Logistics optimization through a social approach for food distribution

Contributors

Purpose: The purpose of this paper is to assess if blockchain technology, which is hailed for its potential to improve supply chain transparency and efficiency, can deliver to this promise (Abreu et al., 2021; Mahyuni et al., 2020; Manzoor et al., 2022; Rennie, 2022; Yiannas, 2018). Supply chains are increasingly complex and full transparency is difficult to achieve and may sometimes be undesired, yet governments worldwide are demanding more transparency especially with respect to the social and environmental sustainability of supply chains (Francisco & Swanson, 2018; Franke, 2021; Najjar, 2021). If blockchain technology allows to close this gap, without compromising corporate confidentiality and efficiency requirements, it might change supply chain management drastically. Most other research in this field remains rather superficial with respect to the fundamentals of blockchain technology and supply chain management. Our study will add to the very limited theoretically founded research on blockchain potentials in supply chains.Study Design: Our paper is conceptual. Based on the extant literature, we compare blockchain technology and its advantages to the characteristics of supply chains and challenges of supply chain management to identify a theoretical fit. We investigate if published case studies of blockchain technology in supply chain management support this. Finally, we devise a conceptual framework for the application of blockchain technology in supply chain management.Findings: There is, currently, very limited use for blockchain technology in supply chain management. It does not provide an answer to several of the challenges that exist in supply chain management. Blockchain technology was primarily devised as encryption technology for virtual entities and transactions involving them and not for tangible objects. Published case studies describing the use of blockchain technology in supply chain management do not contradict these findings.They rely on private blockchain solutions lacking the key benefits of blockchain technology. Our conceptual framework constitutes a reference for a future public blockchain solution for supply chains. It offers solutions for some of the challenges but also cannot solve some fundamental challenges, like power imbalances in supply chains.Limitations: Blockchain technology undergoes rapid development. Future blockchain technology may be faster, less energy-intensive and more integrated with other technologies. This may be aided by developments in hardware, like quantum computing. The findings of this paper are limited to currently available technology.Implications: Companies and governments alike should be very cautious when considering investing in currently available blockchain solutions for supply chain management.

The paper presents possible solutions to small farmers in the agricultural sector in a small island setting grappling with challenges of enhancing their local industry and import substitution through a supply-demand chain framework. In 2005, Hawaiian farms produced approximately 363,200 kg of avocados. However, Hawaiian demand for these fruits required an additional 908,000 kg of avocados that were supplied through imports. At the same time, it is worth noting; almost 49% of locally produced avocados did not reach the market. These facts clearly suggest inefficiencies in the local avocado supply chain. Review of the literature on supply chain management suggests a sustainable avocado industry requires an integrated supply and demand chain management system. Hence, a modified conceptual model of supply-chain management called the Supply Demand Integrated Management (SDIM) model is proposed in this paper. Traditional supply chains contain three functional components: supply coordination, physical production and logistic management. In the new model, we propose adding R4D (Research for Development), a public good provided by the government to agricultural industries as an integral part of the supply-demand chain system. It articulates the significance of research and supply chain partnerships between small growers, industry groups, and public agencies. A well-integrated demand/supply chain in the avocado industry allows sharing of vital market information which can contribute to long-term strategic decisions that help local avocado growers increase their competitiveness against foreign imports. It also provides metrics in terms of consumer satisfaction, profitability, competitiveness, self-sufficiency, import substitution and sustainability. The SDIM model as applied to the avocado industry suggests two hypotheses relevant to the current situation. The hypotheses are: (1) There is a lack of coordination between what the farmers are growing and the chefs' (a high-end customer/buyer) preferences; (2) Forming an avocado farmer cooperative will enhance the profitability of local farmers. Survey data was collected from Hawaiian chefs and avocado farmers. Data was analysed using a Duncan multi-range test and a mathematical model, respectively, to test the hypotheses. The test revealed a lack of coordination between what the farmers are growing and buyer preferences. The results from the mathematical model suggested that formation of an avocado marketing cooperative would enhance the profitability of local farmers. Thus, the SDIM framework expands existing body of knowledge about supply chain management and provides useful information to local avocado growers that can help strengthen their supply and demand chain management.

Implementation and adoption of new technologies are gaining the result of smooth supply chain and logistics operations. Internet of things (IoT) artificial intelligence, including data mining, intensified in all fields of life, particularly in supply chain management and operations. Blockchain technology has the capability to reform the supply chain and logistics operations management. Blockchain provides digital database solutions for all transactions across supply chain and operations management. Radio frequency identification device (RFID) is also helping technology transmit electromagnetic waves to radio-compatible integrated circuits to look after and manage the entire supply chain and logistics operations management. The Fourth Industry Revolution 4.0 refers to the automation, interconnectivity, machine learning, and real-time data that help supply chain and logistics operations management in the 21st century.

The industry is gradually forced to integrate socially sustainable development practices and cross-social issues. Although researchers and practitioners emphasize environmental and economic sustainability in supply chain management (SCM). This is unfortunate because not only social sustainable development plays an important role in promoting other sustainable development programs, but social injustice at one level in the supply chain may also cause significant losses to companies throughout the chain. This article aimed to consolidate the literature on the responsibilities of suppliers, manufacturers, and customers and to adopt sustainable supply chain management (SSSCM) practices in the Pakistani industry to identify all possible aspects of sustainable social development in the supply chain by investigating the relationship between survey variables and structure. This work went beyond the limits of regulations and showed the status of maintaining sustainable social issues. Based on semi-structured interviews, a comprehensive questionnaire was developed. The data was collected through a survey of the head of the supply chain in Karachi, Pakistan. The results of this paper showed that organizational learning was the most important dimension of supplier social sustainability with a value of 40.5% as compared to the effectiveness of the supply chain and the supplier performance with values 37.7 and 9.6%, respectively. In terms of the manufacturer’s social responsibility, the highest score for operational performance was 47%, while productivity was 20%, and corporate social demonstration was 20%. Finally, for the customers’ social sustainability, two dimensions were determined, namely, customer satisfaction and customer commitment with scores of 47 and 40%, respectively. We also solved sustainable social problems from the perspective of suppliers, manufacturers, and customers. The study would help professionals anywhere to emphasize their considerations and would improve the management of social sustainability in their supply chain.

PurposeIt is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM.Design/methodology/approachA structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms.FindingsThe paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers.Originality/valueThe paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

Advancing social sustainability in supply chain management: Lessons from multiple case studies in an emerging economy

Through an international business risk management lens, the widespread and catalytic implications of the 2020 COVID-19 pandemic on the supply chains (SCs) of fashion multinational corporations (MNC) are analyzed to contribute to existing research on supply chain management (SCM). While a movement towards agile, networked supply chain models had been in consideration for many firms prior to the outbreak, the pandemic highlights issues inherent in supply chains that employ concentrated production. We examined the current state of fashion supply chains, risks that have arisen historically and recently, and existing risk mitigation methods. We found that while lean supply chain management is primarily favored for its cost and waste reduction advantages, the structure is limited by the lack of supply chain transparency that results as well as the increasing demand volatility observed even before the COVID-19 outbreak. Although this problem might exist in the agile supply chain, agile supply chains combat this by focusing on enhancing communication and buyer-supplier relationships to improve information exchange. However, this structure also entails an associated increase in inventory and inventory costs. The COVID-19 pandemic has caused supply and demand disruptions which have resonating effects on supply chain activities and management, indicating a need to build flexibility to mitigate epidemic and demand risks. To address this, several strategies that firms can adopt to control for such risks are outlined and key areas for further research are identified which consider parties both upstream and downstream of the fashion supply chain.

Sue Abdinnour (“ Hawker Beechcraft Uses a New Solution Approach to Balance Assembly Lines ”) is a professor of operations management and holds the Verne and Helen Omer Professorship in Business in the W. Frank Barton School of Business at Wichita State University. Her work has been published in journals such as Decision Sciences, International Journal of Production Research, European Journal of Operational Research, and the Annals of Operations Research. Sue engages in consulting that assists companies in modeling their processes and improving their operations. She has won several research and teaching awards during her career. Marc Albers (“ Optimizing Railway Crew Scheduling at DB Schenker ”) works as a consultant for Bayer Business Services. Prior to joining Bayer he worked at the University of Cologne, where he received his doctoral degree. His fields of interest include crew scheduling, supply chain management, and business intelligence. Jude Buquid (“ HD Supply Facilities Maintenance Uses Diluted Discounts to Optimize Purchasing Opportunities ”) is the director of inventory management and logistics planning at HD Supply Facilities Maintenance. He has more than 15 years of experience across the supply chain in duties that include outbound fulfillment and freight cost optimization, applying academic theory to the realities of modern inventory management, and striking the right balance of speed and cost at which products can be delivered. He is currently focused on optimizing the company's network footprint from an inventory perspective as well as developing strategies to increase throughput through distribution centers. He holds a BS in logistics and economics from the University of Maryland at College Park and an MSBA in finance from San Diego State University. Philip Hans Franses (“ Averaging Model Forecasts and Expert Forecasts: Why Does It Work? ”) is professor of econometrics and professor of marketing research, both affiliated with the Erasmus School of Economics. His research interests concern marketing, empirical finance, applied econometrics, and forecasting. He publishes on these topics in a variety of academic journals including Interfaces, the Journal of Marketing Research, and the Journal of Econometrics. Currently he serves as the Dean of the Erasmus School of Economics, serving 5,000 students and 400 staff. Kevin C. Furman (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) has been a leader of various programs and teams related to optimization and logistics research and software application development across multiple ExxonMobil affiliates. He received his PhD in chemical engineering from the University of Illinois at Urbana–Champaign. His eight years at ExxonMobil have focused on research, development, and leadership in the areas of OR and process systems engineering. David Gibson (“ HD Supply Facilities Maintenance Uses Diluted Discounts to Optimize Purchasing Opportunities ”) is the manager of logistics planning at HD Supply Facilities Maintenance and has been working within the field of operations and inventory management for the past seven years. His areas of expertise include process improvement, working capital optimization, and distribution center slotting efficiency. He is a leader in reshaping the corporate culture at HD Supply Facilities Maintenance by demonstrating innovative ways that data can be used to make decisions. He is a graduate of San Diego State University and holds a BS in financial services. Knut Haase (“ Optimizing Railway Crew Scheduling at DB Schenker ”) is a professor in the School of Business, Economics, and Social Sciences at the University of Hamburg. He received his PhD and his habilitation at the University of Kiel. His current research areas focus on crew scheduling, discrete choice models, line planning, pilgrim scheduling, and revenue management. Paul Huppert (“ HD Supply Facilities Maintenance Uses Diluted Discounts to Optimize Purchasing Opportunities ”) received his degree in business administration from San Diego State University. He is a supply chain manager for HD Supply. He has over 12 years experience in real-world inventory optimization including the implementation of supply chain systems that support the function. He is certified by the Institute of Business Forecasting as a Professional Forecaster. Silke Jütte (“ Optimizing Railway Crew Scheduling at DB Schenker ”) is a research assistant in the Department of Supply Chain Management at the University of Cologne. She has studied business mathematics at the University of Kaiserslautern and worked as a process manager at DHL. She is writing her PhD thesis in the field of crew scheduling. Gary R. Kocis (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) is a senior technologist with ExxonMobil Research and Engineering Company, presently working in the supply chain optimization field. During his 10 years with ExxonMobil, he has developed and applied optimization-based solutions to refinery planning and scheduling, and logistics optimization. He has 22 years of experience as a mathematical programming practitioner, having worked in systems development for Shell Oil Company and the ROMeo online optimization tool for Invensys. Michael K. McDonald (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) is an ExxonMobil Global feedstocks trader. He has been trading refining intermediates and products, and natural gas for 15 years. Jin-Hwa Song (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) leads optimization research at Corporate Strategic Research, ExxonMobil Research and Engineering Company. He received his PhD from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Since joining ExxonMobil, he has been working on various projects related to optimization and decision science, as well as science-build research programs with several universities. Ulrich W. Thonemann (“ Optimizing Railway Crew Scheduling at DB Schenker ”) is a professor in the Department of Supply Chain Management at the University of Cologne. He received his PhD from Stanford University and worked as a management consultant at McKinsey & Company. His current research areas focus on crew scheduling, inventory optimization, and behavioral operations. Joris van de Klundert (“ ANWB Automates and Improves Service Personnel Dispatching ”) holds an MSc in computer science from Erasmus University Rotterdam and a PhD in OR from Maastricht University. He has served as the founding director of Mateum, a Maastricht University spin-off delivering OR-based software and consultancy services. After being appointed professor of value chain optimization at Maastricht University, he returned to Erasmus University Rotterdam in 2009 to chair the Department of Health Services Management and Organisation. His main research interests are in the contributions of management science to networks of health-service organizations. Peter van Huigenbosch (“ ANWB Automates and Improves Service Personnel Dispatching ”) is trained as a military officer and holds a bachelor's degree in automotive mechanics. He has worked in various positions at ANWB since 1992. He became involved in dispatch services while doing his MBA, which he earned in 2003. He is logistics manager at ANWB responsible for all supporting departments involved in assisting 1,250,000 stranded motorists annually. Philip H. Warrick (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) is a Distinguished Engineering Associate with ExxonMobil Research and Engineering Company. He is responsible for the supply chain technology team. His 30-year career at ExxonMobil has concentrated on the downstream of the oil business, including responsibilities for supply, transportation and refinery operations and planning, cargo trading, and the development and application of optimization technology to refining and logistics. Laurens Wormer (“ ANWB Automates and Improves Service Personnel Dispatching ”) holds an MSc in OR from Maastricht University. He works for Mateum, a company providing advanced planning and optimization solutions for complex and innovative business processes. His main interests lie in developing decision support systems with state-of-the-art OR techniques that improve customer satisfaction and operational efficiency in health, home-care, and service organizations.

PurposeThe purpose of this paper is to describe the development and teaching of graduate courses providing in‐depth experiential learning employing commercial supply chain management software. The benefits of teaching such courses are described, the challenges in offering such courses are identified, and some solutions to overcome the challenges are offered.Design/methodology/approachThe experiences of the authors in developing and teaching supply chain management courses utilizing commercial software provided a basis for discussing the benefits and challenges associated with teaching students the management of modern supply chains using commercial decision‐support software.FindingsIncorporating commercial software in university programs presents a myriad of challenges and therefore is rarely done. However, providing students with in‐depth knowledge of commercial logistics and supply chain management software improves their understanding of supply chain issues and provides a key differentiator in the marketplace. Modeling real‐world supply chains using commercial software enhances student's education by providing relevant experiential exposure to real‐world problems and decision‐support tools.Originality/valueThe observations of the authors in developing and teaching courses in supply chain management utilizing commercial software afford a unique viewpoint and roadmap for others regarding teaching supply chain management in academic environments.

Supply chain management is essential to a company's success in today's fiercely competitive business environment, regardless of the industry. Businesses are increasingly turning to big data analytics as a useful tool for supply chain improvement to meet the expanding needs of customers, save operational costs, and boost overall efficiency. In order to improve critical areas including demand forecasting, inventory management, logistics optimization, and risk reduction, research has been conducted on the collecting and use of big data in supply chain management. Big data analytics and supply chain management integration can result in more cost-effective operations, better customer service, and increased resilience to disturbances. Organisations that effectively use big data will gain a competitive advantage in the constantly changing supply chain landscape as technology and data capabilities continue to grow.

This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords: Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.

This paper investigates the social sustainability of the supply chain of the Indian manufacturing industry. For the purpose, it examines the socially sustainable business practices adopted by two Indian manufacturing companies from two different sectors - cement and pharmaceuticals. The reason for the choice of companies from these two sectors is their wide divergence as well as economic importance. The study explores various activities under different social dimensions across the supply chain, paying particular attention to the drivers of such business practices as well as the lessons that can be learned from them. The comparative case study method has been used to explore the similarities and differences in the socially sustainable practices adopted by the two companies. To begin with, various social sustainability dimensions have been identified from the literature on the subject. Next, the socially sustainable practices of the two companies have been identified and compared. In the process emerge several important social dimensions related to the Indian manufacturing industry. This research expected to help supply chain managers and policy makers by providing crucial insights for decision making related to social sustainability.

Though the sustainability addresses predominantly on environmental concerns for many decades, the focus on social sustainability in the supply chain was explored less. In developing countries, social sustainability is gaining momentum recently, in the wake of increasing social issues flagged by social organisations. This research on social sustainability unearths the social sustainability practices adopted by two different manufacturing companies, from two different domains that include automobile and fast moving consumer goods (FMCG) industry. In this research, comparative case study methodology is employed, that includes identification of social dimensions which are specific to manufacturing industries, and comparative analysis of various practices adopted by two different manufacturing companies. Major findings imply similarities and differences between companies that can lead to different approaches to sustainability. There were significant differences in the sustainable practices between FMCG and Automobile companies, given their different dynamics. However, all the practices lead to social sustainability in the Indian continent. This research helps the supply chain managers and decision makers who are involved in operations to understand different practices and their impact on different industries, which will act as a guiding tool for decision making.

Integrating Artificial Intelligence (AI) in supply chain management (SCM) signifies a significant advancement with profound implications for modern businesses, including those in Uganda. This research paper critically examines the challenges and opportunities associated with this integration, using Uganda as a case study. A comprehensive analysis of existing literature and specific insights from the Ugandan context identifies critical challenges such as data integration, technology adoption, and organizational readiness within the country. Additionally, it explores AI's diverse opportunities in optimizing supply chain processes for Ugandan businesses, including demand forecasting, inventory management, and logistics optimization within Uganda's unique operational landscape. Furthermore, the paper discusses the potential impact of AI integration on various stakeholders within Uganda's supply chain ecosystem, including suppliers, manufacturers, distributors, and customers. By synthesizing insights from academic research and industry practices in Uganda, this paper provides valuable insights for Ugandan businesses aiming to leverage AI technologies in their SCM strategies. Ultimately, this research contributes to a deeper understanding of the complexities of integrating AI in SCM within the Ugandan context and offers recommendations for addressing challenges while maximizing the opportunities presented by this transformative technology.