If you’re responsible for supply chain planning, you know the stakes: get your forecasts wrong and you’re either drowning in inventory or explaining why customers are angry. You’ve probably seen a dozen “AI-powered” forecasting tools promising the moon. You want honest answers—what actually works, what takes too much time, and how to avoid wasting months on a tool you’ll abandon.
This is for folks in supply chain, ops, or sales who need to make real, actionable forecasts. We’ll dig into Avercast, one of the more established B2B supply chain forecasting tools, and walk step-by-step through setup, use, and pitfalls. You’ll leave knowing whether it’s right for you—and how to not screw up the rollout if you move ahead.
What is Avercast—and Who Should Even Care?
Avercast bills itself as a “supply chain forecasting platform” with modules for demand planning, inventory optimization, sales forecasting, and more. It’s aimed squarely at mid-sized to large companies who have too much data for spreadsheets but don’t want a full-blown SAP or Oracle install.
Good fit if: - You’re juggling forecasts across dozens or hundreds of products/SKUs. - You need to coordinate between sales, ops, and procurement teams. - Your current Excel setup is creaking or held together with duct tape.
Probably overkill if: - You’re a small shop or startup with a handful of products. - You want a plug-and-play tool you can master in a weekend.
Pro tip: Avercast is pretty customizable, but you’ll get the most out of it if you already have some forecasting process in place—even if it’s just a messy spreadsheet.
Avercast Review: The Good, The Bad, and The Meh
Let’s get honest. Here’s what stands out after time spent wrestling with Avercast in the real world.
What Works
- Flexible modeling: You get a lot of forecasting models out of the box (time series, moving averages, regression, etc.). You can try a few and see what fits your data best.
- Scenario planning: You can actually run “what if”s—change a promo, a supplier lead time, or a growth assumption and get updated forecasts quickly.
- Collaboration: Multiple users can tinker with forecasts, add comments, and track changes. This beats sending around version 21 of an Excel file.
- Decent integrations: It’ll connect with most ERPs and can import data from Excel/CSV if you’re not fully automated yet.
What’s Painful
- UI is stuck in 2015: It’s functional, but don’t expect anything slick. There’s a learning curve, and some things take more clicks than they should.
- Setup can drag on: If your data is a mess (let’s be honest, whose isn’t?), getting everything mapped and cleaned is a project.
- Reporting is just OK: You can export lots of data, but building pretty reports for execs will still take some manual work.
- Price: It’s not cheap, especially if you want all the modules. Get clarity on licensing and hidden costs.
What to Ignore
- “AI-powered” claims: Yes, there are advanced algorithms. No, they’re not going to magically fix garbage data or replace human judgment.
- Every module: Focus on nailing demand planning and basic forecasting first. Inventory optimization, S&OP, etc., can wait.
Step-by-Step: How to Actually Use Avercast for Supply Chain Forecasting
Let’s walk through what the process looks like, minus the marketing fluff.
Step 1: Get Your Data Together—Seriously
You can’t forecast what you can’t measure. Before you even log in, pull together:
- Historical sales data (ideally 18+ months)
- SKU/product hierarchy (what’s grouped with what)
- Current inventory levels
- Lead times, vendor info, promotions
Pro tip: Clean your data now, or be prepared to clean it later, under pressure. Bad data in = bad forecasts out.
Step 2: Set Up Avercast (with IT Help)
- Install/integrate: Most companies use Avercast as a cloud/hosted service, but you can run it on-prem if you like pain.
- Connect your data: This is where you’ll spend time mapping fields from your ERP/CRM/Excel files to Avercast’s schema. Expect to make a few mistakes.
- User roles: Assign who can view, edit, and approve forecasts. Don’t give everyone admin—trust me.
Step 3: Build Your First Forecast
- Pick a model: Start simple. Avercast recommends models based on your data shape, but don’t be afraid to try a few and compare.
- Set parameters: Seasonality, minimum/maximum inventory, lead times, etc. The defaults are OK but rarely perfect.
- Run the forecast: You’ll get a baseline—now the real work begins.
Step 4: Collaborate and Adjust
- Review with the team: Sales, ops, procurement—all have a stake. Use Avercast’s comments and audit trails to document changes and arguments.
- Adjust for reality: Promotions, supplier hiccups, and market shifts aren’t always in the data. Human tweaks still matter.
- Scenario planning: Run a few what-if scenarios (e.g., what if demand spikes 20%? What if a supplier is late?) and see how robust your plan is.
Step 5: Monitor, Report, and Iterate
- Track forecast accuracy: The system will tell you how far off you were last cycle. Actually look at this—it’s where you’ll get better.
- Export/share reports: Avercast can spit out plenty of data, but you’ll probably want to build your own exec summary.
- Tweak and repeat: Forecasting isn’t a “set and forget” thing. Schedule regular reviews and tweak as you learn more.
Honest Pros and Cons
Pros
- Handles complexity well (lots of SKUs, locations, etc.)
- Collaboration is much easier than with spreadsheets
- Solid scenario modeling
- Support team knows supply chain (not just IT)
Cons
- Interface feels dated and clunky
- Initial setup is not “quick”—expect weeks, not days
- Pricey for smaller teams
- Reporting is functional, not beautiful
Common Mistakes to Avoid
- Thinking software will fix process problems: If your team doesn’t talk, Avercast can’t make them.
- Skipping the data cleanup: You’ll pay for it later in garbage forecasts.
- Trying to roll out every feature at once: Start with the basics. Get forecasting working before you touch inventory optimization.
- Not training your team: It’s not rocket science, but it’s not Excel either.
The Bottom Line
Avercast is a solid tool for companies that have outgrown spreadsheets and need to coordinate complex supply chain forecasts. It’s not magic, and it’s not cheap—but it does the job if you put in the work, especially around data and process.
Keep it simple: get your data right, start with the basics, and don’t expect miracles overnight. Iterate as you go. Most “failed” forecasting projects fail because people try to do too much, too soon. Take it step by step, and you’ll actually see results—without having to explain another spreadsheet disaster in your next meeting.