According to a research report by PwC, lack of culture and training is the biggest factor preventing the adoption of Industry 4.0. Interestingly, the same companies that provided this feedback are committing significant budget in IIoT infrastructure technologies such as the Digital Twin.
The apparent disconnect between the bullish sentiments of IIoT infrastructure solution providers and factory owners’ concerns about the readiness of their organizations has the potential to slow down the adoption of IIoT.
Industry 4.0 is perceived as a threat to organisational culture
Among the problems that industrial plants encounter when faced with the opportunities Industry 4.0 presents is that there is no common definition for the term “Smart Factory,” nor is there a consensus roadmap for implementation.
Let’s start with the factors that are contributing to concerns about organisational culture in relation to Industry 4.0:
- Industry 4.0 brings the most advanced forms of Artificial Intelligence and Machine Learning to the industrial sector, which is an often a technology laggard.
- Industrial facilities are commonly located in geographical areas with relatively inexpensive labour markets.
- There is a shortage of highly skilled data scientists, who are in any case more likely to be recruited by higher paying industries such as financial services and software.
Despite the production of massive amounts of sensor data, there are very few factories with deep competencies in Big Data analytics. Within the traditional Predictive Maintenance (PdM) arena, only a small percentage of machine asset sensor data is monitored in real-time. Employing statisticians that use off-line sample data to predict machine failure is not a scalable approach.
The human cost of implementing IIoT
Industry 4.0 will cause changes to your organizational culture, though what these exact changes are will vary from company to company. If the prerequisite for successful IIoT deployment in your factories is a new technology infrastructure, you will need to retrain current plant employees on new systems and tools and to recruit employees with the skillset to analyse and operationalise Big Data.
In the case of the Digital Twin, there is a need to “train” the virtual twin on the underlying behaviour of the physical asset. This is not an automated process and requires a substantial time commitment from reliability and asset maintenance resources within the facility.
Adopt an incremental approach
Industry 4.0 is referred to as an industrial “revolution” which has generated much industry buzz. Still, factory owners cannot simply fast forward to full implementation of these technologies. IIoT is based on complex advances in Artificial Intelligence and Machine Learning, and cannot be adopted overnight.
In the past, we have written about the Hybrid Smart Factory where change is incremental and based on best-of-breed technology selection. Culture change can similarly be achievable if it is realistic and incremental. Consider the following principles:
Prepare your people. With a shortage of Big Data scientists and engineers in the market, current employees will need to be re-trained to interpret and act upon Big Data-generated operational recommendations. While there’s no need to transform your employees into “citizen” data scientists, you should be giving them guidance and tools for adapting to a smarter factory environment.
Covid-19 has accelerated the interest in industrial digitalization while demanding companies adopt more agile working practices to stay competitive. The result is that many workers on the plant floor may feel overwhelmed by the amount of change they are being asked to adapt to in a short amount of time. Addressing the uncertainty around new technologies by being transparent about what is required, and the steps to achieve these new KPIs, can go a long way in alleviating fears and successfully onboarding your workforce.
- Technology must enable decision making. If you are going to implement an advanced technology solution that your current employees cannot handle, then it is imperative that your vendor provide a service option. The worst-case scenario is to acquire an IIoT infrastructure solution that cannot be managed with your current staffing resources. When selecting solutions, consider visualisation capabilities so that your employees can interpret data with current analytics skillsets.
- Start planning for the IIoT workforce of tomorrow. New skills will be required to support IIoT and changes to the workforce are inevitable. If you’ve started strategising about the types of AI and Machine Learning that will disrupt your factories you should also be strategising about the types of employees needed to facilitate these processes on the plant floor.
Anne M. Mulcahy, former chair and CEO of Xerox Corporation, once said that “Employees are a company’s greatest asset – they’re your competitive advantage. You want to attract and retain the best; provide them with encouragement, stimulus, and make them feel that they are an integral part of the company’s mission.”
Employees are a factory’s most important asset, and cultivating a rewarding, fulfilling employee experience is still critical for industrial organizations. However, a new asset is emerging: the untapped potential of Big Data. The harmonisation of human and data assets will drive the success of the Smart Factory.
Industrial plants generate terabytes of process data. SKF Enlight AI is a SaaS Predictive Maintenance solution that uses Automated Machine Learning to identify emerging asset failure patterns within this data. It provides early warnings and sensor-level intelligence to help avert unplanned downtime and meet production goals. For more information on how SKF Enlight AI can improve performance and reliability, click here.