Table 2 Improvements based on the D3M... | Emerald Publishing

Table 2

Improvements based on the D3M framework introduction for all the companies interviewed during the research

		Company A	Company B	Company C	Company D	Company E
	Location	Italy	Europe	China	Europe	Italy
	Product	Balancing machines	Robotics and power equipment	Automated guided vehicles	Road construction equipment	Pumps and equipment for the oil and gas market
Stream
Machine As-is	Critical components identification	FMECA to identify the critical components			Critical components identified through designers' experience
	Machine data collection	Few data collected due to privacy constraints Programmable logic controllerPLC) and sensor data available only on the machine Remote data sharing allowed in only a few cases and with few customers		Big amount of data collected PLC and sensor data available on the cloud	Few data collected due to privacy constraints PLC and sensor data available only on the machine
	Machine data analysis	Data processes using basics statistics techniques		Data processed using artificial intelligence (AI)	Data processes using basics statistics techniques

Machine To-be	Critical components identification	Dynamic FMECA to improve and keep updated the critical components' identification Maintenance policies' update based on failure rate analysis and updated components' criticality
	Machine data collection	PLC and sensor data available on the cloud for remote monitoring, respecting privacy constraints Identification of data to collect based on the dynamic FMECA update and the critical components' list Implementation of smart sensors and edge processing for data collection, elaboration and sharing Guided procedures for customers for data collection
	Machine data analysis	Unique approach for data analysis based on data availability, company skills and interests
Service As-is	Services identification	Corrective maintenance, few preventive interventions				Only corrective maintenance
Service As-is	Service data analysis	No analysis of service reports due to lack of time and resources			A unique place for service report storage Basic manual analyses done through pivot tables	No analysis of service reports due to lack of time and resources
Service To-be	Services identification	Preventive interventions offered more frequently (exploiting the new knowledge generated)
Service To-be	Service data analysis	Automated analysis of service data, thanks to the facilitated extraction from the service report Automatic generation of reports Implementation of NLP approaches to improve the analysis of text-intensive service reports
Maintenance delivery As-is	Cross analysis and service delivery decision	Interventions scheduling and allocation based on planner experience
Maintenance delivery As-is	Collection of service and machine data during maintenance	No standardized vocabulary in service report Service report filling is not guided (intense use of free text fields) No feedback collected from customers Problem and solution identified based on technician knowledge and expertise
Maintenance delivery To-be	Cross analysis and service delivery decision	Optimization model merging service and machine data to support the planner and improve intervention allocation
Maintenance delivery To-be	Collection of service and machine data during maintenance	Definition of a standardized vocabulary for service reports Mixed use of free text and close-ended fields in service reports to standardize the filling phase Collection of feedback on maintenance Tools to help technicians/customer care identify problem and solutions

		Company A	Company B	Company C	Company D	Company E
	Location	Italy	Europe	China	Europe	Italy
	Product	Balancing machines	Robotics and power equipment	Automated guided vehicles	Road construction equipment	Pumps and equipment for the oil and gas market
Stream
MachineAs-is	Critical components identification	FMECA to identify the critical components			Critical components identified through designers' experience
	Machine data collection	Few data collected due to privacy constraints Programmable logic controllerPLC) and sensor data available only on the machine Remote data sharing allowed in only a few cases and with few customers		Big amount of data collected PLC and sensor data available on the cloud	Few data collected due to privacy constraints PLC and sensor data available only on the machine
	Machine data analysis	Data processes using basics statistics techniques		Data processed using artificial intelligence (AI)	Data processes using basics statistics techniques

MachineTo-be	Critical components identification	Dynamic FMECA to improve and keep updated the critical components' identification Maintenance policies' update based on failure rate analysis and updated components' criticality
	Machine data collection	PLC and sensor data available on the cloud for remote monitoring, respecting privacy constraints Identification of data to collect based on the dynamic FMECA update and the critical components' list Implementation of smart sensors and edge processing for data collection, elaboration and sharing Guided procedures for customers for data collection
	Machine data analysis	Unique approach for data analysis based on data availability, company skills and interests
ServiceAs-is	Services identification	Corrective maintenance, few preventive interventions				Only corrective maintenance
ServiceAs-is	Service data analysis	No analysis of service reports due to lack of time and resources			A unique place for service report storage Basic manual analyses done through pivot tables	No analysis of service reports due to lack of time and resources
ServiceTo-be	Services identification	Preventive interventions offered more frequently (exploiting the new knowledge generated)
ServiceTo-be	Service data analysis	Automated analysis of service data, thanks to the facilitated extraction from the service report Automatic generation of reports Implementation of NLP approaches to improve the analysis of text-intensive service reports
Maintenance deliveryAs-is	Cross analysis and service delivery decision	Interventions scheduling and allocation based on planner experience
Maintenance deliveryAs-is	Collection of service and machine data during maintenance	No standardized vocabulary in service report Service report filling is not guided (intense use of free text fields) No feedback collected from customers Problem and solution identified based on technician knowledge and expertise
Maintenance deliveryTo-be	Cross analysis and service delivery decision	Optimization model merging service and machine data to support the planner and improve intervention allocation
Maintenance deliveryTo-be	Collection of service and machine data during maintenance	Definition of a standardized vocabulary for service reports Mixed use of free text and close-ended fields in service reports to standardize the filling phase Collection of feedback on maintenance Tools to help technicians/customer care identify problem and solutions