The Sweet Science of Demand Planning – Lessons from a Pop-Tart Lover

Let’s talk Pop-Tarts. Specifically, my favorite: Blueberry Unfrosted. Unfortunately, my local store seems to think Strawberry Unfrosted deserves all the shelf space. What do I do? Begrudgingly buy Strawberry and try to convince myself it’s what I wanted all along.

But here’s the thing: my compromise is silently influencing their demand planning system. My purchase is registered as a “win” for Strawberry, while Blueberry loses shelf space, making me a less loyal customer. It’s a classic case of misaligned demand planning—something businesses everywhere struggle with.

Let’s unravel this problem and uncover strategies to stock what your customers actually want to buy.

Demand Planning: A Critical Component of ERP Systems

Most ERP systems use historical sales data to forecast demand, feeding this data into tools like Material Requirements Planning (MRP). The goal? Predict what customers want and ensure supply aligns with that demand.

But what if the data is flawed?

If customers like me settle for substitutes, purchase quantities vary from orders, or shipments arrive at non-requested dates, the sales history data gets skewed. That’s when your ERP’s demand forecasting goes off track.

Here are a few common pitfalls and how to address them:

1. The Substitute Item Problem

The Issue: Your customer wanted Blueberry Pop-Tarts but settled for Strawberry because that’s all you had.

The Fix:

• Base your forecast on sales orders rather than invoices. Orders better reflect true demand because they capture what the customer originally wanted.
• Beware: If your ERP allows shipments without sales orders, ensure you’re not underrepresenting demand by excluding direct shipments from the forecast.

2. Quantity Mismatches

The Issue: Customers order in non-standard quantities but receive shipments that differ, creating mismatched data.

The Fix:

• Analyze how often these mismatches occur and consider updating standard quantities to better align with customer preferences.
• Focus forecasts on the quantities customers originally requested, not the ones they received.

3. Timing Inconsistencies

The Issue: Supply chain delays or customer agreements to adjust shipment dates skew seasonal demand data.

The Fix:

• Use customer-requested ship dates as the foundation for forecasting rather than actual shipment dates.
• This is particularly important for industries where seasonality influences buying patterns.

4. Unusual or One-Off Shipments

The Issue: Special orders, closeouts, or new store openings create spikes in sales history.

The Fix:

• Exclude one-time events from forecasts to prevent overestimating future demand.

5. Drop-Ship Dilemmas

The Issue: Drop-shipments—where vendors deliver directly to customers—don’t reflect inventory needs.

The Fix:

• Exclude drop-ships from stock replenishment forecasts.
• Use separate forecasting for financial or managerial planning, and share these projections with vendors for better collaboration.

Beyond the Numbers: Improving Forecast Accuracy

While cleaning up your sales history data is critical, some factors impacting demand aren’t reflected in past sales. That’s where forecasting becomes part art, part science.

Here’s how to tackle the intangibles:

• Leverage Big-Picture Trends: Monitor economic conditions, industry shifts, and evolving consumer preferences.
• Engage with Frontline Teams: Sales reps and customer service teams have invaluable insights into customer needs and trends.
• Track Online Buzz: Social media platforms like Twitter, LinkedIn, and Facebook offer a treasure trove of real-time customer feedback.
• Survey Customers: Ask what products they’d like to see—but validate this data before acting on it.
• Win/Loss Analysis: Understand why prospects choose or pass on your offerings to refine demand predictions.
• Gather On-Site Feedback: A simple kiosk or survey on the Pop-Tart aisle could provide game-changing insights.

The Takeaway: A Smarter Path to Demand Planning

Accurate demand planning starts with understanding your sales history data. From there, it’s about applying the right forecasting tools and factoring in qualitative insights to make informed decisions.

For businesses, the stakes are much higher than Pop-Tarts. Inaccurate forecasting leads to stockouts, overstock, wasted resources, and lost customers. By taking the time to refine your forecasting process, you can create a system that doesn’t just meet customer expectations but exceeds them.