How Data Mining Can Be Beneficial for the Supply Chain
Since Covid people who never heard the term “supply chain” have become painfully aware of what it means and how deeply it impacts their lives. It doesn’t take a viral pandemic to create supply chain disruptions. A factory fire, a natural disaster, or resource scarcity — everyday occurrences — can all lead to items disappearing from shelves.
The recent formula shortage was largely due to a single factory being temporarily shut down.
Product shortages can be a significant hardship for families all around the world. In this article, we talk about how data mining can add stability and predictability to supply chain management.
First, What is Data Mining?
Data mining is the practice of looking at large quantities of information already stored in a database to retrieve new insights from it. Basically, it’s the process businesses use to create actionable knowledge. In the context of supply chain management, the data could pertain to anything from consumer habits, transportation routes, product development, or resource excavation.
Every single action that takes a raw resource out of a mine or jungle and turns it into a product on your shelf creates information. More information than any human (or, for that matter, any room of humans) could ever examine in two lifetimes.
With data mining, data processing, and data analysis, that information can be tamed and channeled toward productive means.
Supply Chain Threats
What variables currently threaten supply chain management? Because there are so many steps taken to turn raw material into a physical product, many variables can interrupt the process. Perhaps there is a storm that halts excavation. A viral outbreak that pauses work at a factory.
Disruptions in the transportation sector. Maybe the demand for a product is so much higher than anticipated that it becomes impossible to manufacture it at an appropriate pace.
All of these scenarios can lead to supply chain disruptions. Through data mining, however, many of them can be mitigated or avoided outright.
Let’s say (with unfortunate accuracy) that there is a recession projected to sweep through the country in the not-so-distant future. Naturally, financial downturns can have a significant impact on the way people shop.
But how can stores and supply chain managers use this information to make sure that there is plenty of the things people need and a relatively modest amount of things that will go largely ignored?
Using historic shopping data, supply chain managers can get a vivid forecast of how people are likely to behave during the next recession. This might mean deemphasizing the production and supply creation of luxury items and focusing more on putting staples on the shelves.
The transportation industry is an enormously important component of supply chain management. Using IoT (internet of things) and data, fleet managers now enjoy unprecedented control over their routes. Maps, even GPS-driven maps, tend to be relatively limited in how granular they get. Route recommendations mostly factor in distances. Even programs that account for speed limits, etc. do so for the benefit of personal vehicles.
Trucking is a different animal. Does this route include a short overpass that the truck will need to detour to get around? Maybe the road winds, requiring a large vehicle to slow down to a crawl.
With historical route data, mined through telematics technology (sensors, mostly) fleet managers now get automated reports that recommend the best routes for their trucks. These recommendations not only factor in arrival times, but can also be calibrated to make recommendations most likely to preserve the condition of the vehicle.
Transportation companies run more efficiently. Products arrive at their destinations on time. It’s a win for everyone.
Adjusting the Chain
In a post-Covid world, one needn’t stretch their imagination to imagine a scenario where something could go wrong within a supply chain. Delays and shortages can happen after only a single break in the chain.
With data, supply chain managers can make reasonable forecasts about potential disruptions, and plan accordingly. Already, the supply chain management industry has moved toward keeping a healthy supply of alternative production lines — often closer to home — so that they can pivot immediately into new solutions when problems arise.
With robust access to data, supply chain managers can receive quicker insights as to when they should reach for these solutions.
The result? Fewer disruptions, and significantly more consumer stability. No more months and months of waiting for a new refrigerator or oven.
*This article is written by Andrew Deen. Andrew has been a consultant for startups in almost every industry from retail to medical devices and everything in between. He implements lean methodology and is currently writing a book about scaling up business. You can follow him on Twitter @AndrewDeen14.