We hear it all the time, “Industry 4.0 will replace humans in the factory”. There is certainly a large portion of the industry sold on this macro-level trend, but does your company believe this statement to be completely true? If so, it is probably fair to assume that your organization’s I4.0 strategy has not fully considered the ripples of integrating these new technologies (robots, cobots, AI/machine learning, etc.) into their manufacturing strategy. While a program will likely replace some workers on the shop floor, it will also shift the talent mix within your organization. A holistic strategy is one that considers the people/culture/skills of the organization coupled with the technology. Doesn’t sound like your company? Don’t panic, your organization is not alone! A recent study by the Capgemini Digital Transformation Institute shows only 17% of manufacturers surveyed have achieved a high-level of maturity . Let’s unpack this a bit and see what the drivers could potentially be.
With the steep trajectory of new technology becoming available, automation within the four walls of the factory can take shape in many different forms. For the sake of this article we considered the following technologies involved with automating the line in a manufacturer and some potential causal effects on the company’s workforce.
- Robots/Cobots/Automated Inspection: It is well known by now that this is the topic everyone in the industry most likes to talk about, because robots they are the fun shiny objects in the room. Likely when you walk into a manufacturing plant that has started experimenting with I4.0, this is the first thing that they want to talk about and show. While well and good that the initiative has been taken to begin experimenting, it would not be a surprise to hear that they have not fully thought about the holistic strategy. What do these initiatives mean to your current talent mix or skills within your current workforce? Imagine now that you have designed a part for a product, but when it is on the line, the robot or cobot at a certain stage can’t manipulate the part properly. Engineers now need the ability to design a product with the considerations of how the different automated steps on the line will interact with the parts involved. This could be partially alleviated by a system and workforce that can do all design in 3D through the line, ultimately simulating a product before it hits the line physically (digital continuity). To go one more level down is to consider if the talent mix moves to be weighted from industrial engineers to mechanical engineers or vice versa. Another factor to consider, do your current maintenance groups have the competencies to maintain/work on more technologically advanced equipment than they have dealt with historically? Can you afford to wait for new talent who can, or do you purchase equipment with a robust field-service agreement to bridge the gap?
- Connected OT (operations technology) collecting data: The fundamental backbone of an I4.0 program is data. A data driven culture is a less talked about topic compared to automation. Let’s be honest, to many this is not nearly as interesting as seeing robots on the manufacturing floor. Data is an enabler for full automation of your manufacturing line. Due to the nature and criticality of data, we posit that there needs to be a shift to non-physical skills: HMI design, data analytics, AI, app design, and cybersecurity. Whether your equipment is connected through the cloud or on-premise, has your company considered the outside risks to security of your newly collected data? It was not all that long ago that an Iranian nuclear facility was hacked from an outside party . If you continue to connect more equipment to capture different data elements, are you capturing data for the sake of capturing it or are you using it to become smarter? So, does your current IT structure currently have the skills or do you need a whole new set of skills such as data scientists, software engineers, and cybersecurity experts?
- Predictive Maintenance: Another point to touch on when thinking through automation regards maintenance. If you now have more technologically advanced equipment on the floor as well as newly connected machines, then there is a need to move the needle from reactive to predictive maintenance. Let’s say I now have a sensor that records vibrations on a machine; that’s great if we can automatically trigger an action from that data. A shortsighted approach would be to capture this new data and then not teach the system to identify when a machine is nearing breakdown. Of course, this will inevitably bring to light a culture risk regarding an inherent resistance to an AI-based breakdown prognosis at odds with a maintenance team’s intuition. Most maintenance groups in manufacturers operate on “tribal knowledge” and “the art” of keeping equipment running in a reactive manner. As data is collected and merged with the tribal knowledge, you begin to develop a data set that phases out the need of resource knowledge to pinpoint issues within the equipment. An upstream benefit could be the ability of engineers to use the data to tweak products to be more efficient for the manufacturing equipment itself. This type of exercise requires the use of AI/machine learning to become more accurate as you continue to input real-life scenarios. A 2018 Capgemini benchmark study found that 64% of organizations that implement AI lack the appropriate skills and talent to do so within their organization . It then becomes a question of do you outsource, hire, or develop this skillset from within?
As you begin to think through these scenarios (and there are many more), you must think of the implications beyond technology. Industry 4.0 programs will replace some human elements, but will it really replace them all or simply shift them to other skills needed? All companies embarking on this journey must consider this as a strategic transformation and change their mindsets from that of another legacy IT rollout. Challenge yourself and your organization to consider it from this holistic perspective to answer the questions that really matter…
Blaise is a Senior Consultant at Capgemini Consulting, aligned to the Digital Operations group. With a business background, he focuses on the strategy, processes, people, and transformation of businesses. In his professional career Blaise has accumulated experiences in the industries of CPRD, utilities, IT, and manufacturing.
Zach is a Principal at Capgemini Consulting, and a North American Digital Manufacturing leader. His primary focus is on operational transformation within the plant. In his professional career Zach has accumulated experiences in the industries of manufacturing, mining and metals, oil and gas, CPRD, and aerospace and defense.