As described in Part 1, KPIs (Key Performance Indicators) are measurable values that organizations use to evaluate their progress toward achieving specific goals and objectives. In the context of manufacturing, KPIs are crucial for monitoring and optimizing various aspects of the production process. They provide quantifiable metrics that help manufacturing companies track performance, identify areas for improvement, and make informed decisions to enhance efficiency, quality, and overall productivity.

In this blog, Part 2 of the series, we will delve into the 10 crucial KPIs commonly utilized in the manufacturing industry.

KPIs for Smart Manufacturing

1. On-time Delivery

To uphold customer satisfaction, manufacturers must prioritize punctual deliveries. A perfect score of 100% is the goal. When deliveries fall behind schedule, various factors could be at play, such as delayed supplies, impractical production timelines, or frequent equipment issues. Maintaining a strong track record of on-time deliveries is paramount for retaining existing clients and enticing new ones.

Calculation: On-Time Units Delivered ÷ Total Units Delivered

2. Production Schedule Attainment

This aids manufacturers in evaluating the effectiveness of production planning and the alignment of worker performance with set benchmarks. It enables them to identify potential performance issues that might influence timely deliveries. Simply tracking delivery punctuality isn't sufficient; it's equally important to pinpoint the production aspects that are influencing this performance. This analysis facilitates necessary adjustments to enhance delivery efficiency.

Calculation: (Actual Output ÷ Planned Output) x 100

3. Total Cycle Time

This metric gauges the duration from the commencement of production to shipping, encompassing the entire process of fulfilling a customer order. Excluding intervals and idle periods, it strictly measures the active manufacturing time. The comprehensive cycle time encompasses all stages, spanning from raw materials to the final product. This value is an average derived from the cycle times of all orders. Notably, machine cycle time is part of this metric, revealing machine efficiency and necessitating a comparison with the machine's optimal cycle time.

Calculation: Net Production Time (NPT) ÷ Number of Units Produced

4. Throughput

This indicator provides insight into the efficiency of machine performance. It's monitored in real-time to proactively address emerging concerns before they escalate. Optimal results are achieved in throughput when workers are matched with machines aligning with their proficiency. Furthermore, maintaining machines in excellent condition and streamlining processes to minimize operator involvement yields peak throughput levels.

Calculation: Number of Units Produced ÷ Total Time of Production

5. Capacity Utilization

This measurement assists in evaluating a machine's utilization of its highest potential output. Ideally, the objective is to maintain the machine in constant operational order, minimizing any periods of inactivity. Given the substantial investment in production equipment, idleness should be avoided. The emphasis lies in maximizing machine capacity to enhance efficiency and reduce expenses.

Calculation: (Actual Output ÷ Potential Output) x 100

6. Changeover Time

This metric monitors the duration required to complete all tasks within a production run, including activities like unloading/loading, retooling, calibration, and programming for subsequent jobs. This data aids manufacturers in identifying potential areas for enhancement. It might lead to better organization of setup procedures or highlight the need for additional machinery training for staff. Minimizing changeover time translates to reduced production costs, making swift transitions more cost-effective.

Calculation: Time to Produce First Item in a Product Batch – Time to Produce Last Item in a Product Batch

7. Scrap

Materials that fail to meet quality criteria are classified as scrap. Nevertheless, certain manufacturers categorize any unused raw material as scrap. Monitoring scrap materials is essential for cost control and the production of higher-quality goods.

Calculation: Total Scrap ÷ Total Product Run

8. Predictive Maintenance

When evaluating the planned maintenance KPI, it's crucial to include all instances of emergency maintenance within the overall maintenance calculations. Ideally, the proportion of planned maintenance categorized as emergency work orders should remain below 15%. Emergency maintenance incurs significantly higher costs due to factors such as overtime, expedited parts, and disrupted production. It's imperative to minimize emergency maintenance occurrences to prevent negative impacts on profitability, downtime, and employee morale.

Calculation: (Planned Maintenance Time ÷ Total Maintenance Time) x 100

9. Availability

Availability refers to the assessment of machine operational time versus periods of inactivity. Recognizing the extent of downtime within your organization is crucial, given its substantial cost and status as a primary challenge for manufacturers. When determining availability, it's vital to consider both planned and unplanned downtime. Equally important is maintaining a record of downtime causes, enabling subsequent analysis to pinpoint potential enhancements.

Calculation: Uptime ÷ (Uptime + Downtime)

10. Overall Equipment Effectiveness

This vital KPI evaluates equipment productivity by combining availability, performance, and quality factors. However, it doesn't encompass machine downtime or maintenance periods. It's important to recognize that this KPI offers an incomplete perspective. While a high effectiveness rate may be observed, it's advisable to explore potential underlying issues that might contribute to this figure. In next blog Part 3, we will have a more detailed explanation about OEE because of its importance.

Calculation: Availability x Performance x Quality

Achieve Your KPIs with OMH Offering

The Open Manufacturing Hub (OMH) solution presented by EMQ is a reference architecture for building powerful and scalable Industrial IoT applications that keep your KPIs in control.

When deploying the OMH solution, it provides access to a set of pre-defined KPIs that offer two flexible options: first can either use them as they are, or second can customize them to meet specific business goals. In addition to providing these predefined KPIs, OMH also includes a robust business intelligence component. This business intelligence feature helps to delve deeper into the data, facilitating a comprehensive analysis of KPIs. Explore and extract insights to gain a richer understanding of the performance of a particular KPI and its impact on the manufacturing processes. This analytical capability is critical to identifying areas for improvement and optimizing operations.

The OMH solution allows customizing the KPIs based on unique situations. Should a specific scenario require additional measurements or different performance criteria, the organization has the flexibility to define new KPIs that accurately capture the difference of that situation.

All in all, OMH's comprehensive offering includes both preset and customizable KPIs, along with a powerful business intelligence module. This combination enables the organization to harness the power of data analysis to make informed decisions and continuously refine the manufacturing processes based on real-time insights.

Conclusion

KPIs play a pivotal role in guiding significant business choices. Individuals overseeing these metrics in manufacturing should feel responsible for their designated areas and consistently monitor them to drive enhancements when required. Flexibility is key. KPIs may evolve to address specific circumstances. Their focus should remain on vital management concerns. Ideally, a business should work with approximately 10 KPIs, as an excessive number can overwhelm and lose effectiveness.

In addition, an efficient solution is also crucial for optimizing performance and achieving KPIs. With OMH, organizations can easily build powerful, scalable, and data-driven Industrial IoT applications. They will gain real-time insights to optimize industrial processes, improve operational efficiency, and make better decisions.

Originally published at www.emqx.com