Why 2018 is the Year of Supply Chain Visibility



Supply chain visibility has been on the enterprise to-do list for years. However, very few organizations have achieved it - not because of the lack of transformative technology (IoT, AI, self-service analytics, etc.), but because of the lack of access to high-quality data.

But, in 2018 this changed. The reason: Machine learning’s ability to refine massive quantities of data for deeper analysis has thrust true supply chain visibility into the realm of mainstream adoption.

Photo by thitivong/iStock / Getty Images
Photo by thitivong/iStock / Getty Images

Bringing Machine Learning to Supply Chain Management

Data is the DNA of digital supply chain management. Even a slight defect can have incredible, far-reaching consequences. Most supply chain stakeholders are already aware of how exact of a science they’re dealing with. Consider these findings from GS1’s National Data Quality Program:

  • A package measurement error of a mere 1.5 pounds can make a difference of $100,000 in annual transportation costs.
  • In freight and warehouse measurement processes, a quarter-inch error in case-height measurements can lead to 1,000 fewer product cases per truckload.
  • The addition of 20 more cases per pallet results in the need for 6 additional trucks.

Imagine the impact on transportation costs a single invalid or duplicate data entry might have as it travels the length of the supply chain! In appearance, your processes are optimized for cost-efficiency, but those processes are only as good as the data that informs them. Without a plan to manage data accuracy and supply chain management software to take action on it, you’re succumbing to the principle of “garbage in, garbage out.”  

By definition, that’s the exact opposite of supply chain visibility. It’s a supply chain management mirage that makes you think you’re doing a lot better than you actually could be.

This is where machine learning comes into the picture. Before you can use it to generate insights, you need to train it with clean data. Otherwise, invalid, false or duplicate data will dash your hopes of supply chain visibility.

Supply chain visibility enablement

Data quality has been the missing link in the digital supply chain for a while, according to DATAVERSITY. Discrepancies among supply chain stakeholder data in particular have been a common source of inefficiency and missed opportunities.

By first leveraging machine learning to map, correct, and enhance your data you progressively propagate accurate data across the entire supply chain, giving you end-to-end visibility built upon reality:

  1. Pull and push information from existing solutions (EDIs, TMSs, ERPs, CRMs and custom applications) into a central platform.  
  2. Standardize it, deduplicate it, validate it and then store it for use with your supply chain management resources.
  3. Auto-generate an audit trail to outline the steps taken to enrich data. This verifies data integrity and provides guidance for machine learning algorithms for future data cleansing.

Supply chain stakeholders will need the means to collect data from disparate touch points (IoT endpoints, international trade data, EDI networks, etc.), cleanse it, and then use it to enable end-to-end supply chain visibility. The good news is that we’ve already achieved this at scale with our next-generation supply chain management platform.

ClearMetal has been used time and time again to aggregate data from many different sources within the supply chain, refine it, validate it and analyze it. As a result, enterprises have realized millions of dollars in savings. This is not theory; it’s a reality we have made possible. More importantly, it’s the reason we’re so certain that 2018 is the year of supply chain visibility. We’ve seen it in action, and it works wonders.

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