The Coronavirus disease (COVID-19) crisis has significantly disrupted the normal demand patterns that dictate supply chain decisions across distribution, manufacturing and raw material procurement. Since these disruptions happen so rapidly and often in nuanced and unexpected ways, it is a challenge to determine how much to produce, of what, when and where it needs to go. Within a single product line, demand for certain SKUs may spike up, while others decline steeply, and this pattern can vary by channel, segment and region.
The stakes for getting this calibration right are quite high—companies will need to recapture market share for their products and services as demand recovers, while minimizing overproduction and excess operating expenses. They also need to be able to quickly spot new emerging opportunities in the market. In some areas, they’ll need to right-size their operations to match the post-crisis “New Normal” steady-state. The earlier they can get insight into what this might look like, the better prepared they can be.
Regarding supply chain forecasting and analytics, two key challenges with existing methods are being addressed with next-generation advanced analytics techniques, including those powered by artificial intelligence (AI) technology.
First, forecasting models based on pre-crisis conditions are no longer relevant or reliable. Companies are doing their best to quickly calibrate what the next few weeks and months will look like, but they can’t rely on the well-established techniques that suit them well in a steady-state environment.
Given this, it is key to be able to discover new predictive patterns within the latest data refreshes, train useful models, and get models into use quickly. This is especially true given that the spread and impact of COVID-19 is happening in waves, staggered over time and space, such that learnings from early onset areas can provide foresight into the broader and more widespread impact. But, in order to build accurate models in this context, many granular geospatial and temporal factors must be accounted for. Identifying a robust set of such factors is typically a complex, time-consuming and incomplete data science effort -- given the time constraints, dimensionality limitations and biases of human data analysts. Here, the ability of an AI platform to rapidly evaluate millions of factors in minutes, including across time and space, helps data scientists build useful forecast models quickly based on learnings from early impacted areas. Additionally, AI platforms enable data scientists to quickly incorporate and discover useful predictive signal in external datasets, including data on COVID-19 spread at a U.S. county-level, people mobility data at a hyper-local level and extensive datasets such as maps, web and demographics data.
Companies that can learn from the unfolding situation to optimize their supply chains will have a competitive advantage and be able to optimally calibrate their actions as economic activity revives.
Second, typical reporting and business intelligence methods are revealing their blind spots. They provide an aggregated view of the business which overlooks much of the underlying, and often highly informative, insights which are only visible when 1, 2, or 3 levels of additional depth of factors are considered. Take a simple example of a B2B distribution company. In one month, within a particular customer segment and region, demand for some SKUs increased dramatically while other SKUs plummeted. Standard reports showed no change in demand on aggregate, overshadowing the extreme SKU-level volatility in that segment-region pocket. The company missed this early alert, catching it only in the next cycle of reporting once it had become a major issue, and was weeks behind in making supply chain changes upstream to get demand where it needed to be. Their standard static reports didn’t provide a sufficient level of dimensionality and nuance. And, the reports only showed what had been explicitly asked of the data based on past experience, but given this was a novel pattern that popped up early in a granular pocket, they missed it until it was too late.
This company then turned toward AI-powered trend detection. Given the speed that opportunities and threats are emerging, the ability to rapidly and exhaustively detect emerging trends is key. An AI-powered solution provides a level of exhaustive, multi-dimensional and dynamic trend detection that cuts through the blind spots and noise of the company’s traditional reporting and provides timely and actionable intelligence on a near-real-time basis to supply chain decision makers.
Now more than ever, supply chains must react quickly and nimbly. Having the right insights at the right time and refreshed, relevant forecasts are key to getting their actions right. With the right techniques and tech capabilities, supply chain analysts and data scientists can be more valuable than ever in helping their companies respond effectively to the New Normal.