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- Legacy systems and technology integration challenges: Outdated machinery and systems that are not designed for digital data capture or easy integration with advanced analytics platforms hamper seamless data flow and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.
