A systematic approach for supply chain improvement using design structure matrix

Supply chain is a complex system that involves many system elements from various functional areas. Performance of a supply chain heavily depends on the effectiveness of communication and coordination among these system elements and functional areas. However, a large and complex supply chain usually makes it difficult to coordinate and thus degrades its performance. This paper focuses on the development of a systematic approach with the following objectives: (1) to identify and quantify the interactions among the system elements in a supply chain; (2) to decompose the large interdependent group of system elements into smaller and manageable sub-groups; and thus (3) to improve the structure of the supply chain system. A supply chain system is first decomposed into subsystems and system elements from which the interactions (i.e., independent, dependent and interdependent relationships) are studied and documented by design structure matrix (DSM). Next, the interaction strengths among the related system elements are quantified. Cluster analysis is used to decompose the large interdependent group into smaller ones in order to provide a better supply chain system structure. The effectiveness of this systematic approach is demonstrated by an illustrative example. The result shows that it is able to improve the system structure of a supply chain that will be useful for the supply chain reengineering.     

Economic value added of supply chain demand planning: A system dynamics simulation

The development of a successful demand plan is typically a joint effort between different functional units such as Logistics, Marketing, Sales and executive management at one hand and between different business units on the other. Starting a project to structurally improve the demand planning often requires convincing all parties involved in such an effort. The key is to quantify the bottom-line impact of an increased demand planning reliability in the supply chain. This paper proposes a system dynamics simulation modeling framework that allows different managers to examine how improvements in their demand reliability will impact the overall corporate bottom-line. For example, supply chain managers can investigate how proposed changes in the supply chain demand forecasting structure, different suppliers, different logistics routes, or alternative inventory methods, may increase the overall profitability. The simulation model has been tested, validated with a real-life case of LG. Philips Displays Europe.     

System for supply network management

Strategies and algorithms for operational planning and control are quite important to successful operations of a supply network. Implementation of a strategy requires substantial information system support, but few detailed designs of such systems have appeared in the literature. We designed an information system for a centralized management concept that could handle any type of strategy or algorithm. We developed algorithms for two different strategies deemed important by researchers and practitioners, push and pull, and implemented them in system modules. This information system performed well in simulations of the management of operations of an example six-stage supply network. This system provides an example for operational development as well as a platform for laboratory experiments.     

A model measurement system for collaborative supply chain partners

The purpose of this paper is to understand business performance in the context of an electronic component company. This paper developed a system dynamics model that describes supply chain process structure and examines scenarios, as well. Thus, this study adopted the signal-to-noise (SN) ratio defined by the Taguchi method to evaluate the robustness of a specific supply chain behavior. Resulting in poor inventory cost performance with uncertainty demand, this paper shows how the factor delivery time and lead time of an order can improve performance. Finally, this paper serves as a guideline for decisions that require different inventory strategies.     

A system dynamics framework for integrated reverse supply chain with three way recovery and product exchange policy

With the technological advancements and rapid changes in demand pattern, diverse ranges of products are entering into the market with reduced lifecycle which leads to the environmental disasters. The awareness of product take-back and recovery has been increasing in various supply chains not only due to the obligation imposed by legislation but also competitive economics worldwide. In this paper, we develop a system dynamics framework for a closed-loop supply chain network with product exchange and three way recovery policy, namely; product remanufacturing, component reuse and remanufacturing, and raw material recovery. In the simulation study, we investigate the significance of various factors including product exchange, collection and remanufacturing; their interactions and the type of their impact on bullwhip and profitability through sensitivity and statistical analysis. Our results suggest that the inclusion of three way recovery in reverse channel and product exchange policy in the forward channel reduce the order variation and bullwhip effect at both retailer and distributor level and increases the profitability of RL operations. In addition, we redefine the reverse logistics framework with “open-loop” in which the remanufactured products are redistributed only in the secondary market and compare the performance of open-loop model with that of closed-loop. Extended numerical investigation provides insights to the decision makers regarding the actions which can lead to better performance of the system.     

Performance evaluation of supply chain networks with assembly structure under system disruptions

Global supply chain networks (SCNs) with assembly structure are common in industry. System disruptions, due to various causes, can significantly influence the performance of the whole system. Differing from previous studies on supply disruptions, this paper aims to develop a discrete time model, which could describe the characteristics of network-wide system disruptions and provide rapid performance evaluation of SCNs with assembly structure. Considering system disruptions at any stage of the whole system, we develop new iterative methods to obtain the key performance measures of SCNs with assembly structure in both lost sales and backorder scenarios. The proposed methods are verified through a series of numerical experiments. Our results suggest that component suppliers with higher reliability in the downstream stages will deliver better system performance in both lost sales and backorder scenarios.     

SCOlog: A logic-based approach to analysing supply chain operation dynamics

In a complex business world, characterised by globalisation and rapid rhythms of change, understanding supply chain (SC) operation dynamics is crucial. This paper describes a logic-based approach to analysing SC operation dynamics, named SCOlog. SC operation is modelled in a declarative fashion and it is simulated following rule-based execution semantics. This approach facilitates the automated explanation of simulated SC operational behaviours and performance. The automated explanation support provided by SCOlog is found to improve the understanding of the domain for non-SCM experts. Furthermore, SCOlog allows for maintainability and reusability.     

A new network epsilon-based DEA model for supply chain performance evaluation

Supply chain performance evaluation problems are inherently complex problems with multilayered internal linking activities and multiple entities. Data Envelopment Analysis (DEA) has been used to evaluate the relative performance of organizational units called Decision Making Units (DMUs). However, the conventional DEA models cannot take into consideration the complex nature of supply chains with internal linking activities. Network DEA models using radial measures of efficiency are used for supply chain performance evaluation problems. However, these models are not suitable for problems where radial and non-radial inputs and outputs must be considered simultaneously. DEA models using Epsilon-Based Measures (EBMs) of efficiency are proposed for a simultaneous consideration of radial and non-radial inputs and outputs. We extend the EBM model and propose a new Network EBM (NEBM) model. The proposed NEBM model combines the radial and non-radial measures of efficiency into a unified framework for solving network DEA problems. A case study is presented to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed method to a supply chain performance evaluation problem in the semiconductor industry.     

Optimizing supply chain network for perishable products using improved bacteria foraging algorithm

In a supply chain environment, time delay has a significant impact on the success of perishable products. A major concern is therefore aimed at development of a holistic optimized approach in a supply chain environment for perishable products. Thus, integration of production, inventory and, distribution of perishable products in a supply chain environment are the challenging tasks for practitioners and researchers. In general, the standard optimal supply chain model cannot work for perishable products. There is therefore, a need for a holistic model that focuses on the consolidation of the processes. Shorter product shelf-life, temperature control, requirement of strict tractability, large number of product variants, and a large volume of goods handled are the major challenges in a supply chain environment for perishable products. The present work focuses on the development of a holistic model which uses improved bacteria forging algorithm (IBFA) for solving the formulated model. We have proposed and analyzed some general properties of the model and, finally applied it to a three-stage supply chain problem using an IBFA. Two case studies have been considered for support and demonstration of the integrated perishable supply chain network problem. Results obtained from IBFA reveal that the proposed model is more useful for decision makers while considering optimal supply chain network for perishable products. Finally, validation of results has been carried out using bacteria forging algorithm (BFA). The computational performance of the proposed algorithm proves that IBFA is instrumental in effectively handling the proposed approach.     

A review of supply chain complexity drivers

Studies on supply chain complexity mainly use the static and dynamic complexity distinction. While static complexity describes the structure of the supply chain, the number and the variety of its components and strengths of interactions between these; the dynamic complexity represents the uncertainty in the supply chain and involves the aspects of time and randomness. This distinction is also valid when classifying the drivers of supply chain complexity according to the way they are generated. Supply chain complexity drivers (e.g., number/variety of suppliers, number/variety of customers, number/variety of interactions, conflicting policies, demand amplification, differing/conflicting/non-synchronized decisions and actions, incompatible IT systems) play a significant and varying role in dealing with complexity of the different types of supply chains (e.g., food, chemical, electronics, automotive).     

A new approach to supply chain network equilibrium models

We establish nonlinear complementarity formulations for the supply chain network equilibrium models. The formulations have simple structures and facilitate us to study qualitative properties of the models. In this setting, we obtain weaker conditions to guarantee the existence and uniqueness of the equilibrium pattern for a supply chain. A smoothing Newton algorithm that exploits the network structure is proposed for solving these models. Not only is the smoothing Newton algorithm proved to be globally convergent without requiring the assumptions of monotonicity and Lipschitz continuity, but also it can overcome the flaw that the performance of the modified projection method heavily depends on the choice of the predetermined step size. Numerical results indicate the advantages of the nonlinear complementarity formulation and the smoothing Newton algorithm.     

A multi-objective optimization for green supply chain network design

In this paper, we study a supply chain network design problem with environmental concerns. We are interested in the environmental investments decisions in the design phase and propose a multi-objective optimization model that captures the trade-off between the total cost and the environment influence. We conduct a comprehensive set of numerical experiments. The results show that our model can be applied as an effective tool in the strategic planning for green supply chain. Meanwhile, the sensitivity analysis provides some interesting managerial insights for firms.     

An agent-based framework for collaborative negotiation in the global manufacturing supply chain network

This paper applies the multi-agent system paradigm to collaborative negotiation in a global manufacturing supply chain network. Multi-agent computational environments are suitable for dealing with a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving agents. An agent-based multi-contract negotiation system is proposed for global manufacturing supply chain coordination. Also reported is a case study of mobile phone global manufacturing supply chain management.     

Decomposition heuristic to minimize total cost in a multi-level supply chain network

In this paper, the distribution planning model for the multi-level supply chain network is studied. Products which are manufactured at factory are delivered to customers through warehouses and distribution centers for the given customer demands. The objective function of suggested model is to minimize logistic costs such as replenishment cost, inventory holding cost and transportation cost. A mixed integer programming formulation and heuristics for practical use are suggested. Heuristics are composed of two steps: decomposition and post improving process. In the decomposition heuristics, the problems are solved optimally only considering the transportation route first by the minimum cost flow problem, and the replenishment plan is generated by applying the cost-saving heuristic which was originally suggested in the manufacturing assembly line operation, and integrating with the transportation plan. Another heuristic, in which the original model is segmented due to the time periods, and run on a rolling horizon based method, is suggested. With the post-improving process using tabu search method, the performances are evaluated, and it was shown that solutions can be computed within a reasonable computation time by the gap of about 10% in average from the lower bound of the optimal solutions.     

Supporting decisions in real-time enterprises: autonomic supply chain systems

Supporting decisions in real time has been the subject of a number of research efforts. This paper reviews the technology and architecture necessary to create an autonomic supply chain for a real-time enterprise for supply chain systems. The technologies weaved together include knowledge-based event managers, intelligent agents, radio frequency identification (RFID), database and system integration, and enterprise resource planning systems. This article is part of the “Handbook on Decision Support Systems” edited by Frada Burstein and Clyde W. Holsapple (2008) Springer.     

Measuring supply chain resilience using a deterministic modeling approach

To achieve a competitive edge in an uncertain business environment where change is imperative, one of the significant challenges for an organization is to mitigate risk by creating resilient supply chains. This research proposes a model using graph theory which holistically considers all the major enablers of resilience and their interrelationships for analysis using an Interpretive Structural Modeling approach. The uniqueness of this model lies in its ability to quantify resilience by a single numerical index. The quantification of resilience will help organizations assess the effectiveness of various risk mitigation strategies. This will provide tools for managers to compare different supply chains while offering a deeper knowledge of how supply chain characteristics increase or decrease resilience and consequently affect supply chain risk exposure. Thus, the research supports organizations in measuring and analyzing supply chain resilience and facilitates supply chain decision-making. The proposed method could simplify the dynamic nature of environment for managing disruptions in a supply chain. This novel approach for determining the supply chain resilience index (SCRI) advocates the consideration of resilience aspects in supply chain design, thus giving a competitive advantage to achieve market share even during a disruption.     

Supply chain network design under oligopolistic price and service level competition with foresight

In this paper a new bi-level model for designing the network structure of a competitive supply chain (SC) is presented with anticipating variable prices and service levels competition in markets under stochastic price and service level dependent elastic demands with the presence of existing, external rivals. The network structure of the new entrant SC would be designed under the limited production capacity of its producers in a way to maximize its future capturable profit in the competitive markets. The network of the new SC is assumed to be set “once and for all” but further price and service level adjustments are possible. Outer part of this bi-level model deals with strategic decisions of SC network design. Given the SC network structure assigned by the outer model in each iteration, the inner equilibrium model determines the equilibrium retail prices and service levels. Finally, we illustrate the model through several numerical examples.     

Interactions of agents in performance based supply chain management

To acquire a competitive advantage in the expanding market, manufacturing companies should be able to manage their supply chain as much effective as possible. Measuring the supply chain performance is one of the main indicators of business success. Supply chain management (SCM) involves managing the flow of materials from suppliers to manufacturing units. This paper proposes a SCM model with performance measurement capabilities. The model is designed in such a way that it well suits to multi agent systems and related implementations. This paper highlights the components of the model especially pointing out the key parameters of performance indicators.     

Supply chain dynamics and the “cross-border effect”: The U.S.–Mexican border’s case

A system dynamics model is proposed for analyzing the uncertainty caused by delays and disruptions at the U.S.–Mexican border, and how their effects propagate through the cross-border supply chains. Since Mexico’s geographic proximity and low wages provide logistics advantages to North American Free Trade Agreement (NAFTA), it is becoming a favored manufacturing and logistics location. Nonetheless, crossing the border between U.S. and Mexico remains one of the most important challenges to the NAFTA supply chain competitiveness. Based on literature review and real-life information, the security policies at the U.S.–Mexican border and their cost implications to cross-border supply chains are identified. Information regarding the impact of variability on supply chain dynamics due to “cross-border effect” derived of security inspection policies is provided. Results are based on an auto-industry case study that was chosen due to its process standardization; however, results could be applied to other global supply chains. As conclusions, implications for the design of cross-border supply chains are exposed and future research is presented.     

Design of agile supply chain assessment model and its case study in an Indian automotive components manufacturing organization

Agile Manufacturing (AM) paradigm is fast instilled in modern organizations. AM enables an organization to evolve products and services quickly and economically in response to the customers’ dynamic demands. The effectiveness of AM is largely determined by the performance of Agile Supply Chains (ASC). In order to assess their performance, an ASC assessment model was reported in this research paper. This model is encompassed with agile supply chain attributes whose performance levels need to be determined for assessing the overall ASC performance of the organization. The computation was performed using fuzzy logic approach. The working of this model was examined by conducting a case study in an Indian automotive components manufacturing organization. The experience gained by conducting this case study favored the use of a computerized system which will ensure accuracy of computations involving fuzzy logic.