Digital Transformation, End-to-End Visibility and the “Data Problem”


Why Solving the "Data Problem" is the Key to Digital Transformation and True End-to-End Visibility

For decades, businesses have largely derived efficiency and profitability through the size and scale of their operations. This led to a focus on larger store footprints, bigger warehouses, more buffer stock, and the ability to transport goods on mega vessels for unit cost savings. In the last few decades, due to the service that the Amazons of the world provide, customer expectations have gone up. As a result, retailers and manufacturers have been forced to modernize and deliver with much higher reliability and timeliness. Many executives are now looking to transform their supply chains from a reactive, cash-trapped center to a strategic powerhouse. There are many applications of digital transformation but, holistically, it's about businesses using data to transform their supply chains by gaining better access to data, making better sense of their data, and leveraging that data it in a fundamentally different way to drive profitability.

One common goal of digital transformation is to get end-to-end visibility into the supply chain. Many people think visibility is simply seeing information in one place, aggregating data from multiple sources and serving it up in a pretty way. Gaining true end-to-end visibility is about making sense of data across your entire supply chain so that you can see inventory more clearly, plan for exceptions in advance, and use your data to make fundamentally different decisions. Getting access to data and aggregating it is a huge challenge for any industry, but compounding this challenge is the underlying problem that plagues every supply chain organization - bad data.

What do you do when the underlying data being used for visibility can't be trusted?

Data is both the biggest problem and the biggest opportunity in supply chain. It’s a problem because it’s not trustworthy, and if you don't understand and address underlying issues, digital transformation is not possible. Supply chain professionals, from executives down to front line professionals, face challenges on a daily basis. They are constantly making decisions about which shipments to pay attention to, whether to ship via air or ocean, which service provider to choose when booking freight, how to staff warehouses, and much more. The difficulty to answer these questions comes, not from lack of expertise, but because they are forced to rely on systems fueled by flawed data.

To explain the issue tangibly: if you look at the transportation space, you see a complex international, intermodal supply chain that is run off a 315 EDI message set. If you look at the actual EDI messages that are produced at the port or from a carrier (be it motor or ocean), there are inherent errors in the data. For instance, that 315 message will send an ‘Out For Delivery’ message at the same time as a ‘Delivered’ message. This is because the bill of lading may state that the final destination for the ocean carrier segment of that journey is the port, when actually, for the retailer, it's their distribution center or warehouse. Like this, there are thousands of errors happening everyday - you can see why incorrect data creates big problems for a person relying on it to make multi-million dollar decisions.

Though historically, this has been the big “data problem”, it doesn’t have to be this way anymore. There is now technology in place to take data and make sense of it in a way that has never been done before. A common misconception companies have is thinking that in order to begin transformation they need to have tons of data to get started, but that isn’t true. There is no need to boil the ocean. If existing data is appropriately cleaned, canonicalized, and made intelligent, transformation is entirely possible. ClearMetal makes transformation a possibility by leveraging proprietary machine learning to clean and make sense of supply chain data. Unlike others, we do not just collect, aggregate, and show information; our goal is to solve the most crucial problem - bad underlying data.

At ClearMetal, we deliver data you can trust so that true end-to-end visibility can be achieved for your organization.

Our custom-built technologies use machine learning and AI to automatically clean, correct, and make sense of supply chain data by sequencing it properly, cutting out duplicates, imputing certain events, labeling milestones correctly, and throwing out erroneous information in a manner that’s never before done before. Only after the data has been “cleansed”, does the software provide real-time, end-to-end, predictive visibility, risk assessments and planning capabilities via analyses via a cloud-based application. Best of all, no IT resources are required to get started.

For a more detailed explanation of how we use Artificial Intelligence and Machine Learning technology to help supply chain companies experience data transformations, download our whitepaper.

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Nandini Nallasivan