Data is Power and Competitive Advantage in the Digitally Integrated Value Chain,

Editor’s note: This is the first in 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.

Shawn Muma, director of Supply Chain Innovation and Emerging Technologies, DSCI

For years, management experts have championed the power of globally integrating enterprises to optimize business performance and ensure global brand and operational consistency. While that has become part of the fabric of global enterprises, companies have focused on optimizing internal operations only within their four walls. But in an increasingly digital world, extending beyond enterprise boundaries from sourcing to customer engagement is now essential to achieve sustained competitive advantage.

Fundamentally, a digitally integrated value chain is about reimagining your business from the customer back to the beginning of the supply chain in a way that allows the business to respond to changes in customer buying behavior (or other external factors) far faster and align the supply chain with demand dynamically. Demand and sales planning must be seamlessly linked to the supply chain. This requires new, more digitally integrated business models which must also include key external information to improve business performance. 

Digital transformation to create a digitally integrated value chain is not so much about technology, although technology enables the transformation; it’s about understanding all aspects of your business model, defining the right sub-enterprise models and metrics, mapping the way work flows throughout the organization and across suppliers, and making decisions informed by a series of data. Understand this: Digital transformation is not about digitizing your current business model; it is about redefining your business model to serve the New Customer and achieve a competitive advantage. An aging business model, digitized, is still an aging business model.

As business models are rethought, more importance is placed on understanding the end customer and many companies are finding that they do not have the data and end-to-end visibility required to figure out today’s customers and delight them with product or service delivered when they want it. Supply chains need to be more resilient and flexible and that requires data to ensure transparency throughout the value chain. Data needs to be trusted and accessible in the right level of detail and frequency to adjust dynamically to changes in demand and supply.

Once there is an understanding of the desired business model, which incorporates direct to the customer aspects, a data model needs to be developed. Regardless of the business model, the traditional supply chain function must move out of the traditional cost calculus and become customer-centric in partnership with marketing and product development. New metrics need to be identified that reflect performance both within and external to the enterprise and that are true measures of success. It is no longer good enough to say we shipped on time if it was received late by the customer. Metrics need to reflect the customer’s point of view.

While a new data model is needed to support digital transformation, sourcing the data required to populate the model and measure business results enters uncharted territory for many enterprises. Sharing data externally, across enterprise boundaries, to gain end-to-end visibility is new and challenging for most. While companies commonly share transaction data, many are reluctant or not prepared to share strategic data. One option is to participate in data-sharing networks. These networks may be private networks with known suppliers, customers, and competitors where access can be reasonably controlled, and participants are known. Or you may participate in public networks where data access is open and there is less control over who has access and how the data is used. Examples of public networks may be access to shipping container information or traffic at shopping malls. In both these cases, the data you access can also be accessed by your competitors. A new approach is to ‘think small’ and identify very specific data that fills a key gap in your data model. Forward-thinking companies are starting to realize that if they share or trade critical data with key customers or suppliers it can unlock their competitive advantage.

As more data traverses enterprise boundaries, data governance plays a critical role. Responsible data stewardship must be embedded in the operational fabric as more regulations come into effect, like Europe’s GDPR data privacy law. Understanding who has access to your data, what data you are accessing, how it was sourced, how it is being used, and how it is controlled once it leaves your corporate walls is important for both competitive and compliance reasons. The quality of data stewardship and data practices will reflect directly on your brand and its reputation. Establishing corporate policy is essential. As we saw in many recent incidents, like SolarWinds and Colonial Pipeline, effective cybersecurity risk management is critical. Your company is increasingly digitally connected to every company in your value chain. To secure data across your supply chain requires the effective orchestration of people, process, and technology, including rigorous enforcement of basic cyber hygiene. Data protection and cybersecurity must become part of your culture.

The digitally integrated value chain runs on data and companies are creating new data models and exploring innovative approaches for accessing the data they need. Leaders are realizing that to be successful they must have a digital mindset and be actively involved in the development of new business models. New and diverse tools have emerged to support the thirst for digital instrumentation including data trading, IoT sensors, Artificial/Machine Learning integration, integrating external performance data, and adjusting supply by monitoring demand signals. Data may be the new oil but making higher quality decisions requires data to be refined into analytic insights. Data analytics and data science are core capabilities of the new enterprise.

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

About the author: Shawn Muma is director of Supply Chain Innovation and Emerging Technologies for CGE’s Digital Supply Chain Institute (DSCI). To learn more, visit The Center for Global Enterprise.