Struggling to improve inventory forecasting accuracy?

Achieving precise inventory forecasts can feel like chasing a moving target, but it doesn't have to be that way. By leveraging data analytics, embracing machine learning, and refining demand planning processes, companies can transform their inventory management strategies. The key is in aligning technology with strategic insight to not only predict demand but also adapt quickly to changes. But the most important piece is always (human) collaboration between of all nodes in the supply chain.

It is essential to have a database to make forecasting more accurate. Use this data in dedicated systems to support your sales forecasts and regularly replenish your inventory, but never stop using human knowledge to review and make the final decision.

Not only is data quality important but also be mindful where and how the data is stored. Be sure that it is protected, and consistently stored. Restrict access as necessary, create roles and specific criteria for what can be done with the data. Ensure that the data can be pulled and manipulated without undermining the host integrity i.e. any data set shouldn't change as a result of a reporting function. That would mean where the data is stored is compromised. With these, and others, safegaurds in place now the data can be made available. Make sure the data is accessible. If the data can't be understood and digested by those who will be taking action based upon it then it isn't effective.

Ensure that the data feeding into your forecasting models is accurate, complete, and up-to-date. High-quality data is the foundation of any reliable forecast.

Evaluate and refine your forecasting methods regularly. Experiment with different models—such as moving averages, exponential smoothing, or AI-driven algorithms—to find the best fit for your business.

Bring “Demand Creation” to your sales team. Many sales managers focus on pipeline metrics that are inaccurate due to a “Supply side” mindset. Having your salesperson gain customer insights around their needs can improve clarity of value stream.

Integrate demand planning into your forecasting process to align inventory levels with actual market demand. Collaborate closely with sales and marketing teams to incorporate insights that improve forecast accuracy.

Monitor forecast bias as well. Forecast bias refers to a consistent overestimation or underestimation of actual results compared to the forecasted values, over a rolling time horizon. It means there's a systematic error in the forecasting process, leading to predictions that are either predominantly higher or lower than actuals. Once forecast bias is detected, identify root causes and then take actions to remove the bias, increasing forecast accuracy this way.

Track key inventory metrics such as turnover rates, stockouts, and holding costs. These metrics help you understand your inventory performance and identify areas for improvement.

Adopt a mindset of continuous improvement. Regularly review your forecasting accuracy, gather feedback, and make incremental adjustments to your processes.

Leverage advanced technologies such as AI, machine learning, and predictive analytics to enhance your forecasting capabilities. Technology can help process large datasets and uncover patterns that traditional methods might miss.