With ever more data being generated across modern organizations, management are looking for actionable intelligence to drive optimization, increase margins and avoid supply chain distributions. The sheer volume of data can make it difficult to see trends; this is where machine learning (ML) is a revolution for business intelligence.
ML is a type of Artificial Intelligence (Ai) that powers machines with the ability to learn without being explicitly programmed. It excels at finding (anomalies, patterns and predictive insights in large datasets) — the data lakes — by reporting on historical data as well as deploying models built to forecast likely outcomes. In particular, ML automates «what if» analysis by modeling a range of scenarios and prescribing actions that can help the organization achieve optimal results.
How machine learning empowers better decision-making with prescriptive analytics
Traditionally, management have made decisions based on historical data. Increasingly, the availability of real-time data about every aspect of operations is allowing increased agility not just to see issues as they arise but to see trends as they’re developing.
Predictive analytics empowers management to be proactive; it improves decision-making and forecasting based on both historical and real-time data/trends. Companies are leveraging ML and predictive analytics to better forecast demand, minimize program launch delays, discover opportunities for cost reductions or preemptively anticipate cost increases and drive accurate, on-time shipments.
As organizations face increased cost pressure in a rapidly growing and fiercely competitive global marketplace, and as just-in-time models demand precision while raising the stakes, predicting what’s coming is key to maintaining a healthy business. However, ML 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, 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, such as detecting and acting on inconsistent quality or delivery performance.
Where is the data? Leveraging the data lake
Certainly, a wealth of data lives in a company’s enterprise resource planning, product lifecycle management and other enterprise systems, but there is a universe of data outside of them. Stored in spreadsheets, emails or messages, much of that other data is unstructured and incompatible with traditional data warehouses driven by relational databases.
This is why organizations are increasingly turning to the data lake approach. Amazon defines a data lake (as a centralized, secure and durable cloud-based storage platform that allows you to ingest and store structured and unstructured data, and transform these raw data assets as needed). This presents a challenge to many business analytics systems. 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.
According to University of St. Andrews researcher Andreas François Vermeulen, everything from the (data lake) is available for analysis such as «SQL queries, big data analytics, full-text search, real-time analytics and ML.» Machine learning enables a sort of «social listening» to mine the unstructured data in other systems, such as email and spreadsheets. As a result, ML will allow management 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 operations.
This will be even more critical as IoT and advanced robotics become more common, as communication between organizations and their vendors and partners occurs across an ever-evolving variety of channels and as new technologies enter the market.
How you can use machine learning to make smarter business decisions
Discover advantageous relationships. With ML, 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.
Identify suppliers/partners whose performance is trending in the wrong direction and take action. For instance, if a supplier’s defect level or missed shipments has increased recently, this could foreshadow a bigger problem that could cause a major disruption. ML 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 it can proactively award the business to an alternate supplier, mitigating future disruption.
Leverage machine learning to identify timing issues that may delay the launch of a program. A data practitioner can determine suppliers that historically take longer than scheduled to complete a product launch task. This will allow one to select another supplier for the process or adjust the launch schedule based on the supplier’s demonstrated performance.
Model scenarios that project of supplier capacity issues. In particular, 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, ML not only drives decision-making but helps increase transparency while surfacing a significant issue in the supply chain.
What to consider when introducing machine learning into your organization
Engage a vendor who will partner with you, as many organizations do not have in-house data scientists and will need some guidance to make the most of the technology as it evolves.
The knowledge and recommendations that ML uncovers can lead to a paradigm shift in your organization. Be prepared to examine and optimize established business practices.
ML tools require some teaching; they must be trained to learn your strategic goals. Before initiating the project, your team should define what success looks like and recognize that the system must be provided with several data points and feedback that will lead it to anticipate the best course of action in each situation.
Conclusion
Machine learning uncovers opportunities for business optimization hidden in the (data lake) by supercharging analysis of ever-more-complex data. As organizations deploy the next generation of analytics, they’ll have better insight into operations and potential threats for disruption. They realize the benefits through improved program launch, cost avoidance and cost reductions, and they can ensure on-time deliveries.
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