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Studying the effects of variability on supply chains

The harsh competition within the industry forces companies to constantly optimize their processes. One that has the most influencing and long-term impact on the production volume and cost, is the assembly process. By studying the effects of variability using a simulation-based multi-objective optimization tool, such as the one developed at the University of Skövde, it becomes easier to identify where and what to improve and achieve the desired performance.

Figure 1. A demand-stock-production network and tradeoffs among its design/operating objectives.

In supply chain science (Hopp 2008), a manufacturing supply chain is defined as a goal-oriented network of production processes and stocks used to deliver goods to customers. As illustrated in Figure 1, a stock point represents the inter-plant storage that stores the products at the end of a plant to fulfill the demand from a down-stream customer within a serial supply chain. Despite the apparent simplicity of using only a few entities, such kind of demand-stock-production (DSP) networks can be used to represent any complex manufacturing value chains (Pound et al. 2014).

Minimum inventory versus highly responsive

In such a DSP network, trade-offs among several key performance objectives like throughput, lead-time, stock inventory and backlog (i.e., total no. of tardy jobs after due-dates) can be related in the form of efficient frontier (EFs) as illustrated in the figure. While it is solely a strategic decision for manufacturing executives/managers to decide where they want to be to achieve the business objectives suitable for their company, e.g., absolutely minimum inventory cost versus highly responsive with no backlog using an EF, the task of obtaining one is far from trivial.

A model to predict supply-chain network

First, they need an input-output model to predict the performance of their supply-chain network under different inventory settings. Second, but maybe even more challenging, is when a large number of stock points and different sources of variability in the network are involved, the generation of such an EF is intractable for most of the analytical modeling and optimization methods so that some more advanced technologies are needed.

The “variability law"

One of the most important principles emphasized in the Factory Physics framework is the corrupting influence of variability. Expressed as the “variability law”, increasing variability always degrades the performance of a manufacturing system. In a production line, variability can be caused by machine breakdown (availability issues), quality problems (scrap and rework), setups due to technological or logistic constraints, as well as natural work-task variations by human workers (manual assembly processes), to name a few. In a supply chain network, these production-level variations can contribute to the variability like material shortages and delays. The advantage of using simulation-based multi-objective optimization (SMO) is that the corrupting effect on the EFs can be visualized graphically.

For a more detailed application study of simulation-optimization in the supply chain planning level, take a look at this paper.


Hopp, W. J. 2008. Supply chain science. Waveland Press.

Pound, E. S., J.H. Bell, and M.L. Spearman. 2014. Factory Physics for Managers: how leaders improve performance in a post-Lean Six Sigma world. McGraw Hill Professional.

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