Modular supply chain optimization considering demand uncertainty to manage risk

The design of robust value-creating supply chain networks: A critical review

A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply chain network design and operation: Systematic decision-making for centralized, distributed, and mobile biofuel production using mixed integer linear programming (MILP) under uncertainty

Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano’s conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model (“must-be”, “one-dimensional”, “attractive” and “indifferent”) can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.

ABSTRACTAssessing Environmental, Social, and Governance (ESG) indicators in supply chains (SCs) is key to sustainability and operational performance, yet their integration into SC decision‐making remains limited. Supply chain network design (SCND) plays a critical role in this gap. This study empirically examines ESG adoption across centralized, decentralized, and distributed SCNDs, addressing the lack of comparative analysis in this area. Based on a survey of 733 SC professionals, this study statistically analyzes differences in the adoption of 21 ESG indicators across SCNDs. Results reveal that distributed SCNDs exhibit the highest adoption levels of ESG indicators, outperforming centralized SCNDs across all indicators and exceeding decentralized SCNDs, particularly in governance‐related indicators. This study contributes to the limited empirical research on the relationship between SCNDs and ESG adoption, offering actionable insights for decision‐makers to align SC structures with sustainability goals.

Background: In agricultural supply chains, unequal bargaining power often leads to economic inequality, particularly for farmers. The fair profit distribution (FPD) approach offers a solution by optimizing supply chain flows (materials, information, and money) to promote economic equity among members. However, our literature review highlights a gap in applying the FPD approach to the facility location-allocation problem in supply chain network design (SCND), particularly in sugarcane-derived biofuel supply chains. Methods: Consequently, we propose a multi-period optimization model based on FPD to design a sugarcane biofuel supply chain. The methodology involves four steps: constructing a conceptual model, developing a mathematical model, designing a solution strategy, and generating insights. This model considers both investment (crop development, biorefinery construction) and operational phases over a long-term planning horizon, focusing on farm location and crop allocation. Results: By comparing the FPD model to a traditional centralized planning supply chain (CSC) approach, we examine the impact of the planning horizon, number of farms, and sugarcane prices paid by biorefineries on financial performance. While the FPD model results in lower overall system profits, it fosters a fairer economic scenario for farmers. Conclusions: This study contributes to economic justice in supply chains and offers insights to promote fair trade among stakeholders.

Purpose- The purpose of this study is to construct a portfolio based on downside beta as a measure of downside risk. The analysis uses data from all listed companies on the Pakistan Stock Exchange (PSE) over the period from January 2000 to December 2021. Study Design/Methodology/Approach - The study employs a rolling window approach with 36 consecutive months to estimate downside beta values for portfolios ranked from lowest to highest downside beta. Portfolio returns are evaluated using the Generalized Method of Moments (GMM) in conjunction with the Capital Asset Pricing Model (CAPM) and the Fama-French three- and five-factor models. A six-month Treasury bill rate is used as the risk-free rate. Findings- The study reveals a significant spread between the downside beta values of the constructed portfolios, confirming that downside risk can be effectively captured through this approach. The hypothesis of equal mean downside beta across the portfolios is rejected, indicating that portfolios based on downside beta exhibit different risk-adjusted returns. Furthermore, the rejection of the alpha equal to zero hypothesis suggests that these risk-adjusted returns are statistically significant. However, the study finds that lower partial moments do not fully explain the variation in downside risk. Practical Implications- These findings are particularly relevant for investors focused on managing downside risk. The study offers alternative methods for constructing portfolios that account for downside risk, which could enhance risk management strategies. However, the reliance on historical data from the PSE may limit the generalizability of these findings to other markets. Originality/Novelty - This study contributes to the literature by constructing portfolios grounded in downside beta as a measure of downside risk, offering a novel approach to portfolio formation and risk-adjusted return evaluation. It highlights the importance of incorporating downside risk in portfolio management and presents an innovative application of downside beta within the context of emerging markets.

Optimal design of low-cost supply chain networks on the benefits of new product formulations

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.

The robust Mixed-Integer Linear Programming (MILP) model is an approach to address uncertainty in linear optimization involving integer and continuous variables, which can be solved using the Benders Decomposition method. One of its applications is facility location problems, which often face demand, costs, and capacity uncertainties. This article presents a systematic literature review (SLR) on solving robust MILP models using the Benders Decomposition method and its application to facility location problems. The objectives are to explore the state-of-the-art and research trends, identify issues modeled as robust MILP and solved using Benders Decomposition, and determine the most frequently used uncertainty sets. SLR was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method on the Scopus, Science Direct, and Dimensions databases for the last five years of publication, with bibliometric analysis using VOSviewer and RStudio. The results show that there are limited articles that discuss the solution of the robust MILP model on the problem of facility location with the ellipsoidal uncertainty set. In addition, the Benders Decomposition method is widely used to solve robust MILP problems in energy, logistics, supply chains, and scheduling, with interval uncertainty sets being the most common. This topic is an influential theme and has the potential to be explored further.

We developed a decision support framework for a global manufacturer of specialty chemicals to study the relative impact of demand, supply and lead-time uncertainties on cost and customer service performance. Our approach combines optimisation and simulation methodologies as follows: mathematical models provide optimal plans via a novel approach to the supply chain planning mechanism of the Company. Simulation models execute the supply chain plans so as to allow the examination of the outcomes under the various sources of uncertainty. The iterative use of optimisation and simulation methodologies allows the user the benefit of obtaining optimal solutions while revealing the impact of uncertainties on system performance. Our results indicate that demand uncertainty has the greatest negative impact on performance for the supply chain that we modelled in this study, emphasising the importance of effective forecasting. The relative importance of supply and lead-time uncertainties varies according to the performance measures. While our results are valid for the specific supply chain and the operating environment we modelled, our study emphasises the importance of the ability to model supply chains realistically to obtain valid and useful results.

Designing a closed-loop supply chain network for citrus fruits crates considering environmental and economic issues

Supply chain network design (SCND) plays a crucial role in transforming a supply chain sustainable. Recently, various SCND models have been developed especially focusing on carbon footprint reduction. The current study argues that emphasizing only on carbon footprint cannot entirely transform a supply chain sustainable. There are other dimensions (water footprint, solid waste, and social factor), which need to be taken care of for implementing sustainability. Till now, most of the sustainable SCND models have been developed in deterministic conditions, and very few models have been reported in a stochastic environment. To fill the gaps of existing literature, the current study proposes a multi-product and multi-echelon SCND model by addressing carbon footprint, water footprint, solid waste, social sustainability, service level, different transportation modes and inventories in stochastic condition. The study intends to minimize the total cost and estimate the flow of materials across the various echelons of the supply chain. The model considers the demand and capacity as stochastic variables. Further, chance-constrained programming has been used to model the uncertainty of parameters. In the current research, an individual-level chance constraint with right-hand side uncertainty has been adopted. The applicability of the proposed model has been demonstrated with a numerical example and sensitivity analyses have been conducted by changing model parameters, like probability, carbon footprint, water footprint, solid waste, service level, and social factor. The suggested model facilitates decision-maker to estimate the optimum flow of material across the supply chain for delivering materials of predetermined carbon footprint, water footprint, solid waste, social sustainability, and service level. The model helps the manager to implement the sustainability holistically across the supply chain.

Process design is usually approached by considering the steady-state performance of the process based on an economic objective. Only after the process design is determined are the operability aspects of the process considered. This sequential treatment of the process design problem neglects the fact that the dynamic controllability of the process is an inherent property of its design. This work considers a systematic approach where the interaction between the steady-state design and the dynamic controllability is analyzed by simultaneously considering both economic and controllability criteria. This method follows a process synthesis approach where a process superstructure is used to represent the set of structural alternatives. This superstructure is modeled mathematically by a set of differential and algebraic equations which contains both continuous and integer variables. Two objectives representing the steady-state design and dynamic controllability of the process are considered. The problem formulation thus is a multiobjective Mixed Integer Optimal Control Problem (MIOCP). The multiobjective problem is solved using an ∈-constraint method to determine the noninferior solution set which indicates the trade-offs between the design and controllability of the process. The (MIOCP) is transformed to a Mixed Integer Nonlinear Program with Differential and Algebraic Constraints (MINLP/DAE) by applying a control parameterization technique. An algorithm which extends the concepts of MINLP algorithms to handle dynamic systems is presented for the solution of the MINLP/DAE problem. The MINLP/DAE solution algorithm decomposes the problem into a NLP/DAE primal and MILP master problems which provide upper and lower bounds on the solution of the problem. The MINLP/DAE algorithm is implemented in the framework MINOPT which is used as the computational tool for the analysis of the interaction of design and control. The solution of the MINLP/DAE problems is repeated with varying values of ∈ to generated the noninferior solution set. The proposed approach is applied to three design/control examples: a reactor network involving two CSTRs, an ideal binary distillation column, and a reactor/separator/recycle system. The results of these design examples quantitatively illustrate the trade-offs between the steady-state economic and dynamic controllability objectives.