6 Things to Consider Before Applying Machine Learning to a Manufacturing Process

September 8, 2020

While Industry 4.0 initiatives are being deployed in manufacturing companies, there are increasing conversations around whether processes can be automated and what approaches should be used for that. Although machine learning seems appealing to many, the viability of such solutions and readiness for their deployment should be thoroughly considered.  

To make the decision making process easier, here are six aspects to evaluate before saying yes to applying a machine learning solution to a manufacturing process.

1. Aligning the Project with the Company’s Digital Strategy and Budget

Before considering machine learning, there must a DigitalStrategy in place, determining how the company is planning to go about digitalisation and automation of processes. Often times those strategies are tied to tight budgets that require some creative solutions to implement.

Many start with a proof of concept or gradual implementation to lower the initial cost and validate the efficiency of the approach. These solutions are particularly useful for machine learning projects as the overall costs of implementation can slide due to unexpected infrastructure or data related costs. Therefore, it’s best to divide the development into multiple phases and keep a keen eye on estimated budgets.

At the end of the day, the selected solution should not only fit in the budget but also bring about significant improvements to the process. All alternatives should be considered (discussed in point 5.), before the project starts.

2. Assess The Team’s Readiness for Process Transformation

Changes in software also require a review of the operational process. This is especially true when adopting machine learning technology as it leaves less room for exceptions and creativity. It can also make employees feel afraid of losing their jobs or require a change in how they perform their daily activities.

Create as-is and to-be process comparisons and challenge your thinking of which process steps are needed and how they could be delivered in the most effective way. The end result will require resources for employee communications, trainings and gradual adaptation.

3. Analyse Your Data Integrity

Along with assessing your team for process changes, you need to evaluate your data integrity. Data integrity assessment entails overseeing the accuracy and consistency of data and any system that uses it. This means analysing the risks for manual errors and data validation methods.

The latest manufacturing equipment may include sensors, allowing you to easily obtain accurate data in real time without manual input. However, older equipment may require additional data connections to be established to access the full dataset.

Another consideration is data storage. Cloud data storage has become an increasingly popular option due to the lower cost of implementation.However, large manufacturers may want to consider the value of maintaining local data warehouses. Storing the data locally may allow for high-performance data processing, analysis and increased data security.

4. Develop Process KPIs

What do you hope to achieve? This is an essential question for implementing any new technology. You need to set clear objectives to determine success factors for the project.

Outline the problems that you want to solve or the areas of your production process that you want to optimise. Some of the most common uses ofML software in manufacturing include predictive maintenance, quality assessment, and inventory management.

Identify the issues you want to optimise and set measurable goals.This provides a roadmap for developing your initial strategy into an actionable plan.

5. Seek Alternatives to Machine Learning

Before jumping headfirst into a machine learning project, consider the alternatives. Automation or reducing the scope might be good starting points or even long-term solutions depending on the manufacturing process, available data, and possible savings.

Just limiting the amount of human errors or speeding up the process by automating most common scenarios could be enough to provide a significant boost to the overall manufacturing capability. Therefore, before going for a solution just due to its popularity or buzzworthiness, consider what eases the pain the most.

6. Consider Using Machine Learning in Previously Validated Fields

There are variety of success stories for machine learning solutions in manufacturing but also quite a few that haven’t been brought to the public eye. Although there are no guarantees, it increases the chances of the project succeeding if there are similar solutions already in use. Such as computer vision, predictive maintenance, chemical recipe creation, process parameter optimisation and many more.

Machine learning will revolutionise manufacturing but it won’t happen in a day. Any manufacturing process update should be based on the end goals and maximising the benefits for the business. Even a small step in the right direction in the way of automation will help towards becoming a dark factory in the future.

Feel free to also check out our other posts:

Neurisium: 3 Years of Industrial AI Product Development

Data Sharing Within the Production Industry: Increasing the Available Data Set

Looking Behind Big Data in Manufacturing – 8 steps to ML readiness