Supply chain multi-state risk assessment using universal generating function

Abstract

This paper proposes a new approach to assess the supply risk beyond the classical binary assumption of either delivering the whole quantity or not. Given today’s stochastic nature of supply and the dynamic nature of demand, the different supply activities along with the chain exhibit a multi-state behaviour increasing the complexity of their risk assessment. The new approach utilizes the universal generating function (UGF) to model the different suppliers’ echelons with their various supply risk levels along all the stages of the supply chain as a multi-state risk system. The developed model was successfully implemented to assess the supply risk in a multi-state strawberry supply chain and outperformed classical approaches. Results from the case study and the validation analysis illustrated the ability of the new approach to capture the various supply levels with their associated risks leading to more informative risk assessment process. Furthermore, the developed model improved the visibility for the purchasing managers downstream in terms of the different trade-offs between supply levels and their risks as well as some financial thresholds. The new multi-state approach contributes to the emerging supply chain risk assessment trend by offering a more realistic modelling method to capture the risk of all available supply levels along the delivery chain.

Keywords:

• Supply chain risk management
• risk assessment
• universal generating function

Notation

D  Demand level

P_i  The probability distribution of the supply volume level i

R_i  The risk associated with every supply volume level i

S_i  Supply volume level i

U(Z) Universal moment generating function

Z  The argument of the moment generating function

Ω  Composition operator

π  Parallel composition operator

σ  Series composition operator

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Ahmed M. N. Mohib

Ahmed M. N. Mohib is currently an assistant professor in industrial engineering Department at Nile University (NU). After completing his BSc and MSc in Mechanical Design and Production Department in Faculty of Engineering, Cairo University, he joined the University of Windsor in Canada from which he obtained his Ph.D. in the field of inspection process planning following which he was appointed as a post-doctoral research associate at the IMSC. He worked in Fayoum University since graduation. He spent two years in California polytechnic state University as a visiting professor. Research interests span several areas of industrial and manufacturing engineering research; generally concerned with modeling and optimization of manufacturing and service systems with different applications such as maintenance planning, inspection planning, production planning, facility design, energy optimization, quality and supply chain management.

Ahmed M. Deif

Ahmed M. Deif is an associate professor of supply chain and operation management at Cal Poly. He is also an adjunct professor in multiple universities in Canada, Latin America and Egypt. His current research interests are in supply chain innovation, lean and green manufacturing/service systems and dynamic analysis of operation systems. He has more than 65 publications. Dr. Deif has a diverse portfolio in his industrial experience ranging from automotive industry to steel industry to electronic industry and finally assembly industry at various engineering and consultancy capacities for companies across the world. Dr. Deif is a senior member of SME and member of IIE, APICS, ATMAE and IEOM organizations. He served on the scientific committee of several international conferences as well as a reviewer for many international journals. Dr. Deif received his MS and PhD in Industrial & Manufacturing Systems Engineering from the University of Windsor, ON, Canada. He earned his BS in Mechanical Engineering from the American University in Cairo, Egypt.