If you’re tired of wrestling with spreadsheets and endless “gut feel” meetings, automated demand forecasting is supposed to be the answer. In theory, tools like Avercast let you set up repeatable workflows that do the heavy lifting: crunching the numbers, spitting out forecasts, and (hopefully) making your job easier. But the reality isn’t always plug-and-play. This guide is for supply chain managers, planners, and anyone else who actually has to make forecasts work in the real world—not just in a sales demo.
Here’s how to set up automated demand forecasting workflows in Avercast, step by step—with zero hype, no jargon, and some honest advice about what works and what doesn’t.
1. Get Your Data House in Order
Let’s be blunt: automated forecasting is only as good as your data. If your sales history, inventory, and product master data are messy or incomplete, no software will magically “fix” it.
What you need: - Sales history: At least 18–24 months is ideal. The more, the better. - Product master data: SKUs, categories, units of measure, etc. - Inventory and supply data: Optional, but helps with more advanced workflows.
Pro tip:
Don’t dump everything in. Start with your top 20–30 SKUs or a pilot category. It’s easier to spot issues and you won’t get overwhelmed.
What to ignore:
Avercast can ingest a ton of data fields, but you probably don’t need every last attribute. Stick to the essentials until you’re confident the basics work.
2. Connect Your Data Sources
Avercast offers a few ways to bring in your data: - Direct integrations: ERP systems like SAP, Oracle, and NetSuite can connect directly. Setup can be finicky—expect some IT back-and-forth. - Flat file uploads: CSV or Excel files are fast for pilots. Just map your columns to Avercast’s required fields. - APIs: If you’ve got a data team, they can automate imports via API. Most smaller shops don’t bother.
What works:
Flat file uploads are the quickest way to get started. If you’re under time pressure, start here—even if your IT team wants to build a full integration later.
What to watch out for:
Column mismatches, date format issues, and missing fields are the most common problems. Avercast’s error messages are… not always helpful. Keep your files clean and triple-check your column mappings.
3. Choose a Forecasting Model
Avercast comes with a buffet of forecasting algorithms: moving averages, exponential smoothing, seasonal models, and more. Here’s the honest truth: the “best” model depends on your data.
- Simple moving average: Use for stable, high-volume items.
- Exponential smoothing: Good for items with steady trends.
- Seasonal models: For products with regular peaks and valleys.
- Croston’s method: If you have intermittent demand (think spare parts).
How to pick:
Avercast can auto-select the “best fit” model for each SKU. This usually works… but double-check. Sometimes it picks a model that looks good on paper but produces wild swings.
Pro tip:
Run a few SKUs through different models and compare the results. Don’t just trust the default.
4. Set Up Automated Forecast Runs
This is where the magic happens—if you do it right.
Step-by-step: 1. Schedule forecast runs: In Avercast, you can set forecasts to run daily, weekly, or monthly. Match this to your business rhythm. For most, weekly is a good start. 2. Pick your SKUs: Start small. It’s tempting to forecast everything, but it’s a recipe for noise and confusion. 3. Configure output fields: What do you need? Forecast by SKU? By location? Don’t overcomplicate it. 4. Set up notifications: Have the system alert you (or your team) when forecasts are ready—or if something fails.
What works:
Automated schedules save time and reduce mistakes. But don’t go fully hands-off yet; you’ll want to review the outputs for the first few cycles.
5. Review and Clean Up Outliers
Even the best models spit out weird numbers sometimes. Promotions, supply disruptions, or plain old bad data can throw things off.
How to keep things real: - Visualize results: Avercast’s charting tools are basic, but good enough to spot outliers. - Filter for big changes: Set up rules to flag forecasts that change more than X% week-to-week. - Manual overrides: Sometimes, you just know better. It’s OK to make manual tweaks—just document why.
What to ignore:
Don’t waste time chasing every minor fluctuation. Focus on the big swings that actually matter for planning.
6. Automate Output and Integrate With Other Systems
You’ve got your forecasts—now what? The real value is pushing them where they’re needed.
Options: - Export to Excel/CSV: Still the most common option. Fast, flexible, universally understood. - Direct integrations: Push forecasts into your ERP or planning system. This takes more work, but saves time long-term. - Dashboards: Avercast’s built-in dashboards are… fine. If you have Power BI or Tableau, you’ll probably want to export and build your own.
Pro tip:
Start with simple exports. Don’t get sucked into weeks of integration projects before you’ve validated the numbers.
7. Monitor, Iterate, and Scale Up
Automated doesn’t mean “set and forget.” Forecast accuracy drifts over time, and business needs change.
What to do: - Track forecast accuracy: Avercast reports metrics like MAPE and bias. Use them. - Set a monthly review: Look at where forecasts are off, and why. Adjust models or clean up data as needed. - Expand gradually: Once your pilot SKUs look good, add more products or locations.
What to ignore:
You’ll see fancy features like “AI-powered forecasting” or “collaborative planning” pop up. Ignore the buzzwords until you’ve nailed the basics.
Honest Takes: What Works, What Doesn’t
- Works:
- Automated runs save real time—once you trust the process.
- Starting small helps you catch issues before they snowball.
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Manual overrides are your friend, not a sign of failure.
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Doesn’t work:
- Expecting perfect forecasts. No tool can predict the future.
- Dumping in all your data at once. You’ll create a mess.
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Ignoring model selection. “Best fit” isn’t always best.
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Ignore:
- Overly complex workflows. If you dread checking the forecast, it’s too much.
- Fancy dashboards before the basics work. Eye candy doesn’t drive decisions.
Keep It Simple—Then Tweak
Automated demand forecasting in Avercast isn’t rocket science, but it’s not a magic button either. Start with clean data, a handful of SKUs, and basic models. Let the system run for a few cycles, review the results, and make small tweaks. Once it’s working, then—and only then—should you automate more, integrate deeper, and scale up.
Iterate, don’t over-engineer. The goal is forecasts you can trust, not a Rube Goldberg machine. Good luck!