The Great Promise of Data Analytics in the Manufacturing Sector – And the Roadblocks
Data analytics is now an established enabler of efficiencies across industries such as finance, technology, retail, and healthcare. The manufacturing industry too has now warmed up to the promise of data analytics to increase yields and reduce costs. The manufacturing industry can also leverage data analytics to increase production, optimize supply chains and improve production quality.
Leveraging data analytics is becoming imperative for this sector to keep up with market demands. Analytics is on the mind of manufacturing-focused companies seeking greater agility and increased competitiveness while keeping costs under check.
Data analytics presents multiple opportunities to optimize manufacturing operations by employing operational and business data on finances, inventories, products, human resources, distributors, and partners. With the rise of the industrial internet of things, advances in computing systems, and the maturing of technologies such as AI and machine learning, this industry can now analyze huge data volumes in real-time to turn them into actionable insights.
Here is a look at the promise of data analytics for the manufacturing sector.
Real-time monitoring and measurement
Since we can only manage what we can measure, data analysis becomes critical to drive efficiencies and cost optimizations in this industry. Robust data analytics capabilities help manufacturing companies identify production challenges accurately and make this information available quickly to decision-makers to mitigate problems efficiently.
Real-time or live data from equipment can help in proactive problem identification and assist in identifying the root cause of a primary downtime event with greater accuracy and surety.
Optimize procurement and supply chains
Manufacturing companies can improve their bottom line by gaining a clearer understanding of their procurement and supply chain operations. Purchasing raw materials, for example, is an everyday part of the manufacturing business. Manufacturing supply chains are also long and complex.
Data analytics helps those operating in this space gain greater capabilities to identify the effectiveness and efficiency of every unit across the manufacturing lifecycle. With data-backed insights, organizations can identify error-prone processes, isolate discrepancies, and zero in on process improvement opportunities to drive better outcomes.
Data analytics also helps manufacturing companies predict potential delays and accurately calculate and determine any problem ahead of time. This helps in managing supply chain risks with greater confidence.
Improved demand forecasting
Manufacturing products now must be future-ready if they don’t want the products sitting in the inventory. Most demand forecasts, though, for manufacturing companies are based on historical data. This data only provides a limited view of demand forecasts. However, leveraging data analytics that combines historical data with current and real-time data delivers clearer and better insights for demand forecasting.
Demand forecasting is important for manufacturers since poor demand forecasting can lead to greater expenses. Dealing with things like unplanned production changeovers, impacting manufacturing capacities, incurring supply change issues are just a few of the problems to encounter.
While extra products in the warehouse can help manage unplanned surges, the inventory ends up costing the company money as it sits unutilized.
With accurate demand forecasting capabilities, manufacturing companies can make accurate demand forecasts and ensure that the amount of inventory and production rates are at optimal levels at all times.
Advanced analytic systems can also help manufacturing companies better accommodate seasonal demand, product promotions, causal variables, slow-moving items, product hierarchies, and help with outlier detection.
The term itself is self-explanatory; the data predicts when maintenance is needed. Nothing is assumed.
Predictive maintenance helps manufacturing companies plan maintenance and also make necessary adjustments before any failure event. Predictive data analytics uses manufacturing process data and makes and helps the organization assume a more proactive, rather than reactive decision-making stand.
Predictive maintenance is essential now as depreciation is a vital cost in this industry and advanced equipment is high-priced. Improved asset management capabilities thus become critical for sustainability and business continuity for the manufacturing unit.
Predictive maintenance helps limit unplanned downtime and also helps optimize equipment lifetime.
The power of analytics can only be harnessed to its true potential when it does not reside in silos. Despite having access to large data pools, the data in most manufacturing organizations lies in horizontal and vertical data silos.
Often data is locked in machines or lies in departments. It could have a software dependency or simply be outdated owing to manual collection processes. Manufacturing data from equipment is also complex and may be available in non-standard formats. That apart, this scenario has hundreds of data points that change constantly. Data must thus, not only be integrated and un-siloed but also has to be converted into a common data model to be used easily. This apart legacy systems further contribute to analysis complexity as many assets and systems are connected to legacy infrastructures and not cloud systems.
However, the industry must navigate these challenges and work towards creating an ecosystem that allows data to flow seamlessly and in real-time to leverage the analytics advantage to drive competitive differentiation.
Manufacturing companies thus need to leverage the power of digital technologies and employ a manufacturing Intelligence framework that helps them connect to multiple sources, and platforms. It must help them compile all the data in a powerful data warehouse to provide powerful visual summaries, dashboards, and analytics and provide a single version of the truth to drive impactful decision-making capabilities.