How Machine Learning will deliver in manufacturing Quality, Cost, and Delivery

There’s little wonder that machine learning is regarded by manufacturers as a top strategy for investment and growth. Current advancements in AI and machine learning, when properly harnessed, can streamline every level of your production, starting from benchmarking the performance and quality of your suppliers all the way up to forecasting supply chain demands.

The best digital manufacturing solutions in the market should leverage machine learning technologies. Machine learning breaks down massive amounts of data, allowing them to identify patterns and trends, pinpointing variables conditions that have the most influence on end results like quality, production rates, costs, on-time deliveries, and so on.

Ultimately, the prospect of predictive analytics is what makes machine learning so enticing. Here are some some concrete ways machine learning will deliver results in manufacturing quality, costs, and delivery:

Quality

• Automated defect detection via AI and machine learning can improve human parts-inspections by 90%. AI and machine learning-based quality assurance could also potentially increase productivity by 50% (Source: McKinsey)

• Machine learning improves product quality up to 35% in manufacturing, according to Deloitte.

Costs

• AI can reduce manufacturing conversion costs by up to 20%. Up to 70% of this reduction would be due to higher labor productivity as a result of using AI and machine learning, according to Boston Consulting Group.

• The lifespans of manufacturing assets (such as machinery, equipment, toolings, molds, etc.) can be extended by analyzing usage data collected by IoT sensors. In fact, by identifying and maximizing the lifespans of their tooling assets, Samsung was able to cut their annual spending on toolings by 50%. Machine learning analysis can also generate more accurate KPIs such as Overall Equipment Effectiveness (OEE).

• Machine learning reduces maintenance costs by up to 30%, according to Deloitte.

• By 2020, 60% of leading manufacturers will rely on digital platforms for up to 30% of their revenue. (Source: CNBC, "How manufacturing can harness digital innovation and reap the benefits of growth")

Delivery

• AI and machine learning can find patterns in the quality, performance, and compliance of suppliers and formulate track-and-trace data for the parts produced by each supplier. With up to 80% of a typical company’s components supplied by external contractors, AI can help immensely in tracking said components wherever they are along the supply chain. Manufacturers can thus save time traditionally spent on manual tracking, and predict deliveries far more efficiently and accurately.

• By 2021, 20% of leading manufacturers will rely on AI, IoT, and blockchain applications to automate processes and increase execution times by 25%. (Source: CNBC)

The Best Practice for Machine Learning-based Solutions?

The best way to harness the aforementioned benefits is by combining machine learning with an end-to-end, dedicated analytics platform predicated on real-time data. With IoT sensors, eMoldino provides this real-time data. By monitoring molds and dies in real-time, the eMoldino solution uses tooling data as fuel for its machine learning algorithms. We can thereby give the insights and patterns that OEMs and suppliers will need to transform their quality processes, cost optimizations, and supply chains.

Arrange a meeting with us to talk more about our end-to-end platform. Feel free to ask for use cases on how we use machine learning to create value for our clients.