Digital transformation describes an organization's strategic decision and the implementation process for selecting and adopting digital technologies from available business tools and processes. The transformation is designed to solve identified business constraints and is customized to organizational needs.
Digital transformation in the manufacturing industry is said to improve operational efficiency and client experiences while reducing costs and increasing revenues. The arguments for increased competitive advantage and client retention are compelling, however, digitalization costs can be high, causing manufacturers to question the effectiveness of such an investment.
Those not convinced by marketing need real-world examples of using technology to solve existing constraints and provide tangible benefits. The following examples illustrate the effectiveness of targeted digital implementation.
Examples of Digital Transformation in Manufacturing
Example #1: Tetra Pak
Tetra Pak is a subsidiary of the Swedish Tetra Laval Group and offers global food product processing, packaging and distribution. In 2020, the company sold over 183 billion Tetra Pak packages to manufacturers in over 160 countries. Its packaging is essential for companies that process and package aseptic (bacteria-free) products.
Problem:
Despite constant improvements in maintenance strategies, a production line can be down for a day or more if a machine fails during time-critical activities, such as milk packaging. The spoilage cost from milk that cannot be stored or packaged is a key risk for Tetra Pak's clients, so the company sought answers in technology to reduce client risk and lock in their loyalty.
Tetra Pak cites the example of a servomotor used to form and seal food packages. The servomotor's mean time between failures (MTBF) can range from 2,000 hours to 7,000 hours. When factories operate for 4,000 hours per year, you can change the motor at the six-month or 14-month point. As a result, you either over maintain the equipment or suffer extended downtime, with both scenarios increasing costs.
Solution:
Tetra Pak chose to connect over 5,000 packaging machines worldwide to the cloud, allowing greater insight into in-service experience and failures. The sensors on the carton-filling equipment provide a real-time data stream, with analytics software able to predict arising maintenance issues. Technicians are then dispatched for timely interventions to prevent machine failure and extended downtime.
Upon arrival at the plant, technicians use an app on their mobile devices to access crucial performance data and use Microsoft's HoloLens® mixed-reality headsets to rapidly diagnose and fix maintenance issues. The headsets support video calls to Tetra Pak service centers, where subject matter experts can remotely guide the technician through the repairs required.
Results:
Tetra Pak's digitalization has streamlined clients' equipment maintenance. Maintenance issues are monitored and compared to global in-service data. Issues are predicted well in advance, allowing the timely dispatch of technicians to remote areas of the world with relevant spares. Access to global expertise removes the need for a second technician, lowering the cost of the maintenance intervention while providing access to greater knowledge than that possessed by one individual.
A six-month snapshot of the performance from 11 packaging lines resulted in Tetra Pak diagnosing imminent breakdowns in five of those lines. The prediction initiated preventative maintenance interventions that saved clients over $30,000.
Example #2: Volvo
The Volvo Group employs 95,000 people in production facilities spread across 19 countries. It manufactures industrial engines, construction equipment, trucks and buses, supplying 190 markets.
Problem:
One of Volvo's aspirations is to drive customer satisfaction for all brands in their business segments. To that end, the company has stringent quality and engineering standards, and offers a wide range of product customization and complexity. For example, engines produced in one plant may have 4,500 variants, with 13,000 different quality references.
Such variety challenges Volvo’s strict quality control and quality assurance (QA) inspections, with each engine requiring 40 checks, with 200 possible variants, within eight minutes. The inspections have such a wide variety that training a technician to complete the checks took five weeks using paper-based training methods. Managing up-to-date QA material was also demanding, requiring considerable resources and time.
The company sought to implement a digital system to cope with increasing product complexity and customization. The system would need to tap into the engineering and manufacturing processes while being globally scalable.
Solution:
Volvo chose to implement an Industrial Internet of Things (IIoT) platform, linking computer-aided design, manufacturing and business processes. The company used the provided data to supply a suite of augmented reality tools. QA workers can access current engine configurations and supporting data with augmented reality headsets.
Operators use the headsets in mixed reality modes to overlay 3D data and QA information directly onto the engines they are inspecting, using computer vision to track movement and anchor content. The link to the IIoT platform allows QA inspectors to upload any defects found via the augmented reality system. These are streamed to engineering and manufacturing for continuous improvement activities.
Results:
Volvo has reimagined how its employees are supported and trained. The update and validation of QA checklists with new engine configurations took a day or two, but the new system has reduced this time to less than an hour. The QA technician training time that originally took five weeks has been reduced to less than two weeks, allowing rapid employee onboarding processes.
Volvo predicts savings of thousands of euros for each QA station per annum, promising large savings across the 100-plus QA stations in its 20 plants. The improved quality checking drives down defective products leaving the factory, thus improving client satisfaction. The streamlined process also supports increased customization, further supporting client needs.
Volvo intends to roll out additional augmented reality and IIoT systems in the future, including assembly instructions for operators, service instructions for maintainers, and data mining for greater operational insight.
Example #3: Worthington Industries
Worthington Industries is a steel processor that manufactures and supplies pressure tanks and cylinders globally. Its pressure tank products are manufactured in 10 facilities across five countries and are used for gas, cryogenic, fuel and oil applications.
Problem:
The company was seeing a significant discrepancy between operations on the manufacturing floor and the information used by management personnel for decision-making. Data gathering was suboptimal and paper-based, preventing the capture and timely use of knowledge and learnings from past in-service experiences. Decisions were made on out-of-date or incomplete information, with data fragmented throughout the firm.
Solution:
Worthington chose to implement a lean manufacturing system that identifies the key data needed for operational and business control, and captures it into one repository. The intent is to close the gap between operational reality and managerial understanding by giving every user access to the same breadth and depth of information, thereby empowering decision-making that’s based on indisputable facts.
Results:
By implementing a smart factory platform and agreeing on the performance metrics that mattered, the culture within the organization changed fundamentally. Shop floor workers became more engaged in the business, with conversations on business performance informed by the same data. Within 24 months from implementation, Worthington identified two significant returns on its investment.
Factory productivity increased due to improved business agility, reduced reaction times to equipment failure or downtime, and the rapid identification of bottlenecks. The collected data enabled idle machines to be brought online sufficiently early to maintain optimum manufacturing rates.
The data collected and presented was user-friendly and available to all, and it was considered the one truth upon which all employees could reliably make educated decisions. Communication between departments improved, and personal accountabilities and divisions of responsibility became clearer.
As a result of the operational
benefits Worthington experienced, the smart factory platform was rolled out across all of its manufacturing sites, supporting employee involvement, continuous improvement and waste reduction, and further increasing productivity.
Conclusion
Digital transformation for manufacturers supports the modernization of legacy equipment, data aggregation and mining, and the connection of all steps in the manufacturing chain. While requiring investment, digitalization appears key for organizations seeking increased efficiency, customized product and service offerings, and enhanced business agility. The gains reported by Tetra Pak, Volvo and Worthington Industries suggest the benefits from digital transformation are more than just marketing hype.
