November 19, 2019
Digital transformation in Manufacturing essentially means the elimination of repetitive operations carried out by a human on a production line and replacing it with data driven automated functions. This technological revolution is a mix of data analytics, IoT (Internet of Things) and machine learning in order to increase efficiency of the production line. Digital technology will be integrated into all areas of a business, starting from the manufacturing line, up to top level decision making, enabling to reduce risks in the manufacturing process, make informed decisions and most of all, improve customer satisfaction.
Assessing Digital Maturity
Digital maturity indicates how ready a company or an industry is in their data initiatives to automate and scale their business processes. Digital maturity levels have been linked to the companies financial performance, indicating that companies managing their transformation processes well have been 9% to 26% more profitable over their competitors.
There are multiple ways to assess the digital maturity level of a company, most of which provide a scale of evaluation based on the current operational processes. Digital Maturity assessment is best outsourced as it’s difficult to compare your standing with competitors and best practices in house. If one does want to go about it on their own then the topics to cover should include data storage, data connectedness and accuracy, as well as assessing how the data flow is currently utilised across the company.
A great example of reaching the Digital Twin level is Tesla. Due to having an autonomous production line, consisting mostly of robots they have achieved a safer and more efficient vehicle production while decreasing the possibility of human errors.
Prime Result of Digitalisation - Clean Data
Data can be collected through sensors, day to day production processes, quality assessments and audits but to harness its possibilities, the data needs to be stored in a structured manner. Data warehouse or connected database tables are the most popular options. That is the first building block to digital transformation, enabling to start developing various solutions, such as Business Insights, IoT or Machine Learning systems.
As a first step, the data should be analysed to prove or overthrow hypotheses that are currently used in decision making, such as “we are working at our capacity”.
Three types of analytics possibilities can be defined:
1. Descriptive analytics - common reporting of the status of processes
2. Prescriptive analytics - suggestions on what should be done to improve the result
3. Predictive analytics - analysis of the current data to anticipate the next most efficient move
Businesses that embrace data analytics achieve better production efficiency. The goal is to have real time data flowing through all the time, to get the best and most accurate readings, improve quality control and detect failure.
A great example of a company that has invested into their data management is John Deere. They have created their own John Deere Operations Centre, where farmers can see live data dashboards about their equipment and fields, helping them have a better overview of their agricultural production and equipment. This is also a valuable asset in providing John Deere with data to learn and develop their products.
Industrial IoT use
IoT or Internet of Things enables a data exchange between multiple devices connected to a network. Essentially, IoT allows to create software to automate or optimise processes while using multiple datasources.
It is smart to start with developing solutions to specific business cases in an area / department of the company with the aim of having all company processes digitally connected at a future date. This can mean tackling Logistics, Customer feedback, Quality or any other major influencer of the bottom line.
Automating Repetitive Actions with Machine Learning
Machine learning (ML) has the ability to increase production capacity and efficiency while freeing up valuable human resources from repetitive tasks and offering new data insights. ML can learn from historical data but requires a clean and well structured data set to work. Depending on the task the data requirements can be very different and more often the suitable methods and goals will be dictated by data availability.
Types of Machine Learning:
1. Supervised - requires an existing pool of data with causes and effects;
2. Unsupervised - suitable for finding commonalities or spotting irregularities - faults and malfunctions;
3. Exploratory - allowing ML explore the environment and offer up better ways of optimisation.
Applying ML techniques in manufacturing has shown a great improvement in quality, management and optimisation. For example, Intel uses IoT and AI solutions to predict breakdowns in manufacturing processes, reducing their downtime by 300% and therefore improving their factory floor operations.
Digitally Transformed Future
Digital transformation of manufacturing is inevitable, with clear benefits for those who succeed. We are still a good few years away from fully automated production and digital twins being a part of the common factory setup but the world is certainly moving in that direction.
Manufacturing is one of the most competitive industries, where blows to the bottom line can lead to sudden death. Therefore, knowing where the company stands on the digital maturity scale and having a long-term digital transformation strategy in place are crucial.
Feel free to also check out our other posts:
Fixing Growth Pains in Manufacturing: New Solutions Can Change the Game for New Product Introduction
The End of On-Site Monitoring: How The Pandemic Will Change the Way Engineers Run Their Production Lines