Being able to accurately predict customer demand is vital for all partners in your supply chain. Twenty years ago, this was still an enormous challenge. Fortunately, predictive analytics can greatly improve the reliability of your predictions. And nowadays, this is a lot easier and cheaper than it used to be.
In my work I see it more and more often: logistical chains that are under constant pressure. Chain partners do not want to sell ‘no’. Storing large stocks is expensive and some products have a limited shelf life. As a result, companies work according to the just-in-time principle, in which they coordinate delivery and production in such a way that hardly any stocks are needed. However, this requires that transport runs smoothly. And meanwhile, all links in the chain – such as manufacturers and wholesalers – want to know when they can expect deliveries. As a result, I often get the question from customers: how do I bring peace of mind to my supply chain, which is always under pressure?
Insight into future customer demand
This pressure starts with insufficient insight into your customer’s future demand. This causes all kinds of challenges in your organisation. When demand is higher than expected, you have to make additional purchases at the last minute – usually at high prices. Perhaps you have to hire additional staff. Or you may receive a fine for not meeting delivery agreements. If demand turns out to be lower than expected, you are left with stock and the associated costs. And perhaps part of your product range will be lost because it has a limited shelf life. In short, insufficient insight into future customer demand brings with it a lot of stress. But that has always been the case.
2001 – Talking about big data
When I was working as a Business Intelligence Manager in 2001, there was already a lot of talk about using data to improve the supply chain. Even then, gaining insight into customer demand was a challenge, even though it was already possible to use software to make demand forecasts, for example based on sales history. But back then the software was expensive and could really only be used by IT specialists and statisticians. The computing power of those tools was great for the time, but pales in comparison to current possibilities. Integrating different data sources was possible, but very costly. Moreover, many sources that we have today – such as weather models – were not yet readily available back then.
2021 – Working with big data
Today, we rely on our data when making decisions. Data is available almost without limit. And the computing power of modern computer technology continues to increase. The big difference is the user-friendliness of modern software for predictive analytics. With today’s tools, you can make accurate demand forecasts even without specialised knowledge of software or statistics.
Effective supply chain management starts with demand forecasting
But how can these extensive possibilities help you bring peace of mind to your supply chain? Successfully deploying predictive analytics for effective supply chain management starts with accurately predicting customer demand. From there, you can draw conclusions about the consequences for the rest of your supply chain, such as your planning for purchasing, warehouse management and distribution.
Today’s predictive analytics allow you to make much more accurate predictions than in the past. This is because algorithms are constantly improving themselves. Moreover, you can now bring together many more data sources for analyses, dashboards and reports. Thanks to the improved user-friendliness of the software, you can also easily analyse the results yourself and adjust them based on your expertise.
Peace of mind in the supply chain starts with a good understanding of future customer demand. And that is easier to achieve than ever with predictive analytics.
