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Stop losing money with bad stocking decisions.
If there's one thing every retailer and ecommerce business owner wants, it's "perfect" inventory levels.
Stock enough to meet demand, but not so much that you're stuck with excess when seasonal trends come to an end.
Accurate inventory means:
-> Improved cash flow
-> Increased profit margins
The problem is, effective demand forecasting takes effort.
Unless you have a system that utilizes quality inventory data from multiple sources, you're just guessing at what your customers actually want.
And guess what…
businesses lose up to 11% of annual revenue to poorly executed inventory management. Stockouts and overstocking are draining your bottom line.
Thankfully, there's a better way. Let's dig into how data-driven forecasting can change the way you make business decisions.
Here's Your Free Cheat Sheet:
1. Costs Of Ineffective Inventory Management
2. Data-Driven Forecasting Fundamentals
3. Effective Inventory Forecasting Techniques
4. How To Transition To Data-Driven Forecasting
Costs Of Ineffective Inventory Management
Forecasting isn't something you do to fill your shelves with product.
Inventory forecasting is about protecting your profits.
All of those missed opportunities and inefficient inventory practices come at a cost. Consider:
Excess inventory drains your cash. All of that capital is tied up in shelved products you're not selling. You're paying to store that inventory, you risk losing money to obsolescence, and whatever isn't selling just sits there.
Low stock levels mean lost sales. Someone wants your product, but you can't sell it to them because you're out of stock. Instead, they turn to your competitors.
There's another way bad forecasting decisions cost your business…
Customers who don't receive orders like promised will take their business elsewhere. Sure, they may give you a second chance. But what about that third customer who doesn't come back? Lost forever.
Demand planning reduces total inventory by 10-15%, while service levels increase.
Think of forecasting as investing in your customer's lifetime value.
Data-Driven Forecasting Fundamentals
Demand forecasting analyzes past sales performance to predict future demand.
The key word there is "past".
Data-driven forecasting relies on historical sales data as well as market trends to make accurate predictions.
Instead of using intuition to set your inventory levels, you're:
-> Predicting seasonal variances in demand
-> Reviewing previous years' sales history by week, month, quarter, and year
-> Looking at market factors that affect purchase behavior
-> Understanding your products' life cycles
Sales data is generated automatically by modern POS systems. Smart inventory forecasting tools analyze your historical sales data to identify trends and anomalies then predict future outcomes.
Statistical Forecasting Models That Work
There are several common statistical models used in forecasting.
Many of these methods are still used today because they offer surprisingly accurate predictions.
One of the oldest forecasting methods is known as time series analysis. Time series analysis works by reviewing historical sales data to identify seasonal trends and patterns. This method is ideal for mature products that have been on the market for several years.
Moving averages help you identify underlying trends by eliminating short-term demand fluctuations. This method is typically used for stable products with less variance in demand.
Exponential smoothing is very similar to moving averages, but it puts more weight on recent past data and less emphasis on demand history.
Machine learning forecasting has been gaining popularity in recent years.
Traditional forecasting methods have an average accuracy rate of about 60%. Businesses using ML for demand forecasting reported 90% accuracy.
That's right.
Effective Inventory Forecasting Techniques
So what makes good forecasting great forecasting?
The accuracy of your forecasting model depends on the quality of your data.
Garbage in = garbage out.
Most statistical models rely heavily on historical sales data to make predictions. At minimum you should have 12-24 months of clean historical sales data when forecasting. This allows the forecasting system enough data points to identify seasonal trends and adjust for outliers.
External factors can influence demand as well. A robust forecasting system takes into account market forces when predicting demand.
Economic trends, industry seasonality, competitor actions, and even the weather can influence what your customers are buying.
Real-Time Inventory Demand Adjustments
Let's face it. Business doesn't stand still.
Waiting weeks to identify trends in your inventory performance isn't going to cut it. Demand sensing allows you to react to market changes in near real-time.
These systems pull from POS data, web analytics, and even social media trends to identify demand spikes quickly.
Instead of adjusting your forecasts monthly or quarterly, demand sensing can help you adjust forecasts within days or hours of detecting a market shift.
An upscale clothing brand sees the downtown weekend rugby tournament trending on social media.
They utilize demand sensing tools and instantly recognize they're low on sale items. Before they lose out on a surge in sales, they have enough time to safely replenish their stock.
Two inventory problems solved.
Collaborative Inventory Forecasting
This may come as a surprise to you, but your sales team cares about forecasting.
Forecasting is a team sport, and everyone plays a vital role.
Front line sales teams interact with customers daily. Your marketing team is aware of up-coming promotions. Suppliers know production schedules and lead times.
Why not join forces with your team? Collaborative forecasting allows you to share your data-driven insights with the people who can influence demand. CPFR (Collaborative Planning, Forecasting, and Replenishment) has allowed businesses to generate more accurate forecasts by including the expertise and experience of their employees.
Combining qualitative insights with quantitative data.
Transition To Data-Driven Forecasting
Thinking about making the switch to data-driven forecasting?
You should be.
If you're still forecasting with a spreadsheet and "best guess" estimates, it's time for a change.
Transitioning to a new forecasting method can be simple. Here are some steps to help you along the way:
Start by downloading your historical sales data. You'll need this to generate baseline forecasts. Organize your data with proper attributions such as SKU's, dates, and sales quantities. Be sure to clean any outliers or errors that could potentially skew your forecast.
Pick an inventory forecasting method that fits your business. There's no one-size-fits-all approach.
If you're selling proven products with consistent historical sales, time series methods may be your best bet. For newer products or industries with heavy seasonality or market influences, consider causal models. For volatile markets that require faster response, try demand sensing.
Implement and monitor your new process. Start small by choosing a few products to forecast. Pick your best selling items or items with the tightest margins. Once you master your forecasting process, begin scaling to include more products.
Monitor your forecast accuracy as well. Compare your forecasts to actual sales and calculate your forecast error percentage.
Forecasting is never done. As your business changes, your forecast will need to change as well.
Adjust your forecasts and modeling to fit the evolution of your business.
Data-Driven Inventory Forecasting Cheat Sheet
You made it.
Congrats on reaching the end of our comprehensive guide to data-driven inventory forecasting. Just remember:
Predictive inventory forecasting is about improving your inventory decisions, not making instantaneously perfect ones.
Starting small allows you to iterate your process and establish forecasting as a core part of your culture. Once everyone from executives to warehouse staff understand the importance of forecasting, your business is sure to reap the rewards.
By utilizing forecasting technologies and modern inventory methodologies, you're preparing your business for larger growth.
Information is power. Use it to your advantage.
