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Supply Chain Data: The Need for Continuous Refinement

As companies increase their global footprint – through acquisitions, mergers or expansion — there is often a tremendous disconnect in data communications among the disparate systems. This is an especially cumbersome problem when it comes to supply chain visibility. Executives end up without a single, trusted view into their global logistics spend. Companies regularly invest huge sums of money on enterprise IT integrations to try and tackle the problem. But as anyone that has gone through one of these implementations knows, the process is long, painful and late to deliver value.

Standard systems integrations are not designed for iterative data improvement. They force the world to twist and fit into one-size-fits-all constructs, instead of building a system that is adaptable, flexible and able to embrace change and data diversity. This is especially limiting when it comes to a company’s supply chain, because logistics data is constantly variable, changing and evolving. What companies need is a rapid learning system; a system that is adaptable and able to refine whatever data that flows through it.

Continuous Data Improvement

When you refine data, you don’t usually get it right the first time. It’s necessary to analyze the data, focus on a small number of issues and continuously improve. As you improve, you are able to label a set of data as highly trustworthy.

Trax believes that human and machine algorithms need to work together to solve supply chain and logistics data problems. We don’t depend solely on machine algorithms, nor do we rely completely on human intervention. We classify logistics data with what we like to call trust and confidence. Data, or configurations of that data, can be scored in a way that we can segregate which data is trusted and which is untrusted, and even quantify the degree of trust.

Opportunities in the data

Supply chain and finance executives want to get actionable insights from their logistics data, so they look for tools. But, to be successful, these data warehouse solutions cannot be treated as a one shot project. The attitude that you can invest in a particular tool and your data quality problem is immediately solved is a red herring. Instead, what is needed is a provider/solution that acts as a data curator – someone that cares for the data source and constantly monitors that data to further understand it, refine it and help you use it to reach your revenue goals.

The right partners – made up of both data analysts and data programmers – will help you get a more rapid return out of all your supply chain and IT investments. You’ll benefit from having both a business and a technical orientation. This creates a synergy that you can’t get with either or, and helps you understand both the source and the consumer of the data – and that’s what creates transformational value.

It’s only through continuous improvement of supply chain data using both human and machine algorithms that companies can improve their supply chains and become true revenue drivers for their organizations.

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