Analytics: The Cornerstone of a Resilient Supply Chain

BrandPost By Joanie Wexler
Jun 01, 2020
Supply Chain Management SoftwareTechnology Industry

Developing an enterprise data strategy to unify fragmented data silos can help create the robust analytics foundation businesses need for a resilient supply chain.

Credit: metamorworks

In today’s struggling economy, developing an enterprise data strategy for your supply chain might seem like the last priority on your list.

Yet moving away from traditional spreadsheets and manual processes, which 67.4% of businesses reportedly still use to run their supply chains1, can actually help your business become more resilient in the face of severe disruptions and economic fluctuations.

When companies use traditional spreadsheets and processes, their operational decisions are based almost exclusively on historical data. On its own, that data is insufficient for timely adjustments to unforeseen or unpredictable events such as demand volatility, cost fluctuations, outages, breakdowns, and other circumstances.

However, organizations that can harness data where and when it’s generated—in warehouses, trucks, mobile computers, refrigerators, store shelves, in the cloud, and elsewhere—will gain predictive and prescriptive insights into up-to-the-minute conditions. And that could mean the difference between surviving or not in times of economic uncertainty.

Those insights have the power to revitalize and strengthen the supply chain, as well as create new levels of efficiency to save time and money, reduce delays and shortages, preserve profits, and even help protect a brand’s good name.

Easier Said Than Done

But becoming a data-driven organization involves more than simply deploying an analytics software package. While analytics software represented the top supply chain investment category in 2019, according to the Council of Supply Chain Management Professionals, reaping the rewards of that software requires some upfront work.

For most organizations, reviewing how and where they store different types of data is step one. Corporate departments traditionally collect and manage data that pertains to their own operations and their own view of the world, using their preferred processes, technology, and data formats. This “data silo” approach obscures end-to-end supply chain visibility. To gain transparency, successful analytics programs require:

  • Single-source-of-truth data
  • Data that’s available in real time (or near real time)
  • Comprehensive, integrated data that reflects the entire supply chain

Liberating Siloed Data for Integration

To unlock siloed data, companies must create an enterprise-wide data strategy that includes an inventory of all data sources, applications, and data owners. Once relevant data sources and format have been audited, organizations can prepare the data for integrated use across internal and trading partner environments.

Choosing one analytics platform to aggregate and integrate data is a trusted approach; this way, data imported from data lakes can feed modern analytics engines with structured and unstructured data. In addition, advanced machine learning tools help automate the process of aggregating and classifying data from the various disparate systems.

The Bottom Line

The exponential data growth in supply chains is driving complexity that, if left unmanaged, will create backlogs and drive up cost. By contrast, unifying supply chain platforms and using machine learning to automate allows businesses to harness real-time, aggregated rich data for immediate action. These organizations gain the agility to predict change and quickly pivot in the face of it—essential capabilities for sustainability in potentially uncertain times.

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1 Supply Chain Dive, “Two-Thirds of Companies Consider Excel a Supply Chain System,” August 2018