Big Data Analytics in Supply Chain is Promising but Delayed
We have delayed the promise of big data technologies and analytics in delivery. Most platforms promise efficiencies, waste reductions, collaboration, and better planning, among others. The analytics and data visualization market has grown dramatically as more companies have flocked to business intelligence tools.
In fact, advanced supply chain companies are using analytics to tell them how they perform and inform them what to do. In a 2018 survey, Gartner found that 96% of advanced supply chain companies use predictive analytics, and 85% use prescriptive analytics. Therefore, it’s clear companies are looking for business intelligence to make informed supply chain data-driven business decisions.
Supply Chain Analytics Implementations are Up, Satisfaction is Down
Perhaps expectedly, as companies have wrestled with troves of aggregated data, benefits have proved elusive. A recent study has found implementation of big data projects for the supply chain are up. The study, conducted by Arizona State and Colorado State Universities and published in CSCMP’s Supply Chain Quarterly, found 13% more companies in 2018 implemented analytics or are conducting proof of concepts than in 2017. However, satisfaction with implementations in 2018 is down from 2017. The principal conclusions of the study pointed to unsophisticated big data analytics tools resulting in lack of satisfaction.
It’s Not The Tools. It’s The Data.
We find the challenge companies typically face is not the tools themselves. More sophisticated data platforms may be under-utilized. These great visualization tools usually show companies how bad their data is, but without a way to solve it.
Experts have long discussed the 5 Vs of Supply Chain Big Data—Velocity, Volume, Value, Variety, and Veracity. More recently, they’ve added two more: Variability and Visualization. Data accuracy (veracity) has been overlooked for years, and variability is the likely cause for bad analytics and reduced satisfaction.
Let’s explore an example. Audited freight invoices often have rate tolerances in place. Immediately, the tolerance makes the freight payment data suspect because it could have a $5-$10 “cushion” built into the price.
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Too many manual processes cause variability in the data. The shipper might be manually tendering the shipment to the carrier. Or, the carrier might have a manual process that they have to follow to invoice the shipper. The shipper might have a business rule that the carrier can’t systematically perform. Possibly, neither the shipper nor carrier have the IT resources required to make the process work in their systems.
Manually creating a bill of lading causes the shipper to believe the carrier didn’t pick up the shipment. The handwritten bill of lading will have a manually assigned PRO, so the tracking information sent by the carrier is not referencing the PRO number on the electronic tender. The freight invoice creates an exception because it doesn’t have a matching tender record, causing freight payment issues and compounding the problem.
These issues cascade into the analytics platform. Instead of showing real opportunities, the data is incomplete, missing, or telling the wrong story.
The Right Data Provides the Right Results
Many companies don’t have an excellent way to cleanse their data. Clean, accurate data provides better leverage for strategies to improve supply chain operations. For example, using machine learning-powered cleansed and accurate freight payment data to perform a bid allows a company to stay within its freight budget.
Accurate data also allows you to focus on the real exceptions and reduce your dwell time because you are working on the real shipments that didn’t get picked up. Furthermore, developing supply chain analytics and dashboards of KPIs built on accurate data enables informed and timely decision making. With the right data, predictive and prescriptive analytics provide real tangible insights. Accuracy begets sophistication.
Companies know they can gain incredible insights through supply chain big data and analytics. But when companies build analytics on bad data, it’s no wonder it renders sophisticated tools unsophisticated.