Simulate with a Purpose: Understanding the Key Steps of a Warehouse Simulation

Embarking on a warehouse simulation project holds great promise for streamlining operations, improving efficiency, and optimizing resource allocation. As organizations strive to adapt to ever-evolving market demands, the need to fine-tune warehouse processes becomes more critical than ever. Simulation offers a powerful tool to model, analyze, and refine these processes in a risk-free digital environment. However, amidst the potential advantages, there lie several common perils that can derail the success of your simulation endeavor. It's crucial to follow a structured approach to ensure the success and effectiveness of the simulation.

Here are the key steps to consider.

Define Objectives and Scope

Simulation analysis has an inherit cost. It would be impossible and expensive to use simulation to address all company projects. When assessing the use of simulation, first ask these 7 questions about the problem:

1. Can it be solved with common sense?

2. Can it be solved analytically?

3. Is it less expensive to simulate than to perform direct experiments?

4. Would the cost of simulation exceed the potential savings?

5. Do we have available resources and time to solve this problem?

6. Is the system behavior too complex to define and/or model?

7. Do we have data to support the simulation?

Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. (2010). Discrete-Event System Simulation. (5 ed.) Prentice Hall

If simulation is still an appropriate solution, then it's important to clearly outline the goals and objectives of the project. Determine what scope of the warehousing operations you aim to simulate and improve. Whether it's optimizing layout, evaluating material handling equipment, enhancing picking processes, or assessing throughput capacity, defining the scope is the essential first step.

Collect and Analyze Data

Accurate and comprehensive data is the foundation of any meaningful simulation. Be sure to gather all relevant information, such as warehouse layout, order history, inventory levels, and task times. Utilize various data collection methods like direct observations, database queries, employee interviews, time studies, and live sensor data. Data collected from different sources may be raw or contain inconsistencies, so you'll need to clean and preprocess the data. This involves organizing the data, removing outliers, correcting errors, and ensuring data accuracy. Analyze the data to identify patterns and trends. The two common data types used in simulations are historic (raw) and profiled (summary data). If the latter is used, then the data needs to be prepared by performing a distribution fit test.

Model Development

Now the fun part. Keeping the project objectives in mind, it's time to design and create a simulation model.

The model should represent the real-world warehousing system, considering factors such as layout, processes, material flow, and resource allocation. Choose an appropriate simulation tool that aligns with the complexity and requirements of the project. Remember simulation is as much an art as it is a science. Start simple, before adding in complexity, and only iterate to the level of detail needed to develop a useful approximation of results.

Verify and Validate the Model

Before proceeding further, ensure that the simulation model is accurate and reliable by performing verification and validation.

Verification is the process of determining whether the model functions as intended. A useful checklist when performing model verification:

• Checked by another member of the simulation team

• Relevant logic reviewed by appropriate SMEs

• Created flow diagram of logic and documented variables with a description of each

• Checked output under variety of input settings

• Exported inputs at simulation end to check for inadvertent changes

• Verified animations imitate physical system

• Collected and displayed as many statistics as possible to indicate reasonableness of input output transformations

• If applicable, compared against real-world system

Validation, on the other hand, involves comparing the model's behavior with real-world. Unfortunately, most simulations represent future systems, making the validation step difficult in practice, but not impossible. Here are three approaches you can use to validate a simulation:

1. Check for High Face Validity - these are models that seem reasonable on its face to designers and others who may be knowledgeable with the system.

2. Validate Model Assumptions - review structural and data assumptions. Are these still true based on results?

3. Validate Input/Output Transformations - if possible, use historic data and produce a baseline that compares with real system. Perform experiments by varying inputs and confirming outputs are reasonable and expected. Consider performing a turing test by providing the results to a SME along with a valid data set and ask if they can pick out the simulated results.

If issues arise during any of these steps, adjust and fine-tune the model as needed to achieve the correct level of accuracy.

Experimentation and Analysis

Now the model is ready for testing. Run the simulation using different scenarios and input parameters. Analyze the results to understand how changes in the system variables affect the defined KPIs. Based on the KPI, there are two important estimates you may need to calculate:

• Point Estimate: refer to the single-value estimate of a sample. For example, if the objective of the simulation is to understand the long-run average order cycle time, we could calculate the mean and provide a confidence interval and prediction interval using the formulas below. Confidence intervals provide a range of possible values of the long-run average based on the sample variance after a number of replication, whereas prediction intervals provide a range of possible values that might be observed in individual replications.

• Percentile Estimate: refer to the estimated probability of a sample producing a value within a specified range. A good example would be when determining the expected highest level of work-in-process (WIP) for designing an adequately sized queue. In this case, we can calculate either the likelihood (probability) given a specified bound, or an estimate given a specified percentile. The formulas for both cases are provided below.

Conduct sensitivity analysis to assess the impact of uncertain parameters or variables on the simulation outcomes. This helps in understanding the robustness of the proposed solutions and enables better decision-making. Lastly, compare the outcomes of various scenarios to identify the most effective strategies for optimizing warehouse operations.

Documentation and Reporting

Though often overlooked, it is important to document the entire simulation process, including assumptions, methodologies, data sources, models, results, and conclusions. Prepare a comprehensive report summarizing the findings, insights, and recommendations for stakeholders and project sponsors. Make the report digestible for the audience, whom might not have experience using simulations to make decisions.

Common Simulation Errors

Of course, not all projects follow the above methodology without issue. Here are some the most common modeling errors to look out for:

• Project Management - errors that fall under this category include inaccurately defined process maps, lack of Subject-Matter-Expert (SME) involvement, assumptions that were not checked by stakeholders, and design or operational decisions that were not approved by management.

• Data and Data Models - there are numerous types of data and data modeling errors, but the majority pertain to the misuse of summary statistics (e.g. using a mean value when actual values exhibit a high degree of variability).

• Logic Models - logical errors typically involve faulty assumptions, like how an algorithm allocates work to associates.

• Experimentation - these errors range from lack of statistical analysis to more technical errors like an inadequate simulation warm-up period.

Conclusion

Conducting a simulation project for warehousing involves defining the set of objectives and scope of work, analyzing and preparing input data, developing a model, verifying and validating that model, running it through experiments and analyzing the results, and documenting the methods, assumptions, results, and conclusions. By following these key steps, businesses can expect higher quality and cost-effective results out of their simulation projects, giving them an incredibly powerful tool for optimizing their supply chain.