One of the major hurdles companies face in transforming to a digital supply chain is their inability to get data from customers and suppliers — or even from other departments in their own company. Nothing new, right?
What is new is the idea of “trading data” to overcome that hurdle and use as a catalyst for digital supply chain transformation. Let me explain.
Companies are aggressively turning to artificial intelligence and machine learning (AI/ML) to gain a competitive advantage. But for that strategy to succeed, companies must develop algorithms that rely on AI/ML technology to run their business. And what is the life force behind algorithms? Data. Lots of data. That makes data trading, internally and with customers and suppliers, essential to unlocking the power of AI/ML.
The critical management question is how to do it?
Understanding how to value and trade data with other departments and with value-chain partners starts with thinking about data as you would money. Once you think about data like money, it becomes clear that you have to be strategic in using it.
Consider the negotiations you have with other departments in your organization. Maybe it’s about budgets and who’s going to pay for something from their budget. Just like money, your supply chain department has data that may be really valuable to other departments such as product development or sales. And other departments certainly have data that would help your supply chain to gain more visibility into demand and risks.
The real power of data trading, however, comes from your supply chain. One of the most important aspects of the digital supply chain is collaboration that extends beyond the boundaries of your organization. Just as you exchange money for goods and services, you can exchange your data for data from your suppliers and customers. This is where it gets interesting because unlike with money, the value of data is relative. It depends on the context and how it fits into each company’s strategic puzzle.
Judging from our interviews with business leaders, we find most companies are frustrated by the lack of data sharing with their customers and suppliers. Of course, one issue is the desire to protect proprietary data and not lose a competitive advantage. But another root cause is the vagueness of what data sharing means to each company. Let’s be realistic, no company is going to give you all of its data. And you probably don’t need it all or wouldn’t even use it.
To get things moving, you need to go from the general concept of data sharing with customers and suppliers to the specifics of exactly what data you want and what you are willing to give of value in exchange. As with so many business relationships and business-process improvements, it is more likely to happen if you start small.
That means you need to identify exactly what data would be most useful to you. For example, one consumer-products brand would love to get the age and gender of the end-consumers from a major retailer. One automobile tire manufacturer would love to get the mileage and end-date of leased cars from a car company. One electronics component manufacturer would love to know the desired new product features of the end-consumer from the smartphone manufacturer. In every company, there are very specific pieces of data that would help complete the puzzle. To go from talk to action, companies must move beyond talking in generalities and get very specific.
You also need to get a clear overview of the algorithms driving your digital supply chain. Taking an inventory of the relevant algorithms is important. For many companies, it is an eye-opening exercise when you see how disconnected they can be. You need to get a good understanding of the value of the data you currently have and what data you need that would optimize the performance of current algorithms.
As you become more sophisticated in your data-trading strategy, think about the value of your data to third parties. Think strategically and put yourself in their shoes. Think about what specific pieces of data might help fill a hole in their puzzle. You know what pieces to the data puzzle you’re missing, and you can speculate about the pieces your customer or supplier is missing. We know that companies spend a lot of time and resources on cleaning data. What if you can provide clean data in one very specific area to a customer in exchange for getting a key piece of data that you need? And there may be other things of value other than data that your partners want. For example, they may want an early release of a product or favorable pricing or premier support.
Preparation is critical to effective data trading. We have developed a comprehensive DSCI data-trading framework that outlines a process you can follow to unlock the value of data sharing with your value chain partners. The DSCI framework consists of three key stages.
- Prepare: Think through what a customer or supplier may want. But make sure that you have agreement internally about what data you can offer before entering the negotiation. Be crystal clear about what data you need and how often you need it.
- Negotiate: There are specific issues that need to be addressed in data trading negotiations. Be realistic about your needs and actual use.
- Govern: Incorporate controls for data protection and cybersecurity into how you ultimately structure data-trading agreements.
Digital supply chain transformation has unique challenges, such as getting critical new data from customers and suppliers. Developing a data-trading framework can help you obtain the data you need to unlock the potential benefits of the digital supply chain. Another benefit: It helps break down silos and facilitate collaboration and gives you a way to systematically identify, value and acquire the specific data you need.
Remember, transform or fade away.