Since the first Industrial Revolution, the manufacturing industry has been a victim of several challenges like volatile costs, industrial sickness, poor inventory management, rampant equipment breakdowns and what not.
All these issues had one primary cause: lack of data, or rather its under-utilization. A prime reason why manufacturers failed to realize the true potential of data is because they were looking through the rearview mirror than looking through the windshield. Analyzing historical data to deduce patterns is an inefficient practice. There is a critical need to look at data and spot future trends so that anomalies can be prevented even before they happen.
It is here that predictive analytics can act as a revitalizer for the manufacturing industry.
Predictive Analytics In Manufacturing: The Growth Story
Technologies like Big Data, Artificial Intelligence, and Machine Learning have made tremendous growth in the past one decade. They help manufacturers converge scattered data from IoT devices, ERP systems, and other data sources under one roof for meaningful interpretation.
Manufacturing is one domain where data-driven decision making has enormous influence. The growing adoption of Manufacturing 4.0 and its subsets like Connected Manufacturing, Industrial Internet of Things (IIoT), sensor-fixed equipment, Human-Machine Interface (HMI) have all transformed manufacturing assembly lines into real-time data sources. The volume of data per inch of factory floor has increased by four times.
Manufacturers now have access to more data and the technologies to make sense out of them. Predictive analytics can be a mining machine that can extract value out of chunks of data. It will help manufacturers tame the complexity of their routine operations.
Taming Manufacturing Complexity With Predictive Analytics
Predictive analytics empowers manufacturers to understand and also predict with accuracy what is happening and what would happen shortly within their factory premises. It will help narrow down on previously unknown causes that are causing production bottlenecks or even impacting the bottom line.
Reduced Equipment Downtime
Predictive analytics in manufacturing can help analyze equipment downtime patterns from records. Based on such historical data, as well as data collected from multiple sources, predictive analytics can help forecast possible scenarios when the equipment might break down.
By understanding equipment performance better, spare parts planning, preventive maintenance schedules, and production plans can be tweaked to ensure smooth workflow. This reduces equipment downtimes and maximizes manufacturer reliability.
Reduced routine maintenance costs
Predictive analytics enables manufacturers to plan a proactive maintenance strategy based on accurate equipment diagnostics. The need to replace spare parts at regular intervals rather based on a need-basis is eliminated. Maintenance personnel can collect information from sensors and equipment logs to arrive at the exact estimated useful life of the equipment and each of its spare parts.
This is similar to the connected car concept where the driver can get all information like tire pressure, engine oil levels, brake health, etc. on a mobile app. In a manufacturing scenario, such information will become accessible through portable devices or even directly at the ERP system used by the enterprise. Such data availability combined with proactive planning will help in reducing the frequency of routine maintenance and the downtime costs associated with it.
Digitized Supply Chain Management
Thanks to IoT, Blockchain and even Big Data, Supply Chain Management will now become digitized to every corner. Manufacturers will be able to plan for inventory procurement, logistics, and last mile delivery with nanosecond accuracy.
The advantage that predictive analytics gives to SCM compared to traditional analytic systems is the real-time connect. Data collected on a real-time basis can be combined with historical data to draw patterns. Key metrics like peak times, loading & unloading times, normal & losses and much more can be forecasted with much more benefit with predictive analytics.
Accelerated Engineering Innovations
Dynamic customer demands, rapid digitization strategies of manufacturers, advancements in research & development are all putting manufacturers under severe pressure to reduce their new product release cycle time. But, conducting research, designing, prototyping and manufacturing new products is not an easy task. Enormous amounts of data and their number crunching is required to bring a product from concept to street.
Predictive analytics can help bring together all that intelligence under one roof and help create more customer-targeted products to market quicker than before. It can even compare historical information of sales records to customer demographics, regional population, income levels and much more to fix data-driven pricing models. Ford used this technique to push its sales numbers and also optimize its inventory levels at dealerships.
Final Thoughts
Manufacturing has the most sophisticated of all business processes. It is capital-intensive, requires a massive amount of resources and is time-consuming. A lot of number-crunching and analytics go behind the closed doors of factory managers.
Until a few years ago, they relied on long and broad spreadsheets containing data gathered from manual records. This was an inefficient way of accumulating and analyzing data. Moreover, it lacked the real-time vantage that is much needed for forecasting and planning manufacturing operations.
It is here that predictive analytics positions itself as a continuously evolving and insight providing a digital offering.
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