Let’s talk for a moment about pattern recognition. Human mind is constantly searching the perceptible world for patterns, either to make better decisions or sometimes simply in order to pass the time. The thing is, while the search for patterns is fundamentally human, it’s not something we are good at. As humans its not uncommon making the same mistakes over and over again without identifying the common factor. Habits and inability to see pattens limit our capabilities to improve.
In inventory management, this is also true. Inventory management involves a range of moving parts and disparate sources of information which are often too big to be effectively confronted by a single person. The answer? Implementation of technology to make pattern recognition more effective. Machine learning is what we are talking about here.
Machine learning works on the principle that an algorithm can learn to uncover hidden links in large, complex datasets, given the right feedback. These are typically patterns that a human operator would be virtually unable to see. The immediate result could be improved demand planning by way of a smaller gap between expected demand and real demand, i.e. you face fewer areas of disconnect between expectations and reality, empowering you to plan your production, warehousing, and transport flows in a more optimal manner. In this way, you can avoid the effects of poor demand forecasting: costly storage of unsold goods, late product deliveries due to stock shortages, inefficient bundling of freight, etc.
In many cases machine learning processes gather data from several sources across the supply chain. In an ideal world, that data is being gathered in real-time. But it’s not just the ability to take in data and spit out predictions that has value for your planning processes. On the contrary, the fact that these data streams are all being centralized means that your overall operational visibility should improve, independent of the actual predictions that are being produced.This means that, as a business owner, you’re also putting yourself in a position where you can access individual pieces of information with relative ease.
Knowing in advance that there will be a spike in demand only helps you if you have the capacity to actually meet that demand. Here, again, machine learning presents a potential path forward. How? By powering the kinds of prescriptive analytics workflows that go hand in hand with the predictive ones we discussed above. Using these types of processes, it’s possible to discover not just an accurate picture of your maximum production or shipping capacity, but potential ways to improve that capacity.
This can take many forms, but one common use case involves digitally modeling your entire floor (potentially utilizing IoT data to create real-time information streams) and letting your analytics workflows test various alternative scenarios for, say, shelf arrangement. In this way, you can find smarter, more efficient ways to structure your operations in order to maximize your capacity levels. Thus, you can put yourself in a position to accommodate higher demand levels if and when they arise.
Email us today to set up a free consultation and learn how Waredock digital experts can help you with your machine learning and digital transformation agenda in logistics or retail industy.