Hey there,

Let’s talk about framing your first AI use cases for Manufacturing. AI and analytics are transforming the way manufacturing companies operate and generate revenue. But where to begin? Here are some tips to help you frame your first AI use cases.

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#1: Start with an objective

Before you consider any specific AI technology (vision, text, time series, etc.) define your objective. What do you want to achieve with AI and analytics? Do you want to improve operational efficiency, enhance the customer experience, or generate new revenue? Defining your objective will help narrow down our options in step two: define the right use cases.

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#2: Decide on a use case

The next step is to decide on a use case. For example, if you want to improve operational efficiency, consider predictive maintenance, yield analysis, demand forecasting, etc. It’s important that these use cases speak to real pain points or functions within your organization. For example, a marketing manager cares about “Sale Forecasting”, they don’t care about deep learning neural net models.

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#3: Decide how you'll measure success

Determine how you'll measure the success of your use case. In the case of predictive maintenance, you might measure success by looking at downtime, response time, efficiency, or other relevant metrics. Ultimately, this will be heavily influenced by your “buyer” or “user”. By defining your metrics up front, in close collaboration with your end user, you’ll get a clear line of sight of what “winning” looks like.

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#4: Consider what AI and analytics technology you'll be using

Only after you've defined your objective, use case, and metrics is it time to consider what AI and analytics technology you'll use. There are SEVERAL technologies available, including machine learning, computer vision, natural language processing, and time series analysis, etc. The goal is to subset down to the options that are going to deliver the fastest and most impactful outcome.

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Don't fall into the trap of pursuing the latest AI technology (ChatGPT, vision, speech-to-text, deep learning, etc.) solely for the sake of novelty. This approach can lead to running a science project that fails to secure funding and ultimately goes nowhere. Instead, stay focused on identifying and addressing the business problem at hand and then choose the technology that best serves that purpose.

Questions? Comments? Just hit reply.

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Yours,

Manny
​bigplasma.ai | LinkedIn​
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P.S. Have you seen my latest free training ”How Legacy Midsize Manufacturers Generate NEW Revenue with AI”? You can get instant access HERE. Would love your feedback? What’s unclear? What would you like me to add?