Literature survey shows that supply chain risk management is a fairly new area and knowledge is limited, but the area is very likely to grow as supply chains become more and more exposed to risks (Paulsson, 2003). Accordingly, supply chain risk and disruption management continues to attract more attention, especially in the operations research, supply chain management, and logistics communities (Brindley, 2004). Perea, et al. (2000) and Lin, et al. (2004) consider a simplified supply chain and present an approach based on control theory for managing one type of risk: demand uncertainty. Hallikas, et al. (2004) deals with risk management in supplier networks. They discuss a typical risk management process, which consists of risk identification, risk assessment, decision and implementation of risk management actions, and risk monitoring. They explain the general principles of each phase but do not elaborate the details. Since no structured and elaborate methodology for supply chain risk management has been reported so far, this paper attempts to propose one such methodology adopted from the process safety domain.
The main proposition of this paper is that methods from process risk management can be adopted to supply chains. This is worthwhile since the former is a well-established area and supply chains are in many ways comparable to a process plant. Supply chain operations share common basic features with chemical process operations. Both are structured as a network of entities with flows among them. Both take in some input of raw materials and result in some output of products. Both operations should be optimized, controlled and monitored continuously or continually. Making use of this similarity, this paper proposes to adopt methods from chemical process safety, hazard evaluation, and risk management to the supply chain domain.
Briefly, the typical framework for supply chain risk management consists of the following steps in sequence: risk identification, risk estimation, risk mitigation, risk assessment, and risk monitoring. We look at the risk identification step and study the efficacy of the proposed idea. There are a number of methods commonly used for hazard identification, e.g. checklist, what-if analysis, concept hazard analysis, HAZOP, scenario-based, hazard index, etc. In this paper, the HAZOP method is selected because it is one of the most widely used, structured and comprehensive hazard identification techniques.
The starting point in a HAZOP study is a detailed and well defined process design and description. Within the chemical (continuous) industries, this comes in the form of process flow diagrams (PFDs), process and instrumentation diagrams (P&IDs), and plot plans. To develop equivalent models in the supply chain context, we propose a Grafcet-like work-flow diagram to represent supply chain operations. Grafcet is a graphical language that has been accepted as an industrial standard for PLC-level sequential logic control (David and Alla, 1992). Each entity involved in the supply chain operations has its own work-flow diagram, which describes the sequence of tasks it performed for different signals. Each task requires some resources, takes in some input of flows from other entities, and results in some output of flows to other entities. Flows could be material, information, or finance. Resources reside in the entities for longer time scale than flows, e.g. manpower, equipment, etc. Risk identification is performed by using a set of guidewords (e.g. high, low, no, late, early, etc.) in combination with specific parameters (e.g. raw material flow, purchase order, etc.) to seek deviations from the design intention or normal operation. These deviations are studied to identify their causes, consequences, what safeguards exist for them, and propose possible mitigating actions.
A case study of risk identification in a refinery supply chain using the proposed method has been performed. The case study demonstrates that risk identification could be done in a structured and systematic way using the proposed method. Furthermore, the method is elaborate and detailed such that there is high probability of identifying most of the important risks. The disadvantage is the high resource requirement for the study, both manpower and time, from across supply chain entities. This paper provides a platform for future studies of employing other chemical process hazard evaluation techniques, both qualitative and quantitative, for supply chain risk management.
References: 1) Brindley, C. (2004). Supply Chain Risk. Ashgate, Hampshire. 2) David, R. and Alla, H. (1992). Petri Nets and Grafcet: tools for modeling discrete events systems. Prentice Hall International. 3) Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V. and Tuominen, M. (2004). Risk management processes in supplier networks. International Journal of Prodution Economics, 90, 47-58. 4) Lin, P. H., Wong, David S. H., Jang, S. S., Shieh, S. S., Chu, J. Z. (2004). Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis. Journal of Process Control, 14, 487-499. 5) Paulsson, U. (2003). Managing risks in supply chains – an article review. Presented at NOFOMA 2003, Oulu, Finland, June 12-13. 6) Perea, E., Grossmann, I., Ydstie, E., Tahmassebi, T. (2000). Dynamic modeling and classical control theory for supply chain management. Computers and Chemical Engineering, 24, 1143-1149.