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What it takes to realise smart manufacturing and logistics

Nurdianah Md Nur | June 30, 2016
Speakers at the Computerworld Singapore Manufacturing and Logistics Forum 2016 provided tips on digital transformation specific to those two industries.

Changing customer demands are pressurising manufacturers and logistics providers to digitalise their business or be left behind. But how exactly should such organisations digitally transform? What new technologies should they explore? And what challenges should they be prepared for as they adopt and experiment such technologies? These were the questions discussed at the Computerworld Manufacturing and Logistics Forum 2016, held at the Marina Bay Sands, Singapore, on 16 June 2016.

According to Dhiraj Garg, Vice President, Solutions of T-Systems APAC, we're moving into the age of smarter production/manufacturing, which includes smart factories, smarter supply chain, automated controls and machine to machine. The logistics industry will need to smarten up too by using technology for real-time tracking, space utilisation optimisation and higher vehicle utilisation, as well as to reduce idle times.

Echoing Garg, Amit Dhupkar, VP Group Technology (eCommerce Logistics) at SingPost, said that logistics providers should perceive IT a "revenue enabler, differentiator, solution design and an enabler of improved customer experience." This is in contrast to previously, when IT is seen as a "cost centre that controls and ensures compliance".  

Game-changing technologies
Enablers for smart manufacturing and logisitcs include miniaturised sensors for the Internet of Things (IoT);  fast, mobile and fixed networks to ensure continuous availability and security; and cloud as it is necessary to house the massive amount of data coming from IoT devices on a single platform, said Garg and Dhupkar.

The value of big data lies in the actionable insights derived by analysing it. However, it is not enough to only rely on traditional analytics (also known as Analytics 1.0), in which business intelligence systems drive reporting and descriptive analytics to a specific set of data, said Shanmuga Sunthar, Chief Technology Officer of SAS Singapore. While analytics 2.0 caters to big data as it features faster and visual analytics, as well as fosters experimentation and data mash ups, it is also insufficient to solely support organisations that want to thrive in the "smart" age.  

"Analytics 3.0 [for smarter production and logistics] needs to be more than just generating reports. Besides offering the capabilities of analytics 1.0 and 2.0, it needs to be able to provide statistics, analyse text from unstructured data, mine data for hidden insights and trends, identify areas most likely to produce profitable results, and leverage historical data to drive better decision-making," Sunthar explained.

Manufacturers and logistics providers should also consider using cognitive systems and blockchain to improve "visibility, transparency and provenance of their supply chain to help them respond to regulations and be more competitive", said Janet Ang, Chairperson, Institute of Systems Science, National University of Singapore.

"Cognitive systems will take organisations to next realm as they are machines that understand, reason and learn [unlike traditional programmed systems]. They can be used to bring efficiency into planning processes and improve customer engagement," she added. For example, a cognitive system can forecast spikes in demand based on changing weather conditions and thus optimise supply, operations and pricing. It can also be used to send customers promotions and recommendations dynamically based on weather forecasts. 

 

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