Table A1 Coding qualitative data from...

Table A1

Coding qualitative data from the interviews with the panel of 12 experts

Question (referring to production management)	Code	Initial coding	Grouping	Theoretical themes
What do you think are/will be the impacts of AI in relation to simulation and optimisation? What do you think are/will be the impacts of AI in relation to predictive processes? What do you think are/will be the impacts of AI in relation to production planning and scheduling? What do you think are/will be the impacts of AI in relation to environmental management (including energy management)? What do you think are/will be the general impacts of AI on production management?	A₁	Digital twin simulation	T₁ {A₁A₃A₄A₆ A₈A₁₃A₁₅A₁₆ A₂₀A₂₁A₂₂A₂₃ A₂₈}	Production routing simulation
	A₂	Linking production deviations to scheduling	T₂ {A₆A₇ A₁₂A₁₃ A₁₄A₁₇ A₁₈}	Machine parameters and yield optimisation
	A₃	Scheduling potential deferments	T₃ {A₁A₂A₃A₄ A₆A₇A₈A₉A₁₅ A₁₆}	Production scheduling optimisation
	A₄	Learning from historical production troubleshooting	T₄ {A₅A₆A₁₁ A₁₂A₁₄A₁₆}	Predictive and preventive maintenance
	A₅	Anticipating future performance	T₅ {A₅A₁₀A₁₁ A₁₆A₁₇A₁₈ A₂₈}	Predictive quality control
	A₆	Predicting process behaviour	T₆ {A₆A₇A₈ A₁₀ A₁₂A₁₆A₁₇ A₁₈ A₂₅A₂₆}	Root-cause analysis and identification
	A₇	Predicting and preventing bottlenecks	T₇ {A₆A₁₂A₁₃ A₁₄A₁₇A₁₈A₁₉ A₂₀ A₂₃A₂₄}	Resource consumption optimisation
	A₈	Reducing unplanned postponement events	T₈ {A₁₂A₁₃A₁₈ A₁₉A₂₀A₂₃A₂₄ A₂₈}	Reduction of environmental impacts
	A₉	Real-time product deviation and routing adjustments	T₉ {A₁₂A₁₃ A₁₈A₁₉A₂₀ A₂₃ A₂₇A₂₉}	Energy efficiency
	A₁₀	Solving potential production problems before they occur
	A₁₁	Planning preventive scheduled maintenance based on process evolution
	A₁₂	Early warning from machinery and assembly lines
	A₁₃	Machine and station parameters optimisation and adjustment
	A₁₄	Increasing machine yield and overall equipment effectiveness
	A₁₅	Calculating possible trajectory of the production flow
	A₁₆	Real-time detection of abnormalities and undesirable events
	A₁₇	Predicting process variability
	A₁₈	Predicting the evolution of the most relevant process variables
	A₁₉	Predicting potential environmental impacts
	A₂₀	Running simulations based on previous data
	A₂₁	Trail-and-error on production processes
	A₂₂	Finding similar behaviours and patterns
	A₂₃	Machine parameters optimisation for reducing consumption of resources
	A₂₄	Machine parameters optimisation for reducing air and water pollution
	A₂₅	Finding root causes through pattern recognition
	A₂₆	Solving most production problems
	A₂₇	Optimising energy efficiency
	A₂₈	Analysing and preventing production risks
	A₂₉	Energy consumption control

Question (referring toproduction management)	Code	Initial coding	Grouping	Theoretical themes
What do you think are/will be the impacts of AI in relation to simulation and optimisation? What do you think are/will be the impacts of AI in relation to predictive processes? What do you think are/will be the impacts of AI in relation to production planning and scheduling? What do you think are/will be the impacts of AI in relation to environmental management (including energy management)? What do you think are/will be the general impacts of AI on production management?	A₁	Digital twin simulation	T₁ {A₁A₃A₄A₆ A₈A₁₃A₁₅A₁₆ A₂₀A₂₁A₂₂A₂₃ A₂₈}	Production routing simulation
	A₂	Linking production deviations to scheduling	T₂ {A₆A₇ A₁₂A₁₃ A₁₄A₁₇ A₁₈}	Machine parameters and yield optimisation
	A₃	Scheduling potential deferments	T₃ {A₁A₂A₃A₄ A₆A₇A₈A₉A₁₅ A₁₆}	Production scheduling optimisation
	A₄	Learning from historical production troubleshooting	T₄ {A₅A₆A₁₁ A₁₂A₁₄A₁₆}	Predictive and preventive maintenance
	A₅	Anticipating future performance	T₅ {A₅A₁₀A₁₁ A₁₆A₁₇A₁₈ A₂₈}	Predictive quality control
	A₆	Predicting process behaviour	T₆ {A₆A₇A₈ A₁₀ A₁₂A₁₆A₁₇ A₁₈ A₂₅A₂₆}	Root-cause analysis and identification
	A₇	Predicting and preventing bottlenecks	T₇ {A₆A₁₂A₁₃ A₁₄A₁₇A₁₈A₁₉ A₂₀ A₂₃A₂₄}	Resource consumption optimisation
	A₈	Reducing unplanned postponement events	T₈ {A₁₂A₁₃A₁₈ A₁₉A₂₀A₂₃A₂₄ A₂₈}	Reduction of environmental impacts
	A₉	Real-time product deviation and routing adjustments	T₉ {A₁₂A₁₃ A₁₈A₁₉A₂₀ A₂₃ A₂₇A₂₉}	Energy efficiency
	A₁₀	Solving potential production problems before they occur
	A₁₁	Planning preventive scheduled maintenance based on process evolution
	A₁₂	Early warning from machinery and assembly lines
	A₁₃	Machine and station parameters optimisation and adjustment
	A₁₄	Increasing machine yield and overall equipment effectiveness
	A₁₅	Calculating possible trajectory of the production flow
	A₁₆	Real-time detection of abnormalities and undesirable events
	A₁₇	Predicting process variability
	A₁₈	Predicting the evolution of the most relevant process variables
	A₁₉	Predicting potential environmental impacts
	A₂₀	Running simulations based on previous data
	A₂₁	Trail-and-error on production processes
	A₂₂	Finding similar behaviours and patterns
	A₂₃	Machine parameters optimisation for reducing consumption of resources
	A₂₄	Machine parameters optimisation for reducing air and water pollution
	A₂₅	Finding root causes through pattern recognition
	A₂₆	Solving most production problems
	A₂₇	Optimising energy efficiency
	A₂₈	Analysing and preventing production risks
	A₂₉	Energy consumption control

[ViewLarge]

Sharing Unavailable