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The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

The aim of this research is to underscore the pivotal role of warehouse management in the current turbulent global landscape exacerbated by the confluence of a health crisis and geopolitical instability in Europe. In today's interconnected global economy, the turbulence of the global supply chain causes a lack of its resilience among companies. Facing this critical crisis context, companies are refocusing on business processes and outsourcing support processes such as logistics. In this paper we have empirical and methodological objectives. Methodologically, we employ a qualitative research approach utilizing action research in a collaborative framework that involves academics and practitioners. The purpose of this methodology is to empirically investigate warehouse outsourcing as a solution for enhancing a company's performance and agility within the crisis context.

The authors’ action research based on case study approach is conducted through an immersion within the ALCL French multinational company located in Morocco. The authors mobilize the theory of constraints, which allows us to set up a process of identification and optimization of managerial constraints (Goldratt, 1990). The approach allows to set up a retroactive loop to increase the performance of the constraint.

The study shows that ALCL has a storage over-dimension constraint due to the decrease of physical flows caused by the global crisis. The results of action research protocol show that the optimization of warehousing constraint is achieved by the total outsourcing of the process.

The study provides new insights into how action research can improve management practices within companies and explore concrete solutions to the logistical challenges faced by businesses.

The main purpose of this study is to present a new approach to managing process changes in uncertain conditions. The proposed approach is applied in one of the largest production companies in Turkey to manage the changes in their warehouse processes which formed after the merger.

In the model, interval-valued hesitant fuzzy the decision-making trial and evaluation laboratory (IVHF-DEMATEL) methodology is integrated into one of the most used BPR tools, change matrix. The main focus of the proposed model is to increase both flexibility and applicability in uncertain conditions. Thus, while the change matrix enables companies to be agile and responsive to changes, IVHF-DEMATEL provides a better way to continuously evaluate and determine critical processes, and strategies to align with evolving conditions.

Initial analysis revealed two major problems, the slowness of shipments caused by the increase in costs and the confusion in the organizational structure. However, the conventional methods fall short of effectively determination of critical objectives in terms of dealing with uncertainty. Therefore, a comprehensive roadmap for managing the change is developed with the integration of IVHF-DEMATEL and change matrix so that a successful transition is achieved.

It is believed that the study will contribute to the existing literature by providing a novel approach in which the IVHF-DEMATEL methodology is integrated into the change matrix. Also, the study provides a guideline for practical applications by presenting a step-by-step implementation of the model.

Industry 4.0 and the digital supply chain (DSC) are changing how things are made and moved around the world. This change is all about how smart technologies like the Internet of Things (IoT), artificial intelligence (AI), and blockchain are making supply chains work better. These tools help companies react faster and more clearly to what's needed. By using these new technologies, businesses can get better at guessing what customers want, keeping the right amount of stock, and quickly adjusting to new market trends. With these advanced technologies, companies can see big improvements, like being able to match supply with demand more closely and change their plans fast when things in the market change. It is really important for businesses to get how these tech tools work together as the world of making and selling things keeps changing. This chapter examines the convergence of traditional supply chain systems with Industry 4.0, focusing on the transformative impact of technologies such as the IoT, AI, and blockchain.

This study explores practitioners' perspectives on and definitions of sustainable supply chain management (SSCM), which are then compared to academic definitions to identify new implications for researchers, educators and practitioners.

An abductive, explorative and qualitative approach was followed in the form of a review and classification of 31 academic definitions of SSCM as well as 30 interviews with supply chain (SC) practitioners.

The practitioners' answers show a lack of awareness of upstream and downstream challenges as the practitioners' focus on practices within the practitioners' firms, where the economic and environmental dimensions prevail. However, the practitioners highlighted understudied topics in SSCM: human resources policies, leadership for sustainability and ethics.

This research stimulates discussion on how to teach an SSCM course and which directions to follow to ensure that research has an impact on practices. Practitioners' focus on the practitioners' everyday practices confirms that practice-based theories, amongst others, are relevant in the field and that more interdisciplinary research is needed to highlight the contributions of human resource management (HRM) and business ethics to SSCM.

The proposed framework clearly defines the scope of the practices and research (upstream or downstream of the SC or within the firm), which will allow practitioners to contribute to SSCM more holistically.

Educators and researchers have a crucial role to play in clarifying the meaning of SSCM for students who are future practitioners and consumers. Interacting more with practitioners could help.

This research is targeted not only to researchers and practitioners but also educators.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

This paper aims to show the impact of applied visual management (VM) on performance in logistics operations in the construction industry.

A case study was conducted at a branch of an international company located in Poland on VM implementation in the transport and storage of this firm. Active research was used to include the outlook of top management on the implementation and use of VMs.

This study demonstrates how VM is an effective way to improve performance in the studied logistics functions. The complex nature of the effect is revealed not only in warehouse and transport operations but also in handling operations, improving operational planning and specializing warehouse teams.

Organizational culture, work discipline and value system in the group of production and warehouse workers is of importance in implementing and efficiently using VM resources. Using a VM is complex.

Industry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.

This research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.

The integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.

This study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.

In the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.

For diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.

Testing of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.

Viewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.

As well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.

This paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.