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5 Reasons Demand Forecasting Models in Excel Aren’t Enough in 2024

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Person sitting in front of a large screen displaying demand forecasts.

You might have noticed a shift away from demand forecasting models in Excel. Perhaps you’ve heard about top retailers adopting AI-based models.

Is it worth the investment and time to make the switch? Or is it hype?

There must be a reason that 80% of retail executives surveyed by Analytics Insights, expect their businesses to adopt artificial intelligence automation by 2025.

Let’s look at the challenges modern retailers face when using spreadsheet-based models, and the tangible impact of AI on forecasting demand.

Five shortfalls of using demand forecasting models in Excel

Traditionally, retailers used Excel-based demand forecasting models to collect, sort, and analyze past sales data.

Although these models are still an excellent low-cost option for smaller retailers, they aren’t sufficient for today’s omnichannel and e-commerce needs.

If you have outgrown your demand forecasting model, then chances are you’ve been dealing with these issues for some time.

1.  Inability to scale    

As a retailer grows, every additional sales channel and even SKU results in exponential data growth.

The 100s of thousands of data points and permutations require storage and computational power that traditional spreadsheet models simply cannot handle. Processing the data within a spreadsheet is often a slow process that requires manual intervention.

As a retailer grows they can no longer manage their inventory as closely. The influx of data means retailers must resort to a general, category-level approach to planning. Optimal results require precision, unfortunately, the most accurate category-level forecast will only get you so far.

2. Data Integrity Issues

Today’s omnichannel retailer is pooling data from multiple sources into their forecasting spreadsheet. Data sources often run on a variety of operating platforms and may not be compatible with one another; requiring human intervention.

Inaccurate data leads to an inaccurate forecast and a mistake that is overlooked and not corrected can be repeated through multiple sales cycles.

Consider the following scenario:

While inputting order quantities Kevin accidently types 20 instead of 200 widgets. The widgets are sold out immediately and the company suffers substantial lost sales.

But it gets worse, the sold number of widgets is used as sales history in determining demand for the coming year. The error is repeated causing stockouts and lost sales year after year!

3. Poor Visibility and Collaboration

Managing inventory across channels, locations, and departments is not an easy task. Software such as Excel was not initially developed with sharing in mind. As such, a siloed approach to inventory management emerges.

An optimal demand forecast relies on visibility into inventory data and activity throughout the entire business.

Meanwhile, siloes result in communication breakdowns and version control issues between sales channels and departments. Now you’re dealing with frustrations and redundancies!

4. Lack of Scenario Analysis 

Whether you’re planning to launch a new line of products or running a promotion, new initiatives carry risks.

Time and money are invested, and by the time you discover that the initiative is unsuccessful, it is often too late to pivot.

Running scenarios through a spreadsheet demand forecasting model is time-consuming and requires immense computing power. But the real trouble is that these analyses are not a true representation of real-world scenarios.

Excel-based models are often limited to past-sales data to forecast demand. This does not account for all other factors which influence sales demand such as demographic changes, seasonality, inventory mix, competitor pricing, and many others.

5.    Hidden Sales Opportunities

Because of the vast number of data points sitting in spreadsheets, analysts are limited to looking at baseline operations and trajectories.

Manually sifting through all the data is impossible, so it can be easy to overlook inaccuracies.

Spotting potential opportunities can be even more challenging. If you are analyzing data at the category level, you may miss an opportunity to capitalize on a high-performing SKU.

For instance, there may be an uplift in demand for poetry books during the Valentine’s Day sales cycle. Unfortunately, when planning the seasonal promotions and inventory mix planners cannot look at each individual SKU in excel.

Since books are not in the typical inventory mix for Valentine’s Day this sales opportunity will be overlooked.

How is this impacting you?

It’s easy to overlook red flags that point to an ineffective demand forecast because they are so prevalent in the industry.

You can get used to living with pain. Call it “status quo”, call it “the cost of doing business”.

At the end of the day these red flags are not coincidences, they are the impact your forecasting model has on the business.

You might have been noticing the red flags for a while. You know your current forecasting model is not enough.

You’ve heard that industry leaders are using AI and Advanced Analytics solutions. But what is it,  and does it really make a difference?

The modern approach to forecasting

Today’s consumer expects a smooth, simple, and low-cost shopping experience. Meanwhile, the retail business has advanced, juggling multiple channels from e-commerce to Brick and Mortar.

A modern problem requires a modern solution. This is why leading retailers are investing their IT budgets toward AI-based predictive analytics.

Advanced analytics enables retailers to mitigate the challenges experienced when using spreadsheet-based models and handle today’s business needs.

AI-based Solutions help by:

  1. Scalability, capacity, speed 

The advanced technology is able to store and process millions of data points through cloud connectivity. This means no barriers to growing your business. This capacity means that inventory can be managed at Store/SKU level, leading to a more accurate demand forecast.

  1. Unified Data Analytics 

Advanced Analytics solutions are able to compile and sort through data with no manual intervention. Reducing errors by 30 to 50%. Cutting costs, saving time, and freeing employees to focus on valuable business development tasks.

  1. Clear visibility

Through the use of a unified analytics platform, employees are able to see inventory data updates as they occur at any point in the retail business. Resolving common miscommunication issues and enhancing operational efficiency.

  1. Run real-world scenarios 

Advanced analytics accounts for numerous factors beyond past-sales history to forecast true sales demand. The capacity and speed of these technologies enable users to run multiple scenarios in minutes, save versions and compare possible outcomes before deciding on a course of action.

  1. Prescriptive Analytics

Beyond predicting future sales demand, the solutions comb through the data to identify sales opportunities that have been missed and flag issues that may occur. Enabling retailers to avoid losses while maximizing wins.

Your Next Steps

Don’t fall behind using spreadsheet forecasting. Make the switch to intelligent AI-based retail software and stay competitive in the retail industry.

Here are 5 important steps to getting started with retail analytics.

So go ahead. Innovate, and embrace digital transformation.

If you’re interested in seeing the tangible effect AI-based demand forecasting can have on your business, connect with one of our team members for a personal demo. 

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