4 Ways Machine Learning Drives Success in Smart Manufacturing

Smart manufacturing is a central pillar of advancement in our society. With the substantial impact of recent worldwide events, the industry has boosted efforts to adopt digital transformation and get on the train of Industry 4.0 to better avoid risk, increase efficiency, and make up for narrowing margins and profit losses.

For some of the most successful brands on the planet, like Apple and Tesla, to lesser-known companies in pharma, engineering, textiles, and paper, manufacturing has played a critical role in shaping their success and the global economy. Here’s why AI and machine learning in the manufacturing industry is going to transform how companies will measure success in the production process.

Smart manufacturing delivers less machine downtime. Maintenance is an ongoing challenge in manufacturing. Keeping production line machinery and equipment running smoothly is sometimes a herculean, cost-intensive task. Studies have shown that unplanned downtime costs are estimated at $50 billion per year, as 42% are due to asset failure.

Predictive maintenance has become a vital solution for manufacturers; there’s much to gain from accurately predicting the next failing part, system, or machine. This technology is based on AI algorithms in the form of machine learning that compute and predict projections regarding machine failures, breakdowns, or required maintenance before the issue occurs.

The adoption of this solution allows for extreme reductions in costly unplanned downtimes and the extension of Remaining Useful Life (RUL) of machines and equipment. Even when maintenance is inevitable, technicians can be briefed ahead of time and schedule focused repairs in advance with no halt in production, leading to increases in OEE.

Smart manufacturing uses advanced AI algorithms to alert manufacturing teams of emerging production faults that are likely to cause product quality issues. Some of these faults can include subtle abnormalities in machines’ behavior, deviations from settings, changes in raw materials, and more.

In Industry 4.0, quality assurance is a priority. The challenge for manufacturers is keeping quality stable while boosting efficiency. With the right tech partner and solution, manufacturers who find the balance between stable quality and increasing OEE will also see more flexibility in their production process, such as better handling of changing product portfolios, seasonal changes, team changes, and even input quality.

The industry 4.0 concept is data-driven. Smart data solutions deliver several competitive advantages that improve KPIs like quality, productivity, and efficiency. In manufacturing, OEE is considered the “holy grail KPI”, and since smart manufacturing’s ultimate goal is to increase this number by reducing downtime, documentation time, and demographic risks, the industry stands to gain plenty from the technology.

One of the beautiful things about machine learning and its applications is that over time its algorithms learn more and more. This builds foundational datasets, and allows teams and machines to learn what works best for consistent and continual optimization over time. The more insight manufacturers and their teams have into how their machines operate, the more they can prevent costly bottlenecks, downtime, and micro-stops.