Supply chain resiliency and sustainability are top priorities for CEOs today. To achieve these goals, corporate leadership must focus on two key areas: shift from internally focused supply chains to collaborative supply networks and actively design their supply chains.
Resiliency, which is the ability to withstand supply chain shocks and bounce back quickly, has become the most important requirement for supply chains. Supply chain executives must evolve from cost and service as the key objectives for optimal demand-supply balancing towards the “quadfecta” of cost, service, resiliency, and sustainability. Management practices such as lean manufacturing and just-in-time inventory management, along with globalization, have made tremendous impact on cost and service, but have accentuated risk. Risk events that happen in one part of the supply chain can cause a disruptive effect that is amplified multi-fold given the complex connectivity of labor, raw materials, and capacity. The bullwhip effect is one example of this disruptive effect, when small changes in demand cause huge demand spikes downstream.
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.
Operational vs. disruptive 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
|Types of risks based on where within the supply chain these risks occur||Impact of risks||Example|
|Internal Risks||Operational||Minor quality defect in internal manufacturing process that was caught during inspection|
|Internal Risks||Disruptive||Major quality defect that was unaddressed and caused consumer health issues (pharmaceutical recalls)|
|External Risks||Operational||Third-party supplier not shipping on time|
|External Risks||Disruptive||Supplier’s supplier going out of business due to lack of business compliance (for instance, the German Supply Chain Act is an example of regulatory compliance that suppliers have to adhere to)|
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.
AI assistants uncover hidden opportunities to reduce costs and risks in the supply chain
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:
- Identify the risky elements
- What sites (distribution centers, suppliers, manufacturing plants, etc.) are most exposed to risk?
- Which products would be most impacted?
- What is the Cost to Serve for all the flows to the customer?
- Quantify the costs and risk to the business
- What revenue is at risk?
- What’s your risk tolerance?
- What’s the cost of doing nothing?
- What is the cost impact of making changes?
- Produce recommendations for action
- Models that can show how flow shifts will reduce or increase risk
- Increasing robustness of network design
- Recovery strategy
- Analyze the trade-offs
- Risk tolerance
- Impacts of doing nothing
- Cost of implementing changes
- How it all aligns with business goals
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.
Nari Viswanathan is currently Sr. Director of Product Segment Marketing at Coupa Software, where he brings products to markets in the areas of Direct Material Procurement and Supply Chain Design and Planning. Over the past 20 years, Nari has held VP and Director of Product Management, Research and Marketing roles at Aberdeen Group, River Logic, Steelwedge and E2open. He has significant experience building products from the ground up and managing the P&L for a product suite. He is a proven B2B marketer with expertise in content marketing, competitive intelligence, and positioning. He has published numerous thought leadership articles, whitepapers, blogs and delivered dozens of webinars during his career. Nari Viswanathan is a six times SDCExec Supply Chain Pro to Know award winner. Nari holds a master’s degree in Manufacturing Systems Engineering at the University of Wisconsin-Madison and a bachelor’s degree in Mechanical Engineering at the Indian Institute of Technology, Chennai.