Proactive inventory policy intervention to mitigate risk within cooperative supply chains

Abstract

This exploratory paper will investigate the concept of supply chain risk management involving supplier monitoring within a cooperative supply chain. Inventory levels and stockouts are the key metrics. Key to this concept is the assumptions that (1) out-of-control supplier situations are causal triggers for downstream supply chain disruptions, (2) these triggers can potentially be predicted using statistical process monitoring tools, and (3) carrying excess inventory only when needed is preferable as opposed to carrying excess inventory on a continual basis. Simulation experimentation will be used to explore several supplier monitoring strategies based on statistical runs tests, specifically "runs up and down" and/or "runs above and below" tests. The sensitivity of these tests in detecting non-random supplier behavior will be explored and their performance will be investigated relative to stock-outs and inventory levels. Finally, the effects of production capacity and yield rate will be examined. Results indicate out-of-control supplier signals can be detected beforehand and stock-outs can be significantly reduced by dynamically adjusting inventory levels. The largest benefit occurs when both runs tests are used together and when the supplier has sufficient production capacity to respond to downstream demand (i.e., safety stock) increases. When supplier capacity is limited, the highest benefit is achieved when yield rates are high and, thus, yield loss does not increase supplier production requirements beyond its available capacity.

The moderating impact of supply network topology on the effectiveness of risk management

“…where additional amounts of inventory is kept to prevent the focal company from stocking-out in the case of a disruption. Kurano et al (2014) noted that the amount of additional inventory needed is dependent on the risk profile. Tomlin (2006) highlighted that inventory mitigation is not an attractive strategy in rare and long disruptions, if other options are available because the costs associated with excessive inventory kept for long periods of time would not balance the risk, although it ensures production continuity in case of disruption (Kamalahmadi and Parast, 2017) and absorbs shocks (Mishra et al, 2016).…”

How many to produce? The impact of machine flexibility on the performance of a supply chain

“…We assume deterministic lead time (Dejonckheere et al, 2003) and thus order crossover phenomena (Reizebos, 2006) are neglected in this study. Moreover, for simplicity, as well as on the basis of the studies available in literature (see, e.g., Chatfield et al, 2004;Zhang, 2004;Paul et al, 2014;Kurano et al, 2014), we assume one player per supply chain echelon. Implications of this assumption will be discussed in the conclusions.…”