By Bo Hagler

This article originally ran in Modern Materials Handling.

As manufacturers play in a rapidly growing — and fiercely competitive — global market, prescriptive analysis is allowing them to become more agile. Machine learning automates the analysis of the overwhelmingly complex and diverse data being generated across the supply chain. This provides companies with actionable intelligence to drive optimization, increase margins, and avoid supply chain disruptions.

Machine learning, a type of artificial intelligence (AI), uses algorithms to learn without being explicitly programmed. It excels at finding anomalies, patterns and predictive insights in large data sets. Machine learning tools are able to turn large volumes of passive data into useful information by reporting on historical data as well as deploying models built to forecast likely outcomes. In particular, machine learning automates “what-if” analysis by modeling a range of scenarios and prescribing actions that can help the organization to achieve optimal results.

Transcending Business Intelligence: Decision Recommendations

As manufacturers face increased cost pressure and as just-in-time models demand precision while raising the stakes, predicting what’s coming is key to maintaining a healthy business. However, many business decisions are the result of historical data. Machine learning improves decision making and forecasting based on both historical and real-time data/trends. This empowers organizations to forecast demand, minimize program launch delays, discover opportunities for cost reductions or pre-emptively anticipate cost increases, and drive accurate, on-time shipments.

Predictive analytics empower supply chain managers not just to see issues as they arise, but to discover trends as in the early stages of their development. But machine learning can provide an edge beyond predictive analytics: prescriptive analytics. Prescriptive analytics can drive even better results, because it integrates a decision support system to perform “what-if” analyses and evaluate options under constraints, and make adjustments in real-time. In the analysis, more weight is given to factors that have more impact on desired outcomes; for example, detecting and acting on inconsistent quality or delivery performance.

Leveraging the Data Lake

There is a universe of information outside of a company’s ERP, QMS, Strategic Sourcing, and PLM systems; much of that other information is stored in spreadsheets and email. This data is unstructured, and therefore incompatible with traditional data warehouses driven by relational databases. Increasingly, organizations are turning to the data lake approach.

A data lake stores relational data from business applications, as well as non-relational data from mobile apps, IoT devices, and communications applications and social media. With the vast amounts of data collected across these disparate systems and formats, being able to harness that data to drive operational performance can provide a major advantage. Everything from the data lake is available for analysis, such as SQL queries, big data analytics, full-text search, and real-time analytics.

Making Smarter Supply Chain Decisions

Machine learning enables a sort of “social listening” to mine the unstructured data in other systems such as email and spreadsheets. As a result, companies can discover quickly — even preemptively — who their best and worst suppliers are and flag potential threats for disruption. Historical data relating to every interaction with suppliers can be tracked and analyzed, and this data can be used to determine if a supplier meets or exceeds expectations, if there are opportunities for improvement, or if another supplier needs to be selected.

It is easier to identify suppliers whose performance is trending in the wrong direction and take action. For example, if a supplier’s defect level or missed shipments has increased recently, this could foreshadow a bigger problem that could cause a major disruption. Machine learning automates the detection of this and when flagged, a company can identify a supplier that may be in good standing but is trending in a concerning direction, so they can proactively award the business to an alternate supplier, mitigating future disruption.

Additionally, machine learning can identify timing issues that may delay the launch of a program. A data analyst can determine suppliers that historically take longer than scheduled to complete a product launch task so that another supplier may be selected for the process or adjust the launch schedule based on the supplier demonstrated performance.

Machine learning will allow organizations to leverage the vast and varied data they’re collecting to not only see and respond to trends but also to run scenarios involving any possible influence on the supply chain. In particular, supplier capacity issues: an organization can gain insight into which suppliers would be best suited to respond well to a 20% increase in orders — and which would be unlikely to meet demand — by analyzing contracted capacity measured against demonstrated capacity. Here, machine learning not only drives decision making but helps increase transparency while surfacing a significant issue in the supply chain.

Machine learning uncovers opportunities for supply chain optimization by supercharging analysis of ever-more-complex supplier-related information. This will be even more critical as the supply chain continues to leverage IoT and advanced robotics, as communication between organizations and their suppliers occurs across an ever-evolving variety of channels, and as new technologies enter the market. Equipped with machine learning, supply chain managers will have better insight into suppliers, see early warnings about potential threats for disruption, improve program launch timelines, deliver cost avoidance and cost reductions, and ensure on-time deliveries.

This article originally ran in Modern Materials Handling.