What are some common inventory forecasting errors and how can you avoid them?

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The biggest mistake in inventory planning is relying on time series forecasts (point forecasts) as the foundation. This is an irrecoverable error, akin to building a skyscraper on a bunch of truck tires. What strikes me most is that companies, after failing with one software vendor, immediately turn to another one offering a different flavor of time series forecasts. Uncertainty exists! No level of AI duct-taping can save your point predictions from ignoring this simple fact. The only way to correct this obvious error is to discard your forecasting software as soon as possible. Dealing with uncertainty requires probabilistic forecasts. Making decisions under uncertainty necessitates stochastic optimization. Your vendor should offer both.

One of the biggest challenges in inventory forecasting is dealing with demand variability, caused by factors such as seasonality, trends, promotions, competitors, or external events. To address this variability, the Demand Driven methodology is especially effective. It not only captures patterns and variations in historical data but also allows for a quick response to market fluctuations. Using tools like buffer-based planning, it helps align inventories with actual demand, reducing the risk of overstock or stockouts. Additionally, it integrates real-time adjustments, providing a competitive advantage in volatile markets.

A common error in inventory forecasting is underestimating or overestimating lead time due to delays or disruptions in the supply chain. To mitigate this, the Demand Driven Material Requirements Planning (DDMRP) methodology is especially effective. DDMRP uses buffers and decoupling points to manage lead time variability. Buffers act as shock absorbers, ensuring sufficient inventory. Decoupling points synchronize production and replenishment with market demand, improving agility. With DDMRP, inventory planning accuracy is improved, and the risk of stockouts and excess inventory is reduced, providing a competitive advantage in a volatile market.

Seasonal variance in comparatives is a common pitfall. Factors such as the weather / timing of Easter can impact demand, which are often not reflected when looking at the comparative data.

You want to ensure you are on the same page with your customer on forecasting needs so that you can effectively support their business. A thorough and complete forecast can ensure your inventory levels can be kept to a minimum without safety or banks. If you have to over order or keep a bank due to an incomplete or inaccurate forecast then the process needs reevaluated.

Just last month, I had an unexpected experience where encountered forecast bias being the primary reason for errors in our inventory forecasting. We had been consistently overestimating demand, leading to excess inventory and higher costs. To address this, I shifted to a data-driven approach, analyzing historical trends and incorporating market insights. I also began holding regular meetings with the sales teams to balance perspectives. This helped us improve forecast accuracy and better manage our inventory levels.

It is essential for a company to have a model that optimizes the quantity of products in the warehouse and ensures the fulfillment of sales commitments. This model can be implemented through specialized software or an Excel model, depending on the complexity of the business, the number of SKUs, the ease of accessing and integrating data in the supply chain, etc. It is safer and more advisable to use a digital solution available on the market to improve the reliability of the information.