Tag Archive for: Demand Forecasting

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How Holiday Sales will Impact Retail in 2012

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Data Profits’ Predictions for 2012 that Retailers need to Know Now

The official holiday shopping and Christmas season kicks-off this weekend, and is considered a bellwether for the retail industry, as this is the time of year when retailers earn up to 40% of their annual profits. Everyone is watching to see:  will post-election results and the continuing shaky global economy drive shoppers back into their recessionary, frugal spending habits or will they be more willing to loosen purse strings?

Another major trend that industry insiders are speculating about is the influence of social media and ecommerce on this year’s holiday shopping season. Will consumers seek and find the best deals from their sofas by using their device of choice, avoiding the mob scenes at the Malls all together? With so many big questions looming, what can retailers do on the eve of the 2012 Holiday Shopping season that will make a strategic impact? Read more

Little Known Leverage Strategies for Lost Sales Reductions – Part 3

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Leverage Your Lost Sales Data to Grow Profits

The key to successful Demand Driven retail is leveraging the right data in the right places. Lost Sales data has a lot of leverage that, when ignored, can be your demise; however, those that successfully measure and leverage Lost Sales data will see sales and profit gains. Our Lost Sales blog today outlines how lost sales data can be used to improve inventory optimization and highlights how all the pieces are interconnected. We also dive into the differences between lost opportunity and lost sales, the differences and impacts between the two, and close with some sobering statistics from Lost Sales data collected from the industry.

Our first blog on Lost Sales highlighted the staggering impact of Lost Sales in most businesses today. We outlined some of the methods used to calculate lost sales, and why these methods do not deliver value. Our second Lost Sales blog reviewed how lost sales can add value to demand forecasting and improve the accuracy and value for the service attained calculation.
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Truth, Lies, and Strategies for Lost Sales – Part 2

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Why Lost Sales are Ignored

Our last blog highlighted the staggering impact of Lost Sales in most businesses. We outlined some of the methods used to calculate lost sales, and why these methods do not deliver value. We touched on in-stock and Service Attained as two measures that in the past were acceptable inventory ROI measures but, in the market place today, these methods are dated and focus retail in the wrong direction.

With the advancement of inexpensive hardware, we can calculate a very accurate demand forecast across any block of products in our assortment and measure the business at any individual (or group of) product / locations. We can easily track available inventory for any product/location at a moment’s notice. This makes calculating lost sales a simple calculation: sum demand for the days where available inventory <=0. Today, we will review how lost sales impact demand forecasting, service attained, and inventory optimization.
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The 7 Most Frightening Lost Sales Facts – Part 1

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Companies Miss the Staggering Impact of their Lost Sales

Most companies are totally blind to the amount of lost sales they accumulate each year. Without a Lost Sales measure, a company loses significant opportunities to the competition in the forms of repeat business and gross margin dollars. The real impact of lost sales is often further hidden by the false securities of in-stock reports and service level measures that are based on fill rate.

How often do you review a lost sales report? Do you know how the lost sales are calculated and if they are accurate? Most legacy systems lack a true measure of ‘Lost Sales’ for the many reasons listed above. Many companies miss out due to the age of legacy software (often more than 5 years). The hardware cost to run product/location data even 5 years ago would prevent most companies from buying software that needed mega expensive hardware. Legacy software left out these types of calculations as the customer market that could afford to pay for mainframe hardware was extremely small.
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Top 3 Ways to Crunch Big Data Into Big Dollars

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Note: This is our second blog in our 2-part series on crunching Big Data. If you find this information interesting, be sure to check out part 1 on Big Data & Product Groups as well.

Many are sold on the idea that Big Data provides Retail Demand Intelligence (RDI) and that employing RDI with demand forecasting and inventory optimization will return retail to profitability. The startling truth is that 70-80% of Big Data BI projects fail; typically, we only learn about these projects when we see multi-million dollar accounting write-offs or when cell phone video of winter fur coats in a Hawaii department store shows up on YouTube.

Why was this happening? Perhaps the better question of “Why?” was rooted in the data analysis tools that were used to group products and locations together for allocation and assortment planning. How do we avoid this situation?

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Product Groups: Would you Allocate Winter Coats to Hawaii?

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Got Groups? A major department store sent new winter coats every year to Hawaii.

Consumer Centric Retail is Product and Location

Product Groups and Product Ranking are the most utilized feature in legacy ERP systems. Think of the different ways data is grouped to use in the supply chain. Product Groups and Ranking reports are used for planning, allocation, and analysis reporting in most any retail, wholesale, or grocery line of business. Some businesses go further and use these same reports to run replenishment in Excel or other software, and other companies use product grouping and ranking for inventory optimization, price optimization, and exception management. Read more

Inventory Optimization: Putting it in to Practice

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We’re wrapping up an informative series on inventory optimization today: we’d like to give you some advice on actually putting it all in to practice. If you haven’t been with us throughout the month, the series has included:

What’s the Payoff?

What are the aims of Inventory Optimization? We argue that Inventory Optimization aims to improve your margins through a net reduction of acquisition and carrying costs. So what does this look like?

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The 3 Most Ignored (and Profitable) Factors in Inventory Optimization

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When is the last time your Inventory Optimization (IO) program saw the inside of a warehouse, the bed of a truck, or a container floating across the ocean? Most inventory optimization programs overlook the harsh realities of replenishment and logistics. How does your program stack up?

Note: We’re in week four of our series on inventory optimization.

Oft-Overlooked Inventory Optimization Factors

While it’s true that inventory optimization is largely a math equation, the devil (and the profit) is in the details. Most solutions talk about carry cost, acquisition cost, and profits because it sounds good and seems impressive, but what is really happening under the hood of your IO solution? Read more

Inventory Optimization: Is Your Acquisition Cost Off-Balance?

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Inventory Optimization – Do you know your Acquisition Cost

In today’s tight economy, retailers like you are constantly looking for the best and most profitable ways to grow their business. Last week, we talked about inventory optimization and how a truly optimized inventory results in lower inventory and higher profits by managing a balancing act of acquisition cost, carrying cost, vendor minimums, price breaks, gross margin, sales dollars and service goals. This week we take a closer look at acquisition cost and the role it plays in the balancing act of inventory optimization.

Acquisition Cost and Demand Forecasting

I spend a lot of time with retailers, and every executive I meet is interested in accelerating their sales growth while improving their distribution capability at the same time. One often overlooked piece of the puzzle is managing the Acquisition Cost.
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Does 98% Demand Forecast Accuracy Get Your Attention?

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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.
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