The Influence of Reverse Logistics Strategies (RLS) on the Performance of Food and Beverage Manufacturing Firms in Nairobi City County, Kenya

The Influence of Reverse Logistics Strategies (RLS) on the Performance of Food and Beverage Manufacturing Firms in Nairobi City County, Kenya

Reuben Mwove*, Miriam Thogori, Rael Mwirigi, Anne Ngeretha

Faculty of Business Studies, Departments of Management Science and Business Studies, Chuka University, P O Box 109- 60400 Chuka

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8090206

Received: 09 September 2024; Accepted: 17 September 2024; Published: 15 October 2024

ABSTRACT

This study investigates the influence of Reverse Logistics Strategies (RLS) on the performance of food and beverage manufacturing firms in Nairobi City County, Kenya, as part of a broader exploration of Sustainable Supply Chain Management (SSCM) strategies. Using a descriptive cross-sectional research design, data were collected from a sample of 164 respondents across 138 firms, achieving a 90.9% response rate. Simple and multiple regression analyses, alongside Pearson’s product-moment and Spearman’s Rank Correlation, were employed to analyze the data via SPSS version 28. The findings indicate that RLS have a statistically significant negative impact on firm performance across multiple models, with coefficients ranging from -0.026 to -0.027 and p-values less than 0.000. This negative association persists despite a positive relationship being observed between RLS and original firm performance metrics prior to data transformation. These results suggest that while RLS may incur additional costs or operational challenges that negatively affect short-term performance metrics, they could contribute positively to long-term sustainability and efficiency. This study provides insights into the complex role of reverse logistics in the Kenyan manufacturing sector, offering implications for both managerial practices and policy development aimed at improving firm performance within a sustainable framework.

Key words: Reverse Logistics Strategies (RLS), Firm Performance, Sustainable Supply Chain Management (SSCM), Operational Efficiency, Sustainability

INTRODUCTION

Sustainable Supply Chain Management (SSCM) is integral to ensuring long-term success, particularly in the manufacturing sector, where Reverse Logistics Strategies (RLS) are critical for handling product returns, recycling, and disposal. RLS are typically evaluated using metrics such as returns processing time, the quantity of returned products, and returns handling costs. These metrics not only contribute to cost savings and customer satisfaction but also promote environmental sustainability (Fu et al., 2021; Kinobe et al., 2012). Efficient reverse logistics practices can significantly reduce waste, recapture value, and enhance profitability. However, delays in processing returns often result in increased costs and decreased revenues, which is especially problematic for industries such as retail and e-commerce (Garkavenko et al., 2017; Saad et al., 2018). The quantity of returned products also serves as a measure of the effectiveness of reverse logistics, offering valuable insights into potential product defects or shipping errors. Additionally, returns handling costs, including expenses related to transportation, refurbishing, and restocking, have a direct impact on profitability (Agrawal et al., 2015; Mollenkopf et al., 2010). While there is extensive research highlighting the significance of RLS, there are still gaps in understanding their cross-industry impact, particularly in the food and beverage manufacturing sector. This study seeks to examine how RLS influence firm performance in Nairobi’s food and beverage manufacturing sector, providing crucial insights that can refine SSCM strategies by employing key metrics from Govindan et al. (2017) and Agrawal et al. (2015). This paper will explore the problem statement, research objectives, literature review, methodology, data analysis, results, discussion, and conclusions to provide a comprehensive understanding of the topic.

Statement of the Problem

The manufacturing sector experienced a significant downturn, with a 5% decrease in market share and stagnated sales growth at 2%. Specifically, the food and beverage manufacturing subsector faced a sharp decline in performance, marked by reduced net profit margins and a shrinking market share. From 2017 to 2022, the sector’s overall output declined by 13.4%, operational costs increased substantially, and market share contracted by 12%. This decline underscored the sector’s difficulty in balancing profitability with sustainability. Reverse Logistics Strategies (RLS), which encompass product returns, recycling, and disposal, were recognized as key components of sustainable supply chain management. However, their implementation appeared misaligned with firm performance. Inefficiencies in returns processing time, high quantities of returned products, and elevated returns handling costs contributed to increased operational costs, reduced profitability, and diminished customer satisfaction. Consumer sentiment surveys conducted between 2020 and 2022 revealed a 15% drop in trust and loyalty toward firms perceived as lacking effective sustainability strategies, which further exacerbated the decline in performance. Although previous research had acknowledged the importance of RLS, gaps persisted in understanding their specific impact on firm performance. Disparities in variable selection, inconsistent findings, and varying methodologies hindered a comprehensive understanding of the direct relationship between reverse logistics and firm outcomes. This study aimed to address these gaps by systematically exploring the relationship between reverse logistics strategies and firm performance in the food and beverage manufacturing sector.

LITERATURE REVIEW

Theoretical Literature Review

Closed-Loop Supply Chain Theory

Initially proposed by Rogers (1970), Closed-Loop Supply Chain (CLSC) Theory provides a framework for understanding the integration of reverse logistics strategies with sustainability, cost-efficiency, and overall firm performance. It addresses the limitations of traditional supply chains by emphasizing the importance of incorporating reverse logistics such as recycling, reusing, and remanufacturing into supply chain operations to enhance environmental sustainability and drive cost savings (Malmgren et al., 2020). The theory operates on the premise that integrating reverse logistics strategies can address environmental concerns while improving organizational efficiency. It posits that firms are capable of adopting these strategies to reduce costs and create value (Saruchera & Asante‐Darko, 2021). By focusing on the reverse flow of products, CLSC Theory optimizes resource utilization and minimizes environmental impact, thus improving firm performance (Bai et al., 2020; Simonetto et al., 2022). However, the theory presents challenges, particularly in operational complexity and the substantial costs of implementing closed-loop systems, which may deter smaller firms (Miemczyk et al., 2016). Additionally, encouraging customer participation in reverse logistics activities often requires incentivization and clear communication (Wilson & Goffnett, 2022). Lastly, while the theory emphasizes ecological and operational benefits, it may overlook other critical factors affecting firm performance (Kazemi et al., 2019).

Empirical Literature Review

Numerous studies have investigated the effects of reverse logistics strategies (RLS) on firm performance across various industries, yet significant research gaps persist. Panya and Marendi (2021) analyzed the influence of RLS such as product recalls, manufacturing returns, recycling, and repackaging on the performance of fast-moving consumer goods (FMCG) companies in Kenya. While their secondary data analysis revealed a positive impact of RLS on organizational performance, they highlighted the need for further research to assess the contribution of specific RLS components to performance. Moreover, they suggested integrating qualitative insights to complement the predominantly quantitative data, presenting a gap in understanding the nuanced effects of different RLS strategies.

Sharma et al., (2021) focused on sustainable RLS in the organized retail sector in India, using case studies to identify best practices. Their findings indicated that RLS positively influence environmental performance and overall firm success. However, they recommended future research explore the applicability of these strategies in other geographical regions and retail environments. Additionally, the long-term effects of RLS on firm performance remain unexplored, leaving a critical gap in understanding the sustainability of these strategies over time.

Ebenezer and Zhuo (2019) examined the bottled and sachet water manufacturing sector in Ghana, discovering that RLS significantly enhanced competitive advantage, leading to improved firm performance. However, they called for the application of mixed-methods approaches in future studies to provide a more comprehensive understanding of how RLS function in other industries. This highlights a gap in applying multi-dimensional research approaches across different manufacturing contexts.

Banihashemi et al., (2019) conducted a content analysis to evaluate the relationship between RLS and sustainability performance, covering economic, environmental, and social dimensions. While their study contributed valuable insights into the economic and environmental aspects, they underscored a gap in the literature regarding the social impacts of RLS, calling for comparative research across industries to capture variations in sustainability outcomes.

In the U.S. food and beverage manufacturing sector, Smith and Johnson (2015) utilized a mixed-methods approach to demonstrate the positive relationship between efficient RLS and company performance. However, their findings revealed a gap in understanding the specific mechanisms through which RLS contribute to business success, particularly in terms of profitability, customer satisfaction, and operational efficiency. They recommended further research into the underlying factors influencing the success of RLS in different manufacturing sectors.

Despite significant contributions to understanding the relationship between RLS and firm performance, several research gaps remain. These include the need for sector-specific insights, particularly in emerging markets, the exploration of qualitative dimensions alongside quantitative data, and the investigation of the long-term and social impacts of RLS. This study seeks to fill these gaps by focusing on how RLS influence the performance of food and beverage manufacturing firms in Nairobi. It aims to provide a nuanced understanding of RLS components such as returns processing time, returned products quantity, and returns handling costs and their impact on firm performance, addressing gaps highlighted by previous studies (Fu et al., 2021; Govindan et al., 2017; Agrawal et al., 2015).

Objective of the Study

The general objective of the study was to determine the effect of reverse logistics strategies on the performance of food and beverage manufacturing firms in Nairobi City County, Kenya.

Research Hypothesis

There is no statistically significant relationship between Reverse logistics strategies and performance of food and beverage manufacturing firms in Nairobi city county, Kenya.

Conceptual Framework

A conceptual framework is a graphical representation that illustrates the relationship between variables under investigation, using diagrams or visual aids to clarify these connections (Orodho, 2008). It serves as a structure for presenting hypotheses and outlining the interrelationships among the variables or concepts being studied. A variable refers to a concept that can be measured numerically, such as height, weight, or financial status (Kothari et al., 2011). Mugenda (2008) further defines a variable as a quantifiable characteristic that exhibits differing values among units in a population.

Figure 1: Conceptual Framework

Source: Author Composition (2024)

METHODS AND MATERIALS

The study utilized a descriptive cross-sectional research design (Bryman & Bell, 2018) to investigate the relationship between sustainable supply chain management strategies and firm performance in the food and beverage manufacturing sector. This design was selected for its effectiveness in capturing current strategies and performance outcomes at a specific point in time. The target population comprised 138 food and beverage manufacturing firms in Nairobi City County, Kenya (KAM, 2023). A structured questionnaire was used for data collection, offering a robust method for detailed data gathering, descriptive analysis, and enabling correlation and prediction (Creswell, 2014). The study employed a stratified sampling approach, including all 138 firms, and used the Yamane formula to determine a sample size of 164 respondents. A pilot study was conducted with 14 procurement managers and 14 finance managers from 14 companies in the food and beverage industry. The reliability of the instrument was assessed using Cronbach’s Alpha, with coefficients ranging from 0.734 for returned product quantity to 0.927 for returned product time. These values are significantly above the acceptable minimum threshold of 0.50 and the recommended benchmark of 0.70, indicating strong reliability.

Analysis

Correlation analysis was employed to assess the strength of the relationships between the independent variable and the dependent variable. Regression analysis was utilized to explore these relationships further. The regression model used in the study is expressed as follows:

Y=β0​+β1​X1​+ϵ

In this model, Y represents the dependent variable (firm performance in the food and beverage manufacturing sector), X1   denotes the independent variable (reverse logistics strategies), β0   and 𝛽1 are the coefficients, and ϵ is the error term.

RESULTS AND DISCUSION

The analysis was based on the completed and promptly returned questionnaires from respondents. Out of the 164 questionnaires distributed, 149 were returned and used for the study. All returned questionnaires were accurately filled out and included in the data analysis. This resulted in a response rate of 90.85% based on the sample size.

Reverse Logistics Strategies and Firm Performance

Table 1 reveals the effectiveness of reverse logistics strategies among food and beverage manufacturing firms in Nairobi City County, Kenya. The mean values for the indicators; returns processing time (4.198), returned products quantity (4.151), returns handling costs (4.319), and overall reverse logistics strategies (4.223), are significantly above zero. High t-test values and p-values of 0.000 confirm that these means are statistically significant and not due to random chance. These results indicate that the firms are effectively managing reverse logistics processes, such as handling returns. The significant positive means across all indicators highlight the firms’ strong performance in implementing reverse logistics strategies. This emphasizes the need for ongoing focus and innovation in reverse logistics to sustain competitiveness and profitability in the food and beverage sector. As noted by Sharma et al. (2021), adopting advanced technology and refining reverse logistics processes are essential for maintaining a competitive advantage.

Table 1: Descriptive Statistics of Reverse Logistics Strategies Indicators

N is Number of respondents, SD is standard deviation, SE is standard error of the mean, T is test for equality of means: test value = 0, Ho: mean is not significantly different from 0 at α=0.05. Reject Ho if p < 0.05, df is degrees of freedom.

Source: Primary data (2024)

Correlation of Reverse Logistics Strategies and Performance

The correlations between reverse logistics strategies and various firm performance metrics shown in Table 2 reveal several significant relationships. Returns processing time is positively associated with the quantity of returned products (r = 0.400, p < 0.001) and reverse logistics strategies (r = 0.745, p < 0.001). This indicates that as the processing time increases, both the volume of returns and the overall effectiveness of reverse logistics strategies tend to improve. However, the correlation with returns handling costs (r = 0.214, p = 0.009) and firm performance (r = 0.442, p < 0.001) is weaker, suggesting a more nuanced relationship with these metrics.

The quantity of returned products shows a strong positive correlation with returns handling costs (r = 0.450, p < 0.001), reverse logistics strategies (r = 0.818, p < 0.001), and firm performance (r = 0.345, p < 0.001). This implies that an increase in returned products is closely linked with higher handling costs, more effective reverse logistics strategies, and better overall firm performance. The correlations with profitability (r = 0.255, p = 0.002), sales growth (r = 0.245, p = 0.003), and market share (r = 0.356, p < 0.001) are moderate, indicating that while the quantity of returns affects these metrics, the impact is less pronounced compared to its relationship with handling costs and overall performance.

Returns handling costs are significantly related to reverse logistics strategies (r = 0.698, p < 0.001) and firm performance (r = 0.302, p < 0.001). This suggests that higher handling costs are associated with more effective reverse logistics strategies and better performance outcomes. The correlation with profitability (r = 0.234, p = 0.004), sales growth (r = 0.211, p = 0.010), and market share (r = 0.302, p < 0.001) is relatively lower, reflecting the complexity of its impact on these areas.

Reverse logistics strategies themselves have a strong positive correlation with firm performance (r = 0.486, p < 0.001), profitability (r = 0.388, p < 0.001), sales growth (r = 0.375, p < 0.001), and market share (r = 0.437, p < 0.001). This indicates that more effective management of reverse logistics is strongly linked with improved performance across various dimensions of the firm. Overall, the analysis underscores the significant positive relationship between reverse logistics strategies and firm performance. Effective management in reverse logistics is associated with better profitability, sales growth, and market share, highlighting its importance for enhancing firm performance.

Table 2: Correlation between firm performance and reverse logistics strategies

**. Correlation is significant at the 0.01 level (2-tailed). Source: Primary data (2024)

Regression Analysis for Reverse Logistics Strategies and Performance

The ANOVA Table 3 assesses the overall significance of the regression model, which examines the relationship between reverse logistics strategies and firm performance, where firm performance has been transformed using the power transformation tY=1/Y0.75tY = 1/Y^{0.75}

In the first model, the constant term is 0.446 with a standard error of 0.016, and it is statistically significant with a t-value of 27.277 and a p-value of 0.000. The coefficient for RLS is -0.026 with a standard error of 0.004. This negative coefficient is significant, with a t-value of -6.875 and a p-value of 0.000, indicating that an increase in RLS is associated with a decrease in firm performance.

Model Equation:

The regression model is given by: tY=0.446−0.026X

Where:

tY=1/Y^0.75 is the transformed firm performance.

𝑋 represents the reverse logistics strategies.

This can be interpreted to suggest that, in the context of the transformed variable, increases in reverse logistics strategies are associated with a decrease in Ty. However, since tY=1/Y^0.75, a decrease in Ty implies an increase in the original firm performance variable Y.

In the second model, the constant term is 0.440 with a standard error of 0.021, and it is significant with a t-value of 20.581 and a p-value of 0.000. The coefficient for RLS is -0.025 with a standard error of 0.005. This negative effect is significant, with a t-value of -5.022 and a p-value of 0.000, suggesting a decrease in firm performance with increasing RLS. In the third model, the constant term is 0.453 with a standard error of 0.023, significant at a t-value of 19.335 and a p-value of 0.000. The RLS coefficient is -0.027 with a standard error of 0.006. This coefficient is significant with a t-value of -4.861 and a p-value of 0.000, indicating a negative impact on firm performance as RLS increases.

In the fourth model, the constant term is 0.448 with a standard error of 0.019, and it is significant with a t-value of 23.211 and a p-value of 0.000. The coefficient for RLS is -0.027 with a standard error of 0.005. This coefficient is significant with a t-value of -6.022 and a p-value of 0.000, reinforcing that increased RLS is associated with decreased firm performance. Overall, these coefficients demonstrate a consistent negative relationship between Reverse Logistics Strategies and firm performance, with all coefficients being statistically significant. This suggests that higher levels of RLS are associated with reduced firm performance, as indicated by the negative coefficients across different models.

Table 3: Coefficients^a for RLS and Firm performance

Source: Primary data (2024)

Summary

The analysis of reverse logistics strategies in Nairobi City County’s food and beverage manufacturing sector highlighted their crucial role in improving operational efficiency. Descriptive statistics revealed that many firms focus on effective returns management to cut costs, enhance customer satisfaction, and promote sustainable supply chain practices. Although correlation analysis showed a positive impact of reverse logistics on operational metrics like returns processing time and costs, its direct effect on broader performance metrics such as profitability, sales growth, and market share was less significant. However, regression analysis demonstrated that reverse logistics strategies contribute moderately to overall firm performance, accounting for a meaningful portion of performance variance.

CONCLUSIONS

The findings of this study align with existing literature, underscoring the importance of Reverse Logistics Strategies (RLS) in enhancing operational efficiency and sustainability, particularly within the food and beverage manufacturing sector. Similar to Panya and Marendi (2021), who noted the positive impact of RLS on fast-moving consumer goods (FMCG) companies in Kenya, this study finds that RLS improve operational aspects such as returns processing time and handling costs, which contribute to overall firm performance. However, much like Sharma et al., (2021), who highlighted that the benefits of RLS in organized retail sectors were more pronounced in environmental performance than financial metrics, this study also observes that the direct financial impact of RLS on profitability, sales growth, and market share is not immediately significant.

Further, consistent with Ebenezer and Zhuo’s (2019) study in the bottled water sector in Ghana, which demonstrated that RLS enhanced competitive advantage, this research supports the notion that RLS foster long-term competitive gains by improving customer satisfaction and operational efficiencies. These gains, however, may not translate into immediate financial performance but lay the groundwork for future financial benefits.

This study adds to the literature by suggesting that while the direct financial impact of RLS may be subtle or delayed, the indirect benefits on operational efficiency and sustainability can contribute to financial performance over time. Banihashemi et al., (2019) emphasized the importance of the social dimension of RLS, noting a gap in comparative studies across industries. In line with this, the findings of this study suggest that food and beverage manufacturing firms could also benefit from integrating social sustainability into their reverse logistics processes, further enhancing the long-term value of RLS.

To fully capitalize on the benefits of reverse logistics, firms should continue to invest in advanced technologies, such as automated tracking systems and data analytics tools, to streamline returns management and minimize associated costs. These investments, as supported by Agrawal et al., (2015) and Fu et al., (2021), will not only strengthen sustainable supply chain practices but also improve long-term financial performance as the efficiency of these processes is enhanced. Thus, firms must recognize that the value of RLS lies not only in immediate financial gains but in building a resilient, sustainable, and competitive business model for the future.

RECOMMENDATIONS

The study’s findings highlight several important recommendations for stakeholders focusing on reverse logistics strategies. Policymakers and the government should promote reverse logistics initiatives within the manufacturing sector by developing supportive policies. This could include offering tax incentives or grants to encourage firms to adopt efficient reverse logistics systems. Additionally, setting industry standards for sustainable product design and providing subsidies for eco-friendly practices would further support these efforts. Facilitating greater collaboration between firms and their supply chain partners through dedicated platforms and forums can enhance communication and joint initiatives. Strengthening regulations related to green supply chain practices, with clear guidelines and enforcement, is also crucial for ensuring environmental sustainability within the supply chain.

Manufacturing firms, on the other hand, should focus on enhancing their reverse logistics strategies to boost operational efficiency and reduce costs. Investing in innovative product design can lead to higher quality products and greater customer satisfaction. Firms should also work on optimizing their supply chain collaboration to improve the effectiveness of their reverse logistics efforts. Although green supply chain strategies have had a modest impact, firms need to integrate these strategies more effectively into their operations to better align with business objectives. Overall, while managerial practices are important, firms should prioritize optimizing their reverse logistics strategies independently to drive performance improvements.

Limitations and Future Scope

This study, while providing valuable insights into the role of reverse logistics strategies (RLS) in the food and beverage manufacturing sector, is not without limitations. First, the research is geographically limited to Nairobi, which may restrict the generalizability of the findings to other regions or industries. Second, the study focuses primarily on quantitative data, potentially overlooking qualitative aspects such as managerial perspectives or customer satisfaction related to RLS. Additionally, the long-term financial impact of RLS was not fully explored, as the study captures only a snapshot in time, which may not reflect the gradual benefits of these strategies. Future research could address these limitations by conducting comparative studies across different industries and geographical regions, integrating both qualitative and quantitative methods. Longitudinal studies could also provide a deeper understanding of the long-term financial and sustainability benefits of RLS, offering more robust insights into their full potential.

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