On the link between inventory and responsiveness in multi-product supply chains

Abstract

Manufacturing systems in many industries face the challenge of manufacturing products that are assembled from multi-variant components. Demand for these component variants is correlated, and often subjected to an overall capacity constraint (e.g. fixed production volumes in the final assembly plant). Therefore, the modelling of supply chain systems under multi-variant product conditions is conceptually difficult as the demands for the individual variants are not independent. Previous approaches to modelling supply chain systems have commonly dealt with this complication by either focusing on single-product multi-tier systems, or single-tier systems with multiple products. In this article, we propose a modelling approach capable of studying the responsiveness of multi-tier systems with correlated demands, which is used as the input for a simulation model. Based on the simulation outcomes, generic relationships between product variety, responsiveness and inventory levels in a supply chain system are derived; and a novel safety stock formula for such settings is developed.

Methodological approach to study the dynamics of production networks: discrete-event simulation modelling

“…In last decade, an increasing attention has been devoted to the phenomenon and to its deleterious consequences. Several issues have been investigated within the BE and supply chain field such as: reverse logistic (Zhou and Disney, 2006), multi-product (Reichhart et al, 2008), stochastic lead times (Boute et al, 2007), capacity constraints (Yuan and Ashayeri, 2009), flexibility (Jain et al, 2009;Hanna et al, 2010), batching (Potter and Disney, 2006;Hussain and Drake, 2011), collaboration (Matopoulos et al, 2007;Darwish and Goyal 2011;Sujatha, 2011), order policies (Azadeh et al, 2011;Cannella et al, 2011), pricing (O'Donnell et al, 2009), and performance measurement (Wong and Wong, 2008;Keebler and Plank 2009;Pettersson and Segerstedt, 2011) amongst others. Analogously, several studies have addressed the methodological approaches to study the dynamics of supply chains (Riddalls et al, 2000;Kleijnen and Smits, 2003;Dejonckheere et al, 2004;Holweg and Disney, 2005;Geary et al, 2006;Lemmens, 2006, Towill et al, 2007;Disney and Lambrecht, 2008;Nilakantan, 2010;Ciancimino and Cannella, 2011;Lättilä, 2011;Scholz-Reiter et al, 2011).…”

Operations research models and methods for safety stock determination: A review

“…Reichhart et al. [68] developed a novel and accurate safety stock formula for multi-variant products and responsive systems, by means of a Monte Carlo simulation process. An adjusted term for the standard deviation of forecasting errors is included in their formulation.…”

Reliability evaluation of a multistate network subject to time constraint under routing policy

“…That implies a transmission line has several states in which state p means p cables are normal. The multistate network model can be applied to supply chains (Lin 2006(Lin , 2010a, which in many industries face the challenge of manufacturing and delivering commodities that are multi-variant (Reichhart, Framinan and Holweg 2008). Anbuudayasankar, Ganesh, Lenny Koh, and Mohandas (2010) solve the routing problem for a supply chain by genetic algorithm which is a great computational performance in many kinds of optimisation problems.…”