Performance Measurement of a Rotavirus Vaccine Supply Chain Design by the Integration of Production Capacity into the Guaranteed Service Approach

Abstract

Previous research has integrated multi-echelon inventory management into the design of a responsive supply chain by the use of the guaranteed service approach. We build further upon this work by integrating the production capacity and product flow to minimize the supply chain's inventories. The production capacity is modeled with a queuing network to handle the variability of the batch production processes as well as the demand variability. We test and validate our model with adapted instances from literature and apply it to the rotavirus vaccine supply chain. This vaccine supply chain is seen as complex on the manufacturing side as well as on the distribution side. For our industrial application we show how this work is embedded in a scenario approach and the contribution of our model to evaluate a single scenario according to multiple performance indicators. For this paper, our scenarios consist of different lead time reduction programmes and varying demand levels. We demonstrate how to extract the best performing scenario.

Planning capacity and safety stocks in a serial production–distribution system with multiple products

“…Graves and Schoenmeyr (2016) made the assumption that lead times are fixed, independent of utilization, which is restrictive in the presence of congestion effects due to variability (Hopp and Spearman, 2011). Lemmens et al (2016) and Kumar and Aouam (2018b) present models that capture the relationship between the capacity, batch size, and lead times using queuing theory. While the former expresses the mean and variability of the PCT as a function of the batch size in a G/G/m system, the latter considers a G/G/1 queue and optimizes batch sizes.…”

Finite inventory budgets in production capacity and safety stock placement under the guaranteed service approach

“…In this way, authors are able to analyze the effect of different types of production decisions on safety stock placement, and formulate tractable problems to jointly optimize these decisions in large-scale supply chains. Lemmens et al (2016); Kumar and Aouam (2018a,b) use queuing theory to formulate the problem of jointly optimizing lot-sizing and multiechelon inventory in supply networks. They analyze the effect of lot-sizes and production capacity on inventory decisions.…”

“…In order to sustain a competitive advantage, supply chains are under unprecedented pressure to coordinate production, transportation, and inventory decisions over complex supply networks, while delivering products at a lower cost and higher customer service. With the intent to model and analyze the interaction between production and inventory decisions, several studies have adopted the guaranteed service approach (GSA), for example Tian et al (2011); You and Grossmann (2011); Graves and Schoenmeyr (2016); Lemmens et al (2016); Kumar and Aouam (2018b); Aouam and Kumar (2019); Ghadimi et al (2020). Under GSA, which was first developed by Simpson Jr (1958), a supply chain stage uses a combination of inventory and extraordinary measures to fulfil all customer demand within a guaranteed service time.…”