Flow Software offers a practical analytics solution for manufacturers aiming to improve their operations. With so many possibilities available, sometimes it can be difficult to identify where you can start using Flow. Here are some suggested Key Performance Indicators (KPIs) that provide clear insights and can drive real value. This KPI guide focuses on tangible benefits and actionable steps that manufacturers can take to see immediate improvements in their processes using Flow.
To gain a competitive edge, manufacturers must harness the power of analytics to enhance their production efficiency. This involves tracking a suite of KPIs that not only measure output but also provide insights into the effectiveness of the production process.
Machine Downtime Rate with Cause Coding:
Downtime is an inevitable part of manufacturing, but its impact can be mitigated by understanding its causes. This KPI measures the frequency and duration of equipment downtime and categorizes each incident to identify patterns. Categories may include mechanical issues, operator errors, or supply chain disruptions, allowing for precise troubleshooting and strategic planning to reduce future occurrences.
Mean Time Between Events (MTBE):
While MTBF focuses on failures, MTBE expands the scope to any event that interrupts production. This KPI helps to paint a more detailed picture of equipment and process reliability by tracking the average time interval between all types of production-stopping events.
Average Production Speed:
Monitoring the rate at which goods are produced is essential for identifying production bottlenecks and optimizing process flow. This KPI provides the baseline speed at which the production line operates, offering valuable data for improving throughput.
Adherence to Plan (ATP):
This critical KPI measures the degree to which actual production aligns with the planned production schedule. High adherence indicates a well-organized operation, whereas deviations may signal inefficiencies or issues in the production planning or execution processes. ATP is pivotal for assessing the reliability of production scheduling and the effectiveness of the manufacturing process.
By integrating these KPIs into their performance management systems, manufacturers can obtain a 360-degree view of their production efficiency, identify areas of improvement, and take informed actions to enhance their operations.
Flow leverages its historical data analytics, such as run rates, to project future production values, enabling manufacturers to engage in forward-thinking strategies. Here’s how this predictive capacity translates into practical applications for production KPIs:
Run Rate Predictions for Production Planning:
By analyzing historical run rates, Flow can project future production outputs. This allows manufacturers to fine-tune production plans, ensuring they meet demand forecasts without overproducing, which can lead to excess inventory costs.
With historical maintenance records, Flow projects when machinery might require maintenance or is at risk of failure. Manufacturers can plan maintenance activities during non-peak periods, optimizing uptime without disrupting production.
Inventory Level Optimization:
Flow's projection of future production needs, based on historical run rates, informs inventory management. Manufacturers can thus maintain optimal inventory levels, preventing shortages or excesses that could impact production flow.
Energy Consumption Forecasting:
Using historical energy usage data correlated with production volumes, Flow predicts future energy needs. Manufacturers can anticipate energy costs and implement conservation measures in advance, improving sustainability and reducing expenses.
Incorporating Flow's predictive analytics into the monitoring of production KPIs equips manufacturers with a proactive approach to operational management. This foresight enables them to streamline their processes, reduce costs, and ensure they are prepared to meet future challenges and demands effectively.
Overall Equipment Effectiveness (OEE) is a widely accepted benchmark for manufacturing productivity, but to address various nuances and needs within the manufacturing process, several variants of the traditional OEE metric should also be considered.
Overall Equipment Effectiveness (OEE):
This comprehensive KPI represents the gold standard for measuring manufacturing productivity. It reveals the percentage of manufacturing time that is genuinely productive by combining three critical components: availability, performance, and quality.
Total Effective Equipment Performance (TEEP):
TEEP extends the concept of OEE by considering the total calendar time as the denominator, which includes all the time the equipment could theoretically be used, not just the scheduled time. It gives a sense of the total utilization of the equipment, accounting for both production and non-production time.
Overall Operations Effectiveness (OOE):
OOE modifies OEE by incorporating the impacts of not just equipment performance but also takes into account the entire manufacturing operation, including factors like scheduling delays and constraints on the supply chain that can affect production time.
Asset Utilization (AU):
Asset Utilization is a broader measure that looks at the utilization of the entire manufacturing system rather than individual pieces of equipment. It considers the capacity of the entire production line and how effectively it is being used.
Overall Factory Effectiveness (OFE):
OFE expands upon OEE by including additional factors that affect the entire factory’s performance, such as labor and material availability, to provide a more holistic view of the factory’s output capabilities.
Overall Plant Effectiveness (OPE):
Similar to OFE, OPE takes a plant-wide view of effectiveness, often integrating additional metrics such as energy consumption, safety incidents, and quality rates across the entire plant, not just specific equipment or lines.
Overall Asset Effectiveness (OAE):
OAE measures the effectiveness of an asset throughout its lifecycle, taking into account its performance, quality, and availability during the whole period it's in service, not just during production times.
Overall Labor Effectiveness (OLE):
While not focused on equipment, OLE is a human-centric variant of OEE that measures the effectiveness of labor. It examines how labor contributes to productivity, including aspects like labor utilization, performance, and quality of work.
Overall Process Effectiveness (OPE):
OPE focuses on the effectiveness of a manufacturing process. It goes beyond equipment and labor to include process flow, waste reduction, and other lean manufacturing principles.
These variants of OEE allow organizations to fine-tune their productivity and efficiency measurements to align better with their operational goals and challenges. They can be used to identify specific areas for improvement that may not be captured by the standard OEE formula.
Batch processing is a pivotal component of manufacturing that demands precise management to ensure operational efficiency and product quality. To achieve this, a suite of KPIs can be employed to scrutinize every aspect of the batch process. Here's an inclusive look at key metrics that can be built and monitored within Flow to drive substantial improvements in batch processing.
Average Batch Cycle Time:
The average time taken to complete a batch process, setting a benchmark for production efficiency.
Batch Cycle Time Variability:
A measure of the consistency in cycle times, where lower variability indicates more predictable and controlled operations.
Shortest and Longest Batch Cycle Time:
Identifies the range of cycle times, shedding light on potential process irregularities and areas ripe for optimization.
Batch Cycle Time Trend Over Periods:
Tracks changes in cycle times across periods, useful for spotting trends indicative of process improvements or degradation.
Frequency Distribution of Batch Cycle Times:
Analyzes the commonality of specific cycle times, assisting in identifying the most frequently occurring operational tempos.
Average Yield Per Batch:
Reflects the mean output of acceptable product per batch, crucial for gauging productive efficiency.
Yield Per Batch Rate:
The proportion of the batch that meets quality standards, offering a direct look at process efficacy.
Yield Per Batch Over Time:
Monitors yield changes batch-over-batch to detect shifts in production effectiveness.
Yield Variance Against Targets:
Compares actual yields against predefined targets, pinpointing discrepancies and areas needing attention.
Average Set-Up and Clean-Up Time:
Tracks the mean duration for equipment preparation and post-production clean-up, key for optimizing production schedules.
Set-Up and Clean-Up Time Variability:
Assesses the consistency of set-up and clean-up durations, indicating process stability.
Standard Deviation of Set-Up and Clean-Up Time:
Measures the spread of set-up times, aiding in identifying equipment or processes that may disrupt flow.
Frequency of Exceeding Standard Set-Up Time:
Monitors occurrences where set-up exceeds the norm, signaling potential inefficiencies or training gaps.
By integrating these KPIs into the Flow platform, manufacturers gain a granular view of their batch processing performance. These metrics not only serve as indicators of current efficiency but also guide strategic decisions for continuous process improvement. Manufacturers can now challenge themselves to pick two or three KPIs from the above and start building them in Flow, setting the stage for enhanced productivity and a stronger bottom line.
Alarm management is essential in manufacturing for maintaining operational efficiency and safety. The effectiveness of alarm management hinges on the careful tracking and analysis of specific KPIs. Here's a summary of each key KPI in this approach.
Total Alarm Count by Area/Cell:
This KPI quantifies the total number of alarms triggered in specific areas or cells, broken down by hour, shift, and day. It's used to identify areas with frequent alarms, directing focus on targeted troubleshooting and preventive measures.
Mean Time Between Alarms (MTBA):
MTBA calculates the average interval between consecutive alarms by dividing the total operating time by the total number of alarms. It's a critical metric for assessing system stability, where frequent alarms may suggest persistent operational issues.
Average Time in Alarm State:
This KPI measures the typical duration for which an alarm remains active. It's calculated by dividing the sum of all alarm durations by the total number of alarms. It helps identify delays in addressing alarms and highlights areas where response times can be improved.
Average Time from Alarm to Acknowledgement:
This metric evaluates the response time to alarms by calculating the average duration from an alarm's occurrence to its acknowledgement. It ensures alarms are attended to promptly and is crucial for maintaining a responsive operational environment.
Alarm Chatter Frequency:
This KPI tracks the frequency of alarms that are quickly reset without any apparent issue. It's used to identify and rectify faulty alarms or overly sensitive triggers, thereby reducing nuisance alarms.
These KPIs, when effectively monitored and analyzed, empower manufacturing teams to quickly address and prevent issues, ensuring smoother operations and enhanced safety.
Quality control stands as a cornerstone of manufacturing excellence, providing both the lens and the mirror to reflect on the production process's efficacy and the product's reception in the market. It's not just about minimizing the cost of non-conformance; it's about building a reputation for reliability and customer trust. By implementing and tracking the following KPIs, manufacturers can maintain high-quality standards and continuously improve their processes.
This KPI serves as a critical indicator of manufacturing accuracy and the overall health of the production process. By measuring the number of defects relative to the total units produced, manufacturers can pinpoint issues within their operations, allowing for targeted quality improvement initiatives.
First Pass Yield (FPY):
A direct measure of process effectiveness, FPY evaluates the percentage of products that meet quality standards at the first attempt, without the need for additional touch-ups or rework. High FPY rates are indicative of efficient production lines and robust quality control measures.
Quality-Related Waste Cost Percentage:
Quantifies the financial impact of waste resulting from quality issues as a proportion of total production costs. By indicating how much of the production budget is being consumed by defective products, it serves as a critical indicator for assessing the cost-effectiveness of quality management processes and identifying opportunities for process improvement and cost savings.
Often an eye-opener, this KPI assesses the percentage of products that customers return due to defects or dissatisfaction. It not only impacts the bottom line through return costs but also affects brand reputation. A low return rate is the aim, signifying that products meet or exceed customer expectations in the real-world usage.
This KPI measures the percentage of materials processed that do not result in a sellable product. A high scrap rate indicates waste and inefficiencies in the manufacturing process, which can be due to poor material quality, inadequate machine operation, or suboptimal production processes. Tracking and analyzing scrap rate helps in identifying the stages where quality issues occur and prompts actions to reduce waste and improve material utilization.
Customer Satisfaction Index (CSI):
While not always considered a traditional manufacturing KPI, the Customer Satisfaction Index is an invaluable measure of product quality from the customer's perspective. This metric reflects how well the product meets or exceeds customer expectations. It is derived from direct customer feedback and surveys post-purchase. A high CSI indicates that the quality of the product aligns well with customer needs and expectations, whereas a low score can signal the need for product improvement or better customer education regarding the product's use.
Through diligent tracking and analysis of these KPIs, manufacturers can assure that quality is not just a checkpoint but a defining feature of their production lifecycle, leading to superior products and enhanced customer satisfaction.
Inventory management is a balancing act that ensures the right products are available at the right time without tying up excessive capital in stock. In manufacturing, inventory management is not just a logistical concern; it's a strategic element that can significantly influence cash flow, production cycles, and customer satisfaction. Here are four KPIs that can help manufacturers master their inventory management.
Inventory Turnover Ratio:
This KPI reflects how often inventory is sold and replaced over a certain period. High turnover may indicate efficient inventory management and strong sales, whereas low turnover might suggest overstocking or insufficient demand. This ratio helps businesses optimize inventory levels and reduce holding costs.
Days Inventory Outstanding (DIO):
DIO measures the average number of days that inventory remains in stock before being sold. It's a liquidity indicator that shows how quickly a company can turn its inventory into cash. A lower DIO is generally preferred, indicating that the company manages its inventory efficiently and has less money tied up in unsold goods.
The stockout rate tracks how often inventory levels are insufficient to meet customer orders. Frequent stockouts can lead to lost sales and dissatisfied customers, while a low rate suggests that inventory is well-managed and aligned with demand.
This KPI compares the actual on-hand inventory to the inventory levels recorded in a company's database. High accuracy ensures that production planning is based on reliable data, which is essential for maintaining production schedules and meeting customer demand.
Order Lead Time:
Order lead time measures the time from when an inventory order is placed until it's received and ready for use or sale. Shorter lead times can improve a company's agility and responsiveness to market demand, while longer lead times may require higher inventory levels to buffer against delays.
By closely monitoring these KPIs, manufacturers can make informed decisions about production planning, supply chain management, and customer service strategies. Effective inventory management leads to reduced costs, improved cash flow, and better alignment between production output and market demand.
In today’s environmentally conscious market, sustainability is not just an ethical choice but a business imperative. Manufacturers are increasingly called upon to minimize their environmental impact while still meeting production demands. Here are key performance indicators that can help manufacturers track and achieve their sustainability objectives.
Energy Consumption per Unit Produced:
This KPI measures the amount of energy used to produce a single unit of product. It’s a direct reflection of the energy efficiency of the production process. Lower energy consumption per unit indicates more efficient use of resources, which not only reduces costs but also lessens environmental impact.
Water Usage per Unit Produced:
Similar to energy consumption, this KPI assesses the volume of water utilized in the production of each unit. It’s particularly relevant for manufacturers whose processes are water-intensive. Reducing this ratio is crucial in regions where water scarcity is a concern and can also result in significant cost savings.
This KPI quantifies the total greenhouse gas emissions (typically measured in CO2 equivalents) produced by the manufacturing activities. It encompasses direct emissions from manufacturing processes and indirect emissions from auxiliary activities like raw material transportation. Manufacturers strive to lower their carbon footprint to mitigate climate change and comply with regulations.
Utility Cost per Production Unit:
This financial KPI ties the sustainability efforts directly to the bottom line by measuring the cost of utilities (electricity, water, gas) consumed per unit of production. It helps businesses evaluate the economic impact of sustainability initiatives and can be a driving force for investing in more energy-efficient technologies.
Waste Reduction Rate:
Measuring the percentage decrease in waste generated over a period, this KPI is essential for tracking progress in reducing the environmental impact of waste. It also indicates improvements in material utilization and process efficiency.
For manufacturers, the proportion of waste material that is recycled rather than discarded is a key sustainability metric. A high recycling rate not only contributes to environmental goals but can also reduce costs associated with waste disposal.
By integrating these KPIs into their operational metrics, manufacturers can monitor and improve their sustainability performance, demonstrating their commitment to environmental stewardship while also uncovering opportunities for cost reduction and process improvement.
Optimizing the supply chain is a multifaceted approach that ensures the right components are available at the right time, quality, and cost. It's a strategic orchestration that affects every facet of manufacturing. The following KPIs are vital in measuring the efficiency and reliability of a manufacturer's supply chain:
Supplier Defect Rate:
This KPI measures the percentage of materials received from suppliers that do not meet quality standards. It is an essential metric for assessing the impact of supplier quality on overall production and the need for rigorous quality control measures upstream.
Average Lead Time:
Lead time is the duration from when a purchase order is placed until the goods are received and ready for production. This KPI is crucial for inventory planning and maintaining uninterrupted manufacturing operations.
This KPI assesses the ability of suppliers to meet order requirements on the first shipment. A high fill rate indicates a reliable supply chain, reducing the need for safety stock and the risk of production delays.
Supplier Downtime Impact:
A critical KPI that evaluates the extent of production downtime attributable to issues with supplied goods, whether it’s material shortages, defects, or delivery delays. It shines a light on the supply chain’s effect on production efficiency.
Supplier On-time Delivery (SOTD):
SOTD tracks the percentage of supplier deliveries that arrive as scheduled. Timely deliveries are essential to maintain a lean and responsive supply chain.
For manufacturers, maintenance and reliability are about ensuring that machinery and equipment are in optimal working condition to avoid interruptions and maintain productivity. The KPIs in this area are critical for predicting and preventing equipment failure and for planning maintenance activities effectively.
Mean Time to Repair (MTTR):
This KPI measures the average time required to repair a machine or component after failure. It is a direct indicator of maintenance efficiency and equipment reliability.
Mean Time Between Failures (MTBF):
MTBF indicates the average time between each equipment failure. A high MTBF suggests that equipment is reliable and less prone to breakdowns.
Planned Maintenance Percentage:
This KPI shows the proportion of maintenance activities that are scheduled and preventive, as opposed to reactive. Higher values indicate a proactive maintenance strategy, which can prevent unplanned downtime and extend equipment life.
Equipment Downtime Rate:
The percentage of time that production equipment is not available for use. This includes both planned downtime for maintenance and unplanned downtime due to failures.
Maintenance Cost as a Percentage of Replacement Asset Value (RAV):
This KPI compares the total maintenance cost to the cost of replacing assets. It helps manufacturers understand whether they are spending too much on maintaining aging equipment that might be cheaper to replace.
Operational Condition Index (OCI):
This KPI provides a weighted hour count that reflects the operational conditions to which machinery is subjected. It factors in the variance from optimal operating ranges, such as speed, temperature, or load. Operating outside these parameters increases the weighted hours more significantly than standard operation, indicating a higher likelihood of wear or failure and thus signaling the need for maintenance attention.
Severity Adjusted Maintenance Time (SAMT):
SAMT is a KPI that adjusts the total maintenance time by the severity of the operating conditions. It recognizes that not all operating hours are equal — hours under extreme conditions are weighted more heavily. This metric helps prioritize maintenance activities based on the stress the machine has endured, rather than on a simple chronological schedule.
By tracking these KPIs, manufacturers can achieve a more reliable production line with fewer interruptions, optimizing the supply chain, and ensuring that equipment maintenance is both effective and efficient. The last two suggested KPIs are more advanced and allow for a more nuanced view of equipment usage and maintenance needs, ensuring that maintenance resources are allocated more efficiently and effectively, with an aim to preemptively address conditions that could lead to equipment degradation or failure.
The productivity of the workforce is a testament to how effectively a manufacturer can harness human potential. Here are KPIs crucial for measuring workforce efficiency.
Employee Turnover Rate:
This KPI measures the rate at which employees leave the company. A high turnover rate can indicate dissatisfaction and disrupt productivity.
Average Training Hours per Employee:
Tracks the average number of training hours provided per employee, indicating the company’s investment in skills development.
A broad measure of output per worker or per hour worked, this KPI is essential for gauging overall workforce efficiency.
Measures the frequency of unscheduled absences, providing insight into workforce reliability and potential productivity issues.
Safety and compliance are non-negotiable aspects of modern manufacturing. Here are key KPIs to ensure a culture of caution and conformity.
Safety Training Completion Rate:
This KPI tracks the percentage of employees who have completed mandatory safety training, a direct indicator of the workforce's preparedness to handle workplace hazards.
Compliance Audit Score:
Reflects the results of internal or external audits against compliance standards, indicating how well the company adheres to required regulations.
Near-Miss Frequency Rate:
Counts occurrences that could have resulted in an injury or accident but didn’t, offering insights into potential safety risks.
Personal Protective Equipment (PPE) Usage Rate:
Measures adherence to PPE guidelines, a straightforward indicator of compliance with safety protocols.
Incorporating these KPIs into regular reporting mechanisms equips manufacturers to make informed decisions about environmental strategies, workforce management, and safety protocols, aligning operational efficiency with ethical and regulatory standards.
Short Interval Control (SIC) is a dynamic and proactive management approach that empowers organizations to identify and address production issues rapidly. This method involves frequent and regular checks on production and key performance metrics within short time frames—often hourly or per shift—allowing for quick responses to operational challenges.
SIC can be effectively integrated into virtually any segment of production KPI monitoring, providing a real-time feedback loop that can lead to immediate corrective actions and continuous improvement. Here are specific examples of how SIC can be applied to production KPIs.
This KPI compares the quantity of goods produced in the current hour with the previous hour. It allows managers to quickly identify production slowdowns and investigate potential causes such as equipment issues or operator inefficiencies.
Hour-to-Hour Yield Variance:
Measuring the percentage of quality products made each hour and comparing it with the previous interval can highlight inconsistencies in production quality. Immediate action can be taken to rectify processes before more value is lost.
Downtime Frequency and Duration:
Monitoring the frequency and length of downtime occurrences within short intervals helps in identifying recurring equipment problems. SIC enables a quick response to mitigate issues and reduce overall downtime.
Tracking the time taken to switch from one production batch to another allows for optimizing setup procedures. SIC ensures that prolonged changeovers are flagged and reviewed promptly.
Even small, frequent stoppages can accumulate to significant downtime. By monitoring these stoppages within short intervals, teams can identify and address the root causes more rapidly.
Real-Time Scrap Rates:
Analyzing scrap rates at short intervals helps in quickly identifying when and why waste is occurring. This allows for immediate corrective actions to minimize waste production.
Operator Response Times:
SIC can be used to track how quickly operators respond to issues or alarms on the production floor, ensuring that delays are recognized and addressed to improve reaction times.
Implementing these SIC Production KPIs within Flow provides a framework for ongoing operational refinement. It empowers teams to act swiftly and decisively, ensuring production stays on track and any emerging issues are quickly rectified. With SIC, manufacturers can truly embrace the ethos of continuous improvement, driving productivity and maintaining a competitive edge in the manufacturing landscape.
Developing an operator scorecard requires the identification of key metrics that reflect performance on equipment operation.
Here’s a structured approach to create a KPI for an operator scorecard.
1. Define the Metrics:
• Efficiency: Measure of units produced vs. target output in a given time period.
• Quality: Ratio of units meeting quality standards vs. total units produced.
• Machine Handling: Based on machine wear-and-tear or maintenance requirements, indicating how well operators use the equipment.
• Safety Compliance: Compliance with safety protocols and number of safety incidents.
• Attendance and Punctuality: Tracking adherence to scheduled work times and absence rates.
• Adaptability: Ability to operate different types of equipment or willingness to cross-train.
2. Weight the Metrics: Assign a weight to each metric based on its importance to overall performance. For example, if quality is most critical, it might be weighted at 30%, whereas attendance might be at 10%.
3. Develop a Scoring System: Each metric should have a scoring system, typically 1-10 or 1-100, where the higher the score, the better the performance.
4. Calculate the Score: Aggregate the scores for each metric, adjusted by their respective weights, to come up with a composite performance score for each operator.
5. Standardize the Evaluation: Ensure that the scorecard is applied consistently across all operators to avoid bias. It should be transparent and known to all operators.
6. Set Benchmarks: Establish performance benchmarks or tiers to categorize operator performance levels (e.g., Novice, Competent, Expert).
7. Regular Review: The scorecard should be reviewed regularly (e.g., monthly, quarterly) to ensure it remains relevant and reflective of performance.
8. Integration with Staffing Patterns: Use scorecard outcomes to inform staffing patterns — higher-scoring operators could be assigned to more complex tasks or equipment that requires a higher skill level.
9. Feedback and Improvement: The scorecard should be used as a tool for feedback and to help operators improve. Areas of low scoring can be targeted for training and development.
By following these steps, you create a KPI that not only measures the performance of operators on a piece of equipment but also aligns workforce efficiency with the overall operational goals. The scorecard can become an invaluable tool for supervisors to identify the strengths and weaknesses of their team and to optimize staffing patterns accordingly.
Selecting two or three KPIs from the ones we've explored and integrating them into Flow can ignite a transformation in your manufacturing operations. Whether it's sharpening your quality control with the Quality-Related Waste Cost Percentage or optimizing batch processes, Flow's analytics platform stands ready to turn your data into a conduit for continuous improvement. Embrace the power of Flow and let these KPIs be the compass that guides your manufacturing success to new horizons.
This collaboration aims to bolster 4IR’s platform-as-a-service offerings of FactoryStack and PharmaStack by integrating Flow’s analytics engine and data modeling software as well as provide immediate managed services of Flow for Cloud deployments.
At the forefront of advancing analytics and data readiness in the life sciences sector, Skellig Automation and Flow Software have aligned in a strategic partnership. This collaboration sets a fresh benchmark for actionable KPIs and insights in the industry.
Most medium to large scale manufacturers are in crisis--you may be among them--they cannot scale their analytics efforts to embrace today’s technologies, let alone tomorrow’s. To compete in the new manufacturing landscape, you must unleash the full potential of your data.