Developing lean and responsive supply chains : a robust model for alternative risk mitigation strategies in supply chain designs

This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC’s network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet

The Strategic Supply Chain Management in the Digital Era, Tactical vs Strategic

The perspective of procurement and supply chain management is changing dramatically; traditionally, it was seen as a support function; however, the procurement function is receiving increased attention and investment as an essential contributor to the strategic success and a business enabler. While an end-to-end digital supply chain is an opportunity as it unleashes the next level of strategic growth and involves minimal investment in infrastructure, it is still a challenge to optimize and transform. Furthermore, the recent pandemics and geopolitical disruptions of Covid-19, the Ukraine-Russian war, Brexit and the US-China trade war; have structurally changed the global economy and revealed a new risk assessment that will result in the re-introduction of buffers, boundaries across industries and a partial return to regionalization with sort of de-globalization in which existing just-in-time getting replaced by just-in-case strategy

Dynamic joint investments in supply chains under information asymmetry

Supply chain management involves the selection, coordination and motivation of independently operated suppliers. However the central planner's perspective in operations management translates poorly to vertically separated chains, where suppliers may have rational myopic reasons to object to full in- formation sharing and centralized decision rights. Particular problems occur when a downstream coordinator demands relation-specific investments (equipment, cost improvements in processes, adaptation of components to downstream processes, allocation of future capacity etc) from upstream suppliers without being able to commit to long-term contracts. In practice and theory, this leads of- ten to a phenomenon of either underinvestment in the chain or costly vertical integration to solve the commitment problem. A two-stage supply chain under stochastic demand and information asymmetry is modelled. A repeated investment-production game with coordinator commitment in supplier's investment addresses the information sharing and asset- specific investment problem. We provide a mitigation of the hold-up problem on the investment cost observed by the supplier and an instrument for truthful revelation of private information by using an investment sharing device. We show that there is an interior solution for the investment sharing parameter and discuss some extensions to the work.supply chain management, investment, information

Examining price and service competition among retailers in a supply chain under potential demand disruption

© 2017 Elsevier Ltd Supply chain disruptions management has attracted significant attention among researchers and practitioners. The paper aims to examine the effect of potential market demand disruptions on price and service level for competing retailers. To investigate the effect of potential demand disruptions, we consider both a centralized and a decentralized supply chain structure. To analyze the decentralized supply chain, the Manufacturing Stackelberg (MS) game theoretical approach was undertaken. The analytical results were tested using several numerical analyses. It was shown that price and service level investment decisions are significantly influenced by demand disruptions to retail markets. For example, decentralized decision makers tend to lower wholesale and retail prices under potential demand disruptions, whereas a proactive retailer needs to increase service level with an increased level of possible disruptions. This research may aid managers to analyze disruptions prone market and to make appropriate decision for price and service level. The manufacturer or the retailers will also be able to better determine when to close a market based on the proposed analysis by considering anticipated disruptions. The benefits and usefulness of the proposed approach are explained through a real-life case adopted from a toy supply chain in Bangladesh

Centralized - Decentralized Demand Management And The Bullwhip Effect

Information distortion tends to inflate from downstream to upstream, and results in demand planning errors and this is followed by inaccurate forecasting. This situation is referred to as the bullwhip effect phenomenon. Demand management is also determined based on either a centralized or decentralized distribution strategy. Both strategies will influence the accuracy of demand planning and its effect on the bullwhip phenomenon. Unfortunately, research investigating the relationship between centralization and decentralization strategies for demand and the bullwhip effect is still limited. To answer this shortcoming, this paper has two scenarios for its analysis. First, forecasting is done to determine the accuracy of demand planning, indicated by the smallest forecast error value. Based on the results of the analysis, it is known that single exponential smoothing with alpha 0.5 is the best forecasting method. The second step is to measure the bullwhip effect; the results show that the coefficient is less than one. This coefficient indicated that the company underperformed in the fulfillment of its customers’ needs. Theoretically, this paper extends the literature on demand management in the supply chain by considering centralized and decentralized strategies

Review of Quantitative Methods for Supply Chain Resilience Analysis

Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested

SUPPLY CHAIN RISK MANAGEMENT IN AUTOMOTIVE INDUSTRY

The automotive industry is one of the world\u27s most important economic sectors in terms of revenue and employment. The automotive supply chain is complex owing to the large number of parts in an automobile, the multiple layers of suppliers to supply those parts, and the coordination of materials, information, and financial flows across the supply chain. Many uncertainties and different natural and man-made disasters have repeatedly stricken and disrupted automotive manufacturers and their supply chains. Managing supply chain risk in a complex environment is always a challenge for the automotive industry. This research first provides a comprehensive literature review of the existing research work on the supply chain risk identification and management, considering, but not limited to, the characteristics of the automotive supply chain, since the literature focusing on automotive supply chain risk management (ASCRM) is limited. The review provides a summary and a classification for the underlying supply chain risk resources in the automotive industry; and state-of-the-art research in the area is discussed, with an emphasis on the quantitative methods and mathematical models currently used. The future research topics in ASCRM are identified. Then two mathematical models are developed in this research, concentrating on supply chain risk management in the automotive industry. The first model is for optimizing manufacturer cooperation in supply chains. OEMs often invest a large amount of money in supplier development to improve suppliers’ capabilities and performance. Allocating the investment optimally among multiple suppliers to minimize risks while maintaining an acceptable level of return becomes a critical issue for manufacturers. This research develops a new non-linear investment return mathematical model for supplier development, which is more applicable in reality. The solutions of this new model can assist supply chain management in deciding investment at different levels in addition to making “yes or no” decisions. The new model is validated and verified using numerical examples. The second model is the optimal contract for new product development with the risk consideration in the automotive industry. More specifically, we investigated how to decide the supplier’s capacity and the manufacturer’s order in the supply contract in order to reduce the risks and maximize their profits when the demand of the new product is highly uncertain. Based on the newsvendor model and Stackelberg game theory, a single period two-stage supply chain model for a product development contract, consisting of a supplier and a manufacturer, is developed. A practical back induction algorithm is conducted to get subgame perfect optimal solutions for the contract model. Extensive model analyses are accomplished for various situations with theoretical results leading to conditions of solution optimality. The model is then applied to a uniform distribution for uncertain demands. Based on a real automotive supply chain case, the numerical experiments and sensitivity analyses are conducted to study the behavior and performance of the proposed model, from which some interesting managerial insights were provided. The proposed solutions provide an effective tool for making the supplier-manufacturer contracts when manufacturers face high uncertain demand. We believe that the quantitative models and solutions studied in this research have great potentials to be applied in automotive and other industries in developing the efficient supply chains involving advanced and emerging technologies

Understanding the Relationship Between Procurement Practices and the Utilization of Forced Labor

This literature review examines the relationship between procurement practices and the utilization of forced labor. As businesses strive to reduce expenses, one of the most significant areas of expenditure is labor. In forced labor, individuals are exploited and coerced into doing jobs or providing services for which they have not voluntarily undertaken. Globally, there are millions trapped in forced labor who are tied to their positions through debt bondage, threats, or fraud. Analysis of the determinants of forced labor, as well as possible techniques for mitigating it, will aid in the battle to end forced labor. A variety of literature sources about forced labor and supply chain management were used in the analysis. This paper discusses the roots of forced labor, supply chain practices and forced labor usage, and the future of forced labor. Within the roots of forced labor, the supply and demand drivers of forced labor are reviewed, as well as its political and institutional reinforcements. Supply chain management practices can be viewed in terms of forced labor mitigation with an understanding of product and labor supply chains, supplier-buyer power dynamics, strategic sourcing strategies, and supplier relationship management. In the future of combatting forced labor in supply chains, there should be a focus on establishing strong reporting standards, government intervention, and consumer awareness. As the structures that facilitate forced labor usage are recognized, they can be broken down to give exploited individuals freedom. As the effort to eradicate forced labor becomes globally united, the structures that facilitate its use can be broken down to give exploited individuals freedom