As we all know, light travels fast but at a finite speed. As a result, what we see or hear happened in the past. In most cases we call it real-time because our senses do not detect the latency. Our senses and analytical skills are very slow compared to what technology can offer. However, our cognitive skills remain superior. At least for the time being! Our ability to reason and arrive at the right conclusion are far superior to what systems can do as long as we have relevant and timely data. In many instances we do not get the relevant data in a timely manner so that risk can be avoided. Machine learning technology is of great help here.
Control Towers in supply chain and current IBP systems tend to show the “current” state of the supply chain or the kind of problems we might run into in the future by way of planning technology. They also show what options we might have when a supplier is late or the demand is expected to rise. However, this information is the ripple effect of what happened a while ago and we are observing the signals after it has traveled through time. What if we could understand the cause and detect the relevant events before it caused the surprise event? For example, we know shopping increases because of Christmas or sparkling wine sales rise because of the new year. We prepare for the event accordingly. But there are so many other events that are detectable before they occur and yet we are not prepared until we get the ripple effects. Examples are knowing a certain supplier is normally late in winter or a potential regional conflict would change the demand for certain products. Knowing about such events and their relevance before they happen is the key to real visibility in the supply chain.
We now have the technology to predict the likelihood of potential events that can disrupt supply chains. We are well aware of weather patterns during certain seasons and regions and the location of our suppliers and customers that might be impacted. Increasing certain types of inventory, because of possibility of a hurricane is the premium we pay to avoid risk of losing revenue and unhappy customers. Monitoring our company’s web site or social media can give us clues as to what products are more likely to sell in the near future. Rising transportation costs after Covid-19 could have been predicted as the economy was expected to bounce back at a rapid rate. Another example is government policies and geopolitical dynamics of regions and their relationship to the company’s products. Not all of these patterns are detectable using AI and ML, however a combination of people, who are focused on risk mitigation, and systems can surely save the company from the majority of such events that can impact operation of the supply chain. Building a culture of resiliency in the supply chain by detecting trends, interpreting relevant information and developing scenarios can guide the business to make the right decisions. Now is the time to appoint your company’s Resiliency tzar.