This year’s CSCMP conference reinforced that we’re in the early days of a massive transformation. The industry is moving in an unprecedented direction that is increasingly digitized and more data-centric than ever, and logistics leaders are looking to increase efficiency with artificial intelligence (AI). However, logistics leaders all know that in order to leverage advanced analytics and AI, they must first solve the core “data challenge.” And at the conference it was clear that the data problem is still huge and everyone needs a better way to solve it.
The Data Problem
“Five years ago, data got sexy,” said Abel Muniz, Panalpina’s Managed Solutions Regional Operations Manager, during a CSCMP panel session. “Everyone went out and gathered it – but with a lot of holes in it.” When data has holes or is “dirty,” it is incomplete and/or un-sequenced, meaning that key pieces of information about a shipment are missing or out of order as the shipment flows from point to point. Dirty data severely hinders the ability of logistics leaders to accurately glean actionable insights from the data. In order to effectively utilize AI in the supply chain, data must first be machine-ready, meaning it needs to be completed, cleanly structured and sequenced properly.
Hordes of people in supply chain organizations spend thousands of hours each year manually cleaning data and reconciling between systems. It’s a tedious and expensive process that has, to date, been required to deploy analytics strategies and get a clear picture of what’s really happening. Alejandra Dorronsoro, Senior International Logistics Manager at Georgia Pacific Cellulose noted at the conference: “We spend so much time gathering data. Cleaning it takes days. If we can do that in hours, that can change our process and allow us to be innovative.”
The Secret to Actionable AI Insights
The reality is that even as the industry dips its toe into digitization, inefficient use of spreadsheets and pivot tables are still in place for planning and forecasting, and email or Electronic Data Interchange (EDI) are the best tools used.
Before any digital transformation tied to artificial intelligence can occur, two steps need to be taken. First, data must be pulled from siloed systems in the supply chain; then, the data must be organized and sequenced so that it’s usable and machine readable.
As Mr. Muniz noted at CSCMP, AI and analytics are the sexier topics of the day. But the reality is that data cleansing – yawn – is a critical prerequisite, and thus a greater priority at the moment. Embarking on a data cleansing initiative is no small task but needs to be done so that advanced analytics efforts can enter the picture. The beauty, and what most don’t understand, is that AI can be used to solve the core data challenge.
What should a data cleansing initiative look like? It should deliver the following:
- The use of AI itself, to clean and structure the data
- The ability to extract and ingest data from various systems, formats and organizations
- Capability to canonicalize and sequence supply chain events
- Dramatic reduction in the need for manual massaging of data
- Machine-readable data that can be fed into an AI engine to deliver actionable insights
Manually sifting through mountains of data is overwhelming and represents a huge yet unnecessary drain on resources. By using an advanced AI-enabled data ingestion engine, retailers and manufacturers can quickly build the foundation necessary for successful analytics efforts and/or the intelligent application of AI. Clean data coupled with AI have already proven to generate unprecedented insights, efficiency and other value metrics at some of the world’s largest organizations throughout the supply chain ecosystem. Just remember, AI is not the step after “clean data” but the necessary component to make sense of data in the first place.
Adam Compain is the CEO of ClearMetal, a predictive logistics company that uses data science and machine learning to unlock new efficiencies for global trade. Compain co-founded ClearMetal after working in Hong Kong at one of the world’s largest container-shipping companies. For five years prior, Adam deployed the newest geo-commerce technologies at Google, and for 16 years he has been the Executive Director of the nonprofit he founded to export charitable goods. Adam holds five technology patents, two degrees from the University of Michigan, and an MBA from Stanford University.