Unveiling a Scholarly Structure for Intelligent Management of Medication Assistance Programs
A recent study from Pusan National University has compiled a comprehensive issue set for Manufacturing Data Analytics (MDA) implementation, offering manufacturers a better shot at making the MDA promise a reality. The study, published in the Journal of Manufacturing Systems, sheds light on the primary barriers hindering the widespread adoption of MDA and provides a clearer map to help manufacturers navigate potential roadblocks.
The Challenges of MDA Implementation
The study highlights that adoption of MDA is still low, not due to a lack of interest, but rather due to inability to see potential roadblocks. The main barriers to MDA implementation include:
- Data quality and standardization issues: Inconsistent and non-standardized data formats, exacerbated by manual data collection, contribute to data inaccuracy, incompleteness, and delays in analytics deployment.
- Legacy systems and technology integration challenges: Outdated machinery and systems that are not designed for digital data capture or easy integration with advanced analytics platforms hamper seamless data flow and real-time analytics capabilities.
- Equipment downtime and reactive maintenance culture: Without proper analytics, companies rely on reactive maintenance, causing costly unplanned downtime.
- Inconsistent quality control and manual inspection processes: Manual quality inspections miss defects, leading to quality issues that can cost significant revenue.
- Data overload and lack of analytical expertise: The exponential growth in data leaves many manufacturers overwhelmed, with a lack of expertise to effectively analyze and convert raw data into actionable insights.
- Supply chain complexity and disruption management: Tracking and using data efficiently for supply and demand forecasting is challenging in complex global supply chains.
- Workforce productivity and cultural resistance: Adoption of MDA requires skilled personnel and a culture open to data-driven decision making, but resistance to change, lack of training, and siloed operations can limit analytics implementation success.
A Comprehensive Approach to MDA Implementation
The study analysed 35 papers on MDA implementation and categorized recorded issues into five different categories: data analysis planning, data preparation, data analysis, evaluation and interpretation, and implementation into manufacturing systems. The study provides a comprehensive set of challenges mapped to implementation stages, offering a broad contextual view of MDA implementation.
Successful MDA implementation requires cross-functional fluency, trust between teams, and a willingness to learn new ways of working. The study aims to present the first CISM for MDA that addresses the full context of implementation. There is a need for tools that cover the entire data lifecycle in MDA implementation.
In summary, overcoming these barriers requires investment in digital infrastructure, upskilling staff, and adopting predictive, AI-driven analytics solutions to maximize manufacturing data benefits. Understanding these friction points is foundational to the evolution of manufacturing into an adaptive, efficient, data-driven system.
- Despite the promising potential of Manufacturing Data Analytics (MDA), its widespread adoption is hindered by various challenges within the industry, such as data quality and standardization issues, technology integration issues, equipment downtime, inconsistent quality control processes, data overload, supply chain complexity, and workforce productivity issues.
- To navigate these potential roadblocks, manufacturers require not only a comprehensive understanding of the entire data lifecycle in MDA implementation but also tools that facilitate cross-functional fluency, trust between teams, and a willingness to learn new methods of work.
- In order to make the most of manufacturing data benefits, it's essential to invest in modernizing digital infrastructure, upskill staff, and adopt advanced technologies like artificial intelligence (AI) to drive more predictive analytics solutions, ultimately fostering a more adaptive and efficient manufacturing industry driven by data and cloud computing.
