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Automation

Maximizing operational efficiency and productivity

Discrete event simulation (DES) on existing processes can help identify and unlock additional production capacity and improve operational efficiency for manufacturers.

By Tim Bednall June 20, 2020
Courtesy of: Courtesy: CFE Media and Technology

Automation has proven to be a valuable asset in addressing many of the issues that face manufacturers today. However, many manufacturers still remain under pressure to improve productivity and output levels even further.

Investing in additional machinery or automation to meet these demands is one option. However, there are instances where subtle changes to an existing process can help yield additional production capacity. The challenge is identifying which areas of the process holds potential for improvement.

Most automated manufacturing systems are designed to meet detailed technical specifications and production efficiency targets. Once installed, they are then fine-tuned to meet these criteria, and the prime objective then is to maintain these production and up-time levels. However, today, customers are pressurizing suppliers to increase production levels. The dilemma for the manufacturer is two-fold, it is highly likely that the customer will want to see the increased capacity quickly, and the manufacturer may not have the time or the capital expenditure available to invest in additional equipment.

Discrete event simulation

Discrete event simulation can be used to analyze the existing process step-by-step and identify bottlenecks or areas where the current process can be improved. We have run many discrete event simulation exercises and have often found that what may have been initially perceived as being the bottleneck restricting output is not actually the case.

Discrete event simulation models break down the process into a series of different events or smaller operational blocks, which can then be triggered as required. The process is effectively analyzed at the lowest levels including the distances that parts travel, where they move to, the speed at which they travel and, of course, process times.

In one example, a manufacturer was tasked to increase production from the current level of 30 units per hour to a throughput of 50 units per hour. An initial overview performed in-house by the customer suggested that a specific part transfer operation was restricting the output from the line. However, when we ran a discrete event simulation it was determined that subtle changes in timings in various areas of the line would allow the system to achieve the 50 units per hour target. In addition to this, the transfer operation initially thought to be the bottleneck by the customer, proved to actually have additional capacity following the simulation.

In another example, the customer was considering linking two consecutive process steps, carried out in separate systems, with a view to having dual systems, each of which performed both processes as a means of increasing throughput.

We performed a discrete event simulation based upon the current configuration, operational sequences and timings of the existing equipment and then followed that with another simulation this time taking into account the changes proposed by the customer. The outcome of this simulation proved categorically that there would be no benefit deriving from the customers proposed changes. However, further evaluation of the application through the simulation model identified that, even with restricted floorspace, it would be possible to integrate additional equipment which would achieve the increase in output desired.

Running discrete event simulation on processes or machines makes it possible to predict output and performance over an extended period, such as a year, in just minutes, by running the simulation at enhanced speeds. It is also possible to build in additional factors including mean-time-between-failure and mean-time-to-repair to provide a holistic view of the process over time.

There is little doubt that manufacturers will continue to seek ways to enhance productivity and output levels from their existing automation systems in the most efficient way they can as a means of responding to customer demands and improving profitability for their business. Discrete event simulations are very often used as a relatively quick and cost-effective contribution to a continuous improvement process, which can identify and unlock efficiency savings, quality improvements and additional production capacity.

Tim Bednall is sales & marketing manager at Wood Automated Systems UK. This article originally appeared on Control Engineering Europe’s website. Edited by CFE Media.


Tim Bednall
Author Bio: Tim Bednall is sales & marketing manager at Wood Automated Systems UK.