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The Role of Advanced Analytics and AI in Reducing Manufacturing Costs

Advanced Analytics
Manufacturing
04 Mar 2024
5 min read

Tom Lin

The relentless pursuit of profitability is steering manufacturers toward innovative solutions. Amid this drive, advanced analytics has emerged as a transformative force, offering a paradigm shift in decision-making processes and operational strategies. However, manufacturers are in an experimental phase, seeking the right use cases and applications to translate these technologies to drive tangible cost reduction and profitability.

In this blog, we dive into key use cases, exploring how manufacturers leverage technologies like predictive analytics, artificial intelligence (AI), generative AI (GAI), and more to navigate complex challenges and reduce costs.

Advanced analytics and AI use cases in manufacturing

Let’s uncover four transformative use cases that are revolutionizing traditional processes, offering a glimpse into the future of efficient and economically sound operations.

Use case 1: AI-accelerated training

Staffing, hiring, and retaining skilled workers in manufacturing pose significant challenges due to not only the manufacturing labor shortage but also the prolonged learning curve for many manufacturing jobs, which spans years or even decades. AI can intervene by absorbing extensive training materials, enabling technicians to query and learn from an interactive knowledge base and accelerating otherwise labor-intensive manual training.

This allows technicians to seek specific guidance on complex issues, such as how to react to error codes, fostering a more efficient learning experience. The integration of AI in training not only creates operational efficiencies but also liberates time for experienced workers. Rather than assisting newcomers extensively, they can focus on strategic tasks, enhancing overall production. This approach facilitates quicker problem-solving, reduces bottlenecks, and has the potential to minimize production flaws.

Wavicle has experience using generative AI to ingest training materials as a method to redefine training for skilled workers. This type of application can process extensive training materials and equip workers to acquire skills and tackle job-specific challenges swiftly. This marks a significant advancement, demonstrating how advanced analytics technologies can revolutionize training methodologies, enhance workforce readiness in manufacturing, and address some of the challenges of the manufacturing labor shortage.

Use case 2: Simplifying master data using AI

Manufacturers often expand through acquisitions, resulting in a network of disparate data systems. This poses a common challenge – difficulty handling master data, especially for products or items, as the same parts can be labeled or defined in different ways in each system. Without a strong master data management (MDM) program, manufacturers often struggle to answer essential questions about their business, like “how much raw material am I purchasing from this vendor across all my supply chain?” or “what is my profit margin on this specific product?”

Traditionally, MDM has relied heavily on manual efforts to harmonize data collected from different sources and systems. AI steps in with an automated solution. Using AI, manufacturers can leverage matching logic to ensure data accuracy during integration with MDM platforms. This improves confidence in data matches and significantly reduces the costs associated with MDM projects.

Essentially, AI emerges as a helpful tool, allowing manufacturers to deal with complexity, improve data accuracy, and make smart decisions more quickly. With a robust MDM program – powered by AI to be more accurate and cost-efficient – manufacturers can easily answer those critical product and business questions, enabling them to make better and more fiscally responsible business decisions.

Use case 3: Automating data catalogs

IT and data teams often struggle to manage metadata, relying heavily on manual processes that data stewards handle. This game is changing with the integration of GAI, which can be used to automate metadata processing and cataloging.

Using GAI to streamline metadata management significantly improves the quality and accessibility of data for projects throughout an organization. With GAI, data teams can automate tasks such as data crawling, tagging, and definition creation to eliminate most of the manual work involved, leaving experts to validate the results. It can also streamline the process and substantially reduce the time and costs associated with governance and data cataloging projects.

The shift towards leveraging AI in metadata management marks a pragmatic improvement, enabling organizations to navigate data complexities and allocate resources efficiently.

Use case 4: Predictive maintenance

In pursuing operational efficiency and cost-effectiveness, manufacturers encounter a formidable challenge in maintaining machinery. This is where predictive maintenance emerges as a promising solution for companies aiming to increase machine uptime and productivity, reduce errors, and operate more efficiently.

However, the real challenge arises in implementing and scaling predictive maintenance. Each machine carries unique data, making applying a single model universally a challenging endeavor. The variability among machine types requires a careful and strategic approach to data capture and storage, as the sheer volumes of data that can be collected can unnecessarily rack up massive data storage costs.

Only with a careful approach can you leverage the full potential of predictive maintenance and operate machinery with optimal efficiency and cost-effectiveness. While predictive maintenance offers a significant ROI when applied correctly, many manufacturers need external expertise to help them determine the right strategy for capturing and storing data and developing predictive models that can accurately predict machinery issues before they happen.

Why it’s beneficial to partner with data experts

The path to cost reduction in manufacturing lies in strategic technology investments, particularly in advanced analytics and AI. These transformative solutions empower manufacturers to enhance operational efficiency and drive down costs.

However, successfully navigating the complexities of these technologies requires the expertise of data professionals. Wavicle’s data experts have guided many manufacturers throughout their data and analytics journeys, helping them modernize systems and take advantage of new opportunities with advanced analytics. If you want to embark on this cost-saving journey, get in touch with Wavicle to collaborate with seasoned data professionals who can pave the way for your manufacturing success.

WIT Leader

Tom Lin

Tom helps manufacturers apply data and AI to improve operations, lower costs, and spark innovation. He brings 20+ years of hands-on analytics and strategy expertise.

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The Role of Advanced Analytics and AI in Reducing Manufacturing Costs

Tom Lin

Advanced analytics and AI use cases in manufacturing

Use case 1: AI-accelerated training

Use case 2: Simplifying master data using AI

Use case 3: Automating data catalogs

Use case 4: Predictive maintenance

Why it’s beneficial to partner with data experts

WIT Leader

Tom Lin

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