• The “Smart Manufacturing & Smart Data” conference on Industry 4.0 (I4.0) organised by SEMI at MITEC recently discussed issues faced by players when adopting I4.0 in a manufacturing plant, and solutions to avoid the “pilot trap”. SEMI is the global industry association representing the electronics manufacturing supply chain.

• According to Alpesh Patel, director of Digital Capability Centre at McKinsey & Company (McKinsey), 79% of Asean companies are aware of I4.0 but only 13% of the companies have launched pilot programme for I4.0 implementation. What’s more concerning is that out of these 13% of companies, 71% of them are still stuck in the pilot phase after 1–2 years. One of the challenges identified is the implementation of too many isolated solutions, resulting in countless data silos.

• In a study conducted by McKinsey and the World Economic Forum (WEF) involving over 1,000 manufacturing companies across all industries, they have identified 16 “lighthouses” or manufacturing sites (Exhibit 1) that have successfully taken I4.0 from pilot to integration at scale.

• Two main success factors were observed across these sites. Firstly, I4.0 needs to be implemented with a holistic approach. Technology decisions should be made to attain longterm comparative advantage as opposed to an isolated technology exercise for an immediate fix. Secondly, building necessary skillsets through internal training or acquiring new talent is vital for overall transformation in an organisation.

• Other than robotic arms and automated equipment, the term “digital twin” was highlighted as an important aspect of smart manufacturing and I4.0. A digital twin is a virtual representation of both the elements and the dynamics of how a device or asset operates and lives throughout its live cycle. Digital twin can be applicable in the semiconductor industry, product manufacturing line, automotive design, oil and gas sector and even the construction and infrastructure sectors.

• When executed correctly with the necessary data and model, a digital twin will influence the design build and operation of an asset or device. This translates into less prototypes, shorter lead time and overall more efficient use of resources.

• David Rogers from Siemens illustrated its digital twin software, MindSphere (Exhibit 2), where it was able to map out the data of individual machines in a production line, e.g., printed circuit board (PCB) milling machine whereby milling dust causes machine downtime. With sufficient data and advance algorithm, the digital twin software was able to monitor motor currents and spindle speed in real time and detect downtimes up to 2 days in advance.

• With a shorter product life cycle of consumer electronics and rising pressure on margins, there is an increasing need for local technology companies to adopt I4.0 to mitigate downtime and allocate resources more efficiently in order to maintain healthy margins.

• Malaysia Pacific Industries (BUY, FV: RM13.79) has started pilot testing automated guided vehicles in its plant to eventually replace human operators. Transformation may not take place as quickly as expected but the 16 successful manufacturing sites identified provide a beacon of light for others to learn from.

Source: AmInvest Research - 10 May 2019