Demand Forecast Accuracy

In today’s world of technology savvy consumers and big data, retailers must find an efficient way to merge business intelligence, forecasting and customer insight into usable information that creates results. We recently worked with a top 100 consumer electronics retailer on an installation of our iKIS™ solution which resulted in improved forecast accuracy from 16.9 percent to 98.2 percent with return on investment realized in the first 60 days.

Unlike legacy systems in the market that have simply upgraded to a web interface, our software is built to work online, keep data secure, analyze the huge amounts of customer data at high speeds and translate the analysis into consumer insight – demand forecast.

Why Does Forecast Accuracy Matter?

Several studies (Gartner, Dr Mentzer, others) consistently show that forecast accuracy delivers a 15% shareholder value increase. The problem is many legacy software apps just do not deliver good forecast accuracy. Using a recent example lets look at the problem and the opportunity as many companies have the same issues and opportunity.

Consumer electronics retailers inventory mix changes by at least 30% on a monthly basis. This intense level of product swapping creates an inventory challenge. The key differentiators that make Data Profits attractive are its demand forecasting software with collaboration tools and its Market Trend Indicator, which is critical for the consumer electronics sector. While nearly every demand forecasting and planning software vendor has seasonality trending mechanisms, Data Profits is the only BI software that provides its clients the ability to watch and predict market dynamics.

How many Demand Forecast Algorithms are Needed?

Multiple algorithms are important but in the past hardware could not run fast enough to allow the forecast programs to choose between multiple algorithms without a heavy price tag. In the 1960’s, the man who co-founded Intel also wrote a rule we refer to as Moore’s Law: hardware will double the amount of data it can process every 18 months. Think about the last laptop or desktop you purchased; did you check the price tag a year later? Moore’s Law still impacts today’s technology, the problem is the legacy hardware many companies are running today was written based on hardware models that are 10 years old.

We can store more data and we can use more algorithms, but if you do not clean the data or apply the data to the algorithms correctly then you will not get the right demand forecast. While we have faster hardware and cheaper storage the old saying garbage in = garbage out still applies. You need some methodology to analyze the data with a set of BI tools and then apply the result data set to the demand forecast algorithms. This is another feature that legacy systems do not have in the base code, meaning you are losing money.

Today we have volumes of data to store and review, but the legacy slower design models need more hardware and cost significantly more dollars to run. A new software system can store and analyze significantly more data faster and at a lower cost than legacy systems. The results in the new systems are found to be far more accurate in the only 10% error range where legacy systems are thrilled to run 15% error and are often in the 20% error range.

By merging business intelligence (BI) analytics, demand forecasting, and planning tools with vendor and buyer collaboration, iKIS provides clear, accurate, and extensive knowledge of consumer buying habits. This knowledge enables retailers to reduce inventories but stay in-stock with the right products at the right location, delivering increased sales for Data Profits’ customers.

Interested and want to learn more? Check out the Data Profits Demand Forecasting video and learn how we can help you “Tighten the Links in Your Chain.™”

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Stuart D

Stuart Dunkin is a strategic visionary who leads by calling on his broad range of experience as a former retail executive as well as a consultant for top retailers and for the E3/JDA software company. He founded Data Profits to solve a real-world problem plaguing retailers today – the disjointed relationship between supply chain data, people and business goals. He has experienced first-hand the challenges and frustrations that occur when decisions are made based on wrong data, which often leads to the wrong actions being taken with crippling results like overstock, out-of-stock, and increased working capital.
Stuart graduated from Auburn University, with bachelor of science in business administration.