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Getting smarter with smart manufacturing

Getting smarter with smart manufacturing
At the heart of any smart factory is the data that is collected, managed, analysed and translated into profitable insights.

In Singapore, manufacturing companies are increasingly beefing up their Industry 4.0 capabilities, and have played a part in driving the nation's overall productivity growth, which rose to a seven-year high of 4.5 per cent in 2017.

However, in their journey to be smart factories of tomorrow, many companies face challenges in their digital transformation - feeling their way as they figure out the resources, skills and infrastructure required. The IDC study revealed that manufacturing data that is stored is not always neatly contained in a common data lake or system and exists in siloed systems. Operators and engineers must manually piece together all the information from these siloed systems in a tedious and time-consuming process - which naturally hampers innovation and discourages collaboration in a company.

According to a survey by McKinsey, some additional challenges faced by Asean companies include managers who lack the understanding of new technologies and innovations to properly implement a transformational strategy, and an insufficient grasp of how all these new solutions can best optimise work processes.

For companies in the region to overcome these challenges and reap the benefits of smarter manufacturing, they will have to commit to a long-term plan and adopt a step-by-step approach towards succeeding in Industry 4.0.

Secure management buy-in

Before embarking on an Industry 4.0 transformation, C-suite executives have to first inform and educate their employees and stakeholders on the merits of smart manufacturing. This will ease the transition and better prepare workers for what is to come, and ensure that everyone is on the same page.

Break down silos through IT infrastructure

At the heart of any smart factory is the data that is collected, managed, analysed and translated into profitable insights. These insights enable companies to gain a competitive edge by improving operational efficiencies and output quality for their customers, ultimately growing their business bottom line. In Asean alone, it is estimated that Industry 4.0 has the potential to deliver productivity gains worth US$216 billion to US$627 billion in the manufacturing sector by 2025.

However, to enable that, the data a company has amassed has to first be integrated into a centralised database, and available across its various divisions, for increased collaboration across the company to promote innovation. Cloud investment should be a top priority as it breaks down data siloes and bridges information. It can also provide greater accessibility to aid in improving processes that leverage emerging technologies and accelerate problem solving.

Optimise processes with AI

AI brings a multitude of applications and benefits that give manufacturing companies an edge in a competitive landscape driven by higher costs and smaller margins. For example, AI has the powerful capability of consuming and evaluating large volumes of data in a way humans are unable to, which allows them to improve processes involving detection of defects, increase manufacturing yield as well as streamline predictive maintenance of equipment on the factory floor, which usually happens in real-time. Over time, AI applications can also detect patterns in the data to offer better insights to complex problems companies are facing today.

However, given that manufacturing companies operate in a complex landscape involving thousands of processes, not all can, or should be, optimised with AI. Managers need to identify the right opportunities that deliver the greatest value and proceed accordingly, instead of assuming that all processes must be transformed "smartly". Seagate's internal AI edge platform is an example of a solution that enables a specific phase of manufacturing to be optimised. The solution is used to identify defects in one of its wafer fabrication facilities, and resolve irregularities more quickly and accurately, ultimately improving production line efficiencies and product quality. Factory subject-matter experts are also able to spend less time on repetitive tasks, and instead focus on innovating and fixing larger issues.

Manage smart factory talent

The increasing use of technological solutions such as AI means that jobs will change too, forcing the need for skills to evolve. Manufacturing companies now require workers who are skilled and adept in data analysis as well as have the ability to manage and leverage emerging technologies including AI and IIoT.

Retraining and reskilling employees to be effective in the changing manufacturing environment should be a key priority. In Singapore for example, the government has proactively implemented schemes such as the Professional Conversion Programmes to aid professionals, managers, executives and technicians (PMETs) retrain in new skills such as data analytics or gain the digital knowledge to operate new technologically-driven processes.

Equally important is sourcing new talent armed with the necessary skills to drive their Industry 4.0 initiatives forward. Success requires companies to build a talent base that is adept with using big data to make decisions.

The manufacturing industry is regularly on the forefront of technological innovation - emerging technologies like real-time analytics, AI and IIoT often are tested and employed on the manufacturing floor before reaching consumers.

Ultimately, companies will have to assess the value that these technologies can bring to their processes, and which delivers the highest return on investment.

The journey to becoming a smart factory is a long one that cannot be rushed. The companies that establish a strong foundation and harness the right technologies will stand to gain a significant competitive advantage and only grow "smarter" over time.

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