Why more supply chain data isn't the answer

3 Minutes Read

The amount of data that companies have access to is growing exponentially. This growth has been particularly significant in supply chains. Companies have access to far more data than ever before, but continually gathering more data is not the answer by itself. 

Instead, businesses need to focus on making the most of the data they already have. Because there is a lot of it. Supply chains generate data constantly and it can either get turned into something helpful or end up overwhelming you and your team.

The key is knowing how to stop the second one from happening.


The data paradox

Data is often seen as an answer to almost any problem. However, there is an inherent paradox in this approach: The more data you have, the more difficult it can be to make sense of it. With more data comes more noise and it can be difficult to separate the two. Supply chain leaders need to be careful not to get overwhelmed by the amount of data they have access to and instead focus on the data that is most meaningful. Which isn’t always easy to do manually.

Simply having access to more data won't necessarily lead to better decision-making. Companies must invest in the right tools in order to make their data usable and useful.

Data is important, but it works best when it makes your company’s knowledge and ability greater than what can be achieved just by humans. Companies have to consider the human element when making decisions. While data can provide insights, it cannot replace the experience and judgment of people entirely. However, businesses that ensure data is used in a way that enhances the capabilities of their team make better great decisions. This means moving from simply having data storage to becoming data-driven.


Crossing the gap from data storage to data-driven

To be data-driven, data must be the main focus when making decisions. To do this, you need to collect data that will answer a particular question. This is an area where human knowledge is really important. AI and data can’t start from nothing. Your team and expertise will need to feed into it to make sure you’re using the right data for answering questions. And to make sure you’re asking the right questions in the first place.

This is how tacit knowledge, like the tips and tricks you know from years of experience in your field, can build explicit knowledge with data. While that may feel counterintuitive, moving knowledge from tacit, implied knowledge to explicit, stated knowledge is really good for your team and business as a whole.

When you pair that shift with data, you both increase the benefits of everyone working from the same page and enable well-informed decisions to be made faster and in greater quantities than human ability alone can manage. Explaining this to your team will help them both adopt the changes you are making and build confidence in them. The ultimate purpose is to get long-term value from data instead of just storing it, thereby making your business data-driven.


How do you do this more practically?

This is an example of a client's engagement process with our software. They used their industry knowledge to identify the issues they want to explore through data.

The process starts with building a data layer. Transportation is not only one of the highest sources of volatility in a global supply chain, but it can also provide a very rich data set to explain a supply chain and measure its performance. We start by capturing invoice data from first-, middle- and last-mile transportation. This gives us a view baseline view of how the supply chain is connected and performing.

We use the invoice data to:

  • Create a data layer - which doesn’t require IT input

  • Measure current performance vs target metrics and hold suppliers to account

  • Finding room through savings to fund your current and future projects

  • Use this data to build a digital twin of your network

  • Run simulations to figure out what is possible and explore your problem and solution space

  • Use output business case to create a prioritised roadmap for to create regular funding and keep your project cashflow positive

  • Move to roadmap execution using a single software platform to minimise training, integrations, and change management

In addition to the setup, expertise and active questioning is important for getting the most out of the simulations you run. Another example of where the tacit/explicit knowledge and data/human input overlap exists.


Evaluate results with the data layer

7bridges was started by looking at supply chains with a data-first lens. Our founders saw that supply chains weren’t making the most of the vast seas of data they had. And that left them full of inefficiency and low-performing systems. The power of data around the world was clear in many industries, but supply chains hadn’t gotten there yet.

However, when combined with smarter, integrated supply chains, data can drive tremendous change for businesses, consumers, and the environment. So more data in isolation isn’t the answer. But wielding it well is.

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