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In the age of Industry 4.0, the manufacturing industry was gaining more momentum than ever—until the outbreak of Covid-19 threw a spanner in the works. By Leon Adato, head geek, SolarWinds

Leon Adato, head geek, SolarWinds

In the age of Industry 4.0, the manufacturing industry was gaining more momentum than ever—until the outbreak of Covid-19 threw a spanner in the works.

Since March, factories across Southeast Asia were forced to shut down as Purchasing Managers’ Index (PMI) surveys saw numbers plummet.

Despite the uptick in the industry according to Singapore Economic Development Board’s recent performance report, a survey by McKinsey saw 54 per cent of manufacturing and supply chain leaders in Asia expecting recovery to take up to 12 months.

Facing added challenges such as material shortages, decreased demand, and worker shortages, manufacturers were forced to start leveraging their digital capabilities.

The environment is proving increasingly advantageous for companies with digital platforms, accessible data, and advanced analytical capabilities, as they will be better poised to face similar challenges in the future.

The implementation of a digital twin can be one such strategy to accelerate this digital transformation. According to a study by P&S Intelligence, Asia-Pacific (APAC) is the fastest-growing digital twin market in the coming decade.

This comes off the back of investments in the IT sector, Artificial Intelligence (AI), IoT initiatives by the government, and technological advancements in the region.

A digital twin works to create a complete digital replica of a physical object and uses the twin as the main point of digital communication.

In the manufacturing industry, it is often used in product redesign, quality management, and even logistics planning—greatly reducing operational costs and alleviating labour shortages.

But to deploy digital solutions in an effective and efficient way, it’s important for the business to select the digital twin solution that makes the most sense for their needs.

IT can kickstart things by detailing the three common types of digital twins being commercially used today:

  1. The Simulation Twin

This is a virtual reconstruction of a physical object which can be manipulated in the virtual space and have those changes replicated in the physical copy. To enable this twin, IT must advocate for investments into greater data collection and compute capabilities, due to the huge data, modelling, and processing requirements simulations of operations will require.

  1. The Operational Twin

Created using data drawn from sensors and databases, the operational twin allows manufacturers to model different conditions and scenarios and see how they would affect physical systems or equipment.

Implementing this twin will require IT teams to acquire proficient data analysis and database management software.

  1. The Status Twin

An unalterable digital twin image used mostly for around-the-clock monitoring. Robust monitoring solutions in data monitoring and application performance will form the bedrock for the creation of status twins to track equipment and applications in real-time.

Looking Beyond The Hype: Data Is The Fuel

Although the concept of digital twins is not new, changes brought about by the pandemic has brought it to the forefront as a disruptive trend.

However, the value of digital twins can bring to manufacturers will still depend on their ability to manage data.

Data fuels the potential of digital twins, but for markets like Indonesia and Cambodia, businesses may be using legacy internal systems lacking the technological capabilities necessary to support digital twins.

Such legacy systems can also pose difficulties in accessing data across organisational silos, further impeding the transition from labour-intensive manufacturing such as clothing production to capital-intensive production like robotics.

To truly realise the business value digital twins can bring, IT teams must shift their priorities towards improving data collection, monitoring, and analysis capabilities.

First, they need to determine the extent of data that must be collected. Depending on the type of digital twins they want to build, they should consider data from manufacturing cycles, telemetry data like temperature, speed, and pressure from sensors and equipment, and data from logistics and warehouses operations.

Second, IT teams must deploy the right database performance monitoring and management solutions—only then can a digital twin be fully operational for modelling and simulations.

Data management solutions work to seamlessly link the various SQL databases employed by manufacturers, resulting in a greater diversity of data that can be used to construct a digital twin.

These solutions will also play a vital part in tracking the execution and data results from simulations run using operational twins, as well as processing those results into actionable insights for management.

Food processing company Tetra Pak is a good case in point. The company has plans to build a new digital twin warehouse in Singapore, allowing them to maintain 24/7 coordination of its operations and resolve safety and productivity issues as they occur.

Warehouse supervisors will also be able to use real-time operational data to reduce congestion, improve resource planning, and allocate workload.

By monitoring controlled areas with management alerts, and IoT and proximity sensors on Materials Handling Equipment (MHE), staff will be able to enhance spatial awareness and reduce potential collision risks.

With support from its digital twin, Tetra Pak hopes to deliver agile, cost-effective, and scalable supply chain operations to meet customer demands.

Digitalisation Is Strategic, Not Technological

Economies in Asia such as in China and Singapore have already started to bounce back. While manufacturing leaders are currently taking short-gap steps to keep their heads above water, it remains critical to plan ahead.

If manufacturers want to learn, grow, and evolve, they need to start looking closely at existing operating models to see what works and identify areas of improvement. Those who fail to plan ahead end up falling further behind the digital transformation race.

Technologies such as digital twins help businesses manage complex interactions at a granular level and across ecosystems of partners.

By building transparency, intelligence, and connectivity across its value chains, they can increase resilience, protect operations, support workers through the crisis, and sustain a competitive advantage to accelerate business growth once economies start to recover.

 

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