Robust Supply Chain Research Articles

Life-cycle Risk Modeling: Alternate Methods Using Bayesian Belief Networks

In recent years natural and man-made disasters have highlighted the need for robust supply chain risk management (SCRM) in manufacturing firms from a life-cycle perspective (pre-manufacturing, manufacturing, use, post-use stages). Bayesian Belief Networks (BBN) provide a means to probabilistically represent risk interdependencies and to proactively identify and manage any existing vulnerabilities. In this work, the BBN method is implemented for a product in the aerospace industry. Risk network maps are developed to identify interdependencies and describe the potential risk propagation behavior during each life-cycle phase and from one phase to another. Due to limited number of respondents and lack of certainty with respect to the post-use phase, enhanced methods of risk likelihood assessment are necessary specifically for the post-use phase assessment. In this paper two alternate techniques are compared for risk modeling using BBN in such situations: Boolean nodes and numeric simulation nodes. Results show that numeric nodes provide a more thorough explanation of the interconnections of the risk items modeled. Further enhancement using an approach that combines both BBN and System Dynamics (SD) for SCRM is discussed and possible variations for linking variables between SD and BBN are also presented.

Robust design of (s, S) inventory policy parameters in supply chains with demand and lead time uncertainties

Demand and lead time uncertainties have significant effects on supply chain behaviour. In this paper, we present a single-product three-level multi-period supply chain with uncertain demands and lead times by using robust techniques to study the managerial insights of the supply chain inventory system under uncertainty. We formulate this problem as a robust mixed-integer linear program with minimised expected cost and total cost variation to determine the optimal (s, S) values of the inventory parameters. Several numerical studies are performed to investigate the supply chain behaviour. Useful guidelines for the design of a robust supply chain are also provided. Results show that the order variance and the expected cost in a supply chain significantly increase when the manufacturer’s review period is an integer ratio of the distributor’s and the retailer’s review periods.

The design of robust value-creating supply chain networks

This paper provides a methodology for supply chain network (SCN) design under uncertainty. The problem is initially casted as a two-level organizational decision process: the design decisions must be made here and now, but the reengineered SCN can be used for daily operations only after an implementation period. The network structure can also be adapted during the planning horizon considered. When making the design decisions, the operational response and structural adaptation decisions taking place during the planning horizon must be anticipated. The methodology recognizes three event types to characterize the future SCN environment: random, hazardous and deep uncertainty events. At the design time, plausible futures are anticipated through a scenario planning approach. Several Monte Carlo scenario samples are generated and corresponding sample average approximation programs are solved in order to produce a set of alternative designs. A multi-criteria design evaluation approach is then applied to select the most effective and robust design among candidate solutions. An illustrative case, based on the location---transportation problem, is finally introduced to illustrate the approach, and computational experiments are performed to demonstrate its feasibility.

Taking health systems research to the district level: a new approach to accelerate progress in global health

The world recently marked the tenth anniversaries of the launch of both The Global Fund to Fight AIDS, Tuberculosis, and Malaria and the United States President’s Emergency Plan for AIDS Relief (PEPFAR). A decade ago, few could have possibly imagined how different the world would look today from the future we feared — a future where the country where one lived determined if one lived and where the rich increasingly tried to seal themselves off from the poor. Due to the courage of tens of thousands of activists across the globe who put their bodies on the line, the innovation of scientists who refused to relent in the face of complexity, and the vision of policymakers across six continents who imagined a more just and healthy future, we live today in a world that has started to be defined more by our connections than our differences. This is global health: We have come a long way, but the work is not finished. As the world learned through the challenge of scaling up access to prevention, care, and treatment for HIV, tuberculosis, and malaria in countries with sparse health infrastructure, simply having the tools is not enough. Bypassing the public sector — weak as it might be at the outset — to get pills to patients faster may have been easier and did address some of the symptoms of decades of nonexistent access to quality health care. But this approach could never guarantee the poorest and most vulnerable access to health as a right[1]. That, it is now clear, requires a financially and geographically accessible health system complete with robust supply chains, an adequately staffed workforce with the right skill level and skill mix, and feedback loops driven by information and research — all rooted in the foundation of accountability provided by a strong and transparent public sector is required [2].

Super facilities versus chaining in mitigating disruptions impacts

Dealing disruptions has increasingly attracted researchers’ attention in the last decades due to recent events: weather deregulation, natural disasters, financial crisis, etc. Researchers often dealt with the strategic aspect of the problem while making facility location decisions to build a robust supply chain. In this paper we address the flexibility aspect. We consider the problem of allocating demand arising from a set of products to a set of dedicated facilities. The facilities are subject to disruption and the demand is then lost. To mitigate disruption impacts, we consider the use of a super facility that can hold the demand of products when the dedicated facilities are under failure. In systems with identical products and facilities, we propose an algorithm that can be used to determine the optimal capacity of the super facility so as to minimize the sum of capacity investment, demand allocation and lost sales cost. Finally we compare the performance of the super facility configuration to that of the single chain configuration. The single chain refers to a facility configuration where each facility is configured to fulfill only two products and each product can be assigned to only two facilities and the whole system forms a closed chain.

IBM Blends Heuristics and Optimization to Plan Its Semiconductor Supply Chain

IBM uses operations research techniques to plan its enterprise semiconductor supply chain. The scale and complexity of this planning problem make developing robust supply chain optimization tools a challenge. Pure optimization methods are computationally infeasible, and fast heuristic methods alone generate poor results. Consequently, we developed a method that decomposes the problem by dividing the bills of materials product structure horizontally and vertically into complex and simple portions that are based on the major stages in semiconductor manufacturing and the choices of supply chain paths for building parts. The method then solves the complex portions with a mixed-integer program and the simple portions with fast heuristics that contain small embedded linear programs. A unique pegging algorithm, an explosion heuristic, and an implosion linear program enable coordination among these portions. The result is a unified production, shipping, and distribution plan with no evidence of the original decomposition. This method has helped IBM to improve its asset utilization, customer service, and inventory levels.

Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case

We have developed a stochastic mathematical formulation for designing a network of multi-product supply chains comprising several capacitated production facilities, distribution centres and retailers in markets under uncertainty. This model considers demand-side and supply-side uncertainties simultaneously, which makes it more realistic in comparison to models in the existing literature. In this model, we consider a discrete set as potential locations of distribution centres and retailing outlets and investigate the impact of strategic facility location decisions on the operational inventory and shipment decisions of the supply chain. We use a path-based formulation that helps us to consider supply-side uncertainties that are possible disruptions in manufacturers, distribution centres and their connecting links. The resultant model, which incorporates the cut-set concept in reliability theory and also the robust optimisation concept, is a mixed integer nonlinear problem. To solve the model to attain global optimality, we have created a transformation based on the piecewise linearisation method. Finally, we illustrate the model outputs and discuss the results through several numerical examples, including a real-life case study from the agri-food industry.

Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty

In this study, a general comprehensive model is proposed for strategic closed-loop supply chain network design under interval data uncertainty. The proposed model considers various assumptions such as multiple periods, multiple products, and multiple supply chain echelons as well as uncertain demand and purchasing cost. In addition, bill of materials for each product is considered via a new approach in management of forward and reverse flows of products for producing new products and reusing or disassembling returned products. Uncertainty of parameters in the proposed model is handled via an interval robust optimisation technique. The model assumptions are well matched with decision making environments of food and high-tech electronics manufacturing industries. The factors that make these two industries similar are time-dependent properties of products such as prices and warehousing lifetime period. The computational results of solving the proposed model via LINGO 8 demonstrate efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context.

Supply Chain Risk Prioritization Using a Hybrid MCDM Approach

In recent years, supply chains have become increasingly vulnerable to risks resulting in poor financial performance and customer service. Managing the supply chain risks is a challenging task. Resilience and responsiveness based strategies are being attempted to build a robust supply chain. To select the appropriate mitigation strategies, supply chain risks are to be prioritized. Supply chain risk prioritization is a Multi Criteria Decision Making (MCDM) problem. MCDM models for supply chain risk prioritization needs attention. In this research, a hybrid Analytic Hierarch Process (AHP) and Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) approach is proposed for supply chain risk prioritization. A case example of a typical tractor industry is presented to illustrate the performance of the proposed approach.

Building robust supply chains by reducing vulnerability and improving resilience

Powerful socioeconomic factors have been transforming dramatically supply chain networks, boosting the pursuit for resilience against disruptions, conflicts of interests and economic policies. As companies have to deal with disruptions that their outcome can have significant financial ramifications, both the academic world and industry keep now security and uninterrupted product flow from suppliers to end-users under close scrutiny. In this work, we study one of the dimensions of sustainable supply chains, that of supply chain risk management. To this end we provide definitions, methods, procedures, regulatory interventions aiming in reducing vulnerability and improving resilience as well as related business cases.

Supply chain risk prioritisation using a hybrid AHP and PROMETHEE approach

Supply chain is a network of facilities designed to procure, produce, and distribute goods to customers at right quantities, to the right locations and at the right time. Supply chain studies are focused for improving the operational efficiency through cost reduction. In recent years, supply chains have become increasingly vulnerable to risks resulting in poor financial performance and customer service. Managing the supply chain risks is a challenging task. Resilience and responsiveness-based strategies are being attempted to build a robust supply chain. To select and evaluate the appropriate mitigation strategies, supply chain risks are to be prioritised. Supply chain risk prioritisation is a multi criteria decision making (MCDM) problem. Developing the MCDM models for supply chain risk prioritisation need attention. In this research a hybrid analytic hierarch process (AHP) and preference ranking organisation method for enrichment evaluation (PROMETHEE) approach is proposed for supply chain risk prioritisation. A case example of a typical plastic industry is presented to illustrate the performance of the proposed approach.

A framework for designing robust food supply chains

After years of emphasis on leanness and responsiveness businesses are now experiencing their vulnerability to supply chain disturbances. Although more literature is appearing on this subject, there is a need for an integrated framework to support the analysis and design of robust food supply chains. In this paper we present such a framework. We define the concept of robustness and classify supply chain disturbances, sources of food supply chain vulnerability, and adequate redesign principles and strategies to achieve robust supply chain performances. To test and illustrate its applicability, the research framework is applied to a meat supply chain.

Robust supply chain management

Robust supply chain management

Innovation Impacts on Biomass Supply in Maine's Logging Industry

A robust supply chain is critical to ensure a sustainable supply of feedstock to the existing and emerging bioenergy and bioproducts industries. Logging contractors are a key group in this process, since they provide harvesting and transportation services, and their success is directly linked to innovation activities. Surprisingly, very little is known about the innovation system in the logging industry—especially about how it relates to biomass supply. Failure to understand how logging contractors adopt and implement biomass production technologies could lead to failed innovation efforts, unmet development goals, and a lack of properly equipped contractors. This article presents results from a series of case studies of highly innovative logging contractors in Maine. All of the firms had some experience producing biomass within their operations. The firms had also used multiple biomass harvesting technologies. This study highlights the variation in challenges that led to the adoption (or rejection) of biomass as a product innovation—with particular emphasis on harvesting technologies. A major finding of this study was the need for a high degree of collaboration between landowners, logging contractors, and biomass consuming facilities in the innovation process. The future development of the biomass industry is highly dependent on contractors adopting biomass harvesting and related technologies. The innovation process of logging firms is an area that is not sufficiently studied, and this research provides valuable insight into this important component of the forest biomass industry.

A framework for designing robust supply chains considering product development issues

The primary objective of the design for supply chain (DFSC) is the selection of an appropriate product family. Moreover, it deals with the selection of the optimal combination among the different conflicting criteria while making a trade-off between the supply chain cost, sales profit and the product design complexities. In this research, to address the DFSC issues a product platform approach has been proposed which amalgamates the component modularity as well as the function modularity in the product design. The optimisation model proposed in this paper for the product development and the supply chain design is based on a generic bill of materials (GBOM) representation. The complete framework includes vital decision-making needed for designing a robust supply chain such as locating plants to alleviate the likely dominance of production cost and market mediation cost on product variety and imparting process flexibility of the located plants. The optimisation model proposed in this paper, models the supply chain cost, sales profit and product design complexity as three criteria that altogether determine the robustness of the supply chain and the underlying product development approach. Certain parameters like process flexibility, flow types and drivers of the product variety dominance have been controlled in the design framework. To resolve the complexity of the proposed model a genetic algorithm (GA) technique has been proposed. The proposed GA adopts an arithmetic crossover, a dynamic mutation and a variable penalty strategy to produce optimal results in a very short computational time. To validate the proposed model, a simulated case study of the wiring harness supplier of an AGV manufacturer has been studied.

Contributors

Contributors

Agent-based experimental investigations of the robustness of tactical planning and control policies in a softwood lumber supply chain

This article investigates the robustness of different tactical planning and control policies for a softwood supply chain using an agent-based environment that simulates a distributed advanced planning and scheduling system and its corresponding supply chain operations. Simulations were modelled using a novel agent-based methodology combined with a robust experimental design approach and an industrial data set obtained from two companies in Eastern Canada. Experimental results provide insights about the dynamic relations among factors related to control levels, planning methods and planning horizon lengths. Results indicate that supply chain control levels play a relevant role in defining robust service levels, while the planning horizon and the planning method have lower impact in this context. In addition, from the supply chain inventory level point of view, we verified that the three investigated tactical rules have to be configured together if one desires to maximise their contribution for a robust supply chain system capable of coping with uncertainties from the business environment. When these rules are evaluated individually, it is not possible to make the most of their potential due to interactions between them. The article concludes by proposing an optimum robust configuration of the tactical rules to minimise the impact of supply chain uncertainties.

Application of a scientific approach to evaluate IC industrial supply chain linkage

The current studies on supply chain management are limited in their analysis of the linkages between firms in related industries. This study estimates the degree of linkages among designers, manufacturers and firms of testing and packaging. Significant linkage is demonstrated by the high correlation coefficients of the RSP ratio in IC industrial supply chain. But the magnitude of linkage is low by regression analysis. This paper has contributed to: (1) the development of strategic relationship by collaboration between upstream and downstream of the chain partners between firms in IC industry; and (2) to enhance robust IC supply chain by utilizing supply chain linkages from mutual cooperation. Key words: Supply chain linkage, IC industry, regression analysis.

Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands

A synchronized and responsive flow of materials, information, funds, processes and services is the goal of supply chain planning. Demand planning, which is the very first step of supply chain planning, determines the effectiveness of manufacturing and logistic operations in the chain. Propagation and magnification of the uncertainty of demand signals through the supply chain, referred to as the bullwhip effect, is the major cause of ineffective operation plans. Therefore, a flexible and robust supply chain forecasting system is necessary for industrial planners to quickly respond to the volatile demand. Appropriate demand aggregation and statistical forecasting approaches are known to be effective in managing the demand variability. This paper uses the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating, forecasting and disaggregating two interrelated demands. Through theoretical development and systematic analysis, guidelines are provided to select proper demand planning approaches. A very important finding of this research is that disaggregation of a forecasted aggregated demand should be employed when the aggregated demand is very predictable through its positive autocorrelation. Moreover, the large positive correlation between demands can enhance the predictability and thus result in more accurate forecasts when statistical forecasting methods are used.

Evaluation of forecasting error measurements and techniques for intermittent demand

This paper is a part and contribution to develop flexible and robust supply chain forecasting systems under changing market demands. It suggests new tools and models to evaluate forecasting error measurements. The paper especially studies slow moving or intermittent demand, when for items the forecasting time periods often have zero demand. For the difficult to forecast intermittent demand the Croston forecasting technique is mostly regarded as a better choice than single exponential smoothing. These two methods, Croston and single exponential smoothing, together with two modifications of the Croston method, are discussed and evaluated with real intermittent data. The apprehended performance of a forecasting technique is dependent of the chosen measurement of forecast errors. The main purpose is to examine and evaluate different forecasting error measurements. Traditional measurements of forecast errors are studied, mean absolute deviation ( MAD), mean square error ( MSE), etc. together with new suggested error and bias measurements: “periods in stock” ( PIS) and “number of shortages” ( NOS). PIS considers the time aspect of the forecast error, NOS considers the development of cumulated forecast error ( CFE). A complementary measure for descriptive statistics of time series is also suggested, mean average change. The conclusion, through principal components analysis ( PCA), is that a single measure of forecast errors does not present the total different dimensions of the errors; complementary error measures must be used.