Supply Chain Of Perishable Products Research Articles

Multi‐objective location‐allocation‐routing problem of perishable multi‐product supply chain with direct shipment and open routing possibilities under sustainability

AbstractIn this study, a multi‐objective formulation is proposed for designing a supply chain of perishable products including suppliers, plants, distributors, and customers under sustainable development. In addition to the studies of the literature, direct shipment between producers and customers and also alternative products possibility are allowed. In this problem, the objectives, such as facilities establishment costs, transportation costs, negative environmental impacts, and social impact (fixed and variable employment rates) are optimized simultaneously. As in real situations, most of the transportation activities of such supply chain are performed by hiring transportation devices, the open routing logic is applied to form the traveling path of each hired transportation device. Furthermore, the possibility of direct shipment from the plants to the customers is considered in order to increase profitability of the plants. Because of the NP‐hard nature of the supply chain design problems, some meta‐heuristic solution approaches of the literature are modified to multi‐objective form and applied to solve the problem. Several test problems from small to large sizes are generated randomly to evaluate the meta‐heuristic algorithms. As a result, among the proposed algorithms, the multi‐objective gray wolf optimizer (MGWO) performs better than others by considering four well‐known evaluation metrics. Finally, a case study from perishable products supply chain of Iran is solved and analyzed to show the applicability of the proposed problem.

Benchmarking the failure assessment of perishable product supply chain using fault tree approach: insights from apple case study of India

PurposeThe purpose of this paper is to facilitate perishable product supply chain (PPSC) managers and practitioners to assess PPSC failure events. The paper proposed fault tree methodology for assessing failures associated with PPSC for evaluating the performance in terms of effective PPSC management adoption.Design/methodology/approachInitially, different failure events were identified from literature and semi-structured interviews from experts. Fault tree model was developed from the identified failure events. Probability of failure events was calculated using Poisson distribution based on the annual reports and interviews conducted from experts. Further, qualitative analysis – minimum cut sets (MCSs), structural importance coefficient (SIC) – and quantitative analysis – Birnbaum importance measure (BIM), criticality importance factor (CIF) and diagnosis importance factor (DIF) – were performed for ranking of failure events. In this study, fault tree development and analysis were conducted on apple supply chain to present the authenticity of this method for failure analysis.FindingsThe findings indicate that the failure events, given as failure at production and procurement (A2), that is, involvement of middleman (BE3), handling and packaging failure (BE4) and transportation failure (A3), hold the highest-ranking scores in analysis of PPSC using fault tree approach.Originality/valueThis research uses the modularization approach for evaluation of failure events of PPSC. This paper explores failures related to PPSC for efficient management initiatives in apple supply chain context. The paper also provides suggestion from managerial perspective with respect to each failure event.

A Bi-objective Cold Supply Chain for Perishable Products Considering Quality Aspects: A Case Study in Iran Dairy Sector

Improper handling in the food cold supply chain may compromise food safety and reduce quality, which can lead to economic losses and undesirable effects on food accessibility. Therefore, designing an efficient and reliable cold supply chain is extremely important for the company, suppliers, customers, and society. The majority of the traditional studies in the supply chain do not consider the cost of quality (prevention, appraisement, and failure) in supply chain network design. In this study, all dimensions of the cost of quality in a cold supply chain design such as the cost of quality related to suppliers and the cost of distribution service quality are investigated to close the problem to real-world conditions. Moreover, the quality of suppliers, manufacturers, and distributors is simultaneously considered throughout a supply chain with a new approach. To this end, the problem is formulated as a mathematical model for multi-item and multi-period cases considering two objective functions. The first objective function minimizes the total expected costs and the second objective function maximizes the total quality of the supply chain. The proposed bi-objective model has been transformed into a single-objective model by the solution of the parametric method (normalized weighted summation) and solved for a medium-sized instance considering data of a real-world case study. Computational results and analyzes indicate the efficiency of the proposed model as well as the exact solution method available for small and medium scales. The real case study which involves Kaleh Dairy Company is conducted to illustrate the potential of the proposed model and proper sensitivity analyses.

A multi-objective model for sustainable closed-loop supply chain of perishable products under two carbon emission regulations

PurposeThis paper aims to minimize the total carbon emissions and costs and also maximize the total social benefits.Design/methodology/approachThe present study develops a mathematical model for a closed-loop supply chain network of perishable products so that considers the vital aspects of sustainability across the life cycle of the supply chain network. To evaluate carbon emissions, two different regulating policies are studied.FindingsAccording to the obtained results, increasing the lifetime of the perishable products improves the incorporated objective function (IOF) in both the carbon cap-and-trade model and the model with a strict cap on carbon emission while the solving time increases in both models. Moreover, the computational efficiency of the carbon cap-and-trade model is higher than that of the model with a strict cap, but its value of the IOF is worse. Results indicate that efficient policies for carbon management will support planners to achieve sustainability in a cost-effectively manner.Originality/valueThis research proposes a mathematical model for the sustainable closed-loop supply chain of perishable products that applies the significant aspects of sustainability across the life cycle of the supply chain network. Regional economic value, regional development, unemployment rate and the number of job opportunities created in the regions are considered as the social dimension.

Artificial-Intelligence-Based Time-Series Intervention Models to Assess the Impact of the COVID-19 Pandemic on Tomato Supply and Prices in Hyderabad, India

This study’s objective was to assess the impact of the COVID-19 pandemic on tomato supply and prices in Gudimalkapur market in Hyderabad, India. The lockdown imposed by the government of India from 25 March 2020 to 30 June 2020 particularly affected the supply chain of perishable agricultural products, including tomatoes as one of the major vegetable crops in the study area. The classical time series models such as autoregressive integrated moving average (ARIMA) intervention models and artificial intelligence (AI)-based time-series models namely support vector regression (SVR) intervention and artificial neural network (ANN) intervention models were used to predict tomato supplies and prices in the studied market. The modelling results show that the pandemic had a negative impact on supply and a positive impact on tomato prices. Moreover, the ANN intervention model outperformed the other models in both the training and test data sets. The superior performance of the ANN intervention model could be due to its ability to account for the nonlinear and complex nature of the data with exogenous intervention variable.

Distribution planning problem of a supply chain of perishable products under disruptions and demand stochasticity

PurposeThe emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).Design/methodology/approachThe proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.FindingsThe study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.Originality/valueThe study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.

Random Optimization of the Green Closed Chain Supply Chain of Perishable Products

The nature of the market has changed in many products so that manufacturers are increasingly forced to reconsider their supply chain strategies. In particular, perishable products can mentioned that require better and more accurate planning in terms of inventory and distribution in the supply chain. This paper presents a mathematical model for designing a supply chain network for perishable products. The supply chain includes a set of factories, warehouses, customers and collection centers. The objectives of this mathematical model are to reduce the total cost of the supply chain and reduce environmental pollution. In order to evaluate the performance of the mathematical model, three scenarios have been considered. In the first scenario, the reduction of product prices and in the second scenario, the stability of product prices and in the third scenario, the increase of product prices is considered. Then these scenarios are probably included in the mathematical model. The analysis performed in the study of the effect of each scenario shows that the increase in product prices has a more severe impact on environmental pollution. There is also always a conflict between costs and the environmental impact of the supply chain.

An efficient optimization framework for tracking multiple quality attributes in supply chains of perishable products

Improved supply chain optimization strategies can play a major role in addressing global food security and safety in years to come. In particular, tighter safety regulations, changing consumer quality requirements and more stringent market competition call upon integrated supply chain decision-making frameworks that explicitly consider product quality control. This effort requires metrics of quality that accurately reflect product physico-chemical properties, as well as consumer purchasing preferences. However, a critical challenge linked to embedding the complex dynamics of the evolution of product quality in time within supply chain models is the large-scale nature of the ensuing optimization problems, which are computationally intractable even for moderate-size, single-item systems. In the present work, we introduce a computationally efficient optimal production and distribution planning framework for perishable products having multiple quality attributes that evolve in time as a function of environmental conditions during shipment and storage. We also propose a model reduction strategy and a decomposition framework that enhance the scalability of our approach. We perform extensive numerical simulations using different network instances to validate our theoretical findings, as well as to demonstrate the advantages of the proposed supply chain management scheme.

Modelling the inhibitors of cold supply chain using fuzzy interpretive structural modeling and fuzzy MICMAC analysis.

The Cold Supply Chain (CSC) is an integral part of the supply chain of perishable products. The aim of this research is to examine the inhibitors that have a major impact on the performance of CSC operations in the United Arab Emirates (UAE). This study provides a synthesis and suggests a hierarchical model among CSC inhibitors and their respective relevance. The hierarchical synthesis of twelve (12) primary CSC inhibitors is achieved through a comprehensive literature review and consultation with academics and CSC professionals. This study used semi-structured interviews, a fuzzy interpretive structural modeling (FISM) and a Fuzzy-MICMAC (FMICMAC) analysis to explore and establish the relationship between and among identified inhibitors. FISM is used to examine the interaction between inhibitors, while FMICMAC analysis is used to examine the nature of inhibitors on the basis of their dependence and driving power. The results of the FISM and FMICMAC analysis show the inter-relationships and relative dominance of identified inhibitors. The results show that some inhibitors are of high strategic importance due to their high driving power and low dependence. These inhibitors seek more management attention in order to improve their effectiveness. The result of a hierarchical model helps to understand the influence of a particular inhibitor on others. ‘Higher capital and operating costs’ occupy the highest level in the FISM model. The ‘fragmented cold supply chains’, ‘lack of skilled labor’, ‘inadequate information system infrastructure’ and ‘lack of commitment by top level management’ had strong driving power but weak dependence, which characterizes them as independent inhibitors. Management should be extra careful when dealing with these inhibitors as they influence the effects of other variables at the top of the FISM hierarchy in the overall management of the cold supply chain. The study also suggests a number of recommendations for addressing these inhibitors in cold supply chains operating in the UAE. With due attention and care for these inhibitors, the operation of the cold supply chains is likely to be even more successful.

Assessing dairy supply chain vulnerability during the Covid-19 pandemic

ABSTRACT Frequent occurrence of disruptions in the supply chain due to the Covid-19 pandemic has increased the supply chain vulnerability (SCU), which affects the performance and revenue generation of firms. If the commodity we are dealing with in a supply chain is of a perishable nature, then the situation becomes more complicated, as such products need restrictive storage and transportation facilities. The dairy supply chain is one such perishable product supply chain. This paper, thus, proposes a method to identify key drivers of SCU in a dairy SC followed by establishing a model using interpretive structural modeling (ISM) and a graph theory approach (GTA) to calculate the SCU Index. It is important to quantify the SCU for identifying major factors affecting it and then developing techniques to mitigate it. In order to quantify the SCU, first, the ISM model is used to identify the interrelation between drivers, and then an adjacency matrix is formed by using the interdependence, thereby adding inheritance of each driver. A variable permanent matrix is formed to calculate the SCU Index for the SC. This proposed approach will help managers in mitigating the adverse effects of COVID-19 on the dairy supply chain.

Agricultural Supply Chain Risk Management Under Price and Demand Uncertainty

Agricultural supply chain (ASC) plays a vital role for sustainability as it is the main source of food supply. ASC encounters more sources of risk due to seasonality, perishability, and weather conditions, which makes the global food security system complex. This paper develops an optimization model for a perishable product supply chain to decide the optimal risk management strategy that maximizes the decision maker's expected profit under demand and price uncertainty. A base-case scenario is considered to show the impact of risk management strategy on performance improvement. The expected profit of the decision maker is obtained for different combination of strategies, and sensitivity analysis is performed to show the impact of perishability on the percentage of improvement from the base case scenario. The results show that backup supplier strategy is very effective during the yield disruption, but it is not as effective during harvest disruption. Hence, a single approach is inadequate to provide solution in all types of risk scenarios; thus, the combination of approaches is most effective.

Perishable products supply chain network design with sustainability

Background/Objectives: This study addresses the problem of sustainable supply chain network design for perishable products. Perishable products lose their value or deteriorate in a limited time, from the moment they are produced. Methods/Statistical analysis: A mathematical model is developed to design sustainable, single product, multi period, three-echelon supply network including manufacturer, warehouse, and retailer. The problem is formulated as mixed-integer nonlinear programming. Besides, the model is solved by non-linear Generalized Reduced Gradient solver in Excel. Findings: The behaviour of the model has been verified through solving several scenarios. The effect of demand on system behaviour has been studied. Novelty/Applications: The model considered inventory of perishable products in multi-periods. In addition, it adopts the concept of preventing the expiration of the perishable products, to minimize the environmental impact in addition to maximizing the profit to assure sustainability of the supply chain. The model is solved by non-linear Generalized Reduced Gradient solver in Excel. The proposed model considerably assists the supply chain organizations to design their network more efficiently. Keywords: Sustainability; Supply chain network design; perishable products; MINLP; Inventory; FIFO; expiration; Generalized Reduced Gradient solver

Enhancing resiliency of perishable product supply chains in the context of the COVID-19 outbreak

ABSTRACT Globally, countries are struggling to fulfil customer demands due to the effects of the COVID-19 pandemic on perishable food supply chains (PFSCs). This study aims to analyse the factors influencing PFSCs during the pandemic and improve their resiliency. This is essential as some factors discourage the productive execution of PFSCs and decrease organisational performance, thus lowering stakeholder satisfaction. This study has been conducted in two phases. The first phase, through extensive review and discussion with experts, identifies the influencing factors related to supply chain (SC) disturbances in PFSCs. In the second phase, a hybrid method i.e. g-DANP, a combination of grey-decision making trial and evaluation laboratory and analytic network process, is employed to develop a hierarchical structure to measure their influence. The proposed framework is validated with a case of the current COVID-19 outbreak. The study revealed that factors, restriction on import-export and fear of violation of social distancing guidelines, are the primary ‘cause’ group factors; whereas, price variation of perishable products and panic buying and stockpiling are the crucial ‘effect’ group factors affecting the PFSCs. The findings also enrich existing literature by providing analytical support to relationships between various factors affecting PFSCs during the pandemic.

WITHDRAWN: Profit-sharing contract for a two-echelon supply chain of perishable products

WITHDRAWN: Profit-sharing contract for a two-echelon supply chain of perishable products

Impact of cap-and-trade regulation on coordinating perishable products supply chain with cost learning

<p style='text-indent:20px;'>This paper incorporates carbon emission regulation and cost learning effects to examine a manufacturer-retailer supply chain for deteriorating items over a multi-period planning horizon. We investigate their impacts on supply chain coordination under the assumption that the product demand is affected by the selling price, promotional effort and inventory level. We first propose two algorithms for determining optimal solutions of the centralized and decentralized models. We show that the decentralized system can be coordinated perfectly with a two-part tariff contract. Further, we study necessary conditions under which members of the supply chain can accept this contract. At last, we conduct numerical experiment to illustrate the obtained theoretical results in impact analysis and the robustness of the coordinated model.</p>

Use of blockchain technology as supply chain management system involving air transport

This article explores Blockchain Technology, and how it can be used in the context of information exchange between stakeholders in international supply chains and networks involving air transport. The article reviews the global experience with this technology of iconic companies (Maersk, Lufthansa, Walmart, Unilever, Nestle, Samsung, British Airways) in logistics and supply chain management. It highlights the features of international chains using air transport, which predominantly consist of two categories of goods: goods with high added value (electronic devices and their components, fashion industry products, jewelry) and goods with strict delivery time requirements (pharmaceuticals, live animals, perishable products, e-commerce products and mail). The study shows that perishable goods have great potential for increasing airline cargo turnover. However, at present, there is no single universal tool for managing their supply chains since the tools used (ERP, SRM, TMS, WMS) do not provide complete transparency of information exchange between all stakeholders. The most problematic stage in the delivery to control steps in the delivery of perishable goods by air occur on the ground, while goods are being handled at the airport of departure and the airport of destination. Difficulties arise while checking the correctness of paperwork, labeling and information authenticity about the product origin. The article suggests using block chain blockchain technology in combination with IoT and smart-contract technologies for SCM of perishable products using air transport. The use of this technology allows to reduce the time of document verification procedures. Customs clearance and phytosanitary control could be vastly improved. Overall tracking and monitoring of a product from the moment of its production to the destination simplifies the identification of any counterfeits, improves the quality of food safety control. A greater level of control becomes possible in logistics operations, to determine liability in possible delivery conditions’ violations cases. Overall optimization of the logistic processes, automatization of the execution of verified financial transactions can be achieved. The use of these technologies in combination with innovative technologies for food quality control will significantly reduce logistics costs in the supply chains of perishable products.

General lot-sizing and scheduling for perishable food products

General lot-sizing and scheduling is a well-studied problem in the literature, but for perishable or time-sensitive products is less investigated. Also, most of studies on perishable product supply chains focus on strategic and tactical decision levels rather than operational decision level and integrated operational and tactical decision levels. We focus on a general lot-sizing and scheduling problem faced by perishable food products. The lifespan and shelf life are two important key features of perishable products that are considered in the problem. This problem can be described as a multi-product, multi-parallel line, multi-period general lot-sizing and scheduling problem with sequence dependent change over time. The objective function is sum of production costs, inventory holding costs, waste costs, and lifespan related cost function. We apply two mixed-integer programming based heuristics to solve generated instances. The heuristics are compared in terms of solution quality and computational time. Also, the sensitivity analysis is presented to analyze the effects of parameters’ changes.

Inventory Routing Problem in Supply Chain of Perishable Products under Cost Uncertainty

This paper presents a multi-objective, multi-period inventory routing problem in the supply chain of perishable products under uncertain costs. In addition to traditional objectives of cost and greenhouse gas (GHG) emission minimization, a novel objective of priority index maximization has been introduced in the model. The priority index quantifies the qualitative social aspects, such as coordination, trust, behavior, and long-term relationships among the stakeholders. In a multi-echelon supply chain, the performance of distributor/retailer is affected by the performance of supplier/distributor. The priority index measures the relative performance index of each player within the supply chain. The maximization of priority index ensures the achievement of social sustainability in the supply chain. Moreover, to model cost uncertainty, a time series integrated regression fuzzy method is developed. This research comprises of three phases. In the first phase, a mixed-integer multi-objective mathematical model while considering the cost uncertainty has been formulated. In order to determine the parameters for priority index objective function, a two-phase fuzzy inference process is used and the rest of the objectives (cost and GHG) have been modeled mathematically. The second phase involves the development of solution methodology. In this phase, to solve the mathematical model, a modified interactive multi-objective fuzzy programming has been employed that incorporates experts’ preferences for objective satisfaction based on their experiences. Finally, in the third phase, a case study of the supply chain of surgical instruments is presented as an example. The results of the case provide optimal flow of products from suppliers to hospitals and the optimal sequence of the visits of different vehicle types that minimize total cost, GHG emissions, and maximizes the priority index.

An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network

The transition to sustainable supply chains in industries where perishable products are prominent requires advanced models for logistics management. In this paper, a model of integrated location, routing, and inventory problem, the three key problems in optimizing a logistics system, is introduced. Considering the particular decision-making environment of such industries, we give a two-phase approach to incorporate the three dimensions of sustainability into supply chain practices. After identifying more sustainable-oriented suppliers, a problem is formulated as a multi-objective Mixed-Integer Programming (MIP) model to assist in planning a sustainable supply chain. Based on the proposed two-phase approach, we contribute to the current literature by simultaneously addressing different challenges, e.g., sustainability issue, integrated decision-making on location, routing, and inventory control planning, and real-world assumptions to make further advances in both research and practice. Since finding the optimal solution for this problem is a NP-hard problem, two hybrid metaheuristics as parallel and series combinations of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to solve the problem. The implementation of a set of test instances demonstrates the superiority of the parallel hybridization over the series one. Furthermore, it is observed that when the goal of sustainability is approached, manufacturing sector tends to be decentralized and the local commodity production in more medium- and low-developed locations can exhibit the best sustainability performance.

The design and planning of an integrated supply chain for perishable products under uncertainties

PurposeThis paper aims to design an optimal supply chain network and to develop a suitable distribution planning under uncertainty for perishable product's supply chain. The ultimate goal is to help in making decisions under uncertain environments.Design/methodology/approachIn this paper, stochastic programming is used under conditions of demand, supply and process uncertainties, and a non-linear mathematical model is developed for perishable product’s supply chain. Authors’ study considers disruptions in transportation routes and also within the facilities and investigates optimal facility location and shipment decisions while minimising the total supply chain cost. A scenario-based approach is used to model these disruptions. The retailer level uncertainty due to demand-supply mismatch is handled by incorporating the newsvendor model into the last echelon of supply chain network. In this paper, two policies are proposed for making decisions under uncertain environments. In the first one, the expected cost of the supply chain is minimised. To also consider the risk behaviour of the decision maker, authors propose the second policy through a conditional value-at-risk approach.FindingsAuthors discuss the model output through various examples that are provided via a case study from the milk industry. The supply chain design and planning of the disruption-free model are different from those of the resilient model.Practical implicationsAuthors’ research benefits the perishable products industries which encounter the disruption problems in their transportation routes as well as in the facilities. Authors have demonstrated the research through a real-life case in a milk industry.Originality/valueThe major contribution of authors’ work is the design of the supply chain network under disruption risks by incorporating aspects of product perishability. This work provides insight into areas such as the simultaneous consideration of demand, supply and process uncertainties. The amalgamation of newsvendor model and the approximation of the non-linearity of retailer level cost function especially in the context of supply chain under uncertainty is the first of its kind. We provide a comprehensive statistical study of uncertainties that are present in the supply chain in a unique manner.