Table 1 Summary of the validation phase... | Emerald Publishing

Table 1

Summary of the validation phase

Focus	Strengths	Weaknesses	The solution adopted/proposed
Framework	Sound structure Enables an approach that fosters a “continuous improvement” working mode It can be used at the strategic, tactical and operational levels depending on the needs	Cost and infrastructure should be carefully considered Data collection may be prevented for security reasons	Contractual clauses should be established with customers considering infrastructure's maintenance and data ownership
Identification of the machine critical components	Dynamic FMECA shortens the problem identification and resolution time Dynamic FMECA improve spare parts management	Dynamic FMECA may be too labour-intensive for some companies Causes of component failures must be carefully evaluated	Open review meetings added as an alternative to dynamic FMECA RCA added to support the identification of the components' failure cause
Machine data analysis	ML supports advanced analyses ML allows the introduction of preventive and predictive maintenance strategies	Some companies do not have the competencies to run ML-based analyses or are not interested in it Preventive maintenance policies require historical data	Statistics added as an alternative to ML
Service data collection	Service report structure Possibility to ease even more the filling phase for companies who are developing an app and a platform able to manage automatically general information	Some companies use serial numbers to track components The software/firmware version is not tracked; it could be useful for problem tracking	Added the possibility to track worked components via serial numbers in the service report Added the possibility to enter the software/firmware version
Service data analysis	Statistics for companies interested in descriptive analyses A competencies database is useful for later decisions and resources improvement	Some companies use text-intensive reports that complicate manual analysis and data extraction	Added the possibility to use NLP
Optimization model	General structure and approach validated Data coherent with the current process Support for planners and substitutes	Contingent factors are not considered (e.g. visa problems)	Added contingent factors to the model

Focus	Strengths	Weaknesses	The solution adopted/proposed
Framework	Sound structure Enables an approach that fosters a “continuous improvement” working mode It can be used at the strategic, tactical and operational levels depending on the needs	Cost and infrastructure should be carefully considered Data collection may be prevented for security reasons	Contractual clauses should be established with customers considering infrastructure's maintenance and data ownership
Identification of the machine critical components	Dynamic FMECA shortens the problem identification and resolution time Dynamic FMECA improve spare parts management	Dynamic FMECA may be too labour-intensive for some companies Causes of component failures must be carefully evaluated	Open review meetings added as an alternative to dynamic FMECA RCA added to support the identification of the components' failure cause
Machine data analysis	ML supports advanced analyses ML allows the introduction of preventive and predictive maintenance strategies	Some companies do not have the competencies to run ML-based analyses or are not interested in it Preventive maintenance policies require historical data	Statistics added as an alternative to ML
Service data collection	Service report structure Possibility to ease even more the filling phase for companies who are developing an app and a platform able to manage automatically general information	Some companies use serial numbers to track components The software/firmware version is not tracked; it could be useful for problem tracking	Added the possibility to track worked components via serial numbers in the service report Added the possibility to enter the software/firmware version
Service data analysis	Statistics for companies interested in descriptive analyses A competencies database is useful for later decisions and resources improvement	Some companies use text-intensive reports that complicate manual analysis and data extraction	Added the possibility to use NLP
Optimization model	General structure and approach validated Data coherent with the current process Support for planners and substitutes	Contingent factors are not considered (e.g. visa problems)	Added contingent factors to the model