Table 1 Feature variables considered in...

Table 1

Feature variables considered in the correlation tests

Variables		Description	Range of values	In regression analysis	Proposed effect on schedule variations	Sources of complexity
Categorical	Item	Unique component number	4,977 items
	Item group	Internally defined item categories	Nominal categories
	Supplier	Name of the supplier organization	Nominal categories
	Material receiving address	Plants that receive and use the item	14 addresses
	Scheduled demand date (3 variables)	A combined year and week number	76 weeks: between 2017 (week 31) and 2018 (week 52)
		Year	Two years: 2017 and 2018
		Week number	52 weeks
	Transport lead time in days	The standard time required for delivering items from the supplier to the receiving address	13 categories: 0–9, 11, 13, 41, 45, 46	X	Contingent cause	Insufficient buffering of time, process design
	Pick-up frequency in times per week	The number of days per week when picking up material from the supplier is possible	Five categories: 1–3, 5, 6	X	Independent moderator	Scale dynamics, process design
	Phase-in/-out week	Does the schedule belong to the fixed phase-in/out weeks (two weeks per year)?	Two states: yes, no
	Shifts in scheduled volumes from or to zero	Does the scheduled volume change from any value to zero or vice versa?	Two states: yes, no
	Holiday week	Weeks per year when the plants are closed	Two states: yes, no
	Car Model A	Do the items exist in the model’s BOM?	Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model B		Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model C		Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model D		Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model E		Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model F		Two states: yes, no	X	Independent cause	Scope dynamics, process design
Numerical	Forecasted volume	Forecasted volume X weeks before the delivery date, X = {2, 4, 8, 12}	≥0	X	Contingent cause	Variations in timing, process context
	Unit load per week	Unit load divided by the average weekly demand or reference volume	≥0	X	Contingent moderator	Scale dynamics, process design
	Aggregated production deviation	Number of cars behind or ahead of actual demand	(−) behind (+) ahead	X	Contingent cause	Variations in quality, process context
	Item’s order life cycle ratio	The number of weeks from the first delivery date in the dataset to present over the number of weeks from the first delivery date to the last planned date on the horizon	0–1	X	Contingent cause	Scope dynamics, process design
	Take rate at the plant level	Percentage of manufactured cars at a plant in which the item is used	0–1	X	Contingent cause	Scope dynamics, process design

Variables		Description	Range of values	In regression analysis	Proposed effect on schedule variations	Sources of complexity
Categorical	Item	Unique component number	4,977 items
	Item group	Internally defined item categories	Nominal categories
	Supplier	Name of the supplier organization	Nominal categories
	Material receiving address	Plants that receive and use the item	14 addresses
	Scheduled demand date (3 variables)	A combined year and week number	76 weeks: between 2017 (week 31) and 2018 (week 52)
		Year	Two years: 2017 and 2018
		Week number	52 weeks
	Transport lead time in days	The standard time required for delivering items from the supplier to the receiving address	13 categories: 0–9, 11, 13, 41, 45, 46	X	Contingent cause	Insufficient buffering of time, process design
	Pick-up frequency in times per week	The number of days per week when picking up material from the supplier is possible	Five categories: 1–3, 5, 6	X	Independent moderator	Scale dynamics, process design
	Phase-in/-out week	Does the schedule belong to the fixed phase-in/out weeks (two weeks per year)?	Two states: yes, no
	Shifts in scheduled volumes from or to zero	Does the scheduled volume change from any value to zero or vice versa?	Two states: yes, no
	Holiday week	Weeks per year when the plants are closed	Two states: yes, no
	Car Model A	Do the items exist in the model’s BOM?	Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model B		Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model C		Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model D		Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model E		Two states: yes, no	X	Independent cause	Scope dynamics, process design
	Car Model F		Two states: yes, no	X	Independent cause	Scope dynamics, process design
Numerical	Forecasted volume	Forecasted volume X weeks before the delivery date, X = {2, 4, 8, 12}	≥0	X	Contingent cause	Variations in timing, process context
	Unit load per week	Unit load divided by the average weekly demand or reference volume	≥0	X	Contingent moderator	Scale dynamics, process design
	Aggregated production deviation	Number of cars behind or ahead of actual demand	(−) behind (+) ahead	X	Contingent cause	Variations in quality, process context
	Item’s order life cycle ratio	The number of weeks from the first delivery date in the dataset to present over the number of weeks from the first delivery date to the last planned date on the horizon	0–1	X	Contingent cause	Scope dynamics, process design
	Take rate at the plant level	Percentage of manufactured cars at a plant in which the item is used	0–1	X	Contingent cause	Scope dynamics, process design

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