We cannot ignore it. The complexity of supply chains has increased exponentially in recent years. Whereas once upon a time, a good business strategy alone would be enough to compete, overcoming complexity is now what ultimately sets a business apart from its competitors; a trend which will only become more prevalent in the coming years.
The Power Of Machine Learning
What is machine learning? And what makes it different from the brute-force approach or more traditional mathematics? And why and when should we use it? And what exactly do we need to make it work?
First and foremost, it is the learning component of machine learning that separates it from the brute force approach and traditional mathematics. By this, we mean that the machine has the ability to discover relationships and patterns in a data structure without explicitly naming it. It actually learns the ‘rules’ of the problem. This means that solutions can also work in new, unforeseen situations and can tackle problems with high, underlying complexity and a high degree of uncertainty. And that’s exactly where this concept fits into inventory management.
The fact that machine learning differs from other solution approaches creates new, valuable opportunities. Machine learning makes it possible to improve current techniques in, for example, forecasting, but also to tackle a lot of other issues that were not even considered a few years ago. For instance, identifying the actual costs if we are unable to deliver an item, or determining when an item is at risk of obsolesce before it even reaches the end of the product life-cycle. Likewise, in production management, latency and machine downtime issues are also in the machine learning queue.
There is no question that machine learning can be very powerful. However, with huge power comes huge responsibility. The main pitfall of machine learning is that for managers, the perquisites are simply not clear.
The Conditions for Machine Learning
Machine learning is essentially no more than applied mathematics with an emphasis on integrating the current computer power available today. Given the increasing number of potential data sources, coupled with the rapid rate of evolution in computing power, machine learning can be a tremendously powerful tool in inventory control.
However, it is important to keep in mind that solutions in inventory management do not only rely on quantitative results. Ultimately, it is the people who have to understand and work with the solutions. Management therefore has to monitor this closely. As a result, it is important to facilitate knowledge about machine learning and theoretical inventory management across the company.
There are already some cases where machine learning has proven that it can offer a superior solution. For example:
- Optimising promotions policies.
- Achieving the optimal sourcing strategy based on a variety of sourcing options
- Providing more robust forecasting and insight over irregular and new items
Want to Know How Machine Learning Can Help You in Inventory Management?
Machine learning can be a powerful tool when it is implemented properly. This is where our team comes in- to work together with you and deliver the right strategies and tools in your supply chain management system. Get in touch with us- reach out to us by contacting Erik at firstname.lastname@example.org or visit https://www.slimstock.com/sg/ for more information.
Headquartered in The Netherlands, Slimstock is the leading inventory optimization specialist globally since 1993 and entered the South East Asia market in 2016. Supporting over 1000 companies across a diverse range of industries, covering large, medium and small enterprises, Slimstock's forecasting and demand planning solution (Slim4) is designed to ensure companies get the right stock at the right place and at the right time. It also helps companies strike a balance between working capital, operational costs and the optimal service level.