Measuring unscheduled downtime

The Unknown Cost Variables of Unscheduled Downtime

While reducing Unscheduled Downtime is a major reason plants consider industrial analytics solutions, many factories don't have the full picture when it comes to calculating the total cost of these unforeseen events. The range of costs associated with machine breakdowns is a crucial aspect of ROI.

The rationale for an industrial plant to consider an investment in Industrial Analytics is to reduce Unscheduled Downtime. The International Society of Automation estimates that almost every plant loses at least 5% of production due to downtime and that many lose as much as 20%. The cost to the global processing industry alone is estimated at $20 billion.

It is further estimated that over 80% of plants cannot calculate the cost of Unscheduled Downtime. In fact, many plants underestimate costs by between 200% and 300%. The explanation is simple: It is too difficult to track all the cost components.

Activity-Based Costing (ABC) is the accounting term used for the methodology of accurately assigning all the direct and overhead costs incurred in a production process. The problem with ABC is that it is considered impractical and a “nice-to-have.” Activity-level data is often unavailable and it is too time-consuming to generate.

The result is that when Unscheduled Downtime is calculated, only a few of the obvious variables are considered. The purpose of this document is to outline the various cost elements included in Downtime and to provide tools that can be used to approximate the annual Unscheduled Downtime cost.

The Opportunity Cost of Lost Production

Across all industries, the average Unscheduled Downtime for a plant is 17 days per year. This number varies significantly both across and within industries.

In the processing industries, revenue will change based on fluctuations in commodity prices. The estimate below of the average cost for 5 different industries is a starting point, and should be used only for high-level / back-of-the-envelope calculations.

Even within industries, it is important to recognize variations in the incidence of Unscheduled Downtime. The following data is from a survey conducted by ARC Advisory of senior executives and engineering, operations and maintenance managers in Oil & Gas. As shown below, although the most common production loss was 3-5% per year, there is a significant range from best performers (<1%) to underperformers (>10%).

What can we learn from this data? Although industry averages for Unscheduled Downtime are interesting from a benchmarking perspective, averages must be modified to reflect the operating conditions at the plant level.

Cost Components of Unscheduled Downtime

The following costs are incurred as a result of machine failure:

Labor Costs / Overtime: Employees who are idle until the resumption of production continue to be paid even as output and productivity come to a halt. Furthermore, overtime pay may be necessary for complex repairs or to make up for lost production.

Labor Repair Costs: The repair costs associated with Unscheduled Downtime are higher than planned maintenance activities due to the urgency of restoring production. When Failure Root Cause data is unavailable, there is more of a reliance on trial and error, as technicians must identify the source of the failure before repairs can be made.

Other Variable Costs: The ongoing costs of running a production line, such as utilities outlays, cannot simply be reduced because of a breakdown in a production line or even an entire plant. Overhead and other fixed expenses are still incurred regardless of whether the facility is producing at full or partial capacity.

Tools and Spare Parts: Spare parts and tools are used to repair broken machinery. Many production facilities invest in excess parts. A carrying cost is associated with maintaining an inventory of parts as a protection against unexpected machinery breakdowns.

Damaged Production Output:  The occurrence of a breakdown in industrial machinery is preceded by asset degradation and evolving failure. During this period, production quality is often compromised and does not meet plant standards. In these cases, inferior-quality output must be disposed of.

Excess Inventory: To make up for the potential in lost production, plants may retain access to an inventory of finished goods as a buffer. The cost of holding inventory is typically around 10%-30% of the inventory’s value, per year.

How Unscheduled Downtime Increases Overall Maintenance Costs

The following chart depicts the costs of different asset reliability scenarios. In the case of Under Maintenance or Run-till-Failure, Unscheduled Downtime results in lost production and spikes in maintenance costs to restore production levels.

Another scenario is that plants recognize the potential cost of Unscheduled Downtime and then over-invest in both Preventive Maintenance practices. With Over-Reliability, the industrial plants perform unnecessary maintenance as a precaution. A significant cost element is planned outages – when production is stopped while parts are replaced based on a predetermined schedule and inspections are performed.

How to Calculate Unscheduled Downtime Cost:

Pulp or Paper Mill Example

The following calculation is used to determine Unscheduled Downtime:

The first step is to create realistic assumptions to be used in a cost calculation. In some cases, the data will be readily available or relatively easy to estimate (production capacity). For estimates of costs that are more difficult to calculate (e.g., maintenance costs), we suggest the use of industry benchmarks.

Determine the output of a production line or plant. For this exercise, we have selected a mill with an annual capacity of 100,000 tons.

Select a product grade. Although plants produce multiple product grades, for the purpose of simplicity, select a common product grade and assume 100% production of this grade. In this example, we have assumed a Wood Free Uncoated (WFU) product grade.

Forecast a commodity price. Select a forecast commodity price for a specific product grade. Although pricing fluctuates, one can use a 12-month average price or analyst estimate for a 12-month outlook. For WFU, we have selected a forecast price of 800 Euros per ton.

Estimate annual plant revenue. Multiple the annual plant capacity (100,000 Tons) by the commodity price forecast (800 Euro per ton). In our example, the annual revenue is € 80 Million.

(Based on 1000,0000 Ton Annual Capacity)

Estimate Current Unscheduled Downtime (days). In our example, there are 15 days per year in Unscheduled Downtime or 4.1% of lost production.

(Based on 1000,0000 Ton Annual Capacity)

Estimate Maintenance cost as a percentage of sales. For a mill, industry data suggest that 10% of sales are allocated to Maintenance expenses. It should be noted that if a plant is relatively new or relatively old, the allocation percentage should be decreased or increased, respectively.

In the example of the paper mill, the following calculations are used to determine the annual cost estimate for Unscheduled Downtime.

Estimate Savings from Reduction in Unscheduled Downtime

Once we have calculated the cost of Downtime, we can calculate the cost savings from reducing Unscheduled Downtime using Industrial Analytics.

In our example, we assume that 15 days of Unscheduled Downtime costs the plant 3.5 Million Euros per year. If this is reduced by five days (or 33%), the annual benefit is 1.2 Million Euros. It should be noted that due to the existence of variable production raw material costs, the full 1.2 Million Euros does not equate to operating or net profit

Annual Downtime Cost – 3.5 Million €

Summary and Conclusion

The calculation of Unscheduled Downtime is not simple. It requires an understanding of plant output, revenue drivers and maintenance costs. Because it is unlikely that all this data will be available, there are ways to estimate Unscheduled Downtime by modifying industry benchmark data by applying realistic assumptions.

SKF Enlight AI

Industrial plants generate terabytes of process data. SKF Enlight AI is a SaaS Predictive Maintenance solution that uses Automated Machine Learning to identify emerging asset failure patterns within this data. It provides early warnings and sensor-level intelligence to help avert unplanned downtime and meet production goals. For more information on how SKF Enlight AI can improve performance and reliability, click here.