Predictive analytics for logistics

Predictive analytics is making a big splash in the logistics and supply chain industry. It provides information to act on today, in light of what is to be expected tomorrow. You can imagine the benefits this provides to organizations in markets such as retail, or simply any company that faces challenges in the field of logistics and supply chain. In this video, we will discuss 3 different predictions and how your supply chain will benefit from them.

NR.1-Predict the demand for your products

Knowing the popularity of each of your products within the near future gives you the opportunity to optimize your warehouse and your (costs of) storage. High demand products will be ordered frequently and should be stored in a place that is easy to reach. On the other hand, products that will be less popular in the upcoming period will not be ordered a lot and can be placed in the back of your warehouse. Also, knowing the approximate demand for each product allows you to manage your inventory well. This prevents over- or understocking.

NR.2–Predict the demand for your customers

This is slightly different from number 1 but will give you many new opportunities. It is valuable to know which customer will order your products at which day. Knowing this gives you more time to optimize your transport. You can make better decisions about outsourcing orders to other parties. Having an accurate view of the resources you require in the upcoming time also allows you to make better human resource schedules. If peaks in your workload are known beforehand, you can choose to have fewer people on standby. This reduces your personal costs and provides your employees with a more predictable work environment.

NR.3–Predict machine failure

Machine failure causes parts of your supply chain to stop or slow down until the problem is fixed. This can lead to severe costs. Luckily, most machines are equipped with various sensors for various measurements, like temperature and voltage. The data from these sensors, along with other information about the machine, can be combined to predict machine failure before it occurs. This allows you to schedule maintenance before your machines fail. It also allows you to stall maintenance on machines that are unlikely to fail in the near future, reducing the costs of your operations. This is often called ‘predictive maintenance’.