Risk is unavoidable! Supply chains are no exception facing all kinds of unexpected but inevitable surprises that can be very costly to the company. It is imperative that the management is prepared to deal with such unfavorable incidents without building too much redundancy and increasing the cost of operations. In a typical supply chain, having thousands of SKU's and hundreds of suppliers as well as other factors such as geopolitical challenges, labor related issues and demand volatility, make the supply chain operation very complex. In the absence of appropriate tools, it would be almost impossible to  operate it efficiently . The key is to identify the potential risks before they happen so that adequate measures can be put in place.

Companies often try to tackle the risk issue in a static way. They examine the financial health of their suppliers and analyze their performance and strategic dependency once or twice a year. Some even go further and make sure of their compliance with the laws regarding labor and safety. This kind of static approach, as important as it is, relies on the track record of the supplier and the past trends rather than focusing on future and how the demand patterns are expected to change in the future. The future trends can cause major unexpected stress points in the supply chain, on material, capacity, distribution requirements, subcontractor ability to deliver and the mix of products that are expected.

 

As the volume and mix changes, the risk exposure changes due to shifting "bottlenecks," supplier capability, seasonal variations and so on. Systems are needed to dynamically identify where the highest risk exposures are as the future demand is planned.

 

Some of the more common risks associated with supply chains are:

Trying to identify above potential risks on a day to day basis is not possible using spreadsheets and manual processes. As a result, many companies and planners try to mitigate risk by creating redundancies and padding inventory to be on the "safe" side.

 

But there is more, because of the dynamic nature of the supply chains, there are underlying trends that are changing all the time. Such trends are mostly invisible until it is too late! For example, if demand for a certain SKU is rapidly growing causing your reliance on a couple of suppliers to increase. Can they deliver? Do they have long lead times? Are there alternatives to these suppliers? We are now in a position to detect such underlying trends and can suggest alternatives before it is too late. This has been made possible by the use of machine learning techniques and pattern recognition algorithms.

 

There are many ways to assess risk vs cost and reward. As an example, one can use Multi Echelon Inventory Optimization (MEIO) to assess risk of on-time delivery vs cost. This can be done by SKU and customer. For certain customers, the desired delivery performance must remain (say) at 98% or higher. Some others 90% is adequate at much lower cost. This algorithmic approach, based on probability distribution and queuing theory, is by far superior to the traditional methods of moving averages and/or min-max.

Having visibility into meeting the financial goals of the company is critical. Any risks associated with that must be detected as early as possible and addressed. Optimization models of systems designed to assess the impact of risks can act as a crystal ball to provide visibility to the end users and furthermore provide guidelines and advise end users as to what the best course of action would be. It is a proactive way of responding to potential risks rather than reactive. Predictive is good but prescriptive is better!

For more information, the reader is referred to Supply Chain Risk Resiliency.