Unravelling the Supply-Side Data Black Hole

Supply-side data is often lost in a perfect black hole of inventory distortion, incompatible data systems, and inefficient processes, making it extremely difficult to run high-performing omnichannel operations.

In my last post I noted the importance of improved inventory accuracy for shoring up a retailer’s SFS and BOPIS strategies. This week I’d like to explore the overall dearth of supply-side information and data in comparison to the demand-side.

Rich Demand Side Data

A lot of money and sweat has gone into getting and analyzing data on consumers and their spending habits.

The information, gleaned from social networks, customer reward programs, credit card spending, internet browsing, internet purchases, emails, etc, are fed into models and machine learning algorithms that allow retailers to track, make sense of, and predict consumer behavior based on a host of variables such as age, location, race, hobbies, profession, marital status, socio-economic status, etc, with ever increasing accuracy.

Amazon is notorious for this.

So much so that one of the fears that many marketplace sellers have is that Amazon will take their customer data, crunch it with all the other consumer data Amazon possesses, and end up offering the exact same or similar product — using much more accurate data and analytics — to the seller’s customer base. This has happened a lot and is how many of Amazon’s house brands were developed, including Amazon Basics and its most recent line of over the counter drugs, Basic Care.

In terms of brick and mortar, companies like Starbucks use consumer data and analytics to know what kind of coffee you like, where you buy it from, and at what time of day. This allows them to generate personalized recommendations for customers approaching their stores and encourage average spend through targeted promotions. Kohl’s meanwhile, used their customer data last year to begin offering Under Armour products based on the 400,000 failed searches for such products on their site.

All of this means that if you ask a retail executive to describe their customers and their spending habits, they’ll be able to reel off reams of data, insights, and predictions from memory.

Unfortunately, this isn’t the case with supply side data.

Lack of Supply Side Data

In comparison, it can often be difficult to get accurate answers to what should be simple supply side questions about inventory, such as:

• What’s your total number of SKUs?
• How have your SKUs changed from last year to now?
• Where are all your SKUs located? By store, DC, FC, and brand suppliers?
• What’s the geographically closest item to an online customer?
• How many of your SKUs have availability within 24 hours of each of your major markets?

There are many reasons for the lack of inventory data:

• Store inventory is in a constant state of flux
• The inventory and data exchange systems for DCs, FCs, stores, and drop ship suppliers are not compatible with one another and are seen as different kinds of challenges
• Separate supply chains for wholesale and direct to consumer
• Omnichannel strategies such as SFS and BOPIS are adding extra order volume strain to store inventories
• Legacy systems and processes are not designed for real time inventory accuracy

What all of this comes down to is that there simply isn’t the rich real time data on the supply side to match the needs of the demand side.

This lack of accurate data is costly to the industry and leads to:

• Higher shipping costs, when more distant items are shipped to customers
• Opportunity costs, when retailers don’t have or can’t find an item a customer wants to buy
• Increased costs from all the safety stock needed to avoid out of stocks, cancellations, and late deliveries
• Customers lost due to bad experiences with out of stocks or late deliveries
• Customers lost to competitors who have the item that a customer wants but couldn’t find at a retailer’s store

Suppliers are Important Too

A lot of retailers are beginning to tackle this supply side data challenge by streamlining their supply chains, bringing more visibility to their physical store data (through tech like RFID), and integrating their various inventory systems to allow all of their inventory assets to communicate with one another.

Unfortunately, these attempts to improve inventory visibility miss an important piece of the puzzle: suppliers and brands.

Unless retailers want to invest in building FCs and DCs throughout the country like Amazon, they have to work hand in hand with supply partners to both increase their virtual inventory through drop shipping as well as keep their stores, DCs, and FCs stocked.

Considering how scattered population centers are within the US, a DC/FC infrastructure is too expensive to scale, while relying heavily on store inventory is risky. Incorporating supplier inventory can help bridge the gap.

Without access to accurate inventory data across their entire ecosystem, however, this is a difficult proposition.

And the strain is beginning to show.

Based on some of the consumer research that we have been conducting, the implementation of omnichannel strategies such as SFS and BOPIS have led to a higher frequency of out-of-stock experiences in stores. Customers are reporting more of these types of issues than in previous years.

This is a major reason why consumers are being driven to online giants like Amazon, Alibaba, and JD.com. Such e-retailers run powerful data driven supply chains that make them extremely efficient at matching supply with demand.

Expanding inventory data improvements to include suppliers is therefore essential for brick and mortar retailers to achieve success with digital consumers. It will require data technology that takes the complexity and cost out of inventory integration across hundreds of different systems while processing all that disparate data into a single source of truth in real time.

This will be an important step towards unravelling retail’s supply-side data black hole.

The highest performing omnichannel programs will be those able to discover and implement such a solution.