Industrial AI is rapidly defining the future of manufacturing, and manufacturers that adopt it now will gain a competitive edge in the near future. But how do you roll out an advanced new technology across a large or medium-sized manufacturing organization?

With a growing number of manufacturing leaders reporting significant financial benefits due to AI adoption, it seems that the technology is set to define the future of the industry.

Companies adopting artificial intelligence today will benefit from both early wins now, and a competitive edge moving forward.

And while there are many advantages to incorporating AI in manufacturing, the essence of this technology’s game-changing ability is that it allows manufacturers to know 4 key, strategic insights:

1. WHAT is the correct order of priority across your conflicting production objectives?

Each production line typically has multiple objectives, which often conflict with one another, e.g. quality vs. waste, throughput vs. energy costs, quality and throughput vs. emissions reduction, etc. The ultimate goal is to maximize all of them as much as possible; but in order for process experts to do that, they must first understand their respective “weight” vis-à-vis each other. Namely: in the event of a conflict which objective should take precedence of another, and to what degree?

This question is extremely difficult for a human being to answer – particularly since the answer can change depending on a whole range of factors. That’s where Artificial Intelligence can play a critical role.

Armed with this knowledge, process experts can know precisely how to navigate between the conflicting objectives without harming either unnecessarily.

2. HOW much can you improve your current production process?

On any given production line, there is usually room for improvement. This is true even for many highly-efficient lines. The question is: how much potential is there? This is crucial for setting your business objectives/KPIs on that line.

Here, Artificial Intelligence can provide the answers, by mapping the overall efficiency of your production line, and highlighting the most efficient periods of consistent operation – taking into account all your different objectives (yield, quality, emissions reduction, throughput, etc.).

The end goal here is to replicate that highly efficient behavior more often, which takes us to the next objective…

3. WHICH parameters influence your production process?

Once you’ve quantified the untapped potential of your production line, the obvious question is: how do you get there?

If you already know what the potential room for improvement is (see above), Artificial Intelligence can be used to analyze what precise combination of set points and ranges contributed towards those highly-efficient periods. Conversely, AI can also be used to conduct root-cause analysis on those periods of particularly low efficiency, so you can know what tags, process settings and ranges contribute toward inefficiencies and production losses.

4. WHEN to act to prevent inefficiencies

All of these insights can only provide real value if they can be translated into actions on your line by your operators. AI can provide real-time alerts that proactively inform your production teams of inefficiencies on the line, and recommend how to fix them.

Taking the Leap Forward

So the advantages of AI for manufacturers are as clear as day, but how can the technology be implemented without risking your key business objectives?

To many manufacturers, this may seem like a significant undertaking, but it’s encouraging to note:

There’s a smart way to implement AI in manufacturing.

Let’s begin by taking a look at the journey any manufacturer will typically take in deploying Industrial AI…

The 3 Steps towards Smart Loss Reduction

We can break down the journey to AI deployment into 3 fundamental steps:

Step 1: Data Extraction

Data is collected from the production floor across various input channels including raw materials, process data, quality data, etc. Sometimes this stage also includes data on external factors like weather, temperature and so on.

Step 2: Data organization/visualization

The data is organized and analyzed. It is represented visually in order to better communicate findings to the relevant teams.

Step 3: Deployment of advanced analytics and AI

AI is successfully integrated into production, and using the information provided by the previous two steps, begins to provide actionable insights into production losses.

NOTE: This final step is where manufacturers actually begin to see major financial gains, as the AI works to optimize processes that have significant business impact. Until that point, the financial impact of the first two steps is minimal or none.

The proven strategy to successfully implementing AI on a production line

Because of the complexity of manufacturing processes, it’s important to have a clear AI-adoption strategy for introducing the technology, monitoring progress, and scaling up.

The basis of this strategy is that the adoption plan needs to be specifically suited to the production process in question, and that process should be selected with the utmost care.

Customization is key

There is no absolute, blow-by-blow template for implementing AI into a manufacturing process. There are simply too many variables at play in the process. Of course, there are best practices for approaching the task, but understanding the individuality of a production line is key to discovering an optimal route for successful AI implementation.

Introducing the Lighthouse Strategy

Broadly-speaking, there are two common approaches to deploying AI in manufacturing:

The Horizontal Strategy – AI is deployed throughout the organization, one step at a time (see above).

The Lighthouse Strategy – AI is deployed in a single production line, by fully implementing all 3 steps on that line.

There are a number of problems with deploying AI across an organization as per the Horizontal Strategy, layer by layer. This approach is slow and resource-heavy, and it typically will take years to see the benefits. Another disadvantage is that mistakes made in the deployment process are production-wide and carry a higher price.

More fundamentally, since every production line is unique – with different, dynamic challenges and circumstances – each one has its own unique needs. In some cases, they may not have a real need for advanced AI analytics at all (e.g. if the losses are small, or the potential for improvement slim). The “one-size-fits-all” approach inherent to the Horizontal Strategy means that even after full implementation, the solution itself will most likely not adequately address the challenges on at least some of the lines where it is implemented.

By contrast, deploying AI in a single production line as per the Lighthouse Strategy, leads to a more agile process that is quick and resource-light – and the benefits can be seen very early on, even after a few months. Any mistakes made in the process are controlled within that line and can be corrected and learned from quickly, moving forward.

Using the Lighthouse Strategy leads to a more stable deployment process and early wins that justify budget allocation for scaling.

The Formula for Success

In summary, the Lighthouse Strategy allows manufacturers to adopt AI technology to improve process efficiency, while controlling disruption, minimizing risks, and achieving benefits very early on.

Watch the full video to learn more about the Lighthouse Strategy: