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After hundreds of conversations with bakeries and an analysis of sales data from over 100 locations, we keep seeing the same planning mistakes—regardless of size, region, or product assortment. The good news: All seven are preventable, and most can be avoided even without new software.
Mistake 1: Planning based on gut feeling rather than numbers
A planner’s experience is valuable—but it’s subject to systematic biases: The most recent week carries too much weight, a single frustrating day can skew the data for weeks, and no one can keep track of all the patterns across more than 100 items. The solution: Use sales data from the point-of-sale system as a foundation—intuition can help refine the data, but it shouldn’t replace it.
Mistake 2: Not tracking returns by item
An overall return rate of 12% sounds acceptable—but it hides three items with a 40% return rate that are burning through cash every day. Without item-level return analysis, any improvement is just a matter of chance. The solution: Link production and sales volumes and identify the ten items with the highest return rates.
Mistake 3: Treating All Branches the Same
The flat-rate allocation formula—Branch B always receives 70% of Branch A’s supply—ignores the fact that each location has its own patterns based on the day of the week, weather, and holidays. The solution: Derive quantities for each branch based on its own historical data, not on data from headquarters.
Mistake 4: Planning holidays based on the date rather than the location
Comparing data year-over-year based on calendar dates is systematically misleading when it comes to movable holidays: Easter shifts by weeks, and holidays fall on different days of the week each year. The solution: think in terms of the holiday itself—consider how the week before Easter went, not April 3.
Mistake 5: Ignoring the weather
After the day of the week, weather is the strongest short-term driver of demand—yet in most manual planning processes, it is either not taken into account at all or only considered on an intuitive basis. The solution: systematically incorporate weather forecasts into volume decisions, especially for snacks, salads, and seasonal items.
Mistake 6: Missing the end of the season
The most expensive returns of the year occur after the peak season: Christmas cookies on December 27, Carnival pastries after Ash Wednesday, and Easter cakes on the Tuesday following. The solution: Stock seasonal items with strict expiration dates—clearance sales instead of additional production, discipline instead of hope.
Mistake 7: Focusing Only on Excess Inventory and Ignoring Clearance Sales
Returns are visible, but lost revenue is not: The top-selling item that sold out at 3:00 p.m. doesn’t show up in any statistics, yet it still costs money every day. If you focus solely on reducing returns, you’ll quickly be cutting back in the wrong place. The solution: Track the number of days each item is on sale and optimize both aspects simultaneously—that’s exactly what good forecasting is all about.
The common denominator
All seven errors stem from the same root cause: Planning does not make use of the available data—even though the point-of-sale system has been collecting it for quite some time. This is exactly where “ GoNina ” comes in: The AI automatically analyzes sales history, factoring in weather, holidays, vacation periods, and location-specific patterns, and provides daily order recommendations by item and store—seven days in advance. As a result, businesses reduce their excess inventory by up to 52%, increase sales by up to 6%, and save up to 12 hours of planning time per month.
Frequently Asked Questions
Which error should I fix first?
Error 2—item-level returns analysis. It costs the least, works the fastest, and brings all other errors to light.
Do I absolutely need software to apply the fixes?
No—errors 2, 4, and 6 can be addressed with discipline and Excel. However, errors 1, 3, 5, and 7 are difficult to scale manually: Calculating hundreds of item-store combinations daily, factoring in weather and the calendar, is exactly the kind of work AI forecasts are designed for.
Conclusion
The seven most common planning mistakes aren't a matter of knowledge, but of tools: The data needed to make better decisions has long been available in the point-of-sale system. Those who use it—whether manually or automatically—plan measurably better than even the best gut instinct.
To learn how AI sales forecasts can help, check out our comprehensive guide for bakeries.
