Mapping a Data Strategy for the Manufacturing Industry

In the manufacturing industry, there are many ideas for harnessing data to enhance productivity. The real hurdle, however, lies in transforming these ideas into action, especially, given the constraints of cost and implementation strategies.

So, how can enterprises overcome these obstacles and seize the opportunities presented by emerging technology?

To create a blueprint for success in the manufacturing industry, experts gathered for a roundtable titled “Unlocking the Potential of Data in Manufacturing.” It was organised by Jicara Media, and hosted by Rackspace Technology® and Microsoft®.

Among manufacturers, the concept of a digital thread — which connects data from R&D, down to product development, and into production — often serves as the starting point for any conversation surrounding data democratisation, stated Shwetank Sheel, Director of Public Cloud Data Global Pre-Sales and Solutions at Rackspace Technology.

According to Sheel, a successful data platform for manufacturing needs to have a “digital thread” that connects all the different processes and systems. At the same time, it’s important to identify specific use cases that can drive business value through the platform. These two activities should occur simultaneously, since trying to scale up from proof-of-concept projects becomes difficult without a clear understanding of how the platform will be used to create value.

Real-world manufacturing challenges

An example of this strategy in practice occurred at cereal maker Kellogg Asia Pacific. One of the company’s most persistent challenges was data harmonisation.

"I have come across data in various formats and structures in the companies I’ve worked for," said Saurabh Lal, Senior Director of Supply Chain Network and Digital at the company. "We've got Excel spreadsheets, different ERPs, and we're operating across multiple countries. Getting everything into a usable format is our biggest challenge."

In addition to this problem, Kellogg is also looking into smart manufacturing.

“We’ve got some legacy plants that have old technology and less automation,” he explained. “How do we convert these plants into smart factories? That's the second challenge. And when I say, ‘smart factory,’ it's not just about standard operating conditions. It’s also about enhancing quality assurance, implementing smart maintenance practices and other similar measures.”

In another example, a major drug manufacturer faced consumer safety as its primary consideration, while at the same time, working to streamline its operations.

A director in the drug manufacturer’s IT division said that the challenge lies in connecting data across various areas of the manufacturing process in a way that can drive improvements, including equipment and IT infrastructure.

However, since the end goal is to bring the product to market, it's also important to ensure that the data meets the regulatory requirements of different markets. This can be particularly complex when it comes to AI, which may have different expectations and regulations depending on the market.

“Once we expect people to make decisions, they’re essentially taking responsibility for ensuring that our product is safe,” he continued. “The question is whether we are comfortable delegating that responsibility to an AI."

According to Saj Kumar, Regional Business Leader of Manufacturing, Microsoft Asia, data in the manufacturing industry can be broadly categorised into two main buckets: business data and plant and operational technology (OT) data. Whereas business data is more structured and well-defined, OT data is much more complicated, said Kumar.

"A lot of the OT data in manufacturing consists of time-series data, which involves capturing signals from machines over time for reporting and analytics,” he explained. “However, this type of data alone does not provide complete end-to-end visibility, especially when trying to connect customer or production orders with specific machines, batches and materials. This requires combining different types of data, which can be challenging."

Different paths to a data roadmap

When it comes to data centralisation, conflicts often arise within an organisation due to the wide variety of ways in which different departments use data.

Ajith Narayanan, CTO of robotics systems maker Zoid Labs, believes that while data centralisation is a must, organisations should apply levels of flexibility to allow different people to consume different applications at their own pace.

"The challenge lies in the data architecture,” explained Narayanan. “How we represent the data and the inter-linkages between different pieces of data is crucial. In our product development, we used data to refine the product in the R&D stage. To simplify this process, we decided to use only YAML and JSON formats early on. Our thought process was that if we can get the mindshare within an organisation to think about data differently, maybe a couple of formats like JSON and YAML would satisfy everybody."

For Kellogg's Saurabh Lal, decision makers need to be convinced that a particular solution will not only benefit one function, but also, at the very least, an entire business unit.

“When you say it can help you improve your sales, or it will help improve the outputs, then those teams need to come on board and sign off on that particular solution,” he said.

Shwetank Sheel at Rackspace Technology acknowledged the huge role of the cloud in turning business ideas into relity, a feat that has been proven through many of their customers’ success stories.

“Cloud allows you to experiment on a small scale and validate your ideas,” he explained. “For example, you want to build a business case that shows a potential revenue increase of SG$10 million. By investing a small amount of money, you can see if your efforts are moving the needle. They can determine whether this is the right approach or not.”

The future of AI in manufacturing

AI continues to unlock new possibilities every day, revealing a multitude of use cases. While AI has traditionally found applications in areas like customer experience, the advent of popular solutions like ChatGPT is revolutionising entire industries, introducing innovative solutions and creating new avenues for generating revenue.

Some manufacturers are aware of what AI can contribute to their business. It won’t be long now before mainstream use of the technology begins.

Ajith Narayanan of Zoid Labs, for instance, foresees that predictive maintenance powered by AI will play a crucial role in reducing downtime and potentially even preventing factory accidents.

He stated, “We're not at the stage where AI models are ready yet, but we have a scenario where the data generated by the corrosion measurement system IoT is sent over MQTT to a server. We will use this data with some models to analyse and predict when we should perform maintenance on the machines."

For generative AI, Microsoft’s Saj Kumar thinks some functionality is already present for manufacturers to experiment with. However, the technology’s full potential has yet to be realised.

"As of now, we are seeing it being used for unstructured data,” said Kumar, “whether it's documentation or SOPs — these are all classic structured documents that have been in place in factories for people to refer to. But if a particular situation arises in the factory, for example, a question of what should be done?, AI will be able to pull up all those documents and provide an answer. It can already do this today. So, I think that's where we will see the first revolution in terms of asking these questions and getting answers."

Despite this, AI solutions must go beyond digitising documents or tagged HTML. The goal is for generative AI to be able to provide operators with accurate answers when needed. Until then, companies like Rackspace Technology and Microsoft are doing their part to further research AI possibilities.

(This article originally appeared in Frontier Enterprise.)

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