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Posted on 12/4/2019

Hype vs reality - Machine Learning in Manufacturing

Jeeva Nadarajah

All the buzz

There is so much buzz about machine learning and Artificial Intelligence (AI) today. With the estimated spend of $1.2 trillion to $2 trillion in supply chain and manufacturing, businesses are encouraged to use these new techniques to modernize their operations. We spent the last decade collecting data. Now is the time to use it, right? However, there are few who can identify an area where they have used it to solve a quantifiable problem successfully.

We’ve learned that machine learning can help manufacturing with predictive analytics, preventative maintenance, demand forecasting, etc... How do we get down to doing it?

Hype vs Reality

Let’s briefly talk about AI vs Machine Learning. AI is a more advanced form of machine learning where the machine uses large sets of data and changes the algorithm based on the expected outcome. That’s when we start training a machine to start thinking more like a human. This is no easy task, though, because human beings are said to be irrational in their decision making.

Decades of psychological research have shown most people (and probably most philosophers, too) are pretty irrational in their decision-making. For example, in 2014 the Hong Kong based investment firm Deep Knowledge Ventures made headlines about bringing in an AI algorithm as a board member mostly as a veto mechanism. Today, the company no longer uses the algorithm because big strategy decisions are based on intuition says Brian Uzzi. Largely because we don’t have enough data to make the “more human” decision.

The majority of businesses today are using machine learning (where the program does not morph algorithm) to solve complex business problems.

No magic bullets yet

The reality is that problem solving still matters. There are no magic bullets. We need to close the loop between business and technology by having technologists understand the business goals and outcomes; so instead of picking the tools that are hyped, they will choose the right technology for the business.

Data Scientist Monica Rogati had an interesting article about the AI pyramid. She says, “Think of AI as the top of a pyramid of needs. Yes, self-actualization (AI) is great, but you first need food, water and shelter (data literacy, collection and infrastructure).”

The Data Science Hierarchy of Needs
Image Source: The AI Hierarchy of Needs, Hackernoon, June 2017

Step 1. Defining the problem is 90% of the heavy lifting (Discovery)

As with any technology, this is the first step to a successful outcome. Define the problem you are trying to solve. A Discovery or Assessment is what most technology firms call the process of trying to isolate and validate the problem area. Make it measurable and try to add a price tag or cost to it. This helps validate the need for the tool.

When Bob says this problem costs the company $100,000 and he can have it fixed for $80,000, that’s an easier sell to Katie the CFO.

Step 2. Roadmap (Ideate) - Refer to AI pyramid

Once we have the problem defined, then comes the next step: where can we find the data? How do we collect it? What’s the hardware we use (if any)? How do we store it?

A large part of this portion is working with subject matter experts to validate the solutions against the defined problems. This stage involves low cost ideation where you use simple tools (whiteboard, sticky note, Miro) to communicate an idea across effectively.

Questions about where we store the data (on prem or in the cloud), security of that data, the cost related, can all be discussed at a high level.

Step 3. Software Implementation

Most times this involves setting up applications that collect data. They can be connected devices, mobile devices or Internet of Things devices. These devices then collect the data and push into a storage, either within premises or in the cloud. Data privacy and security come into play when implementing this layer.

The infrastructure for software development environments for developing, testing, staging and production are built for continuous integration and continuous deployment. (Full continuous integration and continuous delivery
pipelines).

How do we transform the data? How do we manage the data integrity? How do we identify and remove the outliers? How do we start versioning the models (machine learning algorithms) used?

Step 4. Validate to drive trust and reliability

In a world where everything is fast paced and we want to keep moving on to the next best thing, it’s easy to pay less attention to this step. Especially because it may seem mundane, but it is crucial.

How do we test and validate the models? It’s not an easy task to pay attention to detail. We need people who specialize in modeling and validating. I would say doing this right is very critical to building end user trust in the system. The reliability of the data and the data model is central to the success of this project.

Wrap up

The pyramid is an important fixture in this article. Paying attention to the infrastructure and being able to collect the right data matters. If we can shorten the time needed to make changes to the implementation of steps down the pyramid so we are gathering the most insightful data in the top, that means we are making real progress.

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About the Author

Jeeva Nadarajah | Serendip

Jeeva Nadarajah is the founder and CEO of Serendip, a community of technologists and problem solvers. She has 15 years experience building custom software products. She is now focused on connecting businesses in a variety of verticals to small software engineering firms that pride themselves on delivery, so clients can count on them for long trusted relationships.

 
 

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