How do you measure and improve the return on investment of your manufacturing data analytics projects?

2 Estimate your costs and benefits

Once you have your goals and metrics, you need to estimate the costs and benefits of your data analytics project. How much will it cost to acquire, store, process, and visualize the data for your project? What are the direct and indirect benefits that you expect to gain from your data analytics project? How will these benefits translate into monetary value for your manufacturing business? You can use various methods and tools to estimate the costs and benefits of your data analytics project, such as cost-benefit analysis, return on investment (ROI) calculation, net present value (NPV) analysis, or break-even analysis. These methods and tools will help you compare the costs and benefits of your data analytics project and determine its feasibility and profitability.

3 Implement and monitor your project

After you have estimated the costs and benefits of your data analytics project, you need to implement and monitor it. How will you execute your data analytics project in terms of data collection, data analysis, data visualization, and data communication? What are the risks and challenges that you might encounter during your data analytics project? How will you mitigate and overcome them? How will you ensure the quality and reliability of your data and your analysis? How will you monitor the performance and impact of your data analytics project on your manufacturing outcomes? You need to have a detailed plan and a robust framework to implement and monitor your data analytics project, as well as a feedback mechanism to adjust and improve it as needed.

4 Evaluate and communicate your results

Finally, you need to evaluate and communicate your results from your data analytics project. How will you measure the actual costs and benefits of your data analytics project? How will you compare them with your initial estimates and expectations? How will you calculate the ROI of your data analytics project? How will you communicate your results to your stakeholders, such as management, customers, suppliers, or regulators? How will you use your results to inform your future decisions and actions? You need to have a clear and consistent method and a compelling story to evaluate and communicate your results from your data analytics project, as well as a way to celebrate and reward your successes.

5 Learn and improve your project

Data analytics is not a one-time activity, but a continuous process of learning and improvement. How will you use the insights and feedback from your data analytics project to improve your manufacturing processes, products, or services? How will you identify new opportunities or challenges that require data analytics solutions? How will you scale up or replicate your data analytics project to other areas or domains of your manufacturing business? How will you update or refine your goals, metrics, costs, benefits, and methods for your data analytics project? You need to have a culture and a mindset of learning and improvement for your data analytics project, as well as a way to share and leverage your best practices and lessons learned.

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?