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Different Stages of Maturity Compared to Digital Masters

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During this part of the research, we are going to analyze which are different steps of maturity in realizing smart factories. At the same time, we want to study in deep, which are possible measures that firms can take in order to overcome the step until become a Digital Master. The real aim of Industry 4.0 is to build digital companies from a business model and production process point of view. In order to analyze the state of the art of each company, the research has taken in consideration two different variables, first of all the level of digital application and integration, second change management for transition from automated manufacture to digital factory.

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Digital Intensity, the “what”, illustrates how far essential processes, such as, production, inventory management, quality, planning and forecasting have been digitized and how much use is made of digital technologies such as robotics, internet of things, artificial intelligence, big data analytics, CPPS and so on.

Transformation management intensity, the “how”, illustrates how well the transformation is being managed to drive benefits, including key aspects such as the manufacturer’s smart factory vision, governance, and the digital skills of its workforce.

Finally, we can conclude that Digital Masters that are found during the research are only 4% in the automotive industry sector. It is composed of which firms that score high on both dimensions of digital and transformation management intensity. They are at an advanced stage in digitizing production processes, and have a strong foundation of vision, governance and employee skills.

Come back to the last research of Capgemini on the automotive sector in 2018. It is the digital maturity of manufacturers that holds the key to achieving full potential. During the study, manufacturers fell into three categories: Struggling, Early stage, Making good progress.

Below, we are going to explore how each of the three categories can graduate to become “digital masters”. By “masters,” it means organizations that have digitally mature manufacturing operations and that consistently achieve greater operational gains and financial benefits.

Summarizing from the figure above we can conclude that Strugglers need a clear vision, strong investment, and a focus on the features utilized by digital masters. Lack of vision is significant. The research has estimated that about two-third of “struggling” organizations admit that they do not have a clear vision for their smart factories. This means they underestimate key elements, such as the disruptive power of smart factories or the continuous investment required. The most effective way to develop a clear vision is to tie it to the strategic goals of the organization. For instance, Faurecia, a leading global automotive supplier, has identified five strategic initiatives paperless shop floors, machine intelligence, enhanced automation, improved traceability, and logistic optimization to develop their vision of smart factory.

In addition to lack of vision, struggling companies often fail to identify the smart factory features and technologies used by digital masters. For example, as we have seen in the second chapter manufacturing analytics and predictive maintenance through the adoption of Cyber physical production system are two of the most crucial features of smart factories, helping improve quality, cost, and productivity performance. In fact the survey finds that more than 80% of digital masters leverage manufacturing analytics, and about two thirds implement predictive maintenance. However, only about 30% of the struggling companies say that they would implement predictive maintenance, and less than 40% say the same for manufacturing analytics. Possible solutions to this problem can be the adoption of roadmap definition and business case analysis, which are considered effective means of identifying the appropriate features and technologies.

Considered as the main part is that struggling organizations need to ensure that their smart factory initiatives are not suffering from lack of investment. In fact, Digital masters have invested more than $1 billion on average over the last five years, compared to the average $380 million investments made by struggling organizations. The investment gap between digital masters and struggling companies can be easily traced and they are due by the lack of clarity over a vision and a failure to find compelling business cases. Can be considered a good idea the launch of pilot project which can demonstrate the potential value added.

Now we are going to focus on early stagers and which are the points on which they have to work on. Auto manufacturers that are at an early stage of their smart factory initiative are doing better on several fronts such as developing a smart factory vision and identifying crucial smart factory features than the companies that are struggling. However, governance is one area where they need to significantly improve. Effective governance starts with appointing a leader as well as forming a committee to guide decision-making and to prioritize actions. In fact, it is considered of primary importance nominate a leader for the smart factory initiatives, the leader has to coordinate with different units of the organization to figure out the next steps and what the best strategy would be. As can be seen from survey data, almost 100% of digital masters have appointed a leader for their smart factory strategy and formed a decision-making committee. However, more than half of the automotive companies with early stage smart factory projects are yet to do the same.

As we have seen in the first chapter, one of the most important factor for a Successful adoption of industry 4.0 is to build a talent pool of workers. Early stage companies success depends largely on how effectively they can scale up their initiatives from pilot runs to industrial level. Scaling up requires employees with digital skills.

Survey data tells us that a lack of digital skills can become a major obstacle. Fewer than 20% of early-stage companies believe that they have adequate skills in areas such as cyber-physical systems and data analytics. But, 50% of digital masters do. In addition, the study reveals how early stagers acquire skilled employees. Usually they upskill their existing talent pool, but as can we see from digital master is also important and critical external hiring for very specialized areas.

Finally, for Early-stage organizations are more likely adopt sectorial technology solutions, such as collaborative-robots and smart displays, than end-to-end transformation of manufacturing operations. The survey displays, 66% of early stagers that they were implementing point technology solutions, while only 32% respondents opted for end-to-end digital transformation.

However, as we have seen in the second chapter only with a combination of all digital technologies and manufacturing leverage that composes smart factories is possible to reach the full potential. In conclusion we are going to focus on companies that are making good progress. They are more likely to have implemented major capabilities, such as manufacturing analytics and smart forecasting, than companies from the other two categories. However, there are some important areas of application that they are not going to exploit, such as smart energy consumption and enterprise asset management. These features, which have been implemented by more than 60% of digital masters, can be crucial to bringing down operation and maintenance costs. However, for organizations making good progress only 38% have implemented smart energy consumption and only a small part 18% have implemented enterprise asset management.

Companies that are making good progress need to ensure that their progresses are monitored through reliable target and KPIs indicators. In fact, careful monitoring is essential. However, the research reveals that only 56% of the companies making good progresses actively track benefits against business cases, this is a practice followed by 100% of digital masters. Using a standard set of KPIs is an important factor in monitoring the progress. This is because progress data needs to be interpreted correctly across different organizational units. We found that only about 50% of companies making good progress use a standard set of KPIs a practice that all digital masters follow.

In order to work efficiently automotive manufacturing industry need to work on standardized work procedures, economies of scale and capital intensiveness. Therefore, given the revolutionize impact of smart factories, firms can disrupt and lost day to day operation introducing industry 4.0.

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For this reason can be considered good to use platform to pilot digital solutions, partnership with start-ups, or launching innovation centers. Emerging digital solutions can be tested in the facilities of start-ups or innovation centers. At the same time, team-members from start-ups and innovation centers can introduce and encourage a more digital first-mindset. In conclusion, the auto industry is looking to smart factories as keystone to boost productivity. In addition we have seen that the investment required for smart factories, if these reach the full potential, can be recouped in less than one year. However, given the benefits and the enthusiasm in the sector, there are a lot of challenges to overcome. Digital masters have to be emulated and firms have to reach the digital maturity, only if these conditions will be satisfied the automotive industry can put itself in the driving seat of the fourth industrial revolution.

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