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Home » Blogs » Think Tank » Minimizing Retail Supply Chain Frustrations With a Strong Data Strategy

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Data Management (Big Data/IoT/Blockchain) / Business Strategy Alignment / Quality & Metrics / Supply Chain Security & Risk Mgmt / Supply Chains in Crisis / Retail

Minimizing Retail Supply Chain Frustrations With a Strong Data Strategy

Empty shelf
Empty shelves in a supermarket. Photo: Getty.
May 2, 2022
Mark Van de Wiel, SCB Contributor

Supply chains are a bit like utilities: Nobody notices them until problems arise. Well, there’s no escaping notice these days, as supply chain setbacks grow even worse than last year.

“Many believe the supply chain disruption issues that have been occurring throughout the pandemic will continue well into 2022,” the National Retail Federation says. “In general, demand for goods and materials continues to outpace the supply of everything needed to produce and move those goods through the supply chain.”

While the supply chain crunch puts retailers largely at the mercy of forces outside their control, better use of real-time, consolidated data within retail systems can go a long way toward not only keeping store shelves stocked, but also managing customer expectations for as long as the crisis persists.

Increasing supply chain resilience is the second-highest strategic priority for retailers in the coming months, according to a report by the Economist Intelligence Unit commissioned by the Association for Supply Chain Management. However, more than half of all companies — retailers and others — don’t have enough visibility into their supply chains.

“Companies lack end-to-end visibility, leaving them vulnerable to dynamic or unexpected risks,” the report says. Too often, “the view of supply chains is based on internal data or relies on siloed or outdated data sets. This limits their ability to detect emerging threats or calculate how a disruption will unfold across supply chains and business units.”

Retailers operate multiple planning processes that are often not integrated. Vendor relationships are developed outside of inventory planning and management. Marketing is isolated from financial planning. Supply chain challenges are handled separate from commercial planning. And more often than not, real estate and franchise planning are isolated strategic activities.

That has led to countless systems and databases for storing and managing myriad slices of data, and they’re often not integrated. Enterprise resource planning (ERP) data, for example, is imperative to many supply chains but is often stuck in a silo. It’s isolated from marketing data often available through platforms like Shopify, Magento, Google Analytics, social media and e-mail marketing. Product assortment plans might be stored in spreadsheets. And where is the customer relationship data?

To bust down silos, building out a data warehouse or data lake — consolidating data from multiple inputs — is an obvious start. Such a datastore can provide insights into trends that are invisible to the individual systems. Getting data into a centralized data warehouse and leveraging it as an asset can provide the 360-degree view that retailers need to integrate isolated activities.

For example, an online retailer based in the U.K. found itself with many disparate systems and couldn’t get a holistic view of the entire business, including customers, the supply chain and inventory.

The retailer unsuccessfully tried to get that view with an on-premises data warehouse. Then it decided to look at more cost-effective cloud-based solutions such as a data lake. By moving data from the disparate systems into the cloud and the data lake, the retailer was able to replicate the data in real time and retire its legacy operational reporting services.

Many retailers are at different stages of their analytical maturity. A surprising number of large national chains are still performing analysis and reporting with Excel. Unfortunately, Excel isn’t going to scale or handle the quantity of data accumulated day over day and year over year.

At the other end of the spectrum, there are retailers further into their analytical maturity that use an on-premises data warehouse. And there are those that have fully moved to the cloud and are using cloud-native tools such as retail-specific artificial intelligence (AI) and machine learning routines that can help them reap the benefits of the cloud and facilitate and grow real-time analytics.

One large global online retailer has a core system that holds data about its retail partners and transactions. The company needed a solution that would allow access to this data without putting a heavy load on the system and affecting other business operations. It’s an extremely high-volume system, but today’s technology allows for getting a real-time copy of the data into the cloud for efficient analysis and reporting.

With real-time insight, organizations can better manage their supply chains, maximize revenue and improve efficiencies. They can gain an outside-in picture that enables better understanding, planning and forecasting.

This helps retailers to become not only less vulnerable to supply chain disruptions, but stronger businesses overall.

Mark Van de Wiel is field chief technology officer at Fivetran, an automated data integration provider.

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