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Inventory Can Be Scary

By ToolsGroup Editorial Team • 31 Oct 2017

“Your inventory problem is scary because you’re selling out before you can make new items to meet demand”. That’s what Robert Herjavec from the TV show Shark Tank said to an aspiring clothing retailer after their pitch in Season 8.

Many retailers agree that inventory can be scary.  Why? Look at the recent performance of retailers J. Jill, Nike and H&M who missed sales projections, upset customers with out-of-stocks, and had to clear excess

inventory through deep discounting. They took a hit to their stock price because of lower earnings and are scrambling to come up with a better plan for 2018.  They are not alone.

The scope of the scary retail inventory problem is so big it can be staggering. A recent IHL research report entitled $1.75 Trillion Reasons to Be Afraid estimated that there is that much lost revenue opportunity from the “Ghost Economy” of overstocks, out-of-stocks and sales returns.  “Inventory distortion” (the combined cost of out-of-stocks and overstocks) accounts for $1.1 trillion of that.  Since $1.1 trillion is a number is so large it’s hard to process, to make this concept a little more meaningful, let’s use a calculation from The Retail Owners Institute.

Find the Cost of Excess Inventory in Your Store 
First estimate the total inventory at cost you currently have on hand. For example, say you have $5,000,000 of inventory at cost on hand.  If you carry only 10 percent more inventory than actually needed, what is that costing you? For the calculation you can use an average identified in multiple studies that shows the annual additional cost of holding excess inventory (not including out-of-stocks and returns) can be 25 percent to 32 percent. For ease of calculating, let’s round that off to 30%. So:

$5,000,000 inventory x 10% excess = $500,000 excess inventory x 30% = $150,000 annual waste expense

When applied at scale to a typical retailer’s inventory, waste represents a significant financial expense. And this doesn’t include the additional cross-functional implications of not making improvements to the forecasting process. That’s because it’s not just the capital tied up in excess items that make up this expense, but rather a multifaceted web of overheads, including discounting (or scrapping) slow moving merchandise, warehouse storage costs, wages for laborers to handle dead or excess inventory, and even administrative staff to figure out the problems.

Here are few ways to reduce these expenses, and ultimately make inventory less scary, by creating more accurate forecasts and better stock mixes.

When forecasting promotions,  72% accuracy is considered good and laggard companies come in as low as 42%. However, pulling in data about the uplift and ROI of past promotional campaigns and blending it with historical sales date to create a better forecast and improve accuracy can lead to better forecast and optimal inventory levels. Getting better at promotion forecasting and placing the right items in the right location for more profitable delivery stores reduces waste from inventory distortion.

New product introductions can be even tougher. Because there is no historical baseline sales data, predicting demand is even more challenging. However, there are existing data sets retailers can leverage into the forecast to determine potential market response for a new item by leveraging performance of existing products with similar attributes such as color, size, style and other features. Initial inventories can then be restocked based on early sales readings.

Early signals like social media and external variables can also be incorporated into a forecast. For example, weather patterns will influence demand for certain products and social media can measure early steps in the buyers’ journey.  Say it’s 10 degrees in Massachusetts in January and a celebrity is seen sporting the hottest blue puffer coat on Instagram that got 10,000 likes. Those two pieces of information could be an indicator of future sales and smart retailers can adjust stock accordingly.

While it’s possible that a planner can monitor these data streams, forecast demand, and do supply chain planning using spreadsheets, the amount of transactional data is generally too big to manage. Therefore, many retailers are looking to technologies like machine learning for supply chain planning to get a firmer grasp on demand and better align their operations.

In fact, leaders say that investing in IT transformation, lowering supply chain costs, and achieving inventory visibility are three of their top five priorities.  Another IHL report, Debunking the Retail Apocalypse, states “leading retailers (those growing better than the average) have decoupled their IT spend growth from the previous year’s revenue and are embarking on transformational IT spending, in some cases growing spend 7X faster than the weakest competitors in their segment to better compete. This is the key: Retailers do not need to outspend Amazon and Walmart, but need to outspend their weakest competitors.”

Technology improvements can help retailers weather the changes in the market, more accurately predict what customers want, stock those items appropriately, and run your business more efficiently. Which isn’t scary at all.

How to Optimize Inventory in the Digital Age

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