The Effect of Supply Chain Management on Competitive Advantage: Mediating Role of Supply Chain Responsiveness in Ethiopian Food Processing Industry

This study aims to examine the indirect impact of supply chain management (SCM) in its five dimensions (supplier relationship, customer relationship, information sharing, information quality and postponement) on the competitive advantage in its five dimensions (Cost, Quality, Delivery Time, flexibility, and innovation) through supply chain responsiveness as a mediator. Furthermore, 234 questionnaires were distributed in food processing industries. The total of 215 questionnaires was analyzed. For a robust result, we conducted exploratory factor analysis (EFA) using Varimax rotation and confirmatory factor analysis (CFA) to check the scale. Furthermore, we test validation of the second-order construct. Structural equations modelling (SEM) using AMOS 24 was used to test the hypotheses. The results of analysis show that the supply chain management practice (SCMP) has a positive and statistically significant influence on supply chain responsiveness (β = 0.909, p-value = 0.000) and the competitive advantage of firms with the standardized coefficient of (β= 0.639, p-value = 0.000). Similarly, the supply chain responsiveness has a positive and significant effect on competitive advantage by (β = 0.352, p-value = 0.000), on the other hand, supply chain management practice (SCMP) has a significant indirect effect on competitive advantage by (β = 0.320, p-value = 0.000) through supply chain responsiveness. Accordingly, we suggest that managers of food processing should properly manage their supply chain management practice, develop a responsive supply chain to gain a competitive advantage. But this study was limited only to food processing with plc and Share Company.


INTRODUCTION
In today's competitive business, most of the firms increased focus on delivering value to the customer. The focus on attention of businesses is providing products and services that are more valuable compared to its competitors. These forces supply chain to be more responsive and create competitive advantage. The growth of supply chain management aims to improve profit, customer response, customer value, interconnection, and interdependence among firms (Inda Sukati et al., 2012). Likewise, Thatte (2007) affirmed that getting the right product, at the right price, at the right time to the customer is not only improved competitive success but also the key to survival.
Supply Chain Management is an approach that is used to achieve a more efficient integration of various organizations from suppliers, manufactures, distributors, retailers, and customers.
This means that goods are produced in the right amount, at the right time and at the right place in order to achieve the minimum overall cost of the system and also reach the desired service level (Levi, 2000). In the Supply Chain there are three types of flows that must be managed, first the flow of goods that flows from upstream to downstream, second is the flow of money and the like that flows from upstream to downstream, the third is the flow of information that can occur from upstream to downstream or information about inventory product, production capacity and shipping information (Pujawan, 2017).
Competitive advantage is the ability of a company to get greater profits from competitors engaged in the same industry (Porter, 1985). Furthermore, Competitive advantage is the advantage achieved by a company over its competitors by offering more value to consumers, either through lower prices for products or services or by providing additional benefits and better services (Attiany, 2014). Additionally, Vargas et al. (2018) define that competitive CA as a contact to original marketplaces comparative to the business's main entrants, design and product growth relation, and upgrading of the organization's status comparative to its key rivals.
Chronic malnutrition, extreme poverty, rapidly rising and young unemployed urban populations, civil and political conflict, and intensifying droughts all strain the country's ability to provide for itself (Michael A. Raynor, 2019). Despite the challenges, the Ethiopian government's vision for the country is to become a lower-middle-income country by 2025.
Ethiopia has now made significant progress in reducing poverty and increasing food production, food security, and nutrition, although challenges remain. Ethiopia's economy experienced strong, broad-based growth, averaging 9.4% a year from 2010/11 to 2019/20, Ethiopia's real gross domestic product (GDP).
The food industry is one of the fastest growing industrial branches in the manufacturing sector in Ethiopia. Agro-industries (food and beverages) contribute approximately 50% of manufactured goods (UNIDO, 2012).This sector has great potential, also owning substantial natural and human resources, as well as a long tradition (Banja Luka, 2014).
In reviewing studies, we found that several research gaps have existed in this area; past studies focused on supply chain management practice were done in developed countries (Al-Shboul, M.A.R., and et. al., 2017); Christopher, M. and Peck, H. (2004) and developing countries (Dr.

Siddig Balal Ibrahim, Abdelsalam Adam Hamid, 2012; Tilahun Woldie Mengistu & Regina
Birner, 2018) on supply chain management practice and competitive advantage by Suhong Li, et. al., 2004;Somuyiwa, 2013;Satria Yunas, 2016;S.K. Chadha, et.al. 2018); and on supply chain responsiveness in relation to competitive advantage (Ashish A. Thatte, 2007;Faheem Gul Gilal et.al., 2017) was conducted with an orientation to developed countries. Although very few research studies on supply chain management practice, supply chain responsiveness, and competitive advantage have been conducted in developing countries (Ashish A. Thatte et al., 2013;Dr. Kamel Mohammad Al-Hawajreh1, et al., 2014), the findings of those studies are inconclusive and non-generalizable for all developing countries like Ethiopia, and they ignore supply chain management practice measurements like information quality, internal leaTo fill this research literature gap, this study was conducted with the development of five dimensions for supply chain management practice and four dimensions for supply chain responsiveness.
The purpose of this study was to examine the effect of supply chain management practices on competitive advantage through supply chain responsiveness in the Ethiopian medium and large food processing industries. Hence, this paper addressed the main objectives of investigating the effect of supply chain management practices on the competitive advantage of the Ethiopian food processing industry through supply chain responsiveness. Supply Chain Management (SCM) has become part of the senior management agenda since the 1990s. Executives are becoming aware that the successful coordination, integration and management of key business processes across members of the supply chain will determine the ultimate success of the single enterprise ( Van der Vorst, 2000). In the Supply Chain there are three types of flows that must be managed, first the flow of goods that flows from upstream to downstream, second is the flow of money and the like that flows from upstream to downstream, the third is the flow of information that can occur from upstream to downstream or information about inventory product, production capacity and shipping information (Pujawan, 2017).

Empirical review and Hypothesis
As Suhong Lia,, et.al, (2004) assert that SCM practices impact not only overall organizational performance but also the competitive advantage of an organization. They are expected to improve an organization's competitive advantage through price/cost, quality, delivery dependability, time to market, and product innovation (Suhong Lia,, et.al, (2004). Likewise, prior studies have indicated that the various components of SCM practices (such as strategic supplier partnership, customer relationship, and information sharing and information quality) have an impact on a variety of aspects of competitive advantage (such as price/cost). For example, strategic supplier partnership can improve supplier performance; reduce time to market (Ragatz GL, Handfield RB, Scannell TV (1997), and increase the level of customer responsiveness and satisfaction (Power DJ, Sohal A, Rahman SU, 2001). Information sharing leads to high levels of supply chain integration (Jarrell JL., 1998) (Walton LW, 1996;Lee J, KimY(1999). Moreover, (Suhong Lia, et.al, 2004;Ashish A. Thatte, et.al, 2013;Somuyiwa, Adebambo 2013;S.K. Chadha, S.K. Sharma, and Maninder Singh, 2018;Siahaan T, Nazaruddin, Sadalia I. 2020) accordingly, H1: Supply chain management practice has a significant influence on the competitive advantage of food processing firms in Ethiopia.
Strategic supplier partnerships including working closely with suppliers to design or redesign products and processes, solve problems, as well as prepare backup plans, are critical in attaining supply chain responsiveness (Storey et al., 2005;Liu and Kumar, 2003). Liu and Kumar (2003) observed that collaborative practices such as 3PL, VMI, and CPFR between supply chain partners led to increased supply chain responsiveness. Customer relationship is essential for attaining supply chain-wide responsiveness (Storey et al., 2005;Martin and Grbac, 2003;Van Hoek et al., 2001;Harris, 2005;Handfield and Bechtel, 2002). Likewise, Ashish A. Thatte, et.al, (2013) assert that supply chain management practices specifically supplier partnership (Inda Sukati, and et.al, 2012;Nur Atiqah Binti Zahari Azar, 2015;Kerwin Salvador P. et.al, 2017). And a great amount of visibility is required through the supply chain in order to attain supply chain responsiveness (Storey et al., 2005) information sharing (Martin and Grbac, 2003;Handfield and Nichols, 2002). Furthermore, Ashish A. Thatte, et.al, (2013)  The improvement of flexibility and speed of response has become increasingly imperative as a method to achieve competitive advantage (Upton, 1997;Martin and Grbac, 2003).
Responsiveness to customers is critical to achieving competitive advantage (Williamson, 1991;Martin and Grbac, 2003). Likewise, Ellinger (2000) argues that competitive advantage accrues to those firms that are responsive to customer needs. Firms with more responsive supply chains will be more adaptive to demand fluctuations and will handle this uncertainty at a lower cost due to the shorter lead time (Randall et al., 2003). More recently Dr. Kamel M. Al-Hawajreh and Dr. Murad S. Attiany (2004); Yusuf et al. (2003;Ashish A. Thatte, 2007 andInda Sukati, et.al, 2012) in their study found out that supply chain responsiveness was positively associated with the competitive advantage of a firm. Accordingly, H3: Supply chain responsiveness has a significant influence on the competitive advantage of the Ethiopian food processing industry.

RESEARCH METHODOLOGY 3.1 Research design
A Causal (Explanatory) research design was used with the quantitative research approach.
This study focused on food processing firms that have the legal establishment as Share Company and private limited company (plc) operate in the Ethiopian food processing industry. The study determined 234 sample respondents using (Yamane's, 1967) but we use 215 for the current analysis. To collect the necessary data from the employees of the food processing industries the researchers employ five point likert scale questioners and it were distributed using convenience sampling Finally, the collected data was analyzed and the hypothesis were tested using structural equation modeling (SEM)-SPSS, AMOS version 23. The study conducted a validity (convergent, discriminate validity) test through loadings, composite reliability (CR), and average variance extracted (AVE) compared with the correlation (r) value of the constructs.

Factor Analysis
Exploratory factor analysis (EFA) was conducted to explore the interrelationship between variables, to remove redundant; unnecessary items, and to simplify interrelated indicators through varimax rotation. Before to this, Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett's test of Sphericity were conducted. The results illustrate the validity of the constructs factor loadings (standardized regression weights) of individual items. The individual item loading is all above the recommended 0.5, ranging from 0.613 to 0.941 (Anderson & Gerbing, 1988), implying that all items converged well. The results of the CR index for all the constructs range from 0.887 to 0.946, thereby exceeding the estimate criteria used. Likewise, the construct had an Average Extracted Variance value that ranges from 0.584 to 0.638 which is above the (0.5) threshold and this provides evidence for an acceptable level of research scale reliability (Fraering & Minor, 2006;Hair et al., 2009). Hence, all the AVE value is greater than the squared correlation value for the entire construct, thereby confirming the existence of discriminate validity.
We also tested the reliability of constructs using composite reliability and Cronbach alpha

Reliability test
We also tested the reliability of constructs using composite reliability and Cronbach alpha. Based on the reliability test of constructs and item to the total correlation coefficient, the minimum coefficient of Cronbach Alpha value is 0.645 and the maximum is 0.942 and the average Cronbach Alpha value of all items with their respective latent variable ranged from 0.755 minimum to 0.869 maximum which is good and acceptable based on the acceptable threshold of 0.7 as of (Nunnally, 1978;Bryman & Bell, 2003). This implies that there are realistically highest Cronbach alpha values and it suggested that the measurement of independent variables supply chain management practice in the current study is generally. In the same vein the item to total (first-order construct) in the current study has a positive and moderate level of correlation this ranges from r= 0.549 to r=0.753) for all items to total correlation. This implies that the measurement or indicator items can measure the latent variables. Exploratory factor analysis (EFA) through varimax rotation was conducted. Before

Test of validity
In study we the researcher conducted validity (convergent, discriminate validity) test through loadings, composite reliability (CR) and average variance extracted (AVE) compared with the correlation (r) value of the constructs which presented as follows

Source: SPSS and Excel result, 2021
The results in the above table illustrate the validity of the construct (dependent variable) factor loadings (standardized regression weights) of individual items on the construct Competitive Advantage (CA). The individual item loading are all above the recommended 0.5, ranging from 0.867 to 0.941 (Anderson & Gerbing, 1988), the results of the factor loadings imply that all items converged well on the construct they were supposed to measure the competitive    (2010). Accordingly all constructs have been forced into three factors and rotated using the VARIMAX rotation method to assess their loadings and the result indicated that all of items have the loading that ranges from 0.613 to 0.955 this greater than 0.50 load on their predicted construct that demonstrate a higher degree of association between the latent items and that constructs (Field, 2013)

SEM Assumption test
The researcher conducted different test of assumptions of structural equation modeling with different method such as melticoliniearity test using Eigen value, variance inflation factor(VIF) level of tolerance and the Pearson correlation coefficient method, and model fit test or assessment using confirmatory factor analysis (CFA) by AMOS 23 as follows.

Test of Multicollinearity Using Pearson's correlation, tolerance and VIF
Among the assumptions of structural equation modeling is that the independent variables should not have very high association or correlation. When the independent variables are highly correlated, it is regarded as a problem in the model and this problem is called multi collinearity.
Multicollinearity among the variables is examined using different methods. Similarly any two independent variables with a Pearson correlation coefficient greater than .9 between them will cause problems (Gujarati and porter (2010). In this study the primary techniques for detecting the multicollnearity are : -i) correlation coefficient, ii) variance inflation factor (Noora Shrestha, 2020) employed to detect multicolinearity problem in the collected data as follows: Multicollinearity among the variables is examined using different methods from these methods Eigen value is the other method of detecting the presence of multicoliniearity problem (Noora Shrestha, 2020). Therefore, the researchers conducted multicolliniarity diagnostics using Eigen value condition index is a function of Eigen values and variance proportion to increase the robustness of the data for better analysis. Accordingly the following table presents the analysis result. In the above table for variable supply chain management practice, higher variance proportions i.e. 0.9 (90%) is associated with dimension 2 that has an eigen value of 0.023 and a condition index of (11.395). The variable supply chain responsiveness has the higher variance of 0.95 (95%) and is associated with the dimension 2, with eigen value of (0.015) and a condition index of (14.268). A condition index greater than 15 denotes a probable problem of multicollinearity.
The higher condition index is (14.268) for dimension 3 but the variance proportions of variables are not associated with this value. This shows there is no evidence of collinearity among the variables. According to the table the value of condition index is below 15 and variance proportion value of the two variable supply chain management practice and supply chain responsiveness were below the acceptable threshold of less than 0.9 but not supported by the condition index (Noora Shrestha, 2020). Accordingly for this study multicollinearity was not a problem.

Confirmatory factor analysis (CFA) and Model fit assessments
CFA was implemented to determine measures of accuracy of the measurement instruments for the respective construct using AMOS Version 23.0. As the results indicate that the conceptual model fit assessment which is discussed hereafter.

Source: AMOS result, 2021
The results in

Source: articulated from the AMOS Model fit result, 2021
As the table demonstrate that all the model fit indices the model perfectly fit with the data and confirmable for further analysis.

Validation of second order constructs
The researcher measures the dependent variable independent variable and the mediating variable with their dimensions than the specific questions in the questioners. As a result the researcher conducted the second order construct validation or confirmation as follows.

Confirmation of supply chain Management practice as 2 nd order constructs
In this study the latent variable supply chain management practice was conceptualized as a high order model composed of five constructs, namely supplier partnership, internal lean practice, customer relationship, information sharing and information quality. Structural equation modeling using AMOS 23 was used to determine whether a higher-order factor model is appropriate for supply chain management practice.

Source: AMOS result, 2021
From the results of AMOS estimation, the fit statistics of second order construct of supply  figure 3 below presented that supply chain management practice can be statistically measured by supplier partnership, customer relationship, internal lean practice, and information sharing and information quality.

4.2.Confirmation of supply chain responsiveness as 2 nd order constructs
Similar to (SCM) in this study the researcher measure supply chain responsiveness using four constructs specifically assembly responsiveness, supplier network responsiveness, operation system responsiveness, logistic process responsiveness and supplier network responsiveness.
Accordingly, the supply chain responsiveness as second order construct was confirmed with structural equation modeling using AMOS 23, to determine whether a higher-order factor model is appropriate for supply chain responsiveness latent variable.

Source: AMOS result, 2021
As the results of AMOS estimation, illustrated in the above figure demonstrated that the standardized estimate indicated that the fit statistics of second order construct of supply chain responsiveness can be statistically measured by its indicator adopted in this study such as assembly responsiveness, operation system responsiveness, supplier network responsiveness and logistic process responsiveness.

Confirmation of 2 nd order construct of competitive advantage
In this study the latent variable supply chain responsiveness was conceptualized as a high order model composed of four constructs, namely time to market, price or cost, product innovation and delivery dependability. Structural equation modeling using AMOS 23 was used to determine whether a higher-order factor model is appropriate for Competitive advantage

Source: Amos result, 2021
As the results of AMOS estimation, the fit statistics of second order construct of Competitive 0.05 , can be concluded that the second order model is valid and reliable. In addition to this, the standardized coefficients of estimate of Competitive advantage is 0.11 for time to market, 1.25 for price or cost, 0.65 for product innovation and 0.14 for delivery dependability this indicated that all constructs are statistically significant at < 0.001. Hence, the higher order latent construct based on the above figure assured that Competitive advantage can be statistically measured by supply chain management practice.

Source: SPSS AMOS result 2021
Generally the above table summarizes the second order construct of the three latent variable, supply chain management practice, supply chin responsiveness and Competitive Advantage in summarized manner. Accordingly, the higher order latent construct in supply chain management practice can be statistically measured by its indicator adopted in this study such as(customer relationship, supplier partnership, internal lean practice information sharing and information quality) supply chain responsiveness by adopted indicators like assembly 19 responsiveness, operation system responsiveness, supplier network responsiveness and logistic process responsiveness and Competitive advantage can be statistically measured by the adopted indicators, time to market, delivery dependability, product innovation and price or cost.

SEM measurement model analysis
after confirmation of the second order constructs in order to verify how well the measured indicators representing the constructs, 53 purified specific questions or measurement items and 13 indicators or dimensions under eight constructs were tested in CFA using AMOS 23.
Naturally, there are many model fit indexes computed by AMOS software hence analyzing and interpreting all these indexes would be mystifying practitioners (Alavi, 2018). Of course, Hu & Bentler(1999) also noted that the most frequently used statistical procedures to evaluate the measurement model in SEM are Chi-square (χ 2 ), square multiple correlations (R 2 ), degree of freedom (DF), factor loading (λ), critical ratio (t-value) and model fit indices:

Source: Path Analysis Result 2021
Based the conceptual framework and the above path diagram illustrated that there is direct relationship between Supply chain management practice with competitive Advantage, Supply chain management practice with supply chain responsiveness and Supply chain responsiveness with competitive Advantage. On the other, there is also indirect relationship between supply chain management practice and competitive Advantage through the supply chain responsiveness. In this path analysis the researcher identify the direct and indirect effect of each explanatory variable on the mediating and outcome variable was analyzed and illustrated as follows:

Source: AMOS result, 2021
The above table illustrates the unstandaredized total, direct and indirect effect between constructs independent and dependent variables. Accordingly, , the unstandaredized total effect of supply chain management practice on supply chain responsiveness and competitive advantage as well as supply chain responsiveness on competitive advantage were (Beta = 0.427, Beta = 1.45 and Beta = 1.134) respectively. In the same vein the unstandaredized direct (unmediated) effect of supply chain management practice on supply chain responsiveness and competitive advantage as well as supply chain responsiveness on competitive advantage were (Beta = 0.427, Beta = 0.966 and Beta = 1.1.134) respectively. Finally the unstandardized indirect (mediated effect of supply chain management practice on competitive advantage was (Beta= 0.484).

Standardized effect
In most inferential statistics analysis standardized coefficient of effect is take in to consideration to make estimation or generalization because the result is after shrinkage of variable and it indicated the exact result of the regression. Accordingly we summarize the Based on the above summery table the study concludes that standardized: total, direct and indirect effect of the dependent variables supply chain management practice on the mediating variable supply chain responsiveness and competitive advantage as well as the standardized direct effect of supply chain responsiveness on competitive advantage is discussed.

Table8. Path coefficient values of mediation effect
Path of relationship (

H1: Supply chain Management practice has positive and significant Effect on Competitive Advantage of Ethiopian food processing industry.
This hypothesis is tested with structural equation model with the path analysis and the result revealed that Supply chain management practice (SCMP) has statistically significant and direct influence on competitive advantage (CA) of food processing firms in Ethiopia supported by coefficient value ( Beta = 0.639, p-value = 0.000). Accordingly, the researcher accepts the alternative hypothesis in turn reject the null hypothesis.

Responsiveness of Ethiopian food processing industry
Based on the result of path analysis result this hypothesis was tested. Accordingly the supply chain management practice (SCMP) has statistically significant and positive effect on the supply chain responsiveness (SCR) evidenced with the (Beta= 0.909 with p-value 0.000).
Therefore, the alternative hypothesis is accepted in turn the null hypothesis was rejected.

H3: Supply chain responsiveness has significant influence on Competitive Advantage of Ethiopian food processing industry.
This hypothesis is tested based on the structural equation model particularly by the path analysis result and it is found that supply chain responsiveness (SCR) has statistically significant direct influence or effect on competitive advantage evidenced by the beta coefficient (Beta = 0.352, p-value 0.000). This implies that any simple improvement on the supply chain responsiveness specifically assembly responsiveness, logistic process responsiveness,

DISCUSSION
The main finding of this study assured that supply chain management practice directly affect the supply chain responsiveness and competitive advantage of Ethiopian food processing industry. In the same vein supply chain responsiveness has direct effect on the competitive advantage. Furthermore, supply chain management practice has significant indirect effect on competitive advantage through the mediating role of supply chain responsiveness.
As the result of this study shows in the estimated effect in the path diagram analysis of the structural equation modelling illustrated that supply chain management practice (strategic suppliers partnership customer relationship, internal lean practice and information quality) has statistically significant positive influence on the supply chain responsiveness of the food processing firms supply chain supported by the (b= 0.909 with p-value 0.000). This means that one step improvement on the supply chain management practice of the firms manager can improve the firms responsiveness by 90.9 %.This finding was consistent with and supports the argument of (Martin and Grbac, 2003;Handfield and Nichols, 2002) and consistent with the research finding of (Ashish A. Thatte, et.al, 2013;Nur Atiqah Binti Zahari Azar, 2015;Kerwin Salvador P. et.al, 2017).
Supply chain management practice has positive and direct effect of (b= 0.639, p-value = 0.000) 63.9% on competitive advantage. This implies that any improvement of Supply chain management practice (strategic supplier's partnership, Customer relationship, and internal lean practice, level of information sharing and information quality) can improve the competitiveness of the food processing firms by the value 63.9% this means that improvement on supply chain management practice can increase the competitive advantage of food processing firms by stakeholders (strategic suppliers, customers, wholesalers and retailers) to achieve common objectives (Axelrod et al., 1995) this relationship in turn enhances the firm's responsiveness to the changing and fluctuating customer demand and operate without any interruption of the firms operation.
With regard to the effect of supply chain responsiveness on competitive advantage, as the current study revealed that supply chain responsiveness has positive and statistically significant effect of (Beta = 0.352, p-value 0.000) on the competitive advantage of food processing firms.
This indicated that if the food processing firm's one step improves their supply chain responsiveness to the changing and increasing food market in Ethiopia particularly assembly responsiveness, supplier network responsiveness, operation system responsiveness and logistic process responsiveness can improve the competitive advantage of the firms by 35.2%. This finding supports the finding of (Ashish A. Finally, in relation to the indirect effect of supply chain management practice on the competitive advantage through supply chain responsiveness the finding illustrated that supply chain management practice has positive and statistically significant effect of (Beta = 0.320, pvalue =0.000) on competitive advantage. as a result any improvnemt on the supply chain management practice can improve the competitive advantage by 32.0% through an improvement of the firms responsiveness. This finding supported the finding of (Nur Atiqah Binti Zahari Azar, 2015, Suhong Lia,, et.al, 2004Ashish A. Thatte, et.al, 2013; Somuyiwa, Adebambo 2013;S.K. Chadha, S.K. Sharma and Maninder, Singh, 2018 and Siahaan T, Nazaruddin, Sadalia I. 2020).

CONCLUSION
The aim of this study was to examine the indirect impact of supply chain management (SCM) on competitive advantage with supply chain responsiveness as mediator. The analysis result based on H1 affirmed that the supply chain management practice (SCMP) has a positive and statistically significant influence on competitive advantage. Likewise based on H2 the supply chain responsiveness has a positive and significant effect on competitive advantage. Moreover H3 asserted that supply chain management practice (SCMP) has a significant effect on supply chain responsiveness finally H4 result indicated that supply chain management practice (SCMP) has a significant indirect effect on competitive advantage through supply chain responsiveness.

Contribution of the study
The contribution of this study is twofold first, the finding of this study propose and develop new literature on the relation between supply chain management practice and competitive advantage through supply chain responsiveness in developing country like Ethiopia. Second the finding of this study paramount important for practitioners to make decision to improve the firms supply chain responsiveness and in turn enhance the competitiveness of the firms. On the other hand this study was conducted in only the food processing industry specifically food complex and editable oil factories and bread bakery company as plc. And share companies) of Ethiopia. Therefore, future research is required on similar topic in other industries and other developing countries.