For decades, the manufacturing sector has relied on data for a competitive advantage. One would see employees with a clipboard in hand walking around the plant to check the machinery. Despite the tedious process, the employees would note down facts and write a detailed report of the operational history.
In time, advanced technologies changed the process of deriving that data and reduced the possibility of inaccuracies and unnoticed errors. Today, plant managers and engineers put their faith in Predictive Analytics (PA).
A few years ago, SkullCandy struggled with the challenge of understanding the return rates on their newly manufactured products. They unleashed Kraken, BigSquid’s PA engine on their historical data and observed the results. With the new data, they were able to ask questions to understand customer behaviour regarding their new products.
Using both historical and real-time data, PA works on predicting future outcomes, reducing risks, cutting costs, and enhancing operations. Experts claim manufacturers leverage tools from PA platforms such as Sisense and Oden Technologies to witness a 10 to 50 per cent reduction in total scrap. Some vendors might suggest companies leverage automated Machine Learning (ML) tools to build, validate, and launch predictive models but experts raise a sign of caution.
Getting Implementation Right
Businesses can reap the benefits of PA, only if they execute the technology properly. A typical PA project working on AutoML includes data preparation, algorithm selection, model training and testing. It can be complex and take over six months of initialisation. It is a highly possible scenario that by the time training and validation is initiated, the AI or ML technique would be outdated.
Traditional PA platforms are usually designed for data science professionals, and it is not compatible with the manufacturing processes. Many manufacturing operation processes require real-time processing rather than a batch processing ML approach. Experts believe smart manufacturing requires an ML solution with streaming analytical capabilities that can process data in less than a second.
Moreover, traditional AutoML platforms leverage the black-box approach, which delivers less than required actionable results. The manufacturing sector does better with a white-box model. This model can generate transparent features and enables the AI team to successfully execute projects.
The Data Strategy Within the PA Strategy
For the analytics to be performed seamlessly, a reliable data strategy that aligns with the company goals and priorities are essential. Experts recommend a centralised database with data ingestion that could consolidate all the data from different business units. Many vendors like Aptitive offer a business assessment and help determine the specifications for the predictive analytics solution for the manufacturing company. Vendors can also provide custom dashboards and portals to help executives manage resources, identify risks, anticipate demands, and increase ROI.
Today’s cutting edge manufacturers claim that creating products for customers is beneficial only if they find a way to present them to customers who demand them. They have begun using PA to predict the demand graph to manufacture the product when demand increases and stop production when demand decreases. IDC expects AI-powered applications, including PA, to reach $95.5 billion by 2022.
The Not-so-predictive Perks of PA
While PA can gauge maintenance conditions and receive automated readings, the ML-powered PA tool can also send out automated requests and reduce maintenance costs by 10 to 40 per cent. It is known to counteract profit erosion and save the manufacturing sector from wasting raw materials. Additionally, the advanced tool can enhance the manufacturing execution systems by detecting cost drivers and identifying bottlenecks in operations.
Demand forecasting is empowered by advanced statistical algorithms, and it can help with raw materials inventory, trade war insights, weather-related troubles, and supplier issues. The added advantage is that it can establish unknown connections between the variable and the drivers that influence demand to enhance the supply management process.
Traditional maintenance plans could merely estimate when to perform a maintenance check. With connected real-time devices, predictive analysis can receive more data points, and it can predict the number of pieces that can be manufactured before a failure. For instance, if an equipment’s amperage increases, the spindle load tracking through the equipment’s software dashboard can provide the required data. The PA solution can then predict the possible number of parts that can be manufactured before complete tool failure.
With the increased ability to track and monitor, PA might increase warranties, subscriptions and insurance policies. Diagnostic analytics could alter what insurances and warranties could cover. The more data insights PA can acquire, the more knowledge is derived. With the increasing remote and mobile asset tracking, experts reckon the high fidelity data gained can increase remote and mobile diagnostic data, which will eventually wipe out the need for most field technicians.
Ultimately, PA is a tool
The data fed to the PA solution is a crucial task. Connecting all data points on a factory floor can be painstaking but manufacturers can use Industrial IoT and applied analytics technology to identify the required data points. Every part of every machine need not require a sensor to perform PA. Meanwhile, if models are trained with incomplete data points, labels or context, the predictions can be inaccurate.
Companies must be mindful that PA solutions might bring up both false negatives and false positives, and they need to take precautionary actions. Experts suggest they provide model feedback for further improvements. While PA can provide insightful data to manufacturers, what they do with the information is completely in their hands. The analytics are not going to solve management problems either.
A wire and cable manufacturer decided to empower plant-floor operators with PA solutions to identify the cause of scrap products. They used Oden’s predictive analytics solution and were able to launch an 85 per cent accurate scrap prediction model that provided insights on the quality of scrap. PA is one of the best ways to save raw material wastage and improve product quality and efficiency. Digitalist magazine reported that PA could save manufacturers $630 billion by 2031.
Also Read: How AI is Improving Predictive Analytics
A PA Case Study: The Electric Vehicle (EV)
The EV manufacturing industry is leveraging PA to build their global revenue and also to establish themselves as a valuable market sector. With the current costs of an electric car much greater than a traditional vehicle, manufacturers are relying on PA tools to help reduce the cost of production and increase manufacturing efficiency.
With PA insights, the industry leaders are beginning to add 3D printing strategies to their manufacturing process, and they are exploring better creation processes that are both environmental and customer friendly.
Similar to the new beginnings of the electric vehicle, PA allows the manufacturing industry leaders to envision better innovations and bring out a seamless production process. It’s high time the industry is known to add economic value and not be known for their raw materials wastage, sitting duck inventory and a chaotic environment.