How manufacturers can find their North Star, according to Brendan Mislin

How manufacturers can find their North Star, according to Brendan Mislin


Avanade’s global Industry X lead reveals why it is difficult to track industrial data across multiple applications and how generative AI and analytics tools are helping to improve operations

Alice Chambers |

Manufacturers face a daunting task in monitoring huge volumes of near real-time data across a diversity of IT and operational technology (OT) software systems. Brendan Mislin, the global Industry X lead at Avanade, draws a parallel between this industrial conundrum and the familiar complexities of managing personal finances. 

“The data challenge is the same,” he says. “Starting out on your personal finance journey seems simple. First, you open a bank account, then you buy car insurance from a second provider before buying a mortgage from a third company a few years later. Fast forward a few years and there are multiple other outgoings to consider. It quickly becomes a complicated web of different accounts. It’s really difficult to ensure you are getting the very best deals and optimising your financial position. 

“Manufacturers follow a similar process when opening a factory. They begin by buying a historian to save all the data that is generated from their machines. But, six months later, they will likely need to resolve a quality problem, so they hire a solutions specialist to build an analytics system, and so on. Fast forward 10 years and a manufacturer can be dealing with potentially hundreds of different systems, all of which are producing their own data.” 

The longer a factory has been operating, the more complex it is to track performance and optimise operations. Traditionally, manufacturers have been able to use solutions from independent software vendors to aggregate different types of data. For example, they could pull industrial data from manufacturing execution systems (MES), programmable logic controllers (PLCs) and historians. Meanwhile, enterprise resource planning (ERP) from solutions, like Microsoft Dynamics 365, provide a unified view of business data, including finance, human resources, services and procurement. Such siloes will become a thing of the past soon. Technology providers like Microsoft and Avanade are helping to consolidate operational and business intelligence by enabling all this data to be accessed via one platform. 

“Going from 50 different systems to a situation where there’s now only two or three is great but that still doesn’t give that north star of a single point of truth for manufacturers across siloed data within a diversity of business function and supplier systems,” says Mislin. “One of the best answers to this problem is Microsoft Fabric’s One Lake.” 

This solution provides a point of integration where all data can be made accessible and visible in one place. Organisations can then build dashboards to understand the performance across complex, inter-dependent and globalised value chains. 

“Manufacturers can use artificial intelligence to understand data from right across their business and partner ecosystem,” says Mislin. “Tools like Microsoft Copilot in Microsoft Fabric play into this. Firms can deploy generative AI at the top level of their enterprise structure and use it to provide answers to various questions within any function. For example, it can compile all the relevant data intelligence for an end-of-shift report, including late deliveries, whether any machines went down or which employees went home sick.” 

Improving efficiency with analytics  

Data analytics tools can be used to both complement human work and replace the manual tasks that can be automated, lifting the burden of work for manufacturing employees who are under pressure to achieve tough output targets to meet customer demand, against the backdrop of worker and skills shortages. For example, Avanade worked with a large automotive manufacturer to add a vision analytics solution to its welding process to save time for engineers and improve safety and quality assurance. 

“One of the critical safety compliance stages for car manufacturers lies in the production of the chassis,” explains Mislin. “The body assembly team builds the car’s metal frame by welding several parts together. Since the chassis is vital for a vehicle to withstand daily usage and the risk of accidents on our busy roads, high-quality welds are vital to provide structural strength, rigidity, and stability to the vehicle. 

“Avanade worked with the manufacturer to develop a solution that can instantaneously analyse photos of welds at incredibly high-definition – something that a human simply can’t do. This a simple example of technology gives humans superpowers, enabling us to pre-empt problems and achieve more within our hectic daily working lives.” 

This move towards more autonomous operations within factories can help firms to reduce waste, improve quality and lessen very high workloads for staff. Avanade’s work with the confectionary and pet food manufacturer Mars exemplifies how digital twin technology helps manufacturers to optimise their supply chains. 

“Working with Microsoft partner Accenture, Avanade assisted Mars in reducing its costly giveaways, which is when too much product is accidentally put into bags,” says Mislin. “We found that the calibration of the Mars industrial scales drifted every few days, which might cause problems for retailers who buy on a weight rather than unit price basis. We developed a digital twin of the production line to predict and alert a person on the factory floor.” 

The solution reduced giveaways by 80 per cent, saving Mars significant amounts of money per year. The firm was also able adopt closed-loop automation where the system no longer requires an engineer to approve recalibrations, saving them time and allowing them to focus on value-added activities.  

Avanade worked with Volvo Cars to develop an environmental impact measurement tool, based on the Microsoft sustainability suite of solutions, that’s being used globally as part of their mission to be carbon neutral by 2040. Automotive firms need to be able to integrate and translate data from multiple sources into actionable insights in real time. The new tool enables data to be gathered from Volvo’s 200 non-manufacturing facilities, 2,500 retailers and manufacturing facilities. By delivering reporting across heating systems and wastewater, data-driven decisions can be made to drive down the environmental impact and carbon footprint. 

“Manufacturers can use similar technology to understand their whole ecosystem of operations. A digital twin model can oversee all processes and predict a manufacturer’s environmental performance and offer recommendations for materials and parts to help it meet its sustainability goals.”

Mars production line factory

Mars has reduced giveaways by 80 per cent with Avanade’s digital twin solution (image credit: Mars)

Design, build and support

Manufacturers can use generative AI to improve operations across the design, make and service process of creating a product.

Traditionally, the design stage for a product can take up to several months, but generative AI helps to reduce this time-frame significantly.

“We build generative AI solutions that run directly within a customers’ infrastructure to generate very innovative and iterative designs in an instant,” says Mislin. “Users can give the system prompts like ‘I want a new prototype of the product and I want it to be five per cent smaller than the previous model’, and it can create hundreds of possibilities within minutes. This supports the creative process by providing customisable product prototypes quickly and effectively.” 

Similarly, if a machine is down during production, businesses can use generative AI to fix the problem. 

“PLCs were built with coding that is no longer commonplace,” explains Mislin. “To find a bug on the factory floor, employees can copy and paste the PLC coding into a generative AI-powered system. It can identify what the coding is designed to do and find any bugs. What’s more, generative AI can then write code to help solve the problem and catch similar issues in the future, saving engineers time and providing relevant, real-world training resources.”

Additionally, manufacturers need to provide customer support for their products once they’ve been designed and produced and are in customers’ hands – from both a customer contact centre and field services perspective. Generative AI can provide succinct, insightful recommendations, drawing on previous cases. Frontline workers are able to access the collective intelligence of their entire organisation to help customers resolve problems faster.  

Volvo Cars production line factory

Volvo is measuring its sustainability performance with help from Avanade and Microsoft (image credit: Volvo Cars)

An aging workforce

These technologies can really help to lift the burden of work for staff. The Manufacturing Institute predicts that there will be 2.1 million unfulfilled manufacturing jobs by 2030. Mislin attributes this to a mismatch in skills in an era where we are still at the early stages of layering on digital world on top of a physical world. 

“The younger generations are digital natives and want to use these skills when they get into factories which tend to run on older technologies. This creates an uncomfortable environment for them to work in,” says Mislin. “But new solutions, like generative AI, are exciting tools that can bridge the gap between OT and IT environments, helping the industry to overcome this problem and bridging gaps in areas like legacy PLC coding.  

As the older workforce retires, manufacturers will need to invest in technology like augmented reality and virtual reality headsets and systems to train their new workforce quickly and effectively. Avanade has helped an industrial equipment manufacturer to do this.

“Our customer manufactures electrical, high-voltage cabinets that are extremely dangerous to work on, so the training process is rigorous,” says Mislin. “We built a virtual reality training environment for them with a step-by-step training guide that gradually presents more complicated scenarios. It also provides less hints as an employee develops so they can make mistakes in the virtual set-up rather than in the real world. This means there are no real consequences and workers know how to maintain safety in real-life scenarios.”

The journey towards integrating and optimising data within the manufacturing sector is a challenging, yet essential, endeavour. Leveraging technological advancements such as Microsoft Fabric’s One Lake and generative AI can help manufacturers to streamline operations, enhance safety and boost efficiency. And, as the aging workforce transitions towards retirement, manufacturers can harness digital transformation to attract a new generation of workers while driving the sustainable growth of their industry.

“The future of manufacturing lies in embracing these innovations to ensure companies remain competitive in a rapidly evolving digital landscape,” says Mislin.

This article was originally published in the Spring 2024 issue of Technology Record. To get future issues delivered directly to your inbox, sign up for a free subscription.

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