The strategic, tactical and operational decision-making within the supply chain are recognized to be important OR/Management Science problems. In the development of analytic models, these decisions have been often considered separately. Recently, practitioners and researchers have realized the close interactions between these three levels of decision-making and new research activities are aimed at modelling these strategic, tactical and operational decisions. Planning decisions are under-pinned by three salient concepts—namely, irreversibility, uncertainty and the choice of timing. First, an investment planning decision is partially if not completely irreversible. Second, the future returns from a given investment (decision) are uncertain. Finally, it is always possible to make a choice in respect of the timing of the (investment) decisions. Also the focus of decision-making has moved from planning for the expected return to the issues which concern the risks of the underlying decisions. Whereas excellent tools for measuring and managing risk have been developed for finance professionals; investment bankers, fund managers, credit analysts; only recently such analyses in the manufacturing and retail industries have attracted attention and consideration of managers and planners.
We believe that there will be an increasing relevance of business analytics which encapsulates the risk and uncertainty in the supply chain. The collection of papers that make up this special issue clearly justifies this assertion. We now briefly consider the focus of the papers in this collection:
In the paper titled ‘An emergent framework for supply chain risk management and performance measurement’, Bob Ritche and Clare Brindley propose a framework consisting of five main components in the supply chain where the risks have to be managed and measured. Through a case study, they highlight the importance of communication between the different players in the supply chain in order to better manage the risk.
In the paper ‘Supply chain operations in the presence of a spot market: a review with discussion’, Cagri Haksoz and Sridhar Seshadri discuss the use of derivatives contract in order to hedge the procurement risk between a buyer and a seller. The authors review the current research literature and discuss the design and valuation of such financial contracts.
In the paper ‘Global Supply chain risk in the consumer electronics industry’, Manmohan S Sodhi and Seongha Lee consider and classify the different sources of risks faced by the supply chain management systems in a consumer electronic company. They elaborate the steps taken by the company in order to mitigate against these risk factors.
In the paper ‘Consumer Risk control and inspection in co-operative supply chain’, the author, CS Tapiero, discusses game theoretic approaches in order to analyse the strategies for the sharing of risks in a supply chain having a single supplier and a single producer. The author uses Neymann–Pearson statistical theory to demonstrate how the risk control and inspection can be mitigated when the supplier and the producer have the flexibility to choose the sampling plan.
In their paper ‘Manufacturer's pricing strategy’, Arcelus, Kumar and Srinivasan analyse the relationship between the wholesaler and a retailer under different situations. These situations involve the sharing of information on the consumer's demand and the manufacturer's knowledge of the risk profile of the retailer.
In the paper: ‘Managing supply chain: An alternative forecasting’, Dutta, Barari, Grainger and Gibbs provide a novel application of time series econometrics in the supply chain planning. Such an approach allows a centralized supply chain model where information is made available to all participating businesses at various stages of the supply chain. Applying this method it is possible to reduce the Bull whip effect and its associated inefficiencies.
In our paper ‘Robust solutions and risk measures for a Supply chain planning problem under uncertainty’ (Chandra A Poojari, Cormac Lucas and Gautam Mitra), we consider a strategic supply chain planning problem formulated as a two-stage stochastic integer programming (SIP) model. The large-scale SIP problem is solved through Benders' decomposition, and we approximate the probability distribution of the random variables using the Generalized Lambda distribution and through simulations, calculate theperformance statistics and the risk measures for the two models, namely the expected-value of the here-and-now.
The papers in this special issue reinforce our claim that the trend is towards the adoption of concepts and techniques used for risk management in finance. We hope that this special issue will stimulate more research in this topic.
Acknowledgements
We wish to thank Mrs Ann Wilkes and Ms Deborah Banyard for supporting us splendidly in the communications with the authors and the referees. We also thank the referees, who contributed their time, benefit of their knowledge and provided insightful suggestions.