AI may as well be the most popular buzzword of the 21st century. A part of every enterprise’s wishlist, but also equally elusive on how it can generate the most value, AI confounds manufacturers, as much as exciting them with its endless possibilities. However, over 85% of AI projects still fail to emerge from PoC stage. Here are 5 steps to make sure your Plant Manufacturing AI Projects fetches the desired results:

Define your goals:

AI may solve multiple problems for your business, but the critical step is to find out the ones that matter the most. Depending on your business & priorities, these can be myriad- ranging from plant monitoring, predictive maintenance, or energy consumption monitoring; the need for clarity is critical. This, along with shared context & impact analysis, can help align the business, IT & management stakeholders on the same page, who often end up having silos & conflicting priorities. Finalizing a SMART goal (Specific, Measurable, Achievable, Realistic, and Timely) can then help you to lay out a clear set of next steps.

Choose the right partner/solution:

AI predictive maintenance manufacturing is a long journey, so you must choose the right partner to join hands with. This is the stage where you can decide if your business would benefit more from an AI platform or a purpose-built AI application. While an AI platform can give you a framework & tools to build your analytics, purpose-built solutions are built to address a specific issue like plant monitoring/operations. 

While platforms only offer software and data science functionality, they have to be adapted to your organization’s use case, which can be a relatively expensive affair. For this, the enterprise needs a team of data scientists, DevOps, domain experts, and IT engineers.

On the other hand, purpose-built applications bring the software, data science, domain knowledge, and workflows into a single application, so the insights are provided in the language your plant engineers can understand. 

For complex use cases such as plant monitoring, purpose-built applications can serve as an excellent testing ground for your AI projects since they are ready-to-use with embedded domain expertise & workflows.  ROI with this is also generally much faster and higher – typically in 6 months or less. Click here to arrange a quick discussion on how a purpose-built solution would benefit your plant operations issues.

Understanding the value of your data:

An AI project thrives on data. However, a typical enterprise has different types of data spread out in multiple silos, across departments, and a multitude of software & systems that do not communicate with each other. Before embarking on this AI journey, one needs to understand what data is available and whether the solution or the approach you chose can work with the available information.

A good AI application can help accelerate this process of evaluating the data, integrating the data, and handling inconsistencies in the data. On the other hand, an AI application that is use case driven can also help you isolate the most relevant data from the clutter, estimate the value of available data, recommendations on additional sensors, and address challenges to maximize the business outcome. Often, customers get stuck in this step as the effort to clean up the data seems impossible. Whenever this happens, remember, outcomes should drive the need for data and not the other way around.

Measure & Iterate:

As more and more pilot data is available, the results & insights both need to be monitored over time to ensure that the targets are being met in accordance with the goals set previously. The monitoring can be done directly by your business users if you have chosen a purpose-built solution or may need business users to work with data scientists to generate insights. In both cases, the idea is to measure continuously while acting upon any suggestions as well. It is essential to choose the right metrics for your business and the AI application. Metrics such as the number of models and model accuracy tell only a partial story.

For example, consider a model with 99% accuracy delivering $150K value per year but requires a full-time data scientist with $100K salary to keep the model working. Now, consider another model that is 85% accurate and delivers only $125K annual value but requires only 25% of the data scientist’s time. The second option offers more value despite having less accuracy. Consider all the costs in measuring the value of a solution.

Planning for adoption:

To ensure that your AI programs attain their desired goals, organizations need to create and prepare the workforce accordingly. Firstly, management at the top level needs to orchestrate a culture that supports enterprise-wide AI adoption. The general misconception that AI might take away the jobs on the floor needs to be cleared, instead outlining the benefits of a data-driven culture. Training programs need to be organized for the existing workforce to make sure they are onboarded & excited around the new way of working. It is crucial that the business users trust the solution. Oftentimes, AI applications end up becoming statistician’s tools and dashboards, resulting in a lack of adoption and scale by the business users. Once again, choosing the correct application for the use case and users can help address mitigate this risk.

The opportunities to use AI for your business are limitless if your project is executed with the right methods & partners. AI can be a manufacturers’ best friend in their quest for productivity, efficiency & quality in an age of fierce competition.

Need to know more about starting your AI journey? Reach out to us at[/vc_column_text]