Digital Transformation is THE buzz word in many industries these days, and no-less in manufacturing. Significant investments are being made to address ways to use technology to reduce cycle times, improve processes, reduce costs, and take the friction out of doing business. McKinsey’s “A Guidebook for Heavy Industry’s Digital Journey” states that the large scale implementation of digital technologies and advanced analytics could boost profit margins by 3 – 5 percentage points. These digital transformation projects follow a traditional transformation journey of the below 5 steps:

Vision – What does success look like? It can be grandiose or it can be very specific and narrowly defined. For one of Mariner’s customers, the vision is to address throughput challenges and quality issues that most effect customers.

Diagnostic – In our experience, the question isn’t to find a problem to solve. It is selecting the most impactful problem from the large collection of possibilities and then prioritizing the rest. We call this step “selecting the problem worth solving.” At Mariner, that means finding a problem for which we have a clear definition of success, access to the necessary information and a business case worthy of the effort. The word “worth solving” implies the development of a business case. You should focus on both the business case for the first phase that you intend to pilot, but also keep your eye on the ROI for the larger vision.

Build the Roadmap – Now that you have a list of projects to tackle, prioritize them. Consider the changes you will need to address in people, process and technology.

Run the Pilot – A pilot is a low cost way of confirming that your hypotheses are on target and that you will be capable of achieving the returns identified in your business case. At Mariner, this embodies a process we call “1 2 3”.

  1. Since our focus is on leveraging AI as the technological centerpiece of digital transformation, our pilot projects begin with building an AI algorithm that demonstrates that an AI model is capable of predicting an outcome with an acceptable degree of accuracy. For example, for a large manufacturer of pumps who is interested in asset management, we are building a model from past telemetry to ensure we can accurately predict failures in time to permit field service to take corrective action.
  2. Continuing with our predictive maintenance example, once the pilot model demonstrates it can predict maintenance needs using historical data, we proceed with a pilot deployment to operationalize the model, first on a limited scale. We’ve developed an IoT platform designed to operationalize analytic models quickly and efficiently. Dubbed “Spyglass Foundation”, we use this framework with great success. We can deploy proven models in 4 weeks, sometimes less.
  3. Model Maintenance – analytic models drift, meaning their accuracy may degrade overtime. For this reason, Mariner provides continuous diagnostic services to identify the optimal frequency to maintain/retrain models.

Scale Up – Once a pilot has been proven out, the next step is to scale it. Mariner has a customer that manufactures automotive glass. Our pilot project on a single production line proved the gains can be attained. The next step is to construct a plan to scale out the entire factory, followed by the remainder of the enterprise.

Manufacturers are hungry for the savings digital transformation can provide. It is essential that they begin the journey in order to stay competitive with their peers who have made progress. However, many manufacturers don’t know where to start. That is where Mariner can help. We have a proven process and tools like Spyglass Foundation to provide a rapid time to value approach to pilot and scale your digital transformation projects. It’s easy as 1, 2, 3.

If you’re interested in learning more, please drop me an email.

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