Unveiling undergraduate production engineering students' comprehension of process flow measures

Torres Júnior, Noel; Azevedo, Américo Lopes de; Simões, Ana Correia; Ladeira, Marcelo Bronzo; Sousa, Paulo Renato de; Freitas, Lauro Soares de

doi:10.1590/0103-6513.20220020

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Research Article • Prod. 32 • 2022 • https://doi.org/10.1590/0103-6513.20220020 copy

Unveiling undergraduate production engineering students' comprehension of process flow measures

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Paper aims

This study analyzes the comprehension of production engineering students about the influence of some key variables on the process performance measures in a service process.

Originality

This paper points out the need for educators to re-evaluate their approaches to teaching the Operations Management (OM) principles related to process flow measures.

Research method

This study used scenario-based role-playing experiments with 2×2×2 between-subject factorial design with three independent variables (variability of activities, capacity utilization, and resource pooling) and four dependent variables related to key internal process performance measures (Flow Time, Overall Quality of service, Quality of service employees, and Queue Size). The sample was composed of 178 undergraduate production engineering students from a large university in Brazil from various institution units.

Main findings

These results show that students perceived the use of resource pooling as an impactful practice. However, the students did not correctly identify the effects of increasing resource utilization and the variability on flow time and queue size when activities are pooled.

Implications for theory and practice

The teaching of basic concepts of OM requires the support of computational tools. Undergraduate courses that contemplate subjects in the field of OM should work more intensely on simulation-based learning.

Keywords
Operations management; Process flow measures; Learning; Scenario-Based Experiment

Factor	Level -	Level +
The extent of change in resource utilization (A)	60% to 85% Arrival process with interarrival time following an exponential distribution $μ = 5^{'} c h a n g e t o 3.8'$ (average time) or $λ = 12 \frac{u n i t s}{h o u r} c h a n g e t o 16 \frac{u n i t s}{h o u r}$ (arrival rate)	60% to 95% Arrival process with interarrival time following an exponential distribution $μ = 5^{'} c h a n g e t o 3.2'$ $μ = 5^{'}$ change to ${3,8}^{'}$ (average time) or $λ = 12 \frac{u n i t s}{h o u r} c h a n g e t o 19 \frac{u n i t s}{h o u r}$ (arrival rate) $μ = 5^{'}$ change to ${3,2}^{'}$
Resource pooling (B)	No change Activities separated	Change Activities grouped
Change in the variability of activities (C)	No change Low variation $μ = 3^{'}$ $μ = 5^{'}$ change to ${3,8}^{'}$ (average time) Activities time following a constant distribution Coefficient of variation: 0%	Change Low variation to high variation $μ = 3^{'}$ $μ = 5^{'}$ change to ${3,8}^{'}$ (average time) or $λ = 20 \frac{u n i t s}{h o u r} (processing rate)$ Activities time following an exponential distribution. Coefficient of variation: 100%

Scenario	Mean flow time			Mean queue size			(A) Utilization change	(B) Resource pooling	(C) Coefficient of variation
Scenario	Simulation's value	Percentage growth compared to the baseline scenario	Degree of change	Simulation's value	Percentage growth compared to the baseline scenario	Degree of change	(A) Utilization change	(B) Resource pooling	(C) Coefficient of variation
Baseline	13.0	0%	-	0.93	0%	-	60% (no change)	No resource pooling	Low Variation
1(I)	22.0	70%	Increase moderately	1.94	109%	Increase moderately	60 to 85%	No resource pooling	Low Variation
2(a)	30.2	132%	Increase moderately	4.61	396%	Increase a lot	60 to 95%	No resource pooling	Low Variation
3(b)	15.3	18%	Increase slightly	0.75	-20%	Decrease slightly	60 to 85%	Resource pooling	Low Variation
4(ab)	20.6	59%	Increase moderately	2.24	141%	Increase a lot	60 to 95%	Resource pooling	Low Variation
5(c)	43.1	232%	Increase a lot	2.97	219%	Increase a lot	60 to 85%	No resource pooling	High Variation
6(ac)	57.4	342%	Increase a lot	6.47	596%	Increase a lot	60 to 95%	No resource pooling	High Variation
7(bc)	16.8	29%	Increase slightly	0.86	-7%	Decrease slightly	60 to 85%	Resource pooling	High Variation
8 (abc)	22.0	69%	Increase moderately	2.88	210%	Increase a lot	60 to 95%	Resource pooling	High Variation

Factor	Level -	Level +
The extent of change in resource utilization (A)	60% to 85% (41.7% increment)	60% to 95% (58.3% increment)
Resource pooling (B)	No change Activities separated	Change Activities grouped
Change in the variability of activities (C)	No change Low variability	Change Low to high variability

Scenario #	The extent of resource utilization change (A) (percent)	Resource pooling (B)	Variability of activities (C)	Sample size
1	60 to 85	Activities remain separated	Remains in low variability	20
2	60 to 95	Activities remain separated	Remains in low variability	19
3	60 to 85	Activities grouped	Remains in low variability	22
4	60 to 95	Activities grouped	Remains in low variability	21
5	60 to 85	Activities remain separated	Low to high variability	22
6	60 to 95	Activities remain separated	Low to high variability	21
7	60 to 85	Activities grouped	Low to high variability	21
8	60 to 95	Activities grouped	Low to high variability	24

Effect	Wilks’ lambda	F _{4, 159}	p-value
Main effects
The extent of resource utilization change (A)	0.973	1.102	0.357
Resource pooling (B)	0.657	20.78	<0.001
Coefficient of variation (C)	0.946	2.260	0.065

Two-way interaction
A*B	0.972	1.145	0.337
A*C	0.983	0.693	0.598
B*C	0.967	1.367	0.248

Three-way interaction
ABC	0.983	0.701	0.592

Independent variable	Flow time		The overall quality of service		Customer satisfaction with service employees		Queue size
Independent variable	Mean	SE Mean	Mean	SE Mean	Mean	SE Mean	Mean	SE Mean
The extent of resource utilization change (A)
60% to 85%	4.05	0.134	4.35	0.166	4.52	0.136	4.56	0.159
60% to 95%	3.89	0.157	4.53	0.178	4.28	0.165	4.46	0.19
Resource pooling (B)
No resource pooling	4.48	0.114	3.68	0.152	3.68	0.133	5.06	0.149
Resource pooling	3.50	0.152	5.15	0.153	5.07	0.130	4.00	0.179
Coefficient of variation (C)
Low variation	3.94	0.141	4.74	0.161	4.62	0.133	4.43	0.188
High variation	4.00	0.150	4.16	0.176	4.19	0.163	4.59	0.164
No resource pooling
The low extent of utilization change	4.50	0.157	3.74	0.213	4.00	0.170	4.98	0.197
High extent of utilization change	4.45	0.168	3.62	0.220	3.35	0.195	5.15	0.225
Resource pooling
The low extent of utilization change	3.60	0.194	4.95	0.221	5.02	0.181	4.16	0.235
High extent of utilization change	3.40	0.234	5.33	0.211	5.11	0.189	3.84	0.270
No resource pooling
Low coefficient of variation	4.51	0.137	3.74	0.190	3.90	0.168	4.16	0.235
High coefficient of variation	4.44	0.180	3.63	0.235	3.49	0.201	3.84	0.270
Resource pooling
Low coefficient of variation	3.42	0.211	5.65	0.156	5.28	0.142	4.16	0.235
High coefficient of variation	3.58	0.221	4.67	0.240	4.87	0.212	3.84	0.270

Hypothesis		Findings
H₁ -The perceived impact in process performance measure … is higher in a situation including changes in the variability of activities duration rather than in a situation with no changes.
	a) In flow time	Not supported
	b) In queue size	Supported
	c) In overall quality of service	Supported
	d) In customer satisfaction with service employees	Not supported
H₂ – The perceived impact in process performance measure … is higher when resource utilization escalates
	a) In flow time	Not supported
	b) In queue size	Not supported
	c) In overall quality of service	Supported^* * Indicates this hypothesis was regarded as significant if we consider the concept of effect heredity.
	d) In customer satisfaction with service employees	Not supported
H₃ – The perceived impact in process performance measure … is higher in a situation in which each resource performs several activities than in a situation in which each resource performs just one activity.
	a) In flow time	Supported
	b) In queue size	Supported
	c) In overall quality of service	Supported
	d) In customer satisfaction with service employees	Supported

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