Contents
Effective demand forecasting is an important factor for success in supply chain operations. Accurately predicting customer demand across product lines allows manufacturing, inventory, logistics, sales, and finance leaders to make smarter decisions. By leveraging demand forecast data, supply chains can operate proactively instead of reacting to product shortfalls or surpluses after the fact.
Demand modeling and planning support revenue growth, boost customer satisfaction, and reduce operational costs. This article discusses how you can predict and plan demand forecasting for your supply chain requirements.
Gather quality historical data.
The foundation for demand planning is high-quality data on past sales, customer orders, returns, inventory levels, etc. Ensure your business intelligence team has robust data collection processes across channels and regions, with sufficient granularity in key attributes like SKUs.
Streamline by filtering out unnecessary columns in datasets to enable clearer pattern recognition. Perform quality checks to confirm accuracy and consistency month over month. Modeling algorithms can only be as smart as the underlying data sets they use for signals. Invest in harvesting the right information to get demand planning in the right direction.
Understand drivers of demand volatility.
Validate assumptions around demand fluctuations based on historical data. Does your business experience seasonal peaks? Large batch orders that spike? Regional variations? Outlier events that boost or decrease orders temporarily? Is demand fairly linear across SKUs, or are specific products larger drivers of volatility?
Map demand variability by product line, fiscal period, location, and customer segment. Overlay factors like pricing, promotions, and new product introductions to determine the cause behind demand changes. This understanding fine-tunes how to best model forecasts going forward.
Leverage qualitative insights
While data analysis provides the foundation for demand planning, don’t overlook straightforward qualitative insights. Tap into your sales reps’ understanding of customer ups and downs or emerging trends from the front lines. Product managers often have pipeline visibility into forthcoming changes that alter demand profiles.
Input from logistic teams managing your inventory and transportation partners with a pulse on warehousing capacity bottlenecks also prevents surprises. Anecdotal intelligence adds helpful context to pure data analytics to improve forecast accuracy.
Build & compare statistical forecasting models.
With sufficient historical data compiled, begin constructing and testing mathematical models to generate estimates of future demand volumes. Establish key metrics like sales or unit volumes over specified time horizons.
Common statistical methods used include:
- Moving averages – forecasts based on averages across a defined past period
- Weighted averages – assigns greater importance to more recent periods
- Exponential smoothing – uses a smoothing factor to avoid drastic fluctuations
- Trend analysis – predicts linear, exponential growth, or seasonal indexes
Leverage spreadsheet software or solutions like stock forecasting software to experiment across techniques using your dataset. Some methods will naturally be more precise, given the dynamics of your products, channels, and customers.
Modify & improve models over time.
Keep iterating upon early demand planning models with new data. Set scheduled times to recalibrate based on recent sales activity, inventory costs, external events influencing your category, and internal priorities.
As your business evolves, so too should your forecasting techniques and demand signals included within algorithms. Leverage automation as much as possible to allow updated data to flow seamlessly into planning tools. But always have analysts double-check that outputs seem reasonable.
Enable demand collaboration across departments.
Break down functional silos between teams to align forecasts, inventory planning, logistics, and finance. Share demand insights and assumptions centrally to foster enterprise-wide coordination. Leverage cloud platforms, providing cross-department access to dashboards visualizing demand forecasts, related KPIs, and potential risks to monitor.
This connects planning teams to procurement, suppliers all employing the same demand projections for smarter supply chain decisions in unison. Identify disconnects allowing certain groups to operate from inaccurate demand assumptions that hamper broader organizational supply chain success.
Continuously analyze forecast accuracy.
Adopt key performance indicators that grade forecast precision against actual demand continuously realized. Common accuracy metrics used include:
- Mean Absolute Deviation (MAD)
- Mean Absolute Percentage Error (MAPE)
- Weighted Mean Absolute Percentage Error
If forecasts are regularly missing by a significant margin in certain areas, revisit your demand algorithms, data inputs, and assumptions made to recalibrate your modeling approach. Demand planning is an evolving, continuous process as complex as your supply chain operations.
Conclusion
With rigorous historical data analysis, cross-functional collaboration, and the right forecasting software, supply chains can leverage demand modeling to optimize planning and delight customers. The above article helps businesses move towards data-driven insights that transform how integrated departments predict, react to, and make smarter decisions within competitive and complex supply chains.