3 steps to solve supply chain disruption with data analytics

Supply chain management through data analytics

Every business has a need for informatics, or the use of discrete information to improve business outcomes. The use of informatics, also called data analytics, helps companies make the best decisions possible from existing business intelligence.

Of all the functions of a manufacturing company, perhaps no other is as ripe for the use of data analytics as supply chain management. Businesses struggle to cope with the art of supply chains: One in three reported losses of more than $1 million in 2015 due to supply chain disruptions.

To solve supply chain disruptions in an organization, the intelligent use of data ought to be priority number one; not to incur analysis paralysis, but to inform decisive actions for the better.

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Step 1: Gather Supply Chain Data

Sixty-five percent of procurement officers lack visibility past their Tier 1 suppliers. This means an overwhelming majority of procurement officers do not know what goes into their companies’ products past the most immediate suppliers, affecting quality and integration potential.

Supply chain disruptions are not always neatly identifiable, however. The portion of companies facing supply chain disruption is likely much higher than the one-third self-reporting their issues.

The key to understanding the supply chain function is to have clean data from which to derive conclusions. This data may consist of lead times, supplier compliance ratings, and credit ratings, for instance. Supply chain data is generally meant to measure the risks affecting (or, potentially affecting) the supply chain.

Step 2: Determine Risk Tolerances

Companies must determine risk tolerances before delving into their data. Chances are that a company’s supply chain professionals can quickly identify various supply chain inefficiencies after implementing data tracking system for the first time.

The key is to not get lost in the noise of the data. A manager may find that one supplier is consistently late in its shipments, affecting lead times. However, this does not necessarily constitute an action item for the manager. The delays might still be well within the company’s production schedule; or, the company may be an industry leader in production turnaround despite the company’s expectations.

Determining risk tolerances, based on both industry standards and on the company’s own historical data, is an important first step before analyzing current-term data. This way, supply chain managers’ and executives’ opinions are not colored by the bias of a single temporal or geographic sample, and more achievable goals can be constructed in the next phase.

Step 3: Employ Data Analytics

After assessing a company’s risk tolerances, the supply chain manager can now use the data gathered in step one to get a better grasp of her expectations versus reality.

If fortune smiles on the supply chain manager and the data suggest total alignment with the company’s risk tolerances, one can still derive new goals to either maintain or improve the supply chain vis a vis risk. Even without continuous improvement principles driving business operations, the supply chain manager is likely able to find deficiencies somewhere along the line; if not, she probably either does not have enough data, or is not employing the full breadth of data analytics possible.

Professor Suresh Acharya of the University of Maryland outlines three methods of data analytics that can drive business decisions: Descriptive analytics, predictive analytics, and prescriptive analytics.

Each of these methods builds on the next for a more robust data analytics approach.

Descriptive analytics takes Steps 1-3 from this article and applies them purely. We ask, “What happened?” in assessment.

Predictive analytics takes the insights from descriptive analytics and applies them with forecasting; it forecasts “What will be?”

Prescriptive analytics, the final stage of data analytics evolution, changes the assumptions of the current state and applies them for more dynamic forecasting. “If things changed in this or that way, then what could be?”

Visionary companies try to answer that “What could be?” question as a reason for being. In today’s data-driven competitive landscape, only the most innovative will be capable of succeeding beyond the margins. Only the most thoughtful organizations will prevail, and data is the first step toward that thought.