Evaluating Industry 4.0 Performance: Five Categories of KPIs You Need to Know

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.

Upkip - KPIs for Evaluating Industry 4.0

Asset Management and Predictive Maintenance KPIs

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:

  • Asset availability: This metric is calculated by dividing the uptime of the asset by the sum of its uptime and downtime. Hence, it is largely determined by the downtime of the asset. Downtime is very important in asset management: Most maintenance approaches strive to minimize downtime towards increasing the efficiency of production operations. Downtime is usually classified as planned and unplanned. The analysis of an asset’s availability considers most of the resources that affect the operation of the asset including machines, operators, and shifts.
  • Overall Equipment Effectiveness (OEE): It is calculated as the product of the availability, the performance, and the quality of the equipment’s operation. OEE indicates the efficiency of machinery or other equipment based on a holistic approach that considers its availability, its performance, and the quality of the products produced at the same time. Due to its holistic nature, it is sometimes used to measure manufacturing excellence.
  • Replacement Asset Value (RAV) and Maintenance Cost: This indicates the price that an enterprise needs to pay to replace an existing asset with a similar asset. It is calculated based on replacement asset value. Moreover, it is widely used to justify and audit maintenance programs and their costs. The latter is usually expressed as a per cent of the RAV which is therefore used as a benchmark for operating asset performance. For instance, when maintenance costs are a high percentage of the RAV, there is usually a need for improving operating and maintenance processes. By investing in technology, business processes, maintenance tools, and people skills, companies can reduce maintenance costs to a very low percentage of the RAV (e.g., 1%-3%).

Upkip - Asset Management and Predictive Maintenance KPIs

Factory Operation KPI

There are KPIs that measure the performance of the plant as a whole. Prominent examples include:

  • Throughput measured as the number of units produced within a certain time interval. It indicates the average performance of a machine, cell or like over time. To increase throughput, manufacturers must employ practices that reduce downtimes, optimize the configuration and maintenance of machines, and change frequently the required raw materials and tooling.
  • Capacity Utilization, which is calculated by dividing the actual output of a machine or cell with its potential output. It is usually expressed as a percentage and used to indicate the potential scale of the production facility. The ultimate goal of manufacturing enterprises is to operate their machines at an ideal cycle time i.e., at 100% capacity.
  • Changeover Time, which is the time needed for unloading, retooling, calibrating, or programming a new job. It is an important metric in cases where there must be a switch from one part to another before production execution. Using the changeover time, plant managers can identify the job types for which setup time must be improved. In this direction changes in operator training and management, materials might be required.
  • Yield, which is calculated by dividing the good parts produced by the total number of units produced. Yield indicates the levels of production efficiency, profitability, and quality. In an ideal scenario, no defective parts are produced, leading to a 100% yield. The latter is a target of a Zero Defects Manufacturing (ZDM) approach.
  • Customer Return Rate, which is the ratio of rejected goods to the total number of delivered goods. This is a quality management measure as customer returns are usually indicative of low product quality. High customer return rates have a direct impact on manufacturing costs as they typically require rework of already produced orders.

Supply Chain Management KPIs

Manufacturers must also track the performance of the supply chains where they engage. To this end, they had better track supply chain metrics like:

  • Cash to Cash Cycle Time, i.e., the number of days between paying for materials and getting paid for finished products.
  • Fill Rate, which indicates the part of the customer order that is filled upon the first shipment. The calculation can be expressed as a percentage of items, Stock Keeping Units (SKUs), or even of the monetary value of the order that is addressed in the first shipment.
  • Average Payment Period for Production Materials, which is the average time from the receipt of materials to the payment for those materials.
  • Perfect Order Measurement, which indicates the percentage of error-free orders. In several cases order perfection is tracked not only end-to-end but also at different legs of the supply chain management process (e.g., procurement, production, logistics, warehousing). For example, production perfect orders are those for which there is no flaw in the production process.
  • Cash to Cash Cycle Time, which refers to the number of days between paying for materials and receiving cash for the finished product. It is an important metric that reflects one of the main goals of effective supply chain management, which is to access customer’s cash faster.

Upkip - Supply Chain Management KPIs - Industry 4.0 - Manufacturing

Sustainability KPIs

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).

Upkip - Financial and Economic KPIs - Industry 4.0

Financial and Economic KPIs

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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