Software tool assesses maintenance inventory

Discrete event simulation is a powerful tool for evaluating what-if processes or processes where a high degree of random variability exists.

By David Saas

PlantServices.com

My father used to tell me, “When all you have is a hammer, everything looks like a nail.” This analogy often holds true for the way we approach plant process problems. We attack them with a trial and error approach because it’s the only hammer commonly available. Discrete event simulation is a powerful tool for evaluating what-if processes or processes where a high degree of random variability exists. Instead of incurring the time and cost of a trial and error technique, the mistakes can be made and studied harmlessly in a computer model. However, simulation isn’t right for every type of problem. Many processes are easily characterized using pen and paper or a spreadsheet, and information gleamed from this analysis is more than adequate to make useful and accurate decisions.

Right tools and skills
Simulation software has made great strides in usability, accuracy and cost during the past five years. Many commercial packages ranging in price from less than $1,000 to more than $10,000. Capabilities vary, but most include a graphical user interface to build the model as well as the ability to represent results graphically. Many packages provide an animation of the simulation. This feature is highly useful in the debugging stage and in final model presentation. In addition, some packages are specialized to handle specific classes of simulation, such as 3D material handling or financial simulations.

The software that performs the simulation is important, but the largest predictor of a successful simulation analysis is the skill of the practitioner. Simulation is an engineering discipline, not just a computer programming exercise. The user should have knowledge of statistics and strong logic skills, and while not essential, experience in the process simulated is a big plus. While most software packages use a graphical interface, all but the simplest models require some type of custom scripting or programming. Therefore, some background in programming is helpful. If the organization is large enough and simulation needs great enough, having a person on staff dedicated (or partially dedicated) to simulation is best.

Tackling material handling
Simulation is an engineering discipline and a simulation project that proceeds in an orderly manner produces the best results. The probability of wasting time and producing dubious results increases dramatically if you lack a solid methodology.

For example, let’s assume that a maintenance department is responsible for maintaining spare parts inventory and performing planned and corrective plant maintenance in several plant areas (Figure 1).
The department manager is being pressured to improve service times and better manage inventory carrying cost. In this example, service time is defined as the time from the moment a part is requested to when that part is delivered for repair. A stockout on any repair part results in expedited replenishment and a longer service time. The manager plans a simulation to determine the optimum inventory to carry to minimize service time and inventory cost.

Know the question
Formulating the problem or question to be answered is crucial to a successful simulation project. Identify the quantifiable key metrics in the process and structure the simulation to gage these metrics. For example, a goal such as “improving the fulfillment process” is much too vague. While the wording might be appropriate for the overall objective, the simulation needs to address measurable variables such as time, capacity and utilization. Better wording that details a measurable goal might be “decrease the maximum fulfillment time by 25%.”

One possible quantifiable goal for managing maintenance inventory is to minimize the service time per inventory dollar carried. Expect planning and running the simulation to produce additional questions. One of the great benefits of a good simulation is that it provides not only data for the process, but also inspires additional insight. And insight often enhances your ability to reengineer a better process.

Data quality is critical
Gathering data is often the most difficult step in a simulation project. Reliable data is required if you expect to achieve meaningful results. Using bad data only leads to erroneous outputs and results in poor and costly conclusions. Project data may already exist as written records, computer logs, spreadsheets and databases. If suitable data doesn’t exist, you’ll need to collect it using a time study with a stopwatch on the shop floor.

If the process modeled varies randomly (stochastic), use statistical methods to characterize the distribution of input data. In turn, run the model many times using the distribution to determine the effect of the random variability on the output. The ability to run a simulation repetitively over a wide range of data is one of a simulation’s most powerful benefits.

The final part of the data gathering stage involves defining the model’s operating logic. This step documents both the existing and desired operating process. Interviewing maintenance technicians, studying procedure reviews and conducting a design review for the new inventory approach are reliable methods for detailing model logic.

The maintenance inventory example will require several sets of input data. First, examine existing inventory SKUs and a spare parts list for the different equipment in the plant and produce a master inventory list of what parts are candidates for including in inventory. Second, determine the population of that part in the plant for each line item in the master inventory list.