To Transform to a Digital Supply Chain, Don’t Overlook People Skills

Editor’s note: This is Part 3 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 4 by clicking on this link.
You can read Part 5 by clicking on this link.

The reason firms strive to create more digitally integrated supply chains comes down to improved business performance. For a digital supply chain, improved performance goals might include reducing the trade-offs between costs to serve customers and their ultimate satisfaction, stimulating demand by accessing new markets or segments, and creating competitive advantage through integrated capabilities that are hard to replicate. This is naturally a tall order, and one that leads us to ask, “how do we get to this digital nirvana”?

Understanding how our customers’ needs and demands are changing is one essential element, as well as the desire and capability to improve our supply chain data models and processes. High quality data and analytics may turn an ocean of disparate data into actionable insights. Technology plays a key role in making this data transformation possible, but equally important is the need for data literacy in the executive ranks.

Most elemental, and most often overlooked in the game of digital transformation are the people and organizational capabilities necessary to lead, collaboratively problem solve and persuade diverse actors comprising complex value chains.

First, let’s focus on how we obtain the data we need to improve supply chain performance. Once we’ve selected a business problem, segment, or product to focus on, we need to develop a data model that includes both narrow selections of structured data, as well as less structured data that may improve the quality of our decisions and recommendations. We will need to collaborate both internally, as well as with various supply chain partners to obtain data that will allow us to develop high quality analytic insights. Internally, we should obtain transaction data from across the enterprise—planning, forecasts, sourcing partner, manufacturing constraints and capabilities, and delivery information are all examples of what the end-to-end supply chain data modeler might seek. Externally, we may seek data and information about our channel partner sales, customer and consumer insights, sourcing strengths and anomalies, manufacturing partner capacities and lead times, as well as transportation partner information.

As we develop the data models, we must ask who owns or controls the data we need. Experienced data analysts know that being able to identify a data source as useful for a model is an early, essential step. But being able to obtain the data you think will help you improve the quality of your process is a different matter. Obtaining data from various supply chain partners both internal and external will at times require compromises or trades, as well as finding adjacent data sets, or perhaps simply doing without. This brings us to, how to improve the people and organizational skills needed to better access the data needed to find answers to supply chain challenges.

Before we discuss the specific skills needed to improve our chances of obtaining data from sources outside of our immediate control, let’s focus on some questions we can ask to prepare for data negotiations:

• What relationships do you currently have with data owners (internal and external) and how much trust you have established with them?
• Do you know how these data owners value the data they control? What restrictions over the data’s use might they enforce?
• What higher level approvals might they require before they can share?
• If agreements to share or trade information are reached, have you established who will manage data extracts (if needed) and their transmittal?
• Have you determined what range of flexibility you have in sharing data you control with others?
• Do you have contacts to approach with external data requests in your supply chain partner organizations?

Finally, with an established data sharing or trading agreement in place do you have a plan on how to govern and maintain them? How will you handle the enforcement of terms and potential breakdowns? How can you continuously improve data sharing relationships?

The questions above might raise some awareness about the complexity of data sharing or trading relationships that go far beyond simple technical requests. An integrated digital supply chain is not a technical solution alone. It requires serious people skills like influence and persuasion, negotiations, data engineering, data management, networking, relationship building, and a talent for true collaboration.

One of the key skills that is often overlooked is strategic influence, described as the “ability to win people over, using emotionally intelligent relationship-based approaches,” according to Richard Shell and Mario Moussa in “The Art of Woo.” In contrast, most negotiations involve simply bringing two parties with differing goals together in an agreement or decision. If you have ever approached someone in another functional department or at another company with a request to share information they control, you have undoubtedly encountered a wide range of reactions, from “no problem” to “why do you want that?” and a fair amount of “no we can’t share that outside our department.”

For the situations where the answer is less than a simple yes, the tools and skills of strategic influence and persuasion are key enablers. They may mean the difference between a data model that has the elements you need to create competitive insights, to one that is fundamentally flawed, or worse, leads to poor quality decisions.

The good news is strategic influence skills, negotiations, data engineering, and others can be developed as part of your data integration transformation. The key is to remember that the people side of the equation is a huge factor in your eventual success driving digital supply chain performance improvements involving data.

About the author: Dave Kurz Ed.D., is a Research Fellow for the Digital Supply Chain Institute. He is an expert on organizational learning and leadership development. Kurz advises global organizations such as Coca-Cola, J&J, Colgate Palmolive, General Mills, and Mastercard on innovative learning and leadership development strategies. He currently holds a faculty appointment as a clinical professor in the management department of Drexel University’s LeBow College of Business.

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