Data-Driven Inventory Forecasting: Best Practices for Businesses

Want to know how to stop guessing how much inventory to order?

Every business owner knows that they need to have the right products, in the right quantities, at the right time. After all, smart inventory forecasting leads to:

• Reduced stockouts
• Lower carrying costs
• Happier customers

The problem is…

Most businesses are still ordering stock by gut instinct and Excel spreadsheets from 1998. Stock levels end up becoming a crapshoot without data-driven forecasting methods.

In this ultimate guide, you’ll find data-driven inventory forecasting best practices to stop the guessing games.

• Why forecasting accuracy matters
• What the most effective forecasting methods are
• How to put smart inventory forecasting into practice
• The mistakes to avoid when forecasting demand

Inventory Forecasting That Works in 2024: Why It Matters

Data-driven inventory forecasting is using historical sales data, market trends and analytics to predict future inventory demand.

It’s all about taking the guesswork out of managing your stock levels. Here’s why it matters so much:

Manual inventory forecasting methods have an accuracy of around 60% at best. Which means that most businesses are getting it wrong at least half the time. No wonder it feels like it’s hard to forecast with precision when so much cash flow depends on it.

The good news is that businesses using proper inventory forecasting solutions with machine learning and AI capabilities can see accuracy rates of 90% or more.

Now that’s impressive accuracy.

But just think about what this means for a business in practice. Higher forecasting accuracy means there is less chance of overstocking products in warehouses. That means avoiding expensive stockouts that frustrate your customers. It means having cash tied up in products where it should be.

Here’s another stat that may grab your attention:

43% of small businesses don’t track inventory effectively or use old, manual methods. This causes huge inefficiencies in their supply chain and lost revenue they could have easily avoided.

Mind-blowing, right?

Inventory Forecasting Methods That Work

Not all inventory forecasting methods are created equal. Some techniques work well for certain business types, while others have better results in different situations.

Here are the core, proven inventory forecasting methods that will help you get the most out of your demand planning.

Time Series Analysis

This method looks at historical sales data to identify patterns.

Time series analysis breaks down past performance into components. It looks for an overall trend in demand direction. It can spot repeating patterns tied to seasons or holidays. It then uses that data to predict what’s likely to happen next.

This type of analysis works best for established products with a lot of historical sales data. For new, untested products, you may need to use a different method.

Demand Sensing

Demand sensing is a little different…

It uses real-time market signals to create short-term forecasts. It combines live sales data from POS systems with statistical forecasting methods.

The aim is to narrow the gap between when changes in the market occur and when the forecasts are updated. Rather than updating on a monthly basis, businesses using demand sensing can quickly adjust to daily changes in demand.

It’s ideal for fast-moving consumer goods and products with highly volatile demand.

Causal Modelling

Causal models go beyond historical sales and look for cause-and-effect relationships between demand and outside factors.

For example:

A hardware store making forecasts around paint sales would need to account for the state of the housing market. More new homes sold equals more interior painting jobs. Weather conditions matter too. Hot and dry weather is good for exterior painting jobs.

Understanding demand drivers and why it changes leads to more accurate predictions.

So how do you implement inventory forecasting best practices?

Inventory Forecasting That Works: How To Implement

Ready to take your inventory forecasting to the next level?

Here’s how you do it…

Start With Clean Data

As the old adage goes, garbage in equals garbage out.

The data behind your inventory forecasting needs to be airtight for any method to work well.

This means:

• Historical sales records are accurate
• Product categories are consistent
• Supplier lead time data is reliable
• Customer demand data is clean

Spend time at the start to make sure you have good data. The process will thank you later.

Choose The Right Tools

Are spreadsheets cutting it for your business’s demand planning? If you want proper forecasting, forget Excel. Spreadsheet solutions have their limits.

Inventory management software and other modern tools provide:

• Automated demand forecasting
• Real-time stock visibility
• Seamless integration with other systems
• AI-powered predictions

Pick tools that match the size and complexity of your business. Start by identifying key pain points, then find the solutions that directly solve those problems.

Build A Feedback Loop

Inventory forecasting is not a set and forget process. Don’t fall into this trap.

Markets and consumer tastes change. New competitors, economic conditions, suppliers can all impact your sales forecasts. Something that worked in the previous quarter might not hit the mark this time around.

Building regular review and adjustment cycles into the process keeps your forecasts relevant and accurate. Continually compare your predicted demand against what’s actually being sold. Find where the model went wrong. Make adjustments.

This continuous improvement model is the difference between good and great forecasting.

Forecasting Mistakes To Avoid

Forecasting well is one thing. Not making common inventory forecasting mistakes is another. Here are the big ones to look out for.

Ignoring Seasonality

Seasonal products are not in constant demand throughout the year. Ignoring these obvious patterns will leave you either with no stock or warehouses overflowing.

Incorporating historical seasonality into your models is relatively simple. Use past sales data to identify these trends.

Over-Reliance On Historical Data

Look at historical sales data but don’t get married to it.

Major shifts in the market can render previous patterns obsolete. New competitors, changing regulations and economic conditions all impact demand. Causal models that include external factors help create a more complete picture.

Failing To Collaborate

Inventory forecasting is not a silo process.

Salespeople have the inside track on upcoming promotions. Marketing have the calendar for campaigns that might impact demand. Suppliers can highlight any potential delays. Finance know what budget and cash flow constraints look like.

Bringing these different perspectives to bear helps create the most accurate forecasts. Cross-functional collaboration is critical.

Not Investing In Technology

Trying to run a complex inventory with a 1998 toolset is not a great idea.

It’s like bringing a knife to a gunfight.

Artificial Intelligence and machine learning can work through vast amounts of data to spot patterns and insights a human might miss. Machine learning technology is out there. Investing in using it to help with forecasting can be the competitive advantage a business needs.

Inventory Forecasting Best Practices Summary

Data-driven inventory forecasting is a game-changer. It helps businesses:

• Minimise carrying costs
• Avoid expensive stockouts
• Optimise cash flow
• Keep customers happy

Getting started is easier than you think. Begin by working with clean data. Choose appropriate tools. Don’t forget the importance of building a feedback loop into the forecasting process. Finally, always keep an eye out for common mistakes.

Businesses that embrace data-driven inventory forecasting will leave those still relying on gut feel and 1998 Excel spreadsheets behind.

Technology is out there, the methods are proven. It’s time to stop guessing and start forecasting with confidence.