Key performance indicators for AI success in manufacturing companies
Scott Schlesinger, US data and analytics lead at PA Consulting, and Scott Siegel, data and analytics expert at PA, explain how manufacturing companies can implement data mesh to achieve Key Performance Indicators in SupplyChainBrain article.
Manufacturing companies are facing more pressure to use data-driven technologies like artificial intelligence (AI) to optimize their operations. A data mesh program can help with the implementation of AI.
A data mesh is an architectural principle for a distributed approach to data management that treats data as a product that can be managed and owned. In short, a data mesh democratizes data while improving quality, integration, business self-service and scalability.
Implementing AI at scale requires access to high-quality data that is properly integrated, governed and discoverable. Traditional data management architectures are often designed with centralized storage, processing and analysis. The main difference between data mesh and traditional architectures is the shift from a centralized to a decentralized approach to data management with a focus on empowering domain experts and collaboration.
Data mesh architecture can reduce costs, increase efficiency and productivity, and enable faster responses leading to innovation and a competitive advantage for businesses. Data mesh approaches must be adopted incrementally while companies still leverage existing investments in infrastructure and other tools.
Data meshes can also allow manufacturing companies to break down data silos, democratize data, improve data standardization, foster a culture of collaboration, and drive innovation. With a data mesh, manufacturing companies can accelerate their AI initiatives and improve the accuracy and efficiency of AI models. Adidas, JPMorgan, Michelin and Equinor are some of the big-name brands that have embraced data mesh architecture.
Organizations that implement data mesh architecture are looking to achieve the following Key Performance Indicators (KPIs):
AI-powered predictive maintenance: AI algorithms can predict equipment failures and schedule maintenance activities. By combining IoT (Internet of Things) data with machine learning models, manufacturers can create predictive maintenance models that identify potential issues before they occur. A data mesh can provide a standardized framework for integrating IoT data and AI models.
Quality control: AI-powered quality control can proactively recognize and address issues and lower waste, reducing customer complaints and warranty claims. A data mesh can enable teams to build their own quality control models, ensuring data is properly integrated, governed and discovered. Furthermore, a data mesh can lead to greater accountability, ownership and transparency. Additionally, a data mesh can facilitate quicker data quality issue identification and resolution while enhancing data discoverability and lineage.
Supply chain and process optimization: Manufacturers can use AI algorithms to enrich supply chain operations by predicting demand, optimizing inventory and improving logistics. A data mesh allows for the easy collection and analysis of data from a variety of sources, helping teams own and manage their own supply chain data domains while providing a holistic view of supply chain operations under a standardized framework.
Customer insights: AI algorithms can help manufacturing companies gain insights into customer behavior and preferences through data analysis from social media, customer reviews, demographics and sales data. A data mesh can provide a standardized framework for integrating customer data while ensuring privacy and security. Real-time insights allow for the development of new products and services, the enhancement of marketing strategies, and the improvement of customer service practices.
Sustainability/Energy efficiency: Manufacturing companies can use AI to optimize their energy usage by identifying energy waste, water consumption and waste generation all while implementing energy-saving measures. Addressing and executing sustainable strategies reduces risks, lowers costs, improves results and creates a brighter future. Data meshes can allow teams to own and manage their energy usage data domains enabling better data management, collaboration and proactive decision-making.
Innovation: AI-powered innovations create new products, services and business models. A data mesh can democratize data, making it available to a wide range of stakeholders and enabling teams to experiment, innovate and create new AI models. Real-time holistic views of data can facilitate untapped areas for innovation. In turn, manufacturing companies are able to best their competitors by rapidly creating new values from their data.
Data can be used by manufacturing companies to optimize operations, deliver value to customers and stay competitive. Adopting data meshes with AI is crucial to achieving these goals. A data mesh can break down data silos, democratize data and foster collaboration providing manufacturers with a wide range of benefits, including predictive maintenance, quality control, enhanced supply chain optimization and sustainability efficiency. As a result, manufacturing companies can drive innovation, accelerate decision-making and ultimately gain a competitive edge in the industry.