Identifying worker density constraints during process outages
How to identify a hidden productivity constraint in your process outage plans and schedules.
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During inspections and maintenance outages, labour cost is usually a significant expense. In order to meet the required cost and duration goals, worker efficiency is critical. As part of the job plans and schedule creation, a planning team was able to model one crucial feature of labour efficiency – worker density, that is the maximum number of workers that can be allocated to a specific work area over a given time span with minimum productivity losses. As a result, small changes were made to the scheduling of individual tasks.
Work Productivity is a Key Consideration in Continuous Process Outages
The fundamental economics of continuous processing or manufacturing industries dictate that planned production outages would have a negative impact on the bottom line. The opportunity cost (buy vs. make) may be millions of dollars a day in some situations. When you factor in the actual expense of doing the inspection, repair, or enhancement work – which can also run into millions of dollars a day – you have a compelling argument for not only minimising the outage’s duration but also ensuring that the plan is a realistic one.
In many cases, lost output due to a scheduled shutdown must be recouped by pre-building inventories, obtaining purchases from other locations or third parties, or reducing customer shipments.
Among other items, published best practices call for diligent front-end loading, with detailed planning and scheduling of all work during the production outage. The estimated duration of the outage is a key output from this program.
Each individual work activity is typically assigned a number of job steps with the corresponding labour requirements, including:
• Number of workers
• Trades involved, e.g., carpenters, electricians, welders
• Hours required by each trade for each step
The typical efficiency of each worker for the job at hand is inherent to these scheduled labour hours. Against this assumption, any major productivity losses may have a significant effect on the duration of the outage.
The real vs. assumed job efficiency would have a significant impact on the outage’s performance. In addition to possibly impacting the overall duration, labour can be the most expensive component of the outage; for example, in the North American petroleum refining industry, labour can account for 60 to 80 percent of the overall cost.
Job productivity is generically defined as the ratio of outputs to inputs. Here are some examples:
Output: Number of tiles installed; surface area painted; volume of concrete placed
Input: Labour hours – individual worker or work team; equipment hours
Our focus here isn’t so much on how to increase productivity performance – there are many methods for doing so – as it is on how to minimise or remove job constraints so that actual productivity meets or exceeds target.
The labour hours allocated to job plans will include a productivity assumption, which estimates how long an employee or group of employees will take to complete a task.
This inference will be based on the following data:
• Similar work in previous outages
• Planner’s own experience or professional judgement
• Input from execution team
• Factors applied to standard planning norms, if available
Many factors may have an impact on actual job efficiency. Here are a few examples:
⇒ Availability of materials
⇒ Availability, adequacy, and condition of tools
⇒ Access to the work front
⇒ Availability and adequacy of work procedures
⇒ Leadership and supervision
⇒ Quality of job plans
⇒ Worker capabilities
A planning team was concerned a few years ago that a large amount of work in a relatively restricted area would result in occasional overcrowding, preventing workers from reaching their assigned work areas on time. The team had no systematic way to model this in the job plans up until this stage. This paper will explain how this was accomplished.
Worker Density Constraint
Individual job plans determine the optimal number of workers for each task. Jobs are then scheduled based on the plan logic (predecessors, successors) and, if specified in the scheduling tool, the total availability of workers.
Unless explicitly stated as a constraint in job plans or schedules, this approach does not generally account for the effect of multiple work fronts in a specific constrained space or area.
Some common execution restrictions, such as only having a single crane or other equipment serving a particular location, will sometimes resolve this crowding of work fronts. Modern planning and scheduling tools can accommodate this.
Although there is a wealth of literature on the subject of worker efficiency, this author was unable to locate any that comprehensively covered industrial maintenance practices. Although worker crowding, also known as stacking, is frequently discussed, it is rarely quantified. The most pertinent information is available for construction work or manufacturing processes. With respect to construction, the literature claims that typical productivity losses are about 20%, with some papers reporting up to 50% losses due to crowding or stacking.
A few of the texts attempt to determine what staff density is best for minimising productivity losses. The results are very divergent – for a quick overview of the literature examined, see Figure 1.
# of workers per Sq. ft. Sq. m. Type of Work
21.5 2 Commercial construction
269.1 25 Industrial projects
169.0 15.7 Concrete foundations
200-250 18.6-23.2 Construction
191 17.7 Electrical work; 0% productivity losses
90 8.4 Electrical work; 50% productivity losses
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