AI in initial stockpiling: Deliverable & efficient despite a lack of experience

Reliable planning where complexity is not an obstacle is what many supply chain managers want. They face the daily challenge of predicting today what will be in demand tomorrow. To plan sales, they rely on experience, historical sales figures and other data.  

But what happens if both experience and a sound data basis are missing? This is precisely the case when launching new products. Questions relating to expected demand, planning the required stock levels and ensuring a reliable after-sales service add up to a mammoth task that is almost impossible to manage. The potential consequences of inadequate replenishment add to the pressure to make the right decisions to ensure customer satisfaction. The more new items are introduced at the same time, the more difficult this task becomes.  

Initial stockpiling with manual planning methods

Until now, managers have generally been forced to carry out their planning using conventional tools such as Excel and based on their gut feeling. This approach is not only time-consuming, but also prone to errors. With manual planning, employees create demand forecasts based on their experience and extensive evaluations of similar products. Purchasing proposals are then made on this basis.  

This approach incurs high costs and always carries the risk of misjudgements. Insufficient stock leads to delayed delivery times, missed sales opportunities and dissatisfied customers. Excessive initial stock, on the other hand, causes unnecessary expenditure and increases the risk of excess stock that is difficult to sell off. Although accurate forecasting is of immense importance to businesses, careful planning is often underestimated at this point.  

In order to guarantee the availability of goods and at the same time reduce capital commitment costs, a balance must be found between supply and demand despite a lack of experience - and this in a dynamic environment that is characterized by numerous influencing factors. Holidays, seasonal factors, sales promotions and external influences such as the weather or unexpected competitive activities are just some of the factors that make it almost impossible for people to make these precise predictions about sales trends.  

Artificial intelligence (AI) as a game changer in initial stockpiling

Despite this complexity, modern AI models can be used to make forecasts for optimal initial stock levels, even if no historical sales data is available. Using a regression approach, for example, AI can draw conclusions about similar products or spare parts and make reliable predictions for new items based on patterns in their sales behavior.  

The AI also takes into account external influencing factors such as seasonal fluctuations or market changes. It recognizes anomalies in the processes and thus increases its decision-making intelligence and the precision of its suggested actions. Thanks to a suitable architecture and sound training methods, the AI can be dynamically adapted and further developed in a constantly changing market environment.  

Initial stockpiling with AI using a practical example

A practical example: A motorcycle manufacturer is planning the market launch of a new product and wants to include the required spare parts directly - despite a lack of sales and repair data. The AI analyzes historical data from similar motorcycle models. It takes into account not only sales figures and repair frequencies, but also external factors such as public holidays and seasonal factors. On this basis, the AI generates forecasts that help the company to determine the optimum order quantities - even before the market launch. This ensures that the right spare parts are available in the right quantities at the right time without incurring unnecessary storage costs.  

The use of AI for initial stockpiling massively reduces the manual planning effort. At the same time, it enables well-founded decisions to be made in order to optimally manage stock levels and minimize costs. The automation of standard processes and the resulting faster decision-making also ensure that employees can focus more on exceptional cases.  

Conclusion: Initial stockpiling with AI brings decisive advantages

With AI-supported methods, companies have the opportunity to take their initial stocking to a new level. The technology enables precise, data-based decisions, even for new items or locations. It optimizes stock levels and helps to avoid bottlenecks. The motorcycle manufacturer's case study shows how companies can already achieve significant business benefits through the use of AI - without speculation.  

Those who utilize the potential of AI in supply chain management ensure that decisions are made faster and more efficiently. This enables companies to optimally secure their supply chains for the future. 

How do you solve the challenges of initial stockpiling?

Further information about artificial intelligence at INFORM can be found here.