Organizational Structures in Advanced Manufacturing: Identifying Four Crucial Support Pillars
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In the modern industrial landscape, data-driven decision-making has become a cornerstone for manufacturing firms seeking to enhance operational efficiency, reduce costs, and drive innovation. A strategic, integrated approach is key to transforming these firms into data-driven organizations.
This approach combines the power of technology, people, and process alignment to maximise the value of data and operationalise insights effectively. Here's how:
Leverage AI-driven manufacturing analytics to convert vast amounts of data from sensors, machines, and production systems into actionable insights. AI-powered analytics have been instrumental in delivering up to a 35% reduction in unplanned downtime and a 92% improvement in equipment effectiveness [1][3].
Implement predictive analytics and digital twins to simulate production processes, optimise workflows, and anticipate equipment failures before they occur. Digital twins enable real-time monitoring, remote control, and scenario testing, significantly improving productivity and minimising costly errors [5].
Ensure high data quality through continuous data cleansing by combining automated and manual methods. Clean and accurate data are essential to avoid misguided decisions and maximise the reliability of analytical outcomes [4].
Integrate data analytics with supply chain and production planning by automating routine planning tasks where appropriate and applying real-time data for informed decision-making. This integration increases supply chain visibility, helps manage disruptions, and aligns operational schedules with demand [2].
Align people with processes and technology by fostering organisational change management, training teams to adopt new data-driven tools, and embedding analytics into daily workflows. Successful transformation requires collaboration across functions, aligning culture, skills, and incentives for data-driven decision-making [2].
Adopt scalable IoT and big data platforms that support collecting, storing, and processing large datasets from the factory floor to enterprise systems, enabling advanced analytics such as machine learning and AI [3].
Projects in ongoing portfolios are monitored, optimised, and evaluated to make adjustments according to changing business environments and priorities. Emerging technologies like Cyber-Physical Systems, Digital Twins, and the Industrial Internet of Things enable this data collection.
Data science, as a multidisciplinary domain, should be managed from an organisational management perspective apart from the technical perspective. Different sponsorship and funding models and options are considered to support and maintain portfolios.
Becoming a data-driven organisation requires an organisational change managed and fostered from a holistic multidisciplinary perspective. Sponsorship and Portfolio Management ensures effective and efficient use of financial resources, projects, and assets to achieve organisational strategy, goals, and business directions.
Organisations evaluate whether financial planning and controlling are managed effectively to fund data science projects supporting their transition to a data-driven manufacturing organisation. Data science can produce business value by supporting strategic decision-making.
Data-driven manufacturing can reduce capital intensity by up to 30% and shorten production life-cycles by up to 40%. Manufacturing companies can collect data from various sources in the product life cycle, including customers, stakeholders, equipment, products, and information systems.
Manufacturing organisations establish a data science strategy and vision to align their organisations and data science strategies, stimulating their transition to a data-driven manufacturing organisation. The strategic plan and roadmap are established to drive alignment among business, data science, and IT units.
The potential for data science to improve operational performance and data-driven decision-making capabilities of business units is recognised. Organisations execute the strategic direction set for their investments in line with organisational vision.
Data science can improve and optimise production processes and asset utilisation for manufacturing companies. It can improve production planning, process optimization, material tracking, equipment maintenance, quality control, and new product design processes.
Data science can provide descriptive, diagnostic, predictive, and prescriptive analytics capabilities for manufacturing companies. Change Management is a process that adapts an organisation's structure, culture, and management capabilities to become data-driven. It involves top-management support, redefinition of organisational structure, leadership, and business processes, and establishing a common data-driven culture.
A model is developed to evaluate and monitor ongoing funded projects to decide whether to continue or terminate funding and resources. This approach drives improved productivity, cost savings, quality, and innovation in manufacturing operations [1][3][5].
- To maximize the financial impact of data-driven decision-making in the manufacturing industry, it's crucial to incorporate data science strategies into business planning and controlling.
- Technology tools like AI-driven manufacturing analytics, predictive analytics, and digital twins can drive innovations in finance by reducing unplanned downtime, improving equipment effectiveness, and boosting productivity.
- As the manufacturing sector shifts towards data-driven operations, the financing of data science projects should be overseen by a comprehensive sponsorship and portfolio management approach to ensure alignment with the overall organizational strategy and business directions.
