July 21, 2024

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With the rapid technological advent, today we live in the midst of a digital transformation where businesses, especially manufacturing companies, are able to collect vast amounts of data from a diverse set of sources in the product life cycle. It is enabled by many emerging technologies, including Cyber-Physical Systems, Digital Twins, and the Industrial Internet of Things. Hence, the manufacturing life-cycle may produce data from many sources, including customers, stakeholders, equipment in production lines, products, and information systems.

Manufacturing companies can benefit from such data to improve and optimize their production processes and asset utilization and outperform competitors. To do so, manufacturing companies need to integrate data science capabilities vertically and horizontally across and beyond the organization and shift towards data-driven manufacturing. Vertical integration corresponds to alignment within the organization from the production floor to the shop floor, whereas horizontal integration aims to integrate multiple production facilities, supply chain partners, and business partners.

Data science employed on such data may provide descriptive, diagnostic, predictive, and prescriptive analytics capabilities to a manufacturer and may create opportunities to improve its production planning, process optimization, material tracking, equipment maintenance, quality control, and even new product design processes. It also allows organizations to produce business value by empowering businesses to support strategic decision-making.

Accordingly, data science paved the way for a data-driven manufacturing paradigm that extracts actionable insight gained from data into manufacturing intelligence, thereby enabling better-informed decision-making. Data-driven manufacturing aims to improve operational efficiency and product quality, together with reducing costs and risks. Even though data-driven manufacturing is able to reduce capital intensity by up to 30% and shorten production life-cycles up to 40 %., Manufacturing firms face difficulties in managing their data science endeavors to reap these potential benefits.

While data science is regarded as a groundbreaking technological breakthrough by both practitioners and scholars, manufacturing firms generally experience a lower return on investment than expected in this field. According to Gartner [1], 80 % of data science projects are not likely to produce any business value through 2020 because these projects are not managed and scaled with a standardized and systematic approach.

Manufacturing firms need to pay attention to data science and organizational capabilities to exploit data effectively as their key strategic assets and realize promising potential. However, only a small percentage of organizations can successfully obtain a business value from their investments due to a lack of organizational management, alignment, and culture [2]. Becoming a data-driven organization requires an organizational change that should be managed and fostered from a holistic multidisciplinary perspective. Accordingly, this post investigates the data-driven manufacturing from the organizational management perspective with four key pillars, (1) Change Management, (2) Skill Management, (3) Strategic Alignment, and (4) Sponsorship and Portfolio Management, to foster the transition to a data-driven organization.

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Change Management

Change Management is continuously adapting an organization’s structure, culture, and management capabilities against the demands of becoming a data-driven organization. Top-management support plays a crucial role in initiating organizational transformation to create a data-driven culture across the organization. To this end, the top management should lead the change management process by redefining organizational structure, leadership, and business processes. One of the main problems of change management is that the people involved in this transformation life-cycle generally do not share the same skills and terminology to serve as a baseline of communication. Thus, organizations should be restructured to share a common data-driven culture and to define an efficient and effective communication channel among data scientists, software developers, analysts, field workers, stakeholders, and top-executive managers to support collaboration and interaction in operating data science.

To sum up, the change management process covers evaluating how organizational policies and directives are established and maintained to restructure and align the organization in transition to a data-driven culture. As a result of the successful implementation of the change management process, organizations are expected to understand the scope and desire to change, assess stakeholders’ and employees’ readiness and willingness for change, identify and deploy action plans to motivate stakeholders and employees, and increase their participation in change management and monitor and sustain organizational changes.

Skill Management

The Skill management efforts on acquiring, training, and integrating skills and talents to build the right multidisciplinary team for data science and improve data literacy across the organization. Manufacturing organizations need to reconfigure and train their human resources according to rapidly changing business environments and technology solutions to sustain competitive advantage in their marketplace. To this end, they need to develop a unified human resource management strategy to determine how required people skills and competencies are identified, developed, or acquired and to evaluate the performance of candidates and employees against the defined performance criteria to meet organizational needs.

Another critical problem in skill management is training employees in the organization about the basic data science principles and methodologies to include them in the analytical decision-making process and improve data literacy across the organizations.

Strategic Alignment

Strategic Alignment defines how to establish a strategic direction and ensure a common understanding of organizational goals and strategic business directions. Manufacturing organizations start their journey to become data-driven manufacturing organizations by first establishing their data science strategy and vision. To do so, they need to align their organizations and data science strategies to stimulate their transition to a data-driven manufacturing organization and improve their profitability and reduce investment risks. This also allows organizations to leverage data science as their core competency and skill. This process enables organizations to understand their business environment and directions, determine their target data science capabilities in line with organizational vision, and establish and maintain a strategic plan and roadmap to drive alignment among business, data science, and IT units.

Sponsorship and Portfolio Management

The Sponsorship and Portfolio Management process aims to ensure that financial resources, projects, and assets are used effectively and efficiently to achieve organizational strategy, goals, and business directions to become a data-driven manufacturing organization. Moreover, sponsorship and portfolio management allow organizations to grasp optimal gain from strategically aligned investments at an affordable cost with a known and acceptable risk level. This process evaluates whether financial planning and controlling are managed in an organization to employ financial resources for efficiently funding projects to support becoming a data-driven manufacturing organization. This also includes developing a model evaluate and monitor ongoing funded projects to decide whether to continue or terminate funding and resources. As a result of successful implementation of this process, organizations execute the strategic direction set for their investments in line with organizational vision, consider and evaluate different sponsorship and funding models and options to support and maintain their portfolios, and monitor, optimize and evaluate projects in their ongoing portfolios to make adjustments according to their changing business environment and priorities.

Data science grasp the potential to improve operational performance and data-driven decision-making capabilities of business units and attain a competitive advantage in their businesses. As data science is a multidisciplinary domain, it should also be managed from the organizational management perspective apart from the technical perspective to manage and coordinate data science endeavors throughout the organization.

References

[1] Gartner. Our top data and analytics predicts for 2019. 2019 (accessed November 19, 2020), https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-a nd-analytics-predicts-for-2019/.

[2] Gökalp, M. O., Gökalp, E., Kayabay, K., Koçyiğit, A., & Eren, P. E. (2021). Data-driven manufacturing: An assessment model for data science maturity. Journal of Manufacturing Systems, 60, 527–546

[3] Gökalp, M. O., Kayabay, K., Gökalp, E., Koçyiğit, A., & Eren, P. E. (2021). Assessment of process capabilities in transition to a data‐driven organisation: A multidisciplinary approach. IET Software, 15(6), 376–390.

Data-driven Manufacturing: 4 Key Organizational Pillars Republished from Source https://towardsdatascience.com/data-driven-manufacturing-4-key-organizational-pillars-f6d08d3da9ff?source=rss—-7f60cf5620c9—4 via https://towardsdatascience.com/feed

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