January 25th, 2025
Production attainment is a critical metric in manufacturing. It provides an accurate measure of the efficiency and effectiveness of a production process. This key performance indicator (KPI) is used to evaluate how well a manufacturing process meets its intended production targets. In this context, understanding production attainment becomes essential for any manufacturing business aiming to optimize its operations and increase profitability.
The formula for calculating production attainment is straightforward:
Production attainment = (Actual production output in units / Target production output in units) x 100
This formula provides a percentage that indicates how closely actual production aligns with target production. A higher percentage indicates a higher level of attainment, which is generally desirable in a manufacturing context.
Production attainment serves as a vital tool for assessing the performance of a manufacturing process. It is what we call at Ferry a ‘red light’ indicator. A poor production attainment measurement indicates that a manufacturer is not hitting key targets. This in turn can impact customer retention and order fulfilment measures.
It can also be a valuable indicator of a company's financial health. A high production attainment rate can signal a well-managed and profitable operation. Conversely, a low rate may indicate inefficiencies that could be negatively impacting the bottom line.
In general, a minimum benchmark for production attainment should be 90%. It can vary depending on the specific industry, product complexity, and production environment.
Highly efficient manufacturing operations may aim for higher rates such as 95% or above.
By measuring production attainment, a company can identify bottlenecks and inefficiencies in its manufacturing process. This information can then be used to implement changes that improve operational efficiency. For example, if a particular stage of the production process consistently falls short of its target output, it may be necessary to invest in additional equipment or training to increase productivity at that stage.
Moreover, tracking production attainment over time can help a company identify trends and patterns. This can provide valuable insights into the long-term performance of the manufacturing process, enabling proactive measures to maintain or improve production attainment rates.
The cadence at which a manufacturer measures production attainment naturally varies according to industry, but as a general rule of thumb we advocate for a weekly review.
Production attainment is closely linked to a company's financial performance. A high production attainment rate can lead to increased revenues and profitability, as it indicates that the company is effectively utilizing its resources to meet its production targets. On the other hand, a low production attainment rate may signal that resources are being wasted, which can negatively impact profitability.
By regularly measuring and monitoring production attainment, a company can ensure that it is on track to meet its financial objectives. This can also aid in budgeting and financial planning, as it provides a clear picture of the company's operational efficiency and productivity.
There are two key metrics that need to be measured to get a good view on production attainment:
Actual output of the manufacturing process
Target outputs to benchmark actuals against
We are advocates of measuring production throughput with digital solutions, ideally on a real-time basis. This helps frontline teams to identify and troubleshoot production problems as they occur.
There are many means to achieve this but the most common are (depending on the manufacturing process):
Line sensors: hardware such as IR sensors to measure product count as they pass along the production line.
PLC tag count: some machines, such as checkweighers, can often output product counts as well as other associated information for other systems to use.
OT software systems: from existing MES, analytics or historian solutions on-site
In batch or discrete settings, it is often ideal to track output on a product or SKU basis, as you may want to measure production attainment within your product portfolio.
Settling on the right measurement for target output is highly subjective, and there a few approaches that you can select from:
Based on Standard Run Rates:
Use engineering specifications, cycle times, or equipment speeds to determine how much product should be manufactured in a given time under ideal conditions.
Example: If a line runs at 60 units per minute and operates for 480 minutes in a shift, the theoretical maximum (target) would be 28,800 units.
Benefits: easy to calculate, interlinked with other core manufacturing KPIs (i.e. the Performance score in OEE).
Drawbacks: as it is a theoretical estimate, it does not take into account demand-supply planning, and is the same across SKUs. Often you will want to measure production attainment within the product portfolio as well as across it on average. Additionally, it does not include interruptions in production, such as expected downtime and changeovers.
Based on Scheduling / Forecasts:
Use production schedules or forecasts that factor in expected downtime, changeovers, and historical performance.
Example: If you plan for 10% downtime in an 8-hour shift, and the line is rated for 28,800 units at full capacity, then the scheduled target might be set around 25,920 units.
Benefits: this is more accurate to the ideal operating conditions as it factors in expected downtime events. Additionally, targets can be adjusted based on demand-supply planning to better reflect what an optimal target should look like, and can be further refined by SKU
Drawbacks: harder to calculate as it requires both historical analysis of expected downtime events as well as frequent assessments of the impact of demand-supply planning.
Based on Rolling Averages or Historical Benchmarks:
Use previous actual performance data to set realistic targets.
Example: If historical data shows you consistently produce 95% of the theoretical maximum, you might set the target at 95% of the engineering rate (e.g., 27,360 units).
Benefits: easy to calculate, and more realistic than a theoretical maximum as it includes the impact of expected downtime events.
Drawbacks: historical data is not necessarily indicative of ideal operations, and is independent of demand-supply implications and SKU variations.
In general, we recommend following the second method of establishing target outputs using scheduling & forecasts, as it balances the use of historical operating information, demand-supply planning, and SKU variations to provide richer insights into production attainment.
Production attainment is a key performance indicator in the manufacturing industry. It provides a measure of how well a manufacturing process is meeting its targets, and can provide valuable insights into operational efficiency and financial performance. By understanding how to measure production attainment, a company can make informed decisions to optimize its operations and increase profitability.
We are strong advocates for including both historical analysis (factoring in expected downtime) as well as forecasts which are based on demand-supply estimates. This helps frontline teams to better understand the nuances of production attainment especially in time of peak demand, and when they are manufacturing more challenging products.