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Enhanced Data Visibility with AI for Improved Operational Efficiencies

Manufacturing is in an era of rapid development, and companies are routinely searching for new innovations to separate themselves from competitors and streamline operations. Artificial intelligence (AI) is no stranger to the industry. The decades-old technology brought robotics and automated processes, assistance in routine tasks, and greater efficiency and production output. Notably in the past few years, AI has expanded across departments and provides companies with data and information faster, more accurately, and more routinely than that done by humans. But there is a caveat – businesses must connect the technology with intention and collaboration, which is a challenge when the historical trends have led to a wide range of diversity in platforms, programming, and connectivity between operations and in-house data tools.  

By 2025, 40% of manufacturers will have enterprise-wide AI-based tools to support decision making and maximize the value of data. The push for data transparency and connectivity between information technology (IT) and operational technology (OT) is moving forward with momentum. IT/OT collaboration may be the untapped secret to determining the success of digital strategies, along with proper stakeholder alignment. To create strong data visibility across IT and OT, organizations may consider data solutions and tools that scale. Manufacturing partners, including Microsoft, offer services such as custom copilots, toolchains, and data transference to cloud networks, which unite, adapt, or create tools to optimize data and analytics into useable information in the theoretical snap-of-the-fingers time thanks to AI.

Where Data Visibility Gives You Decision Empowerment

Collecting data is not the challenge, rather it’s the interpretation and analysis of the data for decision making. One of the tools that can take this on is an AI copilot. AI copilots use generative AI to assist humans with complex cognitive tasks. They can enhance productivity and efficiency by responding to natural language requests with context-aware aid, taking charge and automating tasks, providing fast data analytics and connecting various platforms. ChatGPT, Bard, or Bing Chat offer GenAI assistance and reasoning that many individuals have used. There are opportunities to take it to the next level for organization-centric needs with custom copilots. Logic can tailor a custom copilot to enterprise-specific capabilities that meet organizational needs.

Explore data visibility opportunities that copilots bring to a manufacturing enterprise:

  • Enterprise Optimization: Through the unity of organizational data, enterprise resource planning (ERP) can adapt and visualize multiple scenarios or simulate production with real-time data. Enterprise-wide reports are created by AI with top-level metrics for human action, without the time-consuming analysis from personal oversight, enhancing the business decision-making process.
  • Frontline Support: By collecting institutional knowledge across the departments, employees can access best practices, training materials, and any information needed to successfully complete their work and amplify knowledge attainment.  
  • Accelerate Innovation: Improve product system design and performance with generative AI and find correlations that had not been analyzed. Teams gain access to immediate design or engineering questions to drive productivity forward.

Driving successful GenAI implementation across a manufacturing organization necessitates alignment among key stakeholders about the desired use case. The breadth of applications has created opportunities to engage the technology from the operational front, as seen above, to going beyond historically niche areas and permeating greater functions, from finance with predictive modeling to call center agent support to enhanced customer service performance. With a business outcome in mind, partners can assist in crafting the tool implementation best assessed to achieve strategy alignment.

Through common data foundation technologies and initiatives, converged IT/OT teams can dedicate and leverage the resources universally and tap into the technologies that automate the data integration.

Blending Information with Operation Technology and Vice Versa

One of the core reasons that manufacturers are investing in their IT/OT integration is for improved operational performance. To succeed at that transformation and become a data-driven operation, the two departments require a synergistic deployment of data resources. The technological capabilities needed to converge the two include hardware and hosting infrastructure and common data foundation, providing the business data estate with unified, high-quality data.

Hardware and hosting infrastructure is the first capability to support IT/OT convergence. While previously air-gapped networks must be connected, organizations can ensure data is available to aggregate more broadly and contextualized into a common environment such as the cloud. Remote access for AI and machine learning removes the necessity of having on-site storage and applications. According to IDC's 2022 Worldwide IT/OT Convergence Survey of over 1,000 IT and operations professionals in industrial verticals, 31.2% of manufacturers are performing more than 26% of their operations monitoring and execution activities remotely today. Implementing edge computing technologies helps support the infrastructure of data flows and relieves pressure on staffing in-person technology professionals.  

Moving on to a common data foundation, operations has often been an isolated business function, localizing data to inform local operations and machines. Operational data is difficult to work with, from lack of data connectivity standards to inconsistent metadata, or even the many database formats. This data can be interpreted differently by department or individual, which is a challenge if this non-universal data is being used as part of the configuration in AI applications. Finding clean data and translating it into the business process is a missed opportunity. In fact, “only 28% of manufacturing organizations are using data from equipment, processes, and systems to draw insights for continuous process improvement,” according to IBM Institute for Business Value.

The shift into broader consumption of data is not easy. But, through common data foundation technologies and initiatives, converged IT/OT teams can dedicate and leverage the resources universally and tap into the technologies that automate the data integration. Automating data integration is essential as generative AI and other data tools are only as good as the data it learns and acts on. When will converged success happen? According to Manufacturers Alliance research, 84% of surveyed companies see IT and OT teams working together more closely in the near future, so data integration could be close behind.

Expanding data stakeholders and cross-department collaboration does not have to come with risks for data security. Frameworks built into the data estate can run with conditional access and be developed around the governance standards and regulatory needs of the company. By setting permissions and enabling access to the right data for the right users, those who need the data have real-time capabilities to collaborate with it. And those who don’t, do not have access, enhancing security protocols.

Microsoft’s Cloud Scale Analytics is one solution to building a unified manufacturing data estate. With a single estate for analytics, organizations can monitor access, prepare data for machine learning and AI use cases, and empower decision makers with self-service interaction tools. In fact, the company was able to help Mercedez-Benz when it was looking to make its vehicle production network more intelligent, sustainable, and resilient across its 30 plants worldwide. To find a solution, they collaborated with Microsoft to create the MO350 Data Platform, enabling flexibility and cloud computing power to run AI and analytics globally without compromising cybersecurity or compliance.  

How Will It Transform Your Business?  

Data transparency gives feedback for improved operational performance and improved product and service quality. Additionally, transparency can assist in bridging the gaps between IT and OT and help future-proof operations. Customization can create alignment with global organizational goals and pave the opportunity for longer term indirect benefits, such as footprint optimization or talent access. Many manufacturers report increased product quality, reduced production costs, scalable production, increased throughput, improved equipment efficiency, and improved sustainability across the various applications and teams that now have access to generative AI applications thanks to widespread value chain adoption Dedicated team players and partners establishing set strategies and use cases for GenAI will carry the data movement towards success.

Siemens and Microsoft recently demonstrated how developers and engineers could accelerate the code generation for Programmable Logic Controllers (PLC), the industrial computer systems that control most machines in factories. They showcased how OpenAI's ChatGPT and other Azure AI services can complement Siemens' industrial automation solutions to significantly reduce time and errors by generating PLC code through natural language inputs.

With a variety of use cases for operational improvements and cost savings, build your opportunities for data-based decision making by taking advantage of AI applications.  

Manufacturers Alliance works with a variety of partners in the manufacturing community. It does not endorse any company as a neutral organization.

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