Mention manufacturing, and what comes to mind are conveyor belts, factory floors, and repetitive labour. It’s a far cry from what manufacturing truly looks like today – a sci-fi like scenario combining robotics, advanced software simulations, and big data analytics. At the forefront of the process is global technology leader HP Inc.
 Dominic Chew, Director of Operations for HP Inc.
Dominic Chew, Director of Operations for HP Inc.

In today’s manufacturing, the cutting edge is no longer a saw

Dominic Chew, Director of Operations for HP Inc., has seen big changes over his 30 years with the company.

“There has been a seismic shift in the industry, the transition to industry 4.0,” Dominic says, “where we adopt a lot of new techniques and tools from intelligent automation, industrial Internet of Things (IOT), data analytics, and 3D manufacturing. These are all integrated to make our lives and manufacturing processes more efficient.”

Data analytics, for example, was once considered to be the field of marketing, It was used to interpret and predict customer responses to ad campaigns or measure the effectiveness of product placement. Companies like HP Inc., however, have been quick to see its use in manufacturing.

“With manufacturing lines, there is a lot of information that can be digitised,” Dominic explains, “one of the ways is to apply data analytics for predictive testing.”

Predictive testing allows manufacturers to gauge the quality of a product (e.g. the odds of it breaking), without resorting to physical samples. The “old school way” of doing this was through methods like statistical aggregation – for example, a random sampling of products are put through testing to see whether and how they break, and the statistical results are applied to whole product runs.

Through data analytics however, we can now mitigate the need to physically test products, a process that costs time and money. “We can tell whether a product is likely to pass or fail just by applying data analytics,” Dominic says, “In fact, we can go down to every single unique part of a device with data and even predict which specific parts are likely to fail.”

Predictive testing has allowed HP Inc. to set a high internal goal: a 20 per cent improvement in efficiency (even single digit figures, such as two to three per cent improvements, are a significant achievement in many facets of manufacturing).

“… we can address manufacturing at the unique part level,” Dominic says, “We aim to identify whether every single part will pass or fail and whether it needs customisation.”

A growing need for a new set of skills

With the integration of new technologies, manufacturing has made a 180-degree turn: in its early beginnings, the point of an assembly line was to minimise the need for broader skills : each worker only performed one task, and didn’t need to know the bigger picture.

Today however, the skill sets needed in manufacturing have expanded to include digital technology; and this requires greater breadth (and offers greater opportunity) than before.

Dominic says the classic engineering disciplines – such as mechanical engineering and operational engineering – still have their role on the production line. However, the ideal today is: “To be a T-shaped engineer with breadth and depth of skill sets.” This means having broad knowledge of related fields, while still having depth in your career specialisation.

HP Inc. provides ample opportunities for its staff to develop multidisciplinary skills. The company’s Smart Manufacturing Application and Research Center (SMARC) is an exploratory space that allows employees to try their hand in different focus areas such as 3D printing, automation, IOT, or data analytics.

Also unlike traditional manufacturing, which shied away from exploration for fear of error, the process today is more experimental. As one example, Dominic talked about a fellow HP Engineer named Grace.

Grace was involved in the production of printer cartridges; one of the challenges is that each cartridge must be guaranteed to print a certain number of pages . The traditional way to test this is by printing out hundreds of pages, to see when the cartridge will run out; an expensive and time-consuming process.

Grace believed that predictive testing could eliminate the need for the old process.

“Her method was tested three times over a two-year period, each time we couldn’t get an accurate prediction. She was sent for a data analytics course and five months later, when she came out, she was able to successfully apply her new knowledge.”

Some six months of production data and around 70 variables had to be accounted for to come up with the predictive model; but nonetheless, it was a success, and the company no longer needs to print several hundred pages to test a cartridge.

For these reasons, management teams have grown more accepting of failures, and there is a better understanding of the need for open experimentation. This leaves more room in the industry – be it for tech and engineering staff – to exercise their ingenuity.

Tech skills benefit the administration behind manufacturers too

Patsy Oon, Procurement Associate for Supplies Operations
Patsy Oon
Patsy Oon, grandmother of six and a 40-year HP employee, is now a Procurement Associate for Supplies Operations. However, that isn’t where she started – at the beginning of her career, Patsy worked on the manufacturing line, before moving to more administrative support roles.

Today however, Patsy has upskilled by learning Robotics Process Automation (RPA): the use of software and AI to run business processes.

“I’ve always believed in adding value to anything that I do and constantly improving the way I work,” Patsy says, “So I was attracted to the opportunities that RPA could be applied to the way we work.”

Through her training in RPA, Patsy has been able to turn six- or seven-hour data processing tasks into a simple five-minute procedure. RPA removes the need to manually key in line-by-line data about production quantity and schedules, thus improving speed and accuracy.

“The improvements helped add value to our roles, and gives me time to take on new roles,” she says.

Patsy’s training – which brought her subsequent new role – was done in-house:

“HP always encourages its employees to have a growth mindset,” Patsy says, “We attended our classes every two weeks with an internal trainer during lunch time. As HP catered food for us, we were able to learn something new without sacrificing our lunch time.”

It’s time manufacturing is better recognised as a tech job

Manufacturing and tech are intertwined, with sizeable demand for data analytics, coding, automation, and other jobs that were – in the past - thought of as being a “different field”.

Engineers would do well to consider expanding into fields like data analytics, whilst those already in data can find high demand for their skill sets in manufacturing. Even non-engineers like Patsy, however, can find benefits by expanding into the field of digital technology; machine learning, automation, and applied data science can make almost any task more efficient.

Why Singapore’s Infocomm industry is the next big career opportunity

Even in the early 2000s, the growth of ecommerce hinted at the coming digital revolution. But over the past decade, the growth in the infocomm industry – along with attendant demand for digital professionals – has grown exponentially.

On the private hire vehicle scene, Grab and latent competitor GoJek have emerged as major transport players, within the last five years. In finance sector, the growing demand for online banking, eWallets, and other secured transactions have fuelled a massive demand for cybersecurity experts. In addition, Singapore’s Smart Nation initiative has seen a push for SMEs to go digital, fuelling demand for infocomm professionals at multiple levels – from the smallest start-ups, to companies that are rapidly scaling up.

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