In just a few months, the COVID-19 pandemic demonstrated all the things that can go wrong when a prolonged disruption causes sudden swings to supply and demand. From the stockout of commodity items like paper products and canned goods to supply shortages and factory shutdowns, many businesses have endured what is perhaps the most challenging single event in collective memory.
Reaching a state of full recovery could be a long and uneven process as companies continue to react to each new challenge that arises. But as we continue working toward a normal state of operations, the lessons learned from COVID-19 bring about the opportunity for many business leaders to reassess and rethink their approach to risk. Especially across the supply chain, where proper preparation can go a long way to driving the appropriate response to a disruptive event.
Supply chain modeling is essential to substantiated resiliency analysis and to the planning of risk responses.
A supply chain model is the digital representation of the structure, product flows and policies of a physical supply chain. It is created by transforming data into predetermined structures, so that such data templates can be used in conjunction with mathematical algorithms, to determine improved future state structures, product flows and policies.
Under the hood, modeling techniques can broadly be differentiated into optimization-based and simulation-based algorithms.
Here’s a look at four areas of risk that can businesses can address through modeling and analytics.
Typical resiliency analysis focuses on obvious indicators, such as suppliers with the highest spend, sites with the highest volume, and customers or products with the highest profit contribution. However, potential vulnerabilities of a supply chain are not always in those places.
A supply chain model can uncover hidden breaking points in unexpected places – in commodity suppliers, for example, at small nodes in the network or in ostensibly minor components. Among other insights, such a model can point at assets and processes that are being utilized at capacity, spot single-sourced materials inbound and products outbound, show volume or value concentration at particular nodes, identify bottlenecks in lead times, or quantify the impact of foreign exchange fluctuations on revenue and cost.
Such analyses may lead to the discovery of measures to increase the resiliency of the supply chain that are independent of risks and their identified implications. Hence, these may be implemented even if no disruptions to the supply chain are assumed.
Analyzing risk in a supply chain network consists of the following steps:
- Understanding the implications of a disruption on the network
- Quantifying such implications, also considering the “disruption profile,” in terms of revenue and cost
- Prioritizing the results
- Scaling this process (for a comprehensive understanding of risks, typically many scenarios need to be analyzed and continuously iterated)
To understand the implications of a disruption, scenarios that describe what will happen are defined (e.g., which suppliers, plants, warehouses, customers are affected and how, in what magnitude, for how long, and to what extent do they recover). These serve as the input parameters to the next steps in the analysis.
However, quantifying the effects of these scenarios on the entire supply chain is complex.
First, the effects typically cascade through a variety of nodes and processes. Therefore, simplistic approaches, such as using Microsoft Excel, fall short in showing the full picture. They fail to capture and quantify the effects on the end-to-end supply chain in its entirety. For example, especially in a constrained environment, the breakdown of a production asset at a particular manufacturing site may increase warehousing costs, create capacity shortages and ultimately cause lost sales in entirely different parts of the network.
Second, these effects decrease revenue and increase operating costs over the recovery time period. These additional costs typically outweigh replacement costs of inflicted assets in the supply chain, often determined with methods calculating a probability-weighted replacement cost, like value at risk (VaR).
Third, as mentioned earlier, the nature of HILP events makes forecasting probabilities for these scenarios almost impossible. Therefore, the total cost of each scenario on the supply chain lends itself to being a realistic and neutral measure for ranking and prioritizing them.
Due to the above, such comprehensive and complete analysis of the implications of risks on the supply chain requires the use of supply chain modeling.
Once the supply chain risks have been determined and their effects quantified, the most adequate responses for each scenario need to be selected from a list of options.
As with the scenarios, the biggest challenge is again one of ranking and prioritizing.
First, many qualitative criteria need to be incorporated into the selection (e.g., public perception of the response), which typically are translated into scores that can be quantified and ranked.
Then, the totality of the cost of the disruption (e.g., a plant goes offline for a specific period and assets need to be replaced) plus all costs operating with the best-suited response during the recovery period (e.g., shipping from another plant and holding additional inventory due to increased lead time) plus the (positive) effect of the response (e.g., the partial alleviation of the loss in sales) need to be quantified.
The complexity of the effects of each scenario on the supply chain and the (typically expansive) universe of feasible responses from which to select again make the usage of a supply chain model a necessity.
A company that has implemented sound SCRM standards will likely be able to react quickly to a disruption, with those measures that most effectively support the recovery process.
However, in most cases recovery will take time, and with time comes additional variability and uncertainty. Hence, the recovery process needs to be monitored and adjusted, if parameters assumed in the planning of the response deviate significantly. If, for example, a redundant production line (which was switched on to respond to a manufacturing disruption) provides a lot less output than planned, an adjustment of product-to-line/plant allocations in the network that deviates from the business continuity plan may be required.
Supply Chain Resilience Starts with Design
Modern supply chain networks are complex, and their components are highly interdependent. Therefore, resiliency and risk analyses and the selection of effective responses to identified risk scenarios must be supported with advanced analytics.
Few events are likely to be as disruptive as COVID-19. But even under normal operating circumstances, multi-national enterprises are constantly exposed to risk across the extended supply chain. Each of the above scenarios highlight how modeling and analysis can better position the enterprise for greater resilience and responsiveness to a disruptive event.
 D. Simchi-Levy, W. Schmidt and Y. Wei, Harvard Business Review, Jan/Feb 2014 issue.
 Yossi Sheffi and James B. Rice Jr., MIT Sloan Management Review, 2005.
 Mizgier, Kocsis, and Wagner: “Data Analytics to Leverage the BI Insurance Proposition,” INFORMS Articles in Advance (2018).
 D. Simchi-Levy, W. Schmidt and Y. Wei, Harvard Business Review Jan/Feb 2014 issue.
Evrim Övünç is the Director of Customer Success at LLamasoft Deutschland. Mr. Övünç has over a decade of experience as a supply chain leader. He has a strong financial and analytical understanding as well as experience at strategic and operative levels. Mr Övünç’s industry experience includes FMCG, Retail, Chemicals, Pharma and Business Advisory & Audit.
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