Industry
Manufacturing
TCS Neural Manufacturing: Driving Agility & a Competitive Edge
Highlights
- Manufacturers have traditionally been coy to undertake large scale business transformation processes. They have adopted a rather cautious approach and focused on incremental innovations.
- GenAI has the potential to drive much-desired transformations in manufacturing. This technology will not only have a positive impact on operations among others, but also boost purpose-driven innovations at the workplace.
- However, next-gen technologies are also fraught with risks. Manufacturers need to strike the right balance between the potential they have and the risks they pose to derive maximum value.
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Innovation – yesterday, today, and tomorrow
While manufacturers recognize that innovation is the cornerstone of business growth, most companies have thus far approached it with caution.
By caution, we mean that manufacturers have traditionally preferred localized and incremental process improvements rather than going whole hog and bringing about large-scale transformation. This can mostly be attributed to the notion that if a process is running smoothly, it is best not to tinker with it. Moreover, large-scale transformations are not easy on the pocket by any measure – this could be another deterrent. And while this mindset may pay off decent dividends in the short term, it doesn’t really bode well for the longer term, at least it no longer will, given the pace at which digital technologies are pervading our lives.
Traditionally, manufacturers have focused on two things: consistently producing high-quality products and minimizing the risk of downtime. However, both these aspects end up creating rigid systems in the long run. This narrow focus and reticence often make it difficult for manufacturers to explore innovative ideas using next-gen technologies and business models.
In comes GenAI, and innovation gets a new meaning
Generative artificial intelligence (GenAI) can trigger widespread, purpose-driven innovation in the manufacturing industry.
This will make operations more efficient, sharpen the focus on sustainability initiatives, and elevate customer experience. It will also boost purpose-driven innovations at the workplace, improving employee productivity, which will ultimately translate to business outcomes. Our conversations with global manufacturers corroborate this fact. The generative AI market in the manufacturing industry is expected to be about USD 7 billion by 2032, expanding at a CAGR of 41% between 2023 and 2032.
Traditionally, the creation and deployment of AI solutions were in the domain of data scientists. GenAI has disrupted this exclusivity, enabling the wider community to develop AI solutions for a range of business challenges across industries. While traditional AI systems are designed to perform specific tasks like image recognition and natural language processing, GenAI enables more creative applications like generating new images, videos, or 3D content.
With legacy systems and established management processes dominating traditional manufacturing, GenAI offers a way to do things differently as it interprets complex data to refine operational strategies on both the factory floor and at an organizational level. It therefore allows manufacturers to address data quality, reliability, and availability challenges a tad more holistically.
Amid this excitement, one common concern voiced is the challenge of explaining the outcomes produced by AI systems. The manufacturing sector, like others, grapples with the need for clear insights generated by AI to build trust with customers and maintain transparency. Consequently, the potential of GenAI runs the risk of remaining untapped in various customer-facing scenarios.
This has, however, not diminished the industry's enthusiasm. Manufacturers are actively seeking ways to optimize operations and simplify everyday tasks for their employees, allowing them to invest time in more meaningful, value-adding work.
GenAI and purpose-driven innovation
GenAI will usher in transformative changes for the business, the workforce, and the customer.
Purpose-driven innovation goes beyond profit motives; it looks to resolve real-world problems and create a positive societal impact. Manufacturers can unlock the potential of GenAI through internal initiatives led by such purpose-driven approaches. These initiatives can serve as valuable learning curves, enabling organizations to gain insights into the technology while mitigating the risks associated with change.
By delegating repetitive and mundane tasks to GenAI, employees can reclaim valuable time and concentrate on tasks that align with their interests and expertise. This shift will not only enhance job satisfaction but also nurture a culture of creativity and innovation.
To begin with, GenAI can:
Improve the advisory function: Be it an in-store interaction with a customer on which car to buy, a sales pitch for industrial machinery to an enterprise, or even a contact center conversation with an aggrieved customer, GenAI can empower employees by arming them with a sea of actionable data insights. That will improve the overall quality and outcomes of the advisory function. Let us look at how this would work.
Consider a conversation between a call center executive and a customer for post-sales service support. Here, a GenAI model will serve as an astute observer, scrutinizing every detail, and extracting valuable insights from the customer’s responses. In real-time, this model can anticipate customer requirements and offer the call center executive instant insights, empowering them to provide personalized and impactful recommendations, aligned with the customer's needs. This would not only improve the efficiency of contact center operations but also enhance customer experience and ultimately, strengthen the company’s brand and deepen customer relationships.
Fuel the sustainability agenda: Sustainability ranks high in manufacturing, both from an industry perspective and among customers of manufacturing organizations. Manufacturers can harness the power of GenAI to institute sustainable practices across their operations. By leveraging the data analysis capabilities of GenAI, manufacturers can stay informed about the environmental, social, and governance (ESG) performance of manufacturing processes, market trends, regulatory changes, and sustainability practices.
Manufacturers can tailor their strategies to align with their sustainability goals, fostering an eco-friendlier future. For example, Schneider Electric drives GenAI productivity and sustainability solutions by integrating Microsoft Azure OpenAI. That integration helps to develop solutions that leverage algorithms to generate text, code, and other types of content. This has empowered the manufacturer to reimagine its approach to various operational processes, streamlining time-consuming tasks, optimizing resource allocation, and gaining speed and efficiency.
Enhance employee experience and productivity: Manufacturers use a range of software applications across the value chain. IT, therefore, is a key focus area for manufacturing firms. GenAI can act as an intelligent assistant for software developers, helping them address coding challenges, thereby boosting productivity and learning. Developers can rely on this assistant to analyze codebases, recommend optimized code segments, propose alternative solutions, and highlight potential errors. By serving as a trusted collaborator, GenAI empowers developers to overcome challenges and make informed decisions. Moreover, it facilitates continuous learning and upskilling, enabling developers to focus on problem-solving and innovation. A North American fertilizer company is leveraging GenAI to not only modernize its legacy code base but also improve the overall efficiency of its development process.
A promising future
It's still early days for GenAI adoption to truly take off across sectors, but manufacturers must start blueprinting a strategy right away.
The vast potential of this technology is yet to be unraveled in its entirety. The integration of GenAI with other technologies such as blockchain and biometrics, can further revolutionize manufacturing, At the same time, the threats posed by GenAI are too serious to ignore which include data privacy and security, interpretability, and biases. Striking the right balance between the potential and risks will go a long way in fostering innovation in the manufacturing industry.
