Everyone in industry and, these days, most consumers are aware of the chaos in the world of supply chains. It’s a complex issue, but one thing is clear: Everything starts with demand.
Most companies’ demand horizon tends to be short. Yet, the time required to procure what’s needed to produce their products is longer than the visibility manufacturers can achieve by merely looking at customer orders. A fuller understanding calls for an accurate inventory forecast.
Traditional forecasting methods have worked in the past. But there are limitations because static algorithms are applied to a limited set of data to predict demand. The advent and availability of machine learning (ML) technology, however, provides the capability to take historical demand and apply more and richer contextual data to it. This enables manufacturers to predict the future in a much more accurate and data-driven way.
Improved accuracy in inventory and demand forecasting has a cascading effect on end-to-end supply chain operations. Organizations that can more precisely predict demand will also optimize capacity and material requirements, because they have a higher level of certainty regarding what is needed.
Leveraging ML to analyze heaps of historical data is a new approach to demand forecasting. As such, manufacturers operating on thin margins may be skeptical of the innovative technology and strategy. But companies that deploy ML for inventory management and forecasting will quickly see measurable benefits. This is especially true in times of uncertainty. Under these conditions, achieving even a one percent improvement in accuracy allows for higher levels of customer service because manufacturers can confidently meet shifting volume and timing requirements.
When you compare the accuracy of traditional forecasting methods against ML, one of the things that’s immediately noticed is the cost reduction to maintain existing sales requirements. For example, let’s say you have a 30-day lead time for a product. If you improve the forecast accuracy by 10 percent, you’re able to meet the sales requirements with a lower investment.
Accurate inventory forecasting also helps to reduce the amount of inventory manufacturers need to have on-site. Historically, facilities needed to possess a surplus of parts and raw materials needed to produce finished goods in case demand suddenly increased. Reducing inventory levels creates cash for the business, which equates to more investment and growth. Lowering inventory also mitigates the risk associated with carrying excess and potentially obsolete parts.
Most manufacturers don’t have a data scientist on staff and won’t be able to hire one because of intense demand and high cost. So, companies that want the benefits of precise inventory forecasting need to work with ML technology providers that specialize in optimizing manufacturing operations. These vendors can understand the volumes and types of data already available to manufacturers, then implement a process that allows planners to execute machine learning without realizing they are doing it, because the process is blended into pre-existing workflows.
Everything comes down to the bottom line impact for manufacturers, which are often working against tight budgets and strict requirements.
Improving inventory accuracy results in increased revenue, which opens growth opportunities. When manufacturers deliver goods on time and in full, they will have happy customers—leading to more business and higher levels of profitability.
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