities of different products be delivered to the customers, and 3) operational level; selecting a suitable mode of transportation and quantities in the SCND. To address this challenging problem, vegetables, important perishable products, were examined in a case study by first studying the multi-echelon agri-food supply chain (AFSC) based on the post-harvest quality features.

The remainder of this paper is structured as follows: In the next section, a brief overview of related literature reviews on the quality management of perishable agricultural products is given. Section 3 describes the research methodology, a quantitative supply chain modeling approach in a linear programming framework. The case study and sensitivity analysis results in the optimum point are presented in Section 4, the research conclusions in Section 5, managerial implications in Section 6, and future research and limitations in Section 7.

2. Literature review

2.1. agricultural products supply chain.

Customers pay special attention to the quality and safety of agri-foods because they directly affect their health [ 26 ]. This quality can be measured by such different criteria as the purchasability [ 27 ], lifetime (day) left [ 28 ], color [ 29 ], freshness [ 30 ] and light-greenness of vegetables (L. in the Hunter Laboratory) [ 31 , 32 ]. Creating an efficiency-responsiveness balance in quality-based customer-oriented supply chains is worth considering [ 9 ]. The optimal operation strategy is acquired based on product quality [ 6 ]. Organizations that have instituted a system of quality cost measures have experienced dramatic positive results because it translates the implications of poor quality, activities of a quality program, and quality improvement efforts into a monetary language for managers to understand which factors are important in affecting profitability and the consumer need [ 24 ].

Decisions made in the supply chain of perishable products are strategic, tactical and, operational; strategic decisions that have long-term effects on firms are those made on the network design, supply chain network design [ 33 ] and the location of different equipment in the processing, distribution and, hub centers to make the best use of the capacity of the existing facilities [ 34 ]. In the strategic level of decision-making in the perishable products’ supply chain design, different ways to cope with increasing product quality decay can be identified. On the one hand, the network can be centralized to decrease handling time (for each transport to a hub, a fixed handling time is incorporated in the transport time) and hence decay. On the other hand, more hubs can be opened to decrease transport time and decay [ 9 ]. Moreover, technical models are popular and have public applications in harvest programming, product selection, and labor capacity in agricultural products supply chains. Besides strategic and tactical decisions, the supply chain also involves operational decisions for which it is assumed that the former two are already known and sufficient knowledge is available about production, demand, and transportation [ 35 ]. Pasha et al. studied an integrated bi-objective quality-based production-distribution agri-food MILP supply chain model in which profitability is maximized by defining the quality as a function of such decisions as the location of hubs and transportation strategy throughout the supply chain [ 17 ], whereas making decisions in an integrated way will reduce costs compared to individual decisions made at each level [ 36 , 37 ]. Moreover, in the greenery supply chains, De Keizer et al. presented a model in which decisions made on the greenhouse location (strategic) are based on the plant’s lifetime in that location [ 9 ]. As changes in the temperature and enthalpy levels change the food quality [ 38 ], Khazaeli et al. and Rong et. al determined the temperature of distribution centers and deliveries to meet the expectations of different customers as the operational decision-making in a supply chain management [ 39 , 40 ].

2.2. Quality of agricultural products

In most supply chain designs, cost, profit, quality, responsiveness and environment are the general decision-making factors [ 34 ]. Although cost and profit are still the main criteria in almost all quantitative mathematical programming models of the supply chain of perishable agricultural products, in recent years, other criteria, such as product quality [ 9 , 17 , 18 , 41 , 42 ] and environmental protection [ 43 ] have also been considered in some studies. The quality function of perishable agricultural products can be either complex or simple [ 44 ]. It has been shown that, the decrease of a single quality attribute of agricultural products can be approximated by one of the four basic types of mechanism which are zero-order reactions having linear kinetics, Michaelis Menten kinetics, first-order reactions having exponential kinetics, and autocatalytic reactions with logistic kinetics [ 45 , 46 ]. For the concept of keeping quality, it is convenient to assume zero-order reaction kinetics [ 28 ], and mostly the Michaelis Menten kinetics reduces to a linear one in the initial region of decay, which is the most important in quality assessment [ 47 ]. Therefore, the quality variable of vegetables in the initial region of decay can be considered in a widely used equation, in which the quality function changes by the time linearly. It is shown in Eq 1 .

Where, Q 0 is the initial quality, t is time and k is a degradation rate. In a dynamic environment, the well-known Arrhenius equation shows that the degradation rate (k) depends on the activation energy of the material, and the environmental factors [ 28 , 48 , 49 ].

The perishable products’ quality model shown in Eq 1 has been frequently used to capture the degradation of food products over time. For example, in the grocery retail chain, Wang and Li presented a pricing model to maximize food retailer’s profit in a dynamically identified food shelf life by using Eq 1 [ 50 ]. Chen and Chen proposed an on-site direct-sale dynamic supply chain inventory model, considering time-dependent quality losses for perishable foods [ 22 ]. Lejarza and Baldea presented a closed-loop, feedback-based control framework, that employs real-time product quality measurements for optimal supply chain management [ 51 ]. Moreover, Xu et al. presented a real time decision support framework to mitigate the quality degradation in the journey of agricultural perishable products from farm to the retailer in the supply chain based on the Eq 1 [ 52 ].

Generally, cost, benefit, and quality factors are the most important factors that are to be optimized in network designs. Mostly, agri-food should make a logical balance between two topics, which are the price reduction and the customer service improvement [ 38 ]. In the field of multi-objective supply chain network design, De Keizer et al. and Khazaeli et al. showed that, the quality of agricultural products causes cost in the supply chain’s network [ 18 , 39 ]. A review of quantitative supply chain research on the perishability of agri-food by considering related quality costs is summarized in Table 1 .

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2.3. Research gaps and contributions

Due to the importance and necessity of developing SCM from a larger perspective to provide a win-win situation for each participant in the supply chain, in this paper, we aim to develop a novel mathematical model to design a supply chain network, based on quality function elements in the vegetables’ sector. The summary of the literature review outlines the gaps in the literature as follows:

• Despite the importance of the cost of qualities in designing supply chains due to the perishability of the products, the cost of quality concept has not been widely incorporated by researchers in the design of agricultural products’ supply chains.
• No research has paid attention to the lateral market to look at the quality problems from the side covering some target customers.
• Few researchers have considered the benefits of several stakeholders of the agricultural supply chains simultaneously. The stakeholders in agricultural Products’ supply chain are consumers, farmers, the environment, and society.

The proposed SCND is a multi-product, multi-echelon model with exact (certain) demand that makes decisions at strategic, tactical, and operational levels. It has focused on “quality” by considering the quality deterioration which is time-dependent in the initial region of decay, moreover, by defining costs of quality degradation in the quality-cost functions. Features that differentiate the present research from others are displayed in the last row in Table 1 . As previous researches have demonstrated, traditional supply chain of agri-food is unstructured, which generally leads to low quality and low benefit of agricultural products, the presented research is developed, in which the main contributions are as follows:

• ✓ Providing a network design model for an integrated multi-level supply chain of perishable products wherein profit is optimized by considering quality decay aspect of the products.
• ✓ Optimizing the profit of the supply chain of perishable products considering different quality costs for them due to unmet demand, product waste and reduced revenue of low-quality products.
• ✓ Introducing a strategy of selling perishable products to lateral markets before letting products enter the chain to prevent the production of low-quality products along it.
• ✓ Enabling the purchase of the farmer’s total agricultural product above the contract ceiling due to unpredictable production to prevent waste production and its scattering in the environment.
• ✓ Introducing a strategy of producing semi-processed, low-quality products (from those that did not enter the chain) to meet part of the market demand for lower-quality lower-price products.

The developed model is a four-echelon supply chain of perishable agricultural products in which the time-dependent quality of the products is considered. In addition, a lateral market is considered in the designed supply chain that does not stand higher than vertical marketing and completes the primary market.

In the end, the developed model is applied to a case study of a firm in the agricultural products industry with four echelons of farm-processing-distribution-customer centers. The vegetables selected as candidates for the present supply chain network design are Yarrow , Borage flower , and Melisa , due to their priority in agricultural studies and their application in various industries [ 55 ].

Although there are some studies done to minimize quality losses of perishable products by multi-objective problem-solving approaches [ 17 , 19 , 20 , 21 , 39 ], the programming in the present research is done as a single objective problem solving by profit objective function underlying quality loss costs.

3. Problem description and formulation

From the perspective of the research approach, this research is quantitative, done as a mathematical mixed integer linear programming (MILP) modeling with the objective function of profit by considering the cost of quality factors of products in the multi-echelon perishable products’ supply chain. It is applicable to the related supply chains. It focuses on an integrated multi-product SCND of agricultural products that provides, processes, stores and distributes materials. It considers customer demands and sells the farmers’ in-excess products to the second market. The designed model was solved using GAMS 24.1.2 software by exact solution method by epsilon-constraint. The model is validated by applying it in the case study of a multi-vegetable supply chain of a firm in a fertile area in Iran country. The designed supply chain of the firm is shown in Fig 1 .

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First, products through related contracts and in-excess products are bought from farmers in the study area. In the second echelon of the proposed supply chain, some or all of the purchased products are processed at related centers resulting in different degrees of product quality. Third, the products in the former echelon are stored in cool storage centers until being distributed and fourth, they are sold to wholesalers. Another part of the purchased products are transferred to the second market as lower quality products in different industries (tea bags, spices in food, etc.). Different road modes of transport are used between different echelons of the supply chain.

The modeling makes decisions at different echelons of the supply chain. Decisions made are (1) selecting farms and the quantity of raw products to be purchased from each of them, (2) the quantity of products sold to the second market, (3) the number of processing and storage facilities to be settled in the supply chain, (4) product flow and the vehicles to carry out the transportation between the active facilities i. e. from farms to wholesalers and (5) assignment of processing facilities to the products. They are made based on minimizing the total cost of the supply chain design considering the cost of qualities. In the following, assumptions and the modeling are described.

3.1. Assumptions

• The location of production centers is specified.
• Capacities of the processing centers, and also storage centers are determined.
• Customer demand for each type of processed product is pre-determined.
• Shortage to customer demand is allowed.
• The quality of products post-harvest in the supply chain is considered time-dependent.
• The deterioration rate of each product is considered specific, based on the activation energy of the material.
• The approach of quality costs is considered in measuring the quality of products in the objective function modeling.
• The transportation speed of each mode is assumed uniform.
• In-excess products are sold to the second market.
• Over-time quality loss-related cost, unmet customer demand and product waste are considered as quality costs.
• The cost of the lost product quality equals the price drop in proportion to the quality drop by a factor of ten (The coefficient (10) is proposed by experts based on pairwise comparisons of cost and quality criteria).
• The quality cost of the customer credit for each demand equals the revenue lost due to not meeting one unit demand.
• The quality cost of the product waste equals the revenue from the product sales not realized, causing that product to enter the environment as waste.
• Products are bought from farmers: 1) at a price for first-grade products based on the amount in the contract and 2) at a price for second-grade products for those over that in the contract (According to experts, the purchase-price-drop coefficient is 0.3 in the market).
• Products are sold to the supply chain customers at a price for first-grade products and those outside the supply chain are sold in the second market at a price for second-grade products (According to experts, the sell-price-drop coefficient is 0.3 in the market).

The mathematical model, its objective and its constraints are presented in the following.

3.2. Mathematical modelling

Symptoms used in the model consist of sets, related indexes, parameters and variables, objective functions and constraints, are as follows:

Sets and indexes.

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Parameters.

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Profit objective function and constraints are described as follows:

Profit objective function.

The objective function is defined to maximize the supply chain profit. It is equal to the revenue from both, selling products to customers and the second market minus the total supply chain and quality costs ( Eq 2 ).

Revenue consists of: 1) that obtained by selling the supplied demanded product, which is equal to the unit price of the sold product multiplied by the customer met demand; the latter equals the amount supplied in the supply chain minus that over the customer demand, and 2) that obtained by selling: a) the supply chain-decided products and b) in-excess products sent to the second market which is equal to the price of each unit of the low-quality product multiplied by the amounts in a and b.

Costs relate to: 1) purchasing high-quality (on contract) and low-quality (in-excess) products from farmers (with their own related prices), 2) locating processing and storage centers, 3) processing operations, 4) storing products in storage centers, 5) different supply chain distances (ton-km), 6) ordering different transportation modes, 7) revenue lost due to reduced product quality, 8) credit lost due to unmet demand and 9) unsold wasted product.

Constraints

Quantities equations..

Constraints (3) to (10) ensure the product weight in different supply chain steps—from the farm to the customer (considering the amount of the farm production). Constraint (11) addresses in-excess low-grade products to be sold in the second market; these are produced, but not delivered to customers through the supply chain for different reasons.

Constraints (12) to (15) indicate that quantities of processed and stored products, respectively, in activated processing and storage centers are determined based on the capacities of these centers. If centers are not active, the quantities would be zero.

Travel time in the supply chain equation.

Constraint (16) indicates that the vehicles used in the transportation system of the supply chain have uniform speeds.

Number of vehicles.

Constraint (17) to (20) determines needed vehicles in different modes to transfer materials in different supply chain steps and the whole supply chain assuming full-capacity active vehicles.

Shortage and extra quantities constraints.

Constraints (21) to (23) determine the in-excess and shortage amounts.

Constraints (24) and (25) illustrate non-negativity and binary variables.

4. Case study

In this section, we implement the proposed model in an Iranian raw and processed vegetable products’ company, the Razian Company, as a case study. Iran country has been bestowed with a wide range of climate and physio-geographical conditions and as such is most suitable for growing various kinds of vegetables, its production of vegetables is increasing. Moreover, agricultural products are profitable fields for investment. Since Iran possesses a large variety of flora with manufacturers, in equal measure, analysis of the working of the vegetable market is critical [ 55 ]. There is an apparent shortage of related supply chain in Iran country. The goal of the case study is to evaluate the efficacy of the proposed model under real-world conditions and to address the needs of the firm in question. The case study used a four-echelon SCND, and materials were supplied, processed, and stored (echelons 1–3) in the firm area (origin) while the last-level centers were located all over the country; in addition, a center was established as a second market to collect the in-excess products, as shown in Fig 1 . The mentioned lateral market imposes no costs on the supply chain because it is closest to farms, and customers pay the transportation costs.

At first, the firm seasonally provided the vegetables from the suppliers. Suppliers were specified and contracted in advance in fertilized source centers (i = 4) of selected vegetables (n = 3). The farm centers were, in Kaboudrahang , Razan , Nahavand , and Malayer , and the vegetable products were Yarrow , Borage flower , and Melisa . Secondly, the firm used the related processing on vegetables, or the products remained raw. There are potential processing center (j = 5) candidates in the case study. Thirdly, the firm stored the products in the storage centers for packaging. There are potential storage center (k = 5) candidates in the case study. The five potential processing and storage center candidates were Kaboudrahang , Razan , Nahavand , Malayer , and Asadabad . Finally, the firm delivered the demanded products to the customer centers. The customers were trade representatives of each province all over the country (l = 30). Due to the importance of the case study data for the application of the presented model, some were obtained from the enterprise resource planning (ERP) of Razian company [ 56 ]. In addition, data on fixed and variable costs of different transportation modes were obtained from the recent case study research done in Iran [ 39 ]. Data on the price of different raw and processed vegetable products were gathered from the statistics of the Ministry of Agriculture [ 57 ]. Details of the most critical data of the case study are presented in the table in S1 Table in the supporting information.

The designed mathematical mixed integer linear programming (MILP) model was implemented and solved using GAMS 24.1.2 software and an Intel 2.13-GHz processor by exact solution method by epsilon-constraint. The designed network, product type, amount (tons) produced and sent to, e.g., Tehran (Capital), the transportation mode at different supply chain levels, and amount (tons) delivered to the second market are shown in Fig 2 .

(R indicates Yarrow product, C indicates Borage flower product, T indicates Melisa product). Optimally, 564 trailers and 33068 trucks were needed in the designed supply chain network. Generally, in presented agricultural products’ supply chain, some products have high quality-loss rates as well as demands for distances far from the cultivation center. This leads to a long post-harvest time for the product to reach the customer and, hence, a high rate of quality loss and a drop in the product price. This fact makes the supply chain decision maker set the lateral market due to not delivering those products to those customers and hence delivering them to the second market. It is considered newly in the present research due to make quality loss of products in the supply chain, the less, hence the profit the more.

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4.1. Results

As shown in Fig 2 , in the optimum point of maximizing profit by considering quality costs in perishable vegetables supply chain in the proposed MILP model, the processing, storage, and distribution centers are settled in similar locations, spatially. It leads to set process-storage-transfer type of hub centers, in compliance with the supply chain network proposed by Khazaeli et al. [ 39 ]. Out of 5 potential processing and storage/transfer centers, the model found all for the supply chain No. of facilities based on the center capacity and its setup costs (related parameters are listed in the table in S1 Table ). It is similar to the model proposed by De Keizer et al., in which to decrease transport time and hence decay in related supply chain design, the centers were decentralized, [ 9 ]. Therefore, more hubs were opened.

The model determined the amount and type of the delivered products between all supply chain levels, by the supply chain programming, and provided the information on the product (ton) if it was possible to supply to meet the customer demand. The details of provided products are presented in the table in S2 Table in the supporting file. Here, the supply chain management decides not to offer part of products to the customer and sells them at a second-grade price to the second market to maximize the chain profit by minimizing the quality loss-related cost along the chain (highlighted as unmet demands in the table in S2 Table ). In such a case, saving the low-quality cost of the perishable product will bring more revenue for the chain.

The model also selected the center-to-center transportation mode considering the vehicle speed to reduce time and, hence, the quality degradation and transportation costs. The table in S2 Table in the supporting file lists the number of each vehicle type required to transfer products. In result, the supply chain used trucks about 60 times more than trailers because of being faster. It used trailers, although with higher order costs, only in long distances, e.g., from storage centers to customer centers due to their more than ten times more capacity than trucks which led to fewer vehicle orders and, hence, less vehicle order costs. As shown in Fig 2 , in all supply chain steps, except the last, the model suggests using trucks because of their higher speed than trailers and their less order costs than trailers (The vehicle-related parameters are shown in the table in S1 Table ).

In this chain, some produced, but supply chain-decided undelivered to the supply chain were sold to the second market with price of high-grade products. The products produced more than that guaranteed in the farmer’s purchase contract, were sold to the second market with a much cheaper price (0.3 that of high-grade products). Both, amounted to 1820 tons of product Yarrow in Razan , 10020 tons of product Borage flower in Nahavand and 93.5 tons of product Melisa in Malayer and Nahavand , all were delivered to the second market.

Demands for all types of products were met except for fresh products, for which the demands were responded in centers closer to the previous echelon due, maybe, to their higher corruptibility and quality-loss rate than other types of products (The table in S1 Table in the supporting information lists the perishability rate of each processed products than the fresh one) and, hence, a price decline that makes them uneconomical to deliver to customers.

4.2. Benefit and quality loss of the products in the supply chain

In the designed supply chain, as shown for the optimum solution point in Fig 3A , the revenue and total cost are, respectively, 27.3 and 18.5 million USD; therefore, the benefit is 8.8 million USD. The final product quality and quality loss in the supply chain are 28,357 and 643 (Unit of quality), respectively ( Fig 3B ).

(a). Profit/ cost of the SC designed. (b). Final quality/ quality losses in the SC design.

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The revenue of the supply chain (27.3 million USD) is due to: 1) selling the chain-demanded supplied products 21.9 (Million USD), 2) selling products not supplied to the chain and sold to the secondary market based on the chain management decision 0.08 (Million USD) and 3) selling products supplied more than that specified in the contract 5.32 to the secondary market (Million USD) ( Fig 4A ).

(a). Supply chain revenue parts. (b). Farmers’ revenue parts.

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The revenue of farmers as main stakeholders, is 12.5 million USD, which goes to them by selling: 1) contract-demanded products delivered to the supply chain (10.2 million USD), 2) contract-demanded products supply chain-decided undelivered products (2.07 million USD) and 3) in-excess-of-contract products to the second market (0.24 million USD) ( Fig 4B ).

Total supply chain benefit (8.8 million USD) comes from supplying products to customers considering the demand (5.7 million USD) and products to the second market (3.1 million USD). In addition, the total revenue of farmers is (12.5 million USD) ( Fig 5 ).

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4.3. Supply chain cost breakdown considering quality cost and other supply chain costs

The supply chain cost (18.5 million USD) consists of 8 elements, among which purchasing, including buying raw materials for the supply chain (10.2 million USD) and in-excess materials (2.3 million USD) for selling to the second market, is the costliest, and revenue lost due to reduced product quality along the chain (5.4 million USD) stand next. Other costs in the case studied, in the order of higher values, include quality cost of unmet demand of fresh products in long distances (0.38 million USD), processing (0.1 million USD), logistic transportation (0.07 million USD), storage (0.03 million USD), establishing facility centers (0.02 million USD); product waste has zero cost. The percent share of total costs, including those of the network, supply chain logistics and quality costs is shown in Fig 6 .

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As shown in Fig 6 , 29% of the costs (5.4 million USD) in the supply chain of perishable product supply relate to the revenue lost due to the product quality loss by unmet fresh products. On the other hand, the quality cost of unmet demand for fresh products in long distances is 0.38 million USD. The most part of the mentioned costs are compensated by revenue earned by selling these products to the second market by 5.32 million USD.

The designed supply chain has other profits, which are: 1) preventing low-quality products from being produced at the request of the chain customers and 2) sending products produced over that specified in the contract (due to unpredicted agricultural products produced) to the secondary market and, hence, preventing them from entering the environment as waste.

The model accuracy was verified by changing its parameters and examining its responses to the changes. The validity of the proposed model has also been confirmed by comparing the results of the present SCND, with a vicinity secondary market ( Fig 2 ), and those of the existing chain, without such a market. Related experts have evaluated the proposed model, validated it, and concluded that the chain profit has increased due to its reduced quality costs. The sensitivity analysis is presented to evaluate the effect of changing some parameters on variables and the objective function, in the following.

4.4. Sensitivity analysis

Parameters to which model responses investigated in reaction, are the reaction rate of products and speed of different transportation modes as they relate to the quality loss of products and cost of supply chain during the time after harvest. Model responses to changes have been analyzed and explained orderly in the following:

Quantity of products and revenue versus reaction rate (k) of products.

The quality loss rate (k) of different products varies depending on their reactivity, and processing reduces this rate in fresh products. To prevent the quality cost resulting from the products’ quality loss and price decline, the chain provides just part of the fresh product demands, not far than a specific distance (The table in S2 Table in the supporting information). When the quality loss rate (k) changes, the amount of the customer-demanded met products as well as those not enter the chain change too; the latter are processed at the beginning of the chain immediately after they are purchased and then sold as low-grade products to the second market. The ratio of the customer-offered to customer demand for different types of products and the amount sold to the second market were examined considering the product quality loss rate (k). The effects of the quality loss rate (k) on the stakeholders’ profit and revenue have also been studied. A summary of the results is shown in Fig 7 .

(a) Changes in quantities of products. (b) Changes in revenue/ profit of farmers and SC parts.

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In the current chain, 96% of the demand for fresh products is met, and the rest is sold to the second market. As shown in Fig (7a), an increase in the quality loss rate (k) reduces the amount of fresh products. It increases the amount of those sold to the second market and supplied before entering the chain due to a sharp drop in fresh products, undesirability for customers, quality loss and price drop in the chain over time. A more increase in the mentioned rate (twice more) reduces the meeting rate of the customer-demanded fresh product from 96% to 35%; products sold to the second market increase from 64% to 100%, and the processed, dried and essence products, fully met, remain unchanged. Moreover, as shown in Fig (7b), an increase in the rate of product quality loss (k) does not reduce the farmer revenue, because the contract-specified products are bought from farmers at the original price.

As shown in Fig (7b), an increase in the quality-loss rate (k) of perishable products reduces the chain profit because some of these products, purchased from the farmer at the original contract price, do not enter the chain and are sold in the secondary market at lower prices (here, 0.3 times the contract price). Therefore, considering higher quality-loss rates (k) in the SCND will result in sharper reduced profits for the supply chain and the secondary market.

Supply chain cost/revenue versus speed of vehicles (v) changes.

Under present conditions and the speed (v) of the current fleet in the case study (V trailer = 80 and V truck = 100 (km/ hour)), the model meets 96% of the demand for fresh products and all that for the dried and essence products; Faster fleet speeds enable more demands to be met ( Fig 8A ).

(a) Change in quantities of products (b). Change in number of vehicles (c). Change in cost/ revenue.

https://doi.org/10.1371/journal.pone.0303054.g008

Increasing the speed (v) up to 50% will help the demand for fresh products to be met up to 100% and that for other products stays constant at 100%; however, reducing it up to 80% will not change the amount of processed products, but will cause the amount of the freshly supplied products to reach about 20% ( Fig 8a ).

As shown in Fig (8b), increasing the speed (v) leads to more use of faster vehicles (here, trucks). As shown, increasing the speed (v) to 100% will increase the number of needed trucks by 2%, but will not change the number of needed trailers. As mentioned earlier, trailers are used for outside-province long distances to respond to customers located far from the supply center. This will result in lower total long-distance transportation costs than trucks due to lower ton-km costs despite higher-order costs (The table in S1 Table in the supporting file).

Fig (8c) shows the minor increases in transportation costs and a noticeable reduction in the unmet-demand lost revenue due to the increased vehicle speed (v). Increasing the speed (v) up to 100% will increase the transportation costs by 11%, but reduces the unmet-demand lost revenue by 100%. This increased transportation cost of 0.008 million USD will prevent a revenue loss of 0.34 million USD, which is quite a significant figure.

It demonstrates that increasing the speed (v) will increase the number of vehicles, hence increase the transportation costs and the responded demand and ultimately prevent the revenue loss. Hence, increasing the speed (v) will lead to increased costs and enhanced chain revenue ( Fig 9 ).

https://doi.org/10.1371/journal.pone.0303054.g009

Since the increased revenue is greater than the increased cost, increasing the speed (v) will increase the chain profit; increasing the speed (v) up to 100% will increase the profit by 2.9 million USD (increased by 100%). Increasing the speed (v) will not affect the farmers’ revenue. The results comply with the findings of Patidar and Agrawal in research on traditional agricultural chains in India, in which the transportation share in supply chain costs reached about 92% in the distribution sector [ 12 ]. It shows the importance of transportation strategies in this sector.

A comparison of designed supply chain with traditional supply chain in the case study is demonstrated in Fig 10 .

https://doi.org/10.1371/journal.pone.0303054.g010

Regarding demands for fresh products, as shown in Fig 10 , their amounts in the two cases (with and without a secondary market) are 10868 and 10068 tons, respectively, showing an increase of about 0.08 times; this leads to a quality increase and, hence, customer satisfaction and profit increase. In both cases, demands for dry and essence products are fully satisfied. The comparison between the results of the present supply chain design in the case study and the results with the lateral market indicates that a lateral market in the supply chain will increase the chain profit and farmer income. However, in the optimal mode in this case study, they are increasing from 9.7 and 5.85 to 12.5 (about +50%) and 8.8 (about +20%), respectively.

The results show that newly designed supply chain is applicable in the field of the perishable products supply chain. It confirms the necessity of supplying innovative products of perishable ones such as processed agricultural products to meet new customer needs in a lateral market to the competitiveness. It complies with the findings of Malynka and Perevozova, who proposed the lateral markets in mature and immature markets in the brand creation process [ 25 ].

4.5. Managerial insight

Some lessons and insights for managers are as follows.

• All of the contract products are supposed to enter the chain; if not (for different reasons, e.g., chain management decision), some are sent to the second market, the presence of which prevents the produced products and resources (land, labor, energy, etc.) spent for those not entering the chain (for different reasons) from being wasted.
• Not considering a second market for fresh perishable agricultural products e. g., vegetables will lead to ignoring the post-harvest quality loss-related costs.
• Increasing the fleet speed is of great benefit to all the chain stakeholders including customers, chain management and environment because, on the one hand, it leads to increased response to the customer demand for more fresh products and selling customer-demanded high-quality products at prices proportionate to their grades will increase the chain profit and, hence, the total revenue, on the other, it prevents environmental pollution by letting more products to enter the supply chain and preventing wastes to be generated.

5. Conclusions

In this paper, a new approach is presented to optimize a logistics network design for distributing multiple products that are highly perishable and sensitive in quality and health of products to consumers, such as vegetables. Echelons of supply chain design include supply, processing, storage and customer. Considering the unpredictable amount of production of agricultural products and their perishability post-harvest, the second market which is accompanied by processing technologies to produce innovative products from the perishable products has been considered in the related supply chain network design, beside the main chain. The supply chain network design has been done based on maximization of profit by considering different quality costs in the supply chain. Quality costs include those due to: 1) quality-loss price-drop, 2) product waste and 3) losing credit with the customer for not meeting the desired demand. Since the chain integrity of these types of products is essential, the integrated one considered in this study is managed by the chain management deployed in the product supply center. Programming has been done based on the maximization of profit by the MILP model considering the quality costs of products in the supply chain. To evaluate the modeling, a case study was used on three vegetables cultivated and harvested in a fertile area in Iran country in September 2023. The model was subsequently validated by multiple sensitivity analyses performed on some of the essential parameters that had a greater effect on the results.

In this supply chain design, as it is demonstrated in Fig 2 , different decisions have been made at strategic, tactical and operational levels in order to maximize profit by considering the costs of quality in the supply chain. Decisions made are on the location of processing centers and storage centers, and product flow allocations in the designed supply chain. Moreover, the model decides on the operations of processing after harvest, such as drying and extracting, which leads to mitigated products’ quality decay. In the next echelon after processing in the supply chain, there are storage centers in which products are stored to be distributed to the retailers. In the tactical level of decision making, the presented model decides on the allocation of farmers to processing centers and also processing centers to storage centers, moreover the allocation of storage centers to the retailers as the customers, also the number of products produced by farmers enters the supply chain and remains to be supplied to the second market and not deployed in the supply chain is determined. In the operational level of decision-making, the quantity of products and mode of transportation between different levels in the supply chain have been determined to meet the customers’ needs.

Results of this research were compared with those of related recent studies [ 9 , 12 , 25 , 39 ]. The comparisons demonstrated good conformity, especially, in compliance with recent research in lateral besides vertical markets [ 25 ]. It seems innovative second markets are required to meet other parts of demand. Settling the lateral market seems strategic, especially in perishable products. The lateral market regulates supply and demand and helps reduce the quality-loss-related costs of the chain and responds to another part of the market that has specific customers.

6. Managerial implications

The proposed model is generic and can help managers in food quality, customer service, and other related operations as a tool to assist in decision-making in the perishable agricultural products supply chain. Specially, the research done can have the following applications:

• The decisions stemming from the presented model are determined based on the products’ degradation pattern to maximize its quality. The decisions include supplier selection, supply chain design, processing technology deployment, and vehicle deployment.
• A second market besides the chain and not higher than the vertical one in supply-based products such as vegetables, may result in a considerable increase in the chain profit without changing the resources, no reduction in the farmer income for unpredictable amounts of agricultural products production, and no wasted products preventing the environment from being polluted.
• The usage of lateral marketing is relevant, as it is the most effective way of competition in mature markets. However, when chains are designed for perishable products for optimum profit, the demand for some products with high quality-loss rates is not met due too long distances from distribution centers (if it is met, high-quality costs will be imposed on the chain). The related products are processed for secondary customers and delivered to them in the second market.
• Increased perishability rate of agricultural products reveals the effects and necessity of second markets next to the chain.
• Although, high-speed shipping fleets are expensive, using them will increase the chain profit because they reduce the post-harvest travel time and, hence, reduce the quality-loss-related costs of perishable products significantly. This way, the demands of more customers are met, customer credit costs will be prevented and the supply chain management and customers will both be benefitted. By applying the proposed model in the perishable agricultural products supply chain, the products are sold in the second market to meet the lateral part of the market.

As a result, different stakeholders such as farmers, customers, the environment, and the owner of the supply chain may benefit from the new supply chain network design.

7. Future research and limitations

Our framework is limited in some respects. With that said, this modeling limitations serve as a platform for extending it in future researches. One primary limitation of the presented model is that it does not consider the uncertainty in the amount of customers’ demand. Therefore, the proposed model does not work for the problem in uncertain conditions. Also, the proposed model in this research has been solved by the exact-type solving method of mathematical programming, which is proper for solving the small size of problems such as the studied case. Considering the limitations above, using mathematical models by uncertainty considerations in the supply chain parameters and applying meta-heuristic methods to solve medium and large-sized problems are suggested in the future research. From the managerial perspective, the presented research works by the assumption of that upstream suppliers, freight transportation, processing centers, and storage facilities are integrated and it needs to build alignment between their organizations to deploy the solutions proposed by the output of the proposed framework. For these efforts to be successful, for future research, it is suggested to study how to cooperate all parties involved in the supply chain, and design the coordination infrastructure in the supply chain to yield the positive effects of proposed supply chain network design, in practice.

Supporting information

S1 table. parameters of case study network design..

https://doi.org/10.1371/journal.pone.0303054.s001

S2 Table. Quantity (tons) of each product delivered to customers and number of vehicles in logistics of designed SC.

https://doi.org/10.1371/journal.pone.0303054.s002

Acknowledgments

The authors are indebted to Mr. Ja’fary, the manager of “ Razian” Co. ( https://razian.co/ ), for his invaluable help to gather data in the case study. Also, the authors are grateful to the two anonymous referees for their valuable comments, which have led to significant improvements in this paper.

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• The future of automotive supply chains: Connected, collaborative and resilient

Automotive supply chains are being reshaped by groundbreaking technologies and the continual evolution of consumer demands driving changes globally.

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• Neeraj Bansal , Partner |

In the fast-evolving global marketplace, automotive supply chains are being reshaped by groundbreaking technologies and the continual evolution of consumer demands, driving significant and transformative changes globally.  

Today’s cars are becoming computers on wheels, and given the intricacies involved in creating such sophisticated products—such as sourcing thousands of components from several suppliers and ensuring timely delivery to meet market expectations—building a seamless, transparent and visible supply chain ecosystem becomes a major priority. This is particularly crucial in a rapidly evolving economy like India, which is the world’s third-largest automobile market.

So, let’s explore the key supply chain trends that are dominating boardroom discussions today, which are poised to reshape the automobile industry of tomorrow.

1. Shifting gears with seamless tech integration

Building upon lessons learnt during the pandemic, automakers are rapidly embracing digital transformation to create a more agile, responsive and efficient supply chain ecosystem. Today, emerging technologies, such as AI, cloud computing, IoT, advanced analytics and blockchain, are being widely implemented to reshape automotive supply chain processes. As the industry continues navigating through volatilities, OEMs are now building a more collaborative ecosystem with their suppliers, using technology to anticipate disruptions, identify complexities and develop innovative solutions. For instance, leading Indian automakers are leveraging IoT devices to deploy real-time tracking and predictive maintenance systems to monitor procurement activities and delivery schedules, seamlessly manage inventories and preempt equipment failures.

Besides, Industry 4.0 advancements are resulting in smart and interconnected factories and warehouses that incorporate innovations, such as digital twins, transforming the entire supply chain. Gen AI—the hottest topic in technology—is also expected to bring substantial shifts in supply chain operations. A recent survey reveals that about 94 per cent of automotive stakeholders are considering implementing Gen AI. 1 This trend is expected to continue with the global GenAI in automotive market size projected to grow at a CAGR of 23 per cent by 2033. 2

While tech integration is growing exponentially, the industry also needs to address certain challenges. According to KPMG International’s 24th Annual Global Automotive Executive Survey, only 12 per cent of auto executives are well prepared for advanced technologies. 3 Hence, the industry must focus on comprehensive skill development to ensure readiness for emerging trends. Further, with automotive supply chain being most prone to cyberattacks in India, cybersecurity continues to remain top priority for the sector. 4 Thus, robust regulatory developments along with targeted cybersecurity strategies must be prioritised.  

2. Sustainability: A crucial pit stop

Amidst shifting consumer preferences, government regulations and climate targets, automakers are focusing on reducing their carbon footprint. Sustainable supply chain strategies—such as smart logistics, fleet electrification, renewable energy usage, adopting circular economy principles, etc.—are important differentiators now. For instance, an Indian automaker is making substantial investments to develop an onsite solar project, which will boost the renewable energy capacity at its manufacturing facility. Overall, estimates indicate that about 73 per cent of companies that have actively integrated sustainable design principles have reported higher revenue growth, 5

As the sector advances, more must be done to address climate concerns. This is particularly challenging due to the complexity of automotive supply chains, as a modern vehicle consists of more than 30,000 components that are sourced from numerous suppliers. 6

So how can these complexities be addressed? For one, collaboration and shared responsibility between the government and industry stakeholders can establish a transparent and sustainable automotive value chain. India’s automotive industry, for instance, includes many MSMEs involved in different stages of production. The government can implement planned measures, including tax incentives, subsidies and awareness programmes, to encourage sustainable practices in MSMEs. Similarly, large organisations and industry associations can conduct specialised training programmes and share best practices with smaller players engaged in automotive value chains. Additionally, to sustainably transform the entire automobile industry, the government can offer tax benefits and introduce incentive schemes to boost R&D and promote sustainable innovations. Expanding infrastructure to further strengthen the domestic EV manufacturing ecosystem can also help in creating sustainable supply chains.  

3. A more diversified supply chain ecosystem

As a part of their risk management strategies, automakers are diversifying their supplier base to reduce reliance on a single source. Estimates suggest that offshore procurement will decrease by 19 per cent over the next two years, leading to the adoption of nearshoring strategies that enhance supply chain resilience, presenting India with an opportunity to capitalise on the global demand. 7 The government has already made some headway to boost the production capacity, as evident in the sector-specific PLI schemes. Global investments have surged too, with major players investing in high-value sectors like semiconductors. This also enables India to capture a larger share of global automotive value chains. Lately, India has been meeting global demands, with exports of auto components increasing by 5.2 per cent to reach USD20.1 billion in FY2023. The industry also grew significantly by 33 per cent in the same fiscal. 8

Moving forward, India must continue expanding this momentum. For instance, the government can focus on improving India’s engineering and R&D capabilities according to evolving industry demands. Additionally, India can build domestic capabilities in critical resources that are required for emerging automobile technologies to enhance its position in global value chains. Further, our export potential can be improved by introducing fresh schemes tailored to industry requirements and exploring new global markets opportunities.

As the industry continues to recover and evolve in the aftermath of the pandemic, a connected, collaborative and resilient approach will be the next step in redefining automotive value chains.

[1] Generative AI in Automotive: Why Are Industry Leaders Integrating Virtual Assistants and Other AI-Driven Solutions?, Master of Code, 10 July 2024, accessed on 17 July 2024 [2] Generative AI In Automotive Market Size, Share, and Trends 2024 to 2033, Precedence Research, April 2024, accessed on 17 July 2024 [3] KPMG’s 24th Annual Global Automotive Executive Survey, KPMG International, accessed on 17 July 2024 [4] Automotive Supply Chain Identified as Most Cyberattacked Sector, According to Seqrite’s ‘India Cyber Threat’ Report, The Supply Chain Report, 15 May 2024, accessed on 17 July 2024 [5] Automotive Supply Chain: Pursuing Long-Term Resilience, Capgemini Research Institute, September 2023, accessed on 17 July 2024 [6] How the Automotive Industry is Gearing Up into Sustainability’s Fast Lane, World Economic Forum, 19 January 2023, accessed on 17 July 2024 [7] Automotive Supply Chain: Pursuing Long-Term Resilience, Capgemini Research Institute, September 2023, accessed on 17 July 2024 [8] Indian Auto-Component Industry Achieves 32.8 per cent Growth in FY24, Ministry of External Affairs, 08 August 2023, accessed on 17 July 2024

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Strategic optimization of wheat supply chain network under uncertainty: a real case study

• Original paper
• Published: 16 September 2019
• Volume 21 , pages 1487–1527, ( 2021 )

Cite this article

• Seyyed-Mahdi Hosseini-Motlagh   ORCID: orcid.org/0000-0003-2568-187X 1 ,
• Mohammad Reza Ghatreh Samani 1 &
• Firoozeh Abbasi Saadi 1  

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Today, wheat and its by-products are considered the most important food grain source for humans across the world. Accordingly, integrally investigating the wheat supply chain is of great importance in strategic decisions. In this respect, this paper addresses a real case study of wheat supply chain in Iran as well as the entities involved it. Presenting a new mathematical model, the total cost of the wheat supply chain network design is optimized. The proposed model integrates collection, production, inventory, and distribution echelons of the wheat supply chain, simultaneously. The inherent uncertainty in supply, demand, related costs, and climate changing result in the different quality of wheat which make it challenging to design and manage an optimal structure for the wheat supply chain network. Hence, the role of uncertainty in the mathematical optimization model is highlighted, and then, a robust approach is utilized to tackle the inevitable uncertainty of parameters. The proposed robust model not only manage to overcome the complexity of uncertainty but also outperform the deterministic model. It shows the proposed robust model is more effective than deterministic one that can be applied to make robust strategic and tactical decisions for the wheat supply chain. Moreover, the sensitivity analysis of influential parameters is conducted. Finally, according to the obtained results as well as sensitivity analysis, some managerial insights are provided.

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Hosseini-Motlagh, SM., Samani, M.R.G. & Abbasi Saadi, F. Strategic optimization of wheat supply chain network under uncertainty: a real case study. Oper Res Int J 21 , 1487–1527 (2021). https://doi.org/10.1007/s12351-019-00515-y

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Supply Chain Provides Building Blocks for LEGO’s Growth

LEGO is one of very few organisations whose products and branding are recognisable almost anywhere in the world. 

Founded almost a century ago, its mission today is to inspire and develop the builders of tomorrow through the power of play. 

Despite ongoing downturn in the toys and games market, LEGO continues to well and truly lead the way after posting its strongest-ever H1 results, with double-digit top- and bottom-line growth. 

Much of this can be put down to its efforts in the supply chain and operations space, which is providing the foundations for the group to increase its market share. 

Investment in the supply chain network

LEGO has continued to expand its global supply chain and operations network, ensuring production and distribution facilities are close to major markets. 

In recent months it opened a new European Regional Distribution Centre in Belgium and continued construction of two new factories in Vietnam and Virginia, US, set to open in 2025 and 2027 respectively. 

The business also continued to expand capacity at its existing factories in Mexico, Hungary and China.

Carsten Rasmussen, COO at LEGO Group , said he was “proud” of the role operations had played in helping the organisation achieve revenue growth of 13% and consumer sales growth of 14% – significantly outperforming the toy industry.

Operating profit grew by 26% and net profit by 16% when compared to H1 2023.

Carsten added: “We continue to invest in expanding our global supply chain network, maintain a strong focus on harvesting productivity and have made significant progress on our sustainability ambitions by increasing the amount of sustainable raw material used in our products.”

LEGO’s bid to build a sustainable future

As indicated by Carsten, LEGO has been working tirelessly on sustainability initiatives throughout its supply chains. 

The company continued to increase the amount of resin it purchased from sustainable sources certified under the mass balance principle, with the aim of helping accelerate the industry’s transition to more sustainable, high-quality materials. 

During H1 2024, almost a third (30%) of all resin purchased by LEGO was certified mass balance, which translates an estimated 22% material from renewable and recycled sources.

This represents significant improvement on 2023, when, for the full year, 18% was certified mass balance, equating to 12% sustainable sources. 

In the coming years, LEGO is looking to purchase more than half of its raw materials from sustainable sources via the mass balance principle, reducing its use of virgin fossil materials in the process. 

H1 also saw the business intensify its focus on activity to reduce greenhouse gas emissions , including through the launch of a Supplier Sustainability Programme which requires suppliers to set targets to reduce emissions by 2026 and further by 2028, as well as an annual carbon emissions reduction KPI linked to employee bonuses.

Spending on strategic initiatives intensifies

LEGO’s strong top-line growth is driven by demand for its large and diverse portfolio, especially in the Americas and Europe, in addition to what it calls “excellent execution” in all markets. 

Spending on strategic initiatives designed to drive short- and long-term growth – predominantly in the areas of sustainability, retail and digitalisation – has continued to increase and will be further accelerated in the second half of the year.

“We’re very pleased with our strong performance in the first half,” comments Niels B Christiansen, CEO at LEGO. “We delivered double-digit growth on the top- and bottom-line and made significant progress on increasing the amount of sustainable materials used in our products.

“Our portfolio continues to be relevant for all ages and interests and this is driving significant demand across markets. We used our solid financial foundation to further increase spending on strategic initiatives, which will support growth now and in the future to enable us to bring learning through play to even more children.”

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Operational decisions of construction and demolition waste recycling supply chain members under altruistic preferences.

1. Introduction

2. literature review, 2.1. studies on green demolition technology, 2.2. studies on altruistic preferences, 2.3. studies on the reciprocal altruism theory, 2.4. studies on operational decisions in a cdw recycling supply chain, 3. problem description and relevant assumptions, 4. model developed and solved, 4.1. optimal solutions under non-altruistic model, 4.2. optimal solutions under recycler altruistic model (r), 4.3. optimal solutions under remanufacturer altruistic model (m), 4.4. optimal solutions under mutual altruistic model (mr), 5. analysis of propositions, 6. numerical simulation, 6.1. influence of altruistic preference degree on supply chain members’ operational decisions under unilateral altruistic model, 6.2. influence of altruistic preference degree on supply chain members’ operational decisions under mutual altruistic model, 6.3. comparison of green dismantling technological level under different altruistic models, 6.4. comparison of supply chain members’ utility under different altruistic models, 6.5. summary of comparative differences in different altruistic models, 7. conclusions and management insights, 7.1. conclusions, 7.2. management insights, author contributions, data availability statement, conflicts of interest.

• Proof of profitable altruistic cooperation intervals.
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Share and Cite

Zhu, J.; Zhang, H.; Chen, W.; Li, X. Operational Decisions of Construction and Demolition Waste Recycling Supply Chain Members under Altruistic Preferences. Systems 2024 , 12 , 346. https://doi.org/10.3390/systems12090346

Zhu J, Zhang H, Chen W, Li X. Operational Decisions of Construction and Demolition Waste Recycling Supply Chain Members under Altruistic Preferences. Systems . 2024; 12(9):346. https://doi.org/10.3390/systems12090346

Zhu, Junlin, Hao Zhang, Weihong Chen, and Xingwei Li. 2024. "Operational Decisions of Construction and Demolition Waste Recycling Supply Chain Members under Altruistic Preferences" Systems 12, no. 9: 346. https://doi.org/10.3390/systems12090346

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