Man and Machine in Industry 4.0

Man and Machine in Industry 4.0

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Man and Machine in Industry 4.0

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  • Applying Use Cases to Analyze Industry 4.0’s Effects

    The advances in technology that form the foundation of Industry 4.0 will reshape the business and economic landscapes during the next 10 to 15 years. (See Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, BCG Focus, April 2015.) To analyze the quantitative effects on the industrial workforce, we studied how the ten most influential use cases for these foundational technologies will affect the evolution of 40 job families in 23 industries in Germany. (See Exhibit 1.) A job family comprises job functions that call for related but somewhat different skills.


    To determine the extent to which each use case would affect the number of employees required for specific job families, we worked with 20 industry experts to analyze how each use case would promote productivity gains for existing roles or create new ones. We first determined the effects at a single workplace and then extrapolated the results to the levels of the factory, the industry, related industries, and, ultimately, Germany’s overall manufacturing sector.

    It is important to emphasize that our analysis, which focused solely on Industry 4.0’s incremental effects on job growth, does not forecast changes in overall employment for the period studied. The figures do not account for overall market growth or productivity gains, which vary significantly by industry.

    We selected the ten use cases on the basis of their overall impact on the workforce and the degree to which new skills would be required to complete the related tasks. The following examples of each use case illustrate the possibilities for deployment and the implications for the workforce.

    • Big-Data-Driven Quality Control. A semiconductor company uses algorithms to analyze real-time or historical quality-control data, identifying quality issues and their causes and pinpointing ways to minimize product failures and waste. The application of big data in manufacturing will reduce the number of workers specializing in quality control, while increasing the demand for industrial data scientists.
    • Robot-Assisted Production. A plastics producer uses robots that are similar to humans with respect to their size and hands and that can be easily trained to take on new tasks. Safety sensors and cameras allow the robots to interact with their environment. Such advancements will significantly reduce the amount of manual labor in production operations, such as assembly and packaging, but create a new job—robot coordinator (which we describe later).
    • Self-Driving Logistics Vehicles. A food and beverage manufacturer has deployed automated transportation systems that navigate intelligently and independently within its factory, thereby reducing the need for logistics personnel.
    • Production Line Simulation. A consumer products manufacturer uses innovative software to simulate production lines prior to installation and applies the insights to optimize operations. Implementation of this technology will increase the demand for industrial engineers and simulation experts.
    • Smart Supply Network. By using technology to monitor its entire supply network, an international consumer-goods company has enabled better supply decisions. This application of technology will reduce the number of jobs in operations planning, while creating demand for supply chain coordinators to handle deliveries in smaller lot sizes.
    • Predictive Maintenance. A wind turbine manufacturer offers its customers real-time remote monitoring of equipment and 24-7 access to a diagnostic center. Alarms are automatically generated if one of the vibration-monitoring sensors in a turbine indicates that an abnormality has occurred. Monitoring and sensor technologies will allow manufacturers to repair equipment before breakdowns occur and will foster a significant increase in jobs associated with system design, IT, and data science. These advancements will also create a new job—digitally assisted field-service engineers—while reducing demand for traditional service technicians.
    • Machines as a Service. A German compressor manufacturer sells compressed air as a service instead of selling the machinery itself. The company installs a compressor at a client’s site and maintains and upgrades the equipment as required. In addition to fostering job growth in production and service, this business model requires manufacturers to expand their sales force.
    • Self-Organizing Production. A producer of gears has designed its production lines to automatically coordinate and optimize the utilization of each asset. Although the use of this type of automation will reduce the demand for workers in production planning, it will increase the demand for specialists in data modeling and interpretation.
    • Additive Manufacturing of Complex Parts. Techniques such as selective laser sintering and 3-D printing enable manufacturers to create complex parts in one step, eliminating the need for assembly and inventories of individual parts. New jobs in 3-D computer-aided design and 3-D modeling are being created in R&D and engineering, while jobs are being lost in parts assembly.
    • Augmented Work, Maintenance, and Service. Workers at a German logistics company use augmented-reality glasses to see dispatch information and navigation instructions, including the exact location of an item on a shelf, and to automatically scan bar codes. The system is also designed to enable remote assistance with basic maintenance tasks and provide customer-specific packaging instructions. The use of augmented reality is significantly increasing process efficiency for service technicians, while requiring companies to build extensive new capabilities in R&D, IT, and digital assistance systems.