Consumer demand is the one thing that can decide whether a retailer is successful or not. Of course, there is a whole field of marketing studies to determine how we can influence consumers to purchase. But a really important aspect of how good retailers fare in the market is their ability to “sense” demand, not just influence it.
In a recent study, IHL Group claims Overstocks and Out-of-Stocks cost retailers almost $1.1 trillion world-wide. To put it in perspective, that figure is the size of Australia’s GDP.
What that means is that Overstocks and Out-of-stocks, collectively defined as Inventory Distortion, are a problem that cost retailers world-wide 7.5% of their gross revenue.
The figures translate into poor performance, decreased customer satisfaction, decreased sales and increased costs of inventory warehousing and inventory spoilage. Basically there are two really simple outcomes:
- Either retailers stock up on too much inventory which turns to increased warehousing costs and spoiled products.
- …Or they don’t and they miss on sales opportunities
Either way, one thing is for sure: Inventory Distortion leads to poor commerce performance.
How do you solve Inventory Distortion? (Not exactly) Simple: Demand Sensing
Demand Sensing is a concept and set of technologies that make use of analytical and prediction models to estimate … well … demand. Imagine a retailer that runs a network of 10 stores, one online store and has a mobile app that drives sales also, along side a call center. Maybe they engage in some sort of live shopping to improve their performance.
Said retailer probably has an inventory management system, an warehouse management system, a sales reporting tool and probably some type of integration with suppliers and manufacturers.
Let’s imagine this retailer selling a type of red shirts that is available in one of the 10 stores and that inventory is not available online. If a customer will visit 3 of the stores in search of that particular red shirt and then search for it online and still not find it, it will probably consider it to be out of stock and the retailer would lose a sale opportunity.
You probably see where the problem lies: even though the product was available, it was not available to the customer and opportunities were lost. The same thing goes for products that are not exposed to the customers, or they are, say, unreachable on the shelf or unfindable on the web store if the search engine is not fit for the job.
The opposite situation, where demand is not correctly estimated and out-of-stocks become a reality, are just as bad as sales opportunities are lost.
The solution lies in gathering enough data across all sales channels, compiling this data and using models to predict demand. That easier said than done because …
To make demand sensing a reality, inventory transparency has to be achieved
As you are reading a blog on omnichannel retail, the term was bound to appear somewhere along the line. So here it is. You can’t have Demand Sensing without a connected sales operation and inventory transparency. All inventory sources have to be connected and data should be generally available. So should sales data across channels.
The picture below shows an example of omnichannel supply chain, one where all the operational pieces work together and share data. When such a structure is implemented, demand is easily “sensed” and estimated and thus inventory distortion can decrease.
So now we have the data. Implementing omnichannel retail can lead do a better demand sensing and therefore improve inventory distortion, a small glitch in the global retail system costing “only” $1.1 trillion.