By < Rune Stolan >
April 14, 2021
For over two decades manufacturing operations worldwide are driven by the famous quote of Peter Drucker: “If you can’t measure it, you cannot improve it”. Manufacturing management is largely about tracking and evaluating the status of assets and processes using appropriate Key Performance Indicators (KPIs).
In recent years, the advent of Industry 4.0 has greatly facilitated the calculation of such KPIs thanks to the collection and analysis of large amounts of digital data from production systems and processes. In this direction, Industry 4.0 provides readily available calculations of KPIs, while at the same time facilitating their tracking over time and across different production processes. Furthermore, Industry 4.0 systems integrators are offered opportunities for calculating customized KPIs in line with the needs of the manufacturing enterprise where their systems are deployed.
In this context, it is important for manufacturers and providers of industrial automation solutions to know which KPIs they must track and trace as part of the operation of their automation systems and plants. There is a very rich set of KPIs for almost all manufacturing processes. The details of the various KPIs, including ways and formulas to calculate them, can be found in various papers and books. Nevertheless, it is important for manufacturing enterprises and industrial automation solution providers to understand how the various KPIs are clustered in different categories, as well as how the KPIs of each category can be used for improving production processes and achieving operational excellence.
Asset level KPIs are very important for tracking, evaluating, and improving the operation of manufacturing assets such as machines and tools. Likewise, they are used to effectively manage maintenance and repair operations based on approaches like preventive and predictive maintenance. Asset management processes leverage various KPIs including:
There are KPIs that measure the performance of the plant as a whole. Prominent examples include:
Manufacturers must also track the performance of the supply chains where they engage. To this end, they had better track supply chain metrics like:
In recent years manufacturers pay emphasis on sustainable manufacturing aspects as a means of complying with emerging regulations and respective strategic agendas. One of the main goals of sustainable manufacturing is to optimize the environmental performance of production operations.
To this end, manufacturers must track scrap as the ratio of the total scrap to the total production run. In practice, scrap indicates the discarded or rejected materials during manufacturing processes. It can include waste due to defective items, as well as leftovers from raw materials. Tracking scrap is not only important for improving sustainability: It is also used to reduce material costs and improve quality management processes given that defective products lead to increased scrap.
Apart from scrap, manufacturers track other environment performance KPIs such as air emissions, power consumption, fuel, and materials consumption, as well as the noise pollution that is associated with their production operations. Depending on the manufacturing context, they may also track water and land utilization as well.
Sustainable manufacturing extends beyond environmental efficiency, towards economic and social sustainability. In this direction, manufacturers must track and trace economic KPIs (e.g., labour costs, material costs, inventory costs) and social KPIs (e.g., employee satisfaction, gender balance, accident rates, training, and engagement indicators).
The ultimate objective of manufacturing enterprises is to improve their bottom lines through production processes that are faster, cost-effective and of higher quality. Improvement in the above-listed metrics is therefore reflected in the P&L (Profit & Loss) accounts of the enterprise. Likewise, companies need to track and justify the return on their Industry 4.0 investments. In this direction, they must account for the cost of an investment, while estimating its potential benefits. Manufacturing enterprises and their C-level executives are therefore interested in capital budget indicators like the Return on Investment (ROI), the Internal Rate of Return (IRR) and the Net Present Value (NPV) of their Industry 4.0 projects.
KPIs in the above-listed categories are usually interrelated to one another. For instance, scrap is closely related to the performance of quality management processes and their metrics. Likewise, scrap and waste are greatly influenced by the health condition of the machinery and other manufacturing assets. As another example, improved equipment utilization (e.g., availability and OEE) and supply chain management metrics have a direct impact on corporate bottom lines. As such, they also affect the capital budgeting indicators (e.g., ROI) of Industry 4.0 projects.
Industry 4.0 makes it easier for manufacturing enterprises to collect digital data about the physical production process and to analyse them to track manufacturing performance. This is the reason why digital manufacturing platforms provide inherent support for KPIs calculation.
As a characteristic example, the Upkip platform provides built-in support for a wide range of KPIs, including OEE per factory and per customer order, on-time delivery at order and component levels, machine performance indicators for specific processes, tool usage KPIs, customer order performance indicators, as well as scrap and waste information at various granularities. As such it substantially helps manufacturers in their operational and management excellence journey.
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