Supply chain planning isn’t about predicting the future—it’s about being ready for it. If you’re tired of scrambling when forecasts miss the mark, or you just want to get smarter about what could happen, scenario analysis is your best friend. This guide is for anyone using Avercast (that’s Avercast if you need a refresher) who wants to move past basic forecasting and start making supply chain plans that hold up when reality gets messy.
Why Scenario Analysis Actually Matters
Let’s be blunt: the “perfect forecast” is a myth. Demand spikes. Suppliers ghost you. Costs jump overnight. Scenario analysis lets you stress-test your plans against all kinds of “what if” situations—so you’re not caught flat-footed.
Here’s what you get from doing scenario analysis right: - Fewer expensive surprises - Decisions based on data, not gut feelings - More confidence in your plans (and fewer fire drills)
But here’s what scenario analysis isn’t: a crystal ball, or a way to cover every possible disaster. It’s about preparing for the most likely (and most painful) curveballs.
What You Need Before You Start
Before you dive into scenarios, make sure you’ve got solid basics in place: - Clean, recent data: Garbage in, garbage out. Double-check your sales, inventory, and supplier data. - A clear business goal: Are you trying to cut costs, avoid stockouts, or plan for a new product launch? - Access and permissions in Avercast: You need the right user role to create and save scenarios.
If your data is a mess, fix that first. Scenario analysis just magnifies data problems.
Step 1: Pin Down What You Want to Test
Don’t just run scenarios for the sake of it. Start with a real question, like: - What happens if demand jumps 20% next quarter? - How will a supplier delay affect our inventory? - Can we handle a sudden spike in raw material costs? - What if we want to cut our safety stock in half?
Pick the questions that keep you up at night. That’s where scenario analysis pays off.
Pro tip: Start simple. Tweak one thing at a time—otherwise, it’s a headache to figure out what actually caused the change.
Step 2: Set Up Your Baseline in Avercast
Before you mess with any “what ifs,” save your current plan as the baseline. In Avercast, this is usually your latest consensus forecast or supply plan.
How to do it: 1. Log in and open your main planning workspace. 2. Review your inputs—make sure nothing is outdated. 3. Save this version as your “baseline scenario.” (Most Avercast builds let you copy/rename scenarios; if not, check with your admin.)
Why bother? If you don’t have a baseline, you can’t compare results. You’ll just have a bunch of numbers with no context.
Step 3: Build Your Scenarios
Now the fun part. In Avercast, you can create multiple scenarios by copying your baseline and tweaking variables.
Here’s how to do it, step by step: 1. Copy your baseline scenario. Name it something obvious (e.g., “Demand +20%”). 2. Adjust your variables. Depending on your goal, you might: - Increase/decrease demand forecasts for certain SKUs or regions - Change lead times (what if your main supplier takes 3 weeks longer?) - Adjust costs (fuel, labor, materials) - Reduce or increase inventory targets 3. Save your changes. Each scenario should be a separate record, so you can compare them later.
Don’t overcomplicate it. Three scenarios is plenty for most problems: baseline, best case, and worst case.
Step 4: Run Simulations and Crunch the Numbers
With your scenarios set up, Avercast does the heavy lifting. Run the planning calculations for each scenario to see how your supply chain metrics change.
What should you look at? - Inventory levels: Will you run out or get stuck with too much? - Service levels: Do you still hit your on-time targets? - Costs: How much does each scenario add to your bottom line? - Capacity: Are your suppliers or warehouses overloaded?
Avercast’s reporting tools will spit out a lot of numbers. Focus on what matters most for your business goal.
Watch out for: - Overly optimistic assumptions (don’t just model the “easy” changes) - Ignoring constraints (if your supplier can’t realistically double output, don’t pretend they can)
Step 5: Compare Scenarios Side by Side
Now it’s time to see how each scenario stacks up. Most versions of Avercast have side-by-side reporting, so you can quickly spot: - Which scenario has the worst stockouts - Where costs blow up - Which plan is actually doable with your current setup
Ask yourself: - What’s the biggest risk in each scenario? - Can you live with the worst-case outcome? If not, what needs to change? - Are there any “no brainer” wins—like a tweak that improves service without much extra cost?
Pro tip: Don’t get lost in the weeds. It’s easy to obsess over tiny differences—focus on the big swings that actually change your risk or reward.
Step 6: Share Results and Make a Real Decision
Scenario analysis is useless if it just sits in your inbox. Share the results with the folks who actually make the calls—executives, buyers, logistics, whoever needs to sign off.
When you present your findings: - Show the baseline first, then the scenarios. (People need a reference point.) - Highlight the one or two biggest changes per scenario. - Be honest about what the models don’t cover. (For example: “If China shuts down again, all bets are off.”)
If nobody can decide, you probably made things too complicated. Go back, simplify, and focus on the main risks.
What Works Well (and What Doesn’t) in Avercast
The Good Stuff
- Scenario copying is fast: You can set up and tweak multiple plans without re-entering everything.
- Decent reporting: It’s easy to see inventory, cost, and service impacts for each scenario.
- Customizable: You can model most business realities if your data is solid.
The Frustrating Bits
- Steep learning curve: The interface isn’t exactly beginner-friendly, especially if you’ve never done planning before.
- Data quality is everything: Avercast won’t fix bad master data or broken processes.
- No magic answers: It’s a tool, not a strategy. You still have to make the tough calls.
What to Ignore
- “AI” hype: Don’t get distracted by buzzwords—most scenario analysis is just basic math, not magic.
- Overly complex models: Modeling every possible variable just creates noise. Stick with the scenarios that matter.
Keep It Simple, Iterate Often
Scenario analysis isn’t about building the world’s most elaborate model—it’s about making supply chain plans that won’t collapse the second something changes. Start with your biggest risks, use Avercast to run a few clear scenarios, and actually use what you learn to make decisions.
Then? Rinse and repeat. The world changes, so should your plans. Don’t wait for the “perfect” scenario—just get started, keep it grounded, and keep it practical.
If you do that, you’ll be miles ahead of the folks still hoping their forecast just works out.