Exploring impact of production volume and product quality on manufacturers' profitability: analytical modeling and empirical validation
| Date | 12 February 2025 |
| Pages | 1190-1219 |
| DOI | https://doi.org/10.1108/IMDS-05-2024-0429 |
| Published date | 12 February 2025 |
| Author | Mohit Goswami,M. Ramkumar,Jiju Anthony,Raja Jayaraman,Beth Cudney,Felix T.S. Chan |
Exploring impact of production
volume and product quality on
manufacturers’ profitability:
analytical modeling and
empirical validation
Mohit Goswami and M. Ramkumar
Operations Management and Quantitative Techniques Group,
Indian Institute of Management Raipur, Raipur, India
Jiju Anthony
Newcastle Business School, Northumbria University Newcastle,
Newcastle upon Tyne, UK
Raja Jayaraman
Department of Industrial Engineering, New Mexico State University, Las Cruces,
New Mexico, USA
Beth Cudney
John E. Simon School of Business, Maryville University of St Louis, St Louis,
Missouri, USA, and
Felix T.S. Chan
Department of Decision Sciences, Macau University of Science and Technology,
Taipa, China
Abstract
Purpose –This study aims to develop analytical models that consider product quality and production volume as
essential drivers for profitability in the marketplace. It also considers product demand and price dynamics to
understand related nuances backed by empirical validation.
Design/methodology/approach –The pricing mechanism is influenced by production quality, while product
demand is influenced by both price and quality. The study considers cost elements, including production cost
and quality loss cost which in turn are influenced by production volume and product quality. It establishes
analytical conditions for optimal product quality and applies them to numerical analyses considering four
distinct industry settings.
Findings –The study reveals that unique solutions exist for optimal product quality at each production level in
four industry scenarios. The optimal production volume depends on product quality, and empirical research
validates these findings from analytical models and numerical analysis.
Originality/value –This study represents a pioneering effort to investigate operational strategies in both
analytical and empirical contexts, thus contributing to the existing body of knowledge in this area.
Keywords Operations strategy, Production economics, Quality management, Optimization model,
Scenario analysis, Empirical validation
Paper type Research paper
1. Introduction
Companies that sell physical products rely on factors such as price and product quality for
growth and profitability (Wang et al., 2021). The relationship between product price and
product quality is often intertwined, as high-quality products often lead to higher prices and
vice versa (Xu, 2009). Product quality is an aggregation of quality levels of individual
product attributes, driven by customers who can discriminate among different levels
IMDS
125,3
1190
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 15 June 2024
Revised 9 October 2024
Accepted 10 January 2025
IndustrialManagement & Data Systems
Vol.125 No. 3, 2025
pp.1190-1219
©Emerald Publishing Limited
e-ISSN:1758-5783
p-ISSN:0263-5577
DOI10.1108/IMDS-05-2024-0429
(Voros,2019). Further, in line with that of Li et al. (2024), our work makes an assumption
that product quality level can vary between 0 and 1, where 1 represents perfect quality,
while 0 denotes complete rejection. Previous studies have shown that product price and
quality influence product demand in the marketplace. In addition to product quality and
price, intrinsic technical parameters like production volume are crucial in determining cost
structures and thus, impacting an organization’s bottom line. Factors such as geo-political
shifts, lingering effects of the COVID-19 pandemic, and global supply chain-specific
phenomena can influence these decisions. Manufacturers make long-term decisions about
quality and quantity much earlier than actual production, and due to market conditions and
industry-specific technical dimensions, firms must balance the trade-off between product
quality and production volume to maximize their bottom line. Xu (2009) and Wang et al.
(2021) developed analytical expressions for product demand as a multiplicative function of
price and quality, revealing that demand increases with increasing product quality for a
given price point and decreases when price increases for a given product quality level. The
study used demand intercept and price sensitivity as constants to model the demand
function, while Wang et al. (2021) modeled product price as an increasing function of
quality using constants, quality intercept and quality sensitivity of various costs.
Additionally, Wang et al. (2021) found that production cost per unit is related to product
quality and production volumes. Existing studies have shown that production cost per unit
attributable to production volumes typically follows economies or diseconomies of scale.
Quality loss costs also contribute to production costs if product quality levels are not at the
desired level.
Production volume optimization is crucial for profitability as it enhances productivity,
resource utilization and the optimal deployment of human and non-human resources
(McKinsey Quarterly, 2017). High product quality is also essential for profitability, as it
helps organizations mitigate costs of non-conformance, poor quality, deviations and yield
loss, ultimately improving the operating cost structure. This research aims to develop an
analytical framework considering marketplace parameters and an organization’s technical
parameters for two operational strategies: optimizing production volume level and
optimizing product quality level. Optimizing production volume involves adjusting
manufacturing levels, capacity planning and production control, while production quality
optimization focuses on aligning product attributes with customer desires, considering the
organization’s technical capabilities (Yu et al., 2021). There are several arguments for
organizations to have distinct quality and volume optimization strategies. First, Ketokivi and
Schroeder (2004) argued that companies attempting to jointly optimize production volume
and product quality decisions often face difficulties in aligning their respective strategies
leading to poorer overall performance. This means that joint optimization can neither ensure
volume flexibility nor quality flexibility as important strategic priorities resulting from
volume and quality optimization respectively. Second, Slack et al. (2018) emphasized that
localized quality and volume objectives often conflict because achieving high quality
usually requires more time and resources, which can limit both production speed and
production volume. Third, both strategies require a fundamentally different set of resource
allocations. For instance, quality optimization requires investment in high-skilled labor,
advanced technology and rigorous testing, which are resource-intensive but necessary for
achieving superior product quality. On the other hand, volume optimization may prioritize
automation and scale economies. Finally, separating these strategies often allows
manufacturers to be more flexible in responding to market demands. When quality and
volume are optimized separately, a manufacturer can adjust one without necessarily
compromising the other. For instance, during periods of high demand, a focus on volume
may take precedence, while in periods of low demand, quality improvement may become the
priority (Yu et al., 2023).
Therefore, the study examines the conditions influencing manufacturers’ profitability by
examining the impact of two operational strategies on profit maximization. It examines the
Industrial
Management &
Data Systems
1191
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