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How can you ensure your supply chain optimization model is reliable?

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Supply chain optimization models are powerful tools for improving efficiency, reducing costs, and enhancing customer satisfaction. However, they are also complex and sensitive to various assumptions, data sources, and parameters. How can you ensure that your model is reliable and robust enough to handle real-world situations and uncertainties? Here are some tips to help you build and validate your supply chain optimization model.

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1 Define your objectives and scope

The first step in any optimization project is to clearly define your objectives and scope. What are the main goals and constraints of your supply chain problem? What are the relevant performance indicators and trade-offs? What are the key decisions and variables you want to optimize? What are the boundaries and limitations of your model? By answering these questions, you can establish a clear and realistic scope for your model and avoid unnecessary complexity and ambiguity.

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Connor Makowski

MIT CAVE - Development and International Collaboration Lead
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At the MIT CAVE Lab, we work on many projects like this. The success of our projects is often determined at this phase. A key objective to think about is how to elicit intangible information from decision makers. Not only does this improve accuracy, but also garners decision maker buy in. For example, the data might not show it, but a manager might know that there are upcoming labor shortages at a particular plant. A competitor is building a new factory nearby and will poach workers. Allowing decision makers to bring this knowledge to the table is paramount in getting useful results and driving change.

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2 Collect and analyze data

The next step is to collect and analyze the data that will feed your model. Data is the foundation of any optimization model, so you need to ensure that it is accurate, relevant, and consistent. You also need to understand the characteristics, patterns, and trends of your data, as well as the sources of uncertainty and variability. You can use various data analysis techniques, such as descriptive statistics, visualization, correlation, and outlier detection, to explore and validate your data.

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3 Choose and implement a suitable method

The third step is to choose and implement a suitable method for your optimization problem. There are many methods available for solving different types of optimization problems, such as linear programming, integer programming, network optimization, heuristic algorithms, and simulation optimization. You need to select a method that matches your problem structure, objectives, constraints, and data. You also need to implement your method using a suitable software or programming language, such as Excel, Python, R, or MATLAB.

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Doug Gray

Practitioner | Leader | Educator | Advisor | Author
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Or IBM CPLEX, FICO Xpress, or Gurobi... Vehicle Routing Problems can be solved by including Time Window constraints... heuristics are now being used to solve VRP on Nvidia GRU servers...

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4 Test and refine your model

The fourth step is to test and refine your model to ensure that it works as expected and produces optimal results. You can use various testing techniques, such as sensitivity analysis, scenario analysis, benchmarking, and validation, to check the validity, accuracy, and robustness of your model. You can also compare your model results with historical data, expert opinions, or other models to verify their feasibility and consistency. You should also identify and resolve any errors, bugs, or inconsistencies in your model.

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Doug Gray

Practitioner | Leader | Educator | Advisor | Author
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Test, vigorously, rigorously, early and often throughout the development and deployment process. Verification, including fixing bugs, syntax errors, and logic errors, and validation, including backtesting against historical data and forward testing measuring demand forecasting errors/accuracy and benchmarking asset/resource allocation and scheduling optimization algorithms against formerly used heuristics. Measure the incremental business value and economic impact in terms of cost reduction and avoidance and other KPIs like OnTime-InFull, lost sales reduction.

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5 Communicate and apply your results

The final step is to communicate and apply your results to your stakeholders and decision-makers. You need to present your results in a clear, concise, and convincing way, using charts, tables, graphs, and reports. You also need to explain the assumptions, limitations, and implications of your model, as well as the recommendations and actions derived from it. You should also solicit feedback and suggestions from your audience and incorporate them into your model if needed.

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Doug Gray

Practitioner | Leader | Educator | Advisor | Author
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Tell a story...start with what life was like BEFORE the model then AFTER emphasizing business value captured and economic impact. Save the MATH and CODE for the Appendix! Focus on metrics executives actually care about.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you ensure your supply chain optimization model is reliable?

1

2

3

4

5

6

1 Define your objectives and scope

2 Collect and analyze data

3 Choose and implement a suitable method

4 Test and refine your model

5 Communicate and apply your results

6 Here’s what else to consider

Operations Research

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How can you ensure your supply chain optimization model is reliable?

1

2

3

4

5

6

1 Define your objectives and scope

2 Collect and analyze data

3 Choose and implement a suitable method

4 Test and refine your model

5 Communicate and apply your results

6 Here’s what else to consider

Operations Research

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