Globalization, nearshoring and friendshoring trends are amplifying the supply chain risks. No single company is vertically integrated or isolated enough to be able to withstand the barrage of macro-economic challenges that are happening today. Inflation, pandemics, railway strikes, adverse weather events – the supply chain disruptions keep on coming. With expansion of supply chains into supply networks globally, there is an increased chance of disruptions caused by various kinds of risks.
One of the keys to becoming more resilient is to minimize risks in your supply chain, while another is having the agility to quickly respond to disruptions caused by these risks. A good way to think about risk is anything that causes a supply chain to be impacted in terms of its performance. Risks can be further divided into internal risks or third-party risks. Each of these types of risks can be further subdivided into operational and disruptive risks. Table 1 describes a few examples of these types of risks.
Table 1. Categorization of risks
Operational risks are mainly driven by variability and uncertainty. Balancing supply and demand by orchestrating the flow of materials and information is a key requirement for managing operational risks. Metrics such as lead-times, forecast accuracy, inventory levels, and service are used to measure operational risks. Design and planning software has been utilized for the last several decades to manage these operational risks. Lean manufacturing also focused on managing these operational risks, especially within the four walls of the enterprise.
Disruptive risks on the other hand are harder to predict and manage. These risks are low probability and high impact. Examples of disruptive risks are suppliers going out of business or shipwrecks that result in the loss of cargo containers. Natural risks such as weather, fires, pandemics, etc., fall under this category as well.
Using technology to de-risk supply chains
From a technology perspective, supply chain design tools have been developed from the ground up to handle uncertainty and risks, generate scenarios that identify risks proactively, and provide solutions to mitigate these risks. It is critical that supply chain design tools model real world complexity to effectively model the risks. Lack of adequate risk data and the non-strategic positioning of supply chain design within the organization has been a key inhibitor to success. Another part of the solution that has been missing until recently is the tight integration with upstream processes, such as strategic sourcing and supplier risk management, which have been siloed and operating in their own domains. AI Assistants can also help make managing the process of supply chain design easier.
The sheer quantity of data and possible combinations of those data points might obscure cost-saving and risk reduction opportunities from the sight of supply chain modelers. There might be millions of possible combinations for adding lanes, changing modes, or consolidating volume, making it difficult for modelers to identify which scenarios will provide the highest cost savings. There may be several nodes that are critical and single sourced thus elevating the risk profile of the supply chain. This is where AI can make all the difference. A combination of modeling and machine learning capabilities can provide insights into cost drivers and proactively identify potential cost saving and risk reduction opportunities.
Here is a sample framework of this process might work:
The best AI acts as a partner, handling the more mundane, routine work that can often bog modelers down. The best AI frees humans up to embrace their own creativity and focus on solving more complex problems. Supply chain professionals have been at the forefront of adopting advanced technologies associated with Operations Research, machine learning and statistical analysis. Now they have the opportunity to leverage generative AI combined with machine learning to drive resiliency and cost savings to their organizations.
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