Data is essential to running the digital supply chain

Editor’s note: This is Part 2 of a five-part series exploring the critical role of data in a digital supply chain transformation.

You can read Part 1 by clicking on this link.
You can read Part 2 by clicking on this link.
You can read Part 3 by clicking on this link.
You can read Part 4 by clicking on this link
You can read Part 5 by clicking on this link.

Transforming to a digital supply chain isn’t easy. You need to keep running the supply chain while you transform. Companies are racing to use data, and develop new data models, to provide greater supply chain visibility and resiliency. But traditional data models where the supply chain function is disassociated from the customer no longer work. A customer-centric digital supply chain is needed.

No longer is supply chain a back-office function at the mercy of a demand forecast derived from a sales forecast generated by the marketing and sales department (too often based on intuition). New custom-centric data models are needed that integrate external data (e.g., economy, weather, regulations), customer data (e.g., buying behavior, internet behavior, social media), supplier data (e.g., available capacity, material/component availability), and historical sales data (e.g., actual versus forecast sales) to create far more accurate demand and inventory plans.

Ultimately, the new data models will integrate the supply chain with marketing, sales, and product development. Supply chains will play a role in demand stimulation and demand sensing. Strategies like dynamic pricing will spread to more products and services as one means of matching supply and demand while creating happy and loyal customers.

One of the recurring themes we hear from companies is that new data models and new data are needed to provide better end-to-end visibility. While that’s true, it’s not sufficient to drive performance-improving transformation. More definition is needed to turn the statement into action. Do you need more product visibility or demand visibility or customer visibility? And what about the scope of the visibility? Here’s an example to illustrate the point about the scope. A lot of companies are seeking to start or enhance their direct-to-consumer (DTC) sales channel. This clearly requires a new data model and new data sources to make the shift. One consumer products company that historically sold through wholesale channels is going DTC. They are developing detailed customer profiles to better understand their customer and anticipate their needs. However, the data model being used to build the customer profile ignores their wholesale channel. As a consumer, I could go to the company’s website to browse, or buy occasionally, but primarily go to a retail store to buy. None of that data would make it into my customer profile. 

Creating new data models is only one critical step to the digital supply chain transformation. You’ve got to get the data to populate your data model. That’s where a whole new set of challenges emerge. A new mindset is needed. How do you break down internal silos to get data from other departments that are “too busy”? How do you get suppliers and customers to trade or share data with you to fill critical data gaps? You need to find a way to convince people that collaborating to trade key data generates the biggest ROI for all. As one consumer products executive said to us, “I’ve got retailers that are setting up new departments to sell me data for $5, but if they traded for data I can provide, we can generate $1,000,000 in incremental sales. It’s crazy.”

One of the significant challenges companies face in transforming to a Digital Supply Chain is the ability to share data internally and gain specific, critical data from customers and suppliers. In the case above of the customer profile, the missing data is sales data from the wholesale channel. They need to know who bought their products from the retailer. Even anonymized data with the age and gender of buyers could be matched with DTC data to help understand customer behavior and create better customer profiles. The consumer products company certainly has very specific data that would help the retailer. They should make a trade to create a competitive advantage for both. 

Too often companies think that more data is better. But companies that win aren’t the ones with the most data. The winning companies are those that correctly identify the problem to solve – the performance metric to improve. They define the right goal to achieve. The data model will put a spotlight on the missing data. Of course, this leads to the question, where are we going to get the missing data? Internally, from other departments? Externally from companies in our supply chain? The best approach is to start small. Approach departments or companies with a specific proposal identifying exactly what data you need and what you can offer.

Sophistication in the acquisition and utilization of new data is a competitive advantage. The gap between those that are good and those that are not will quickly widen as the strategic use of algorithms, Artificial Intelligence (AI) and Machine Learning (ML) grows, and as companies evolve to more data-driven decision-making.

Digital transformation requires a new mindset from leadership and a new data-driven culture in the company. Supply chain leaders need to establish new performance-enhancing data models that optimize their existing data and then collaborate to get critical missing data.

In the next three blogs of this series, we’ll deconstruct the digitally integrated value chain by exploring in more detail the topics of data sharing, data governance, and managing cybersecurity risk.

About the author: Craig Moss is Director of Data and Change Management for The Center for Global Enterprise’s (thecge.net) Digital Supply Chain Institute (DSCI). To learn more, visit DSCI (www.dscinstitute.org).