Factors affecting intra-relations of backend cost drivers in multi-brand retail domestic super market under convenience stores format: A customer aligned model of supply-chain optimisation framework

 

A.S. Suresh¹, Dr. S. Ramesh²

¹Associate Professor, Institute of Management, Christ University and Scholar SCSVM University, Kanchipuram.

²Dean, Faculty of Commerce and Management-Pg, Mount Carmel College, Bangalore

 

ABSTRACT:

The main focus of this research paper is in the area of organized retail specifically in the convenience stores format in the city of Bangalore and is confined to Grocery, staples and fruits and vegetables business as food contributes to almost 63% in the organized retail business. The study was conducted during June 2014 and December 2015.

The research revealed that there are not many integrated supply chain models aligned to consumer preference, though there are models on different components of supply chain and retail. This study is intended to understand the drivers and factors influencing the back end operations in the area of supply chain efficiencies to create an integrated consumer aligned model frame work to drive optimizations at all levels. This can assist Indian companies in taking strategic decisions and competing in the emerging eco system of E-tailing on one hand and entry of multi nationals on the other.

Sample unit of this study was two major organized retail companies operating in the convenience stores format in Bangalore catering to about seventy outlets and having distribution centres and the same was derived using probabilistic multi stage sample design, from the overall population of urban retailers operating in the convenience stores format.

Primary data was collected through observation schedules for all the key parameters for both back end and front end consumer operations. Multiple Linear regression was used as inferential model, Linear programming for simulation and optimisation.

 

 

 

INTRODUCTION:

The Indian retail industry has experienced tremendous growth over the last decade with a noticeable shift of consumers’ towards organized retailing. Brick and Mortar organized retail is growing rapidly but is still in the nascent stage. There are many challenges in terms of competition from Multinational giants like Wal-Mart, overall value chain and emerging online retail.

 

Retail and wholesale trade is the single largest component of the services sector in terms of contributions to the Gross Domestic Product and at 14% it is the largest contributor after Agriculture. It employs about 7% of the total work force. The Retail Industry comprises Organized and unorganized sectors.

 

Organized retail which presently accounts for close to 8 percent of total market will increase its share to over 24% percent by 2016, offering huge potential for growth in coming years, says a study, ‘Indian Retailing-The way forward’.(ASSOCHAM, Press release).

 

India is perceived to be a lucrative destination for retail and with opening up of foreign direct investment imminent many multinational giants are eyeing India. There are many challenges in terms of infrastructure, supply chain efficiencies, cost structure and customer adaptability to the emerging retail formats. Indian companies are grappling with many challenges and experimenting with many formats in retail.

 

INTRODUCTION ABOUT THE STUDY:

This study attempts to understand the present scenario and the preparedness of domestic companies in consolidating their existing position to evolve a consumer aligned optimization frame work model. In this study, back end operations Grocery and Fruits and vegetables were analyzed using drivers such as sourcing, Procurement, Logistics for back end and revenue for consumer preference.

 

PURPOSE OF RESEARCH:

The research revealed that there are not many integrated optimization models, though there are studies done on different components of supply chain and retail by various authors. In this study endeavor is to understand the factors impacting back end supply chain drivers, using variables such as Stock keeping units, Cost of procurement, Weighted average gross margin, warehouse rent, sales quantity, sales value, manpower, processing and packing cost and secondary freight to build optimisation framework considering various interrelations.

 

LITERATURE REVIEW:

This study examine s the impact of interplay between shorter cycle times and shared point of sale information on the inventory management and cost efficiencies. Using an experimental design of ordering and shipping lag, this article explores the bullwhip effect, effect of shorter cycle on inventory costs and looks at possibility of optimality bench marks for decision maker using the sterman mental model (Joel, H et al, 2004).

 

A study of the food and grocery market channels was conducted and was found that most of the food and grocery products reach to consumers through the traditional markets which are unorganized. But the very fast changing trends in food and eating habits of consumers have contributed immensely to the growth of Western Retail format such as convenience stores, department stores, supermarkets, specialty stores and hypermarkets for various conspicuous reasons namely, demand, supply (Chetan et al., 2005).

 

This study elucidate the dynamics of customer relationship management in grocery and food buying and how the changing habits will emanate a shift from kiranas. The fast changing trends in lifestyles, food and eating habits of consumers have contributed largely to the growth and development of organised food and grocery retail formats in India. But, this sector is predominantly (99.2 percent) dominated by the traditional kirana (Ch. J. S. Prasad and A. R. Aryasri, 2008)

 

This study examines the viability and imperativeness of farm produce organised retail in the socio economic context. This paper looks into the antecedents of farm produce distribution and consumption in India, the evolving organized retailing and the ensuing movement against such moves in various states of India. (Venkatesh, 2008)

 

In this study focus was on the impact of product assortments, along with convenience, prices, and feature advertising, on consumers' grocery store choice decisions. It specifies a general structure for heterogeneity, and estimates store choice and category needs models simultaneously. Using household-level market basket data, the authors find that, in general, assortments are more important than retail prices in store choice decisions. They find that the number of brands offered in retail assortments has a positive effect on store choice for most households, while the number of stock keeping units per brand, sizes per brand, and proportion of stock keeping units that are unique to the store (a proxy for presence of private labels) have a negative effect on store choice for most households (Briesch et al., 2009).

 

This study conducted in Punjab gives insights on retail purchase behaviour in diversity of demographics and geographic locations. A study was conducted using sample survey method and questionnaire to garner insights on the diversity of purchase behavior amongst customers in food and grocery as per the demography and geography to evolve a retail business strategy for organised retail. Data analysis was done to identify major factors of concern in retail purchase decision making to find out retail purchase factor. Then Bartlett’s test of sphericity was conducted to reduce the data to simplify the interpretation of data and narrow down key factors Supply chain efficiencies starting from decision on the stock keeping units, procurement cost, Inventory Management and distribution has to be integrated and coordinated to have a robust back end (K. C. Mittal and Anupama Prashar, 2010).

 

A study was performed to discuss the core of supply chain management practices and identify five secondary constructs viz technology, supply chain speed, customer satisfaction, supply chain integration and four primary competitive advantage constructs viz inventory management, customer satisfaction, profitability and customer base identification (Rajwinder Singh et al., 2010).

 

This study concentrated on rolling horizon simulation models and performance analysis of partially and fully integrated sales and operations planning (S and OP) as opposed to conventional decoupled planning in a multi-site make-to-order (MTO) based manufacturing supply chain. Three simulation models are developed illustrating, respectively, the fully integrated S and OP model, procurement centrally; the partially integrated S and OP model, and the decoupled planning model. A solution procedure is provided for each model so that a more realistic planning process can be simulated. Performances of rolling horizon simulation models are evaluated against those of the fixed horizon deterministic models (Feng et al., 2010).

 

This research study was oriented towards allocation of production volumes among multiple manufacturing sites and distribution of products among distribution channels involving many variables and constraints. An integrated multi-product, multi-period, multi-site production-distribution planning subject to the production and distribution constraints, distribution system and local customers demand is considered in this paper. A mathematical model has been developed to solve the problem, aiming to decrease the costs of set up, production, inventory, distribution and transportation. (Safaei et al., 2010)

 

A study was performed to explore the dynamics of branding fruits and vegetables and retailing with respect to organised retail and can give qualitative insights. Indian economy has been witnessing significant changes in last few years in the retail sector. This paper is an attempt of authors to understand and study the present Indian retail industry's transition driven by organized retail sector with respect to fruits and vegetables in a detailed manner. This is basically an exploratory study to understand the retailing of vegetables and fruits in Indian context (Hemant and Manita, 2010)

 

A study was performed to analyse the interdependence of the marketing and supply chain in the organised retail scenario with specific focus on shelf facing, pricing and supply chain. Findings suggest that retailers and suppliers must work to integrate marketing activities and supply chain processes both within and across firms to most effectively serve the consumer at the retail shelf and increase market share (Waller et al., 2010).

 

A study was conducted to understand the importance of linking prime producers such as farmers to national and global markets through fresh food retail chain as this will ensure right price and high quality product (Singla et al., 2011)

 

This study deals with leverage of the enormously available customer data for aligning precious resources with customers' needs which makes it imperative for retail managers to deploy advanced tools and techniques for data analysis and generation of reports for effective decision-making. While a host of analytical processes and tools are available, a retailer needs to invest selectively and adapt to those applications that are proven to be successful, and resulted in substantial savings in terms of money, time and shelf space. This paper, apart from highlighting the relevance and efficacy of analytics for retail industry in India, also deals with a live study undertaken at Fresh Greens Bangalore, where Market Basket Analysis (MBA) and advanced demand forecasting techniques have been used for understanding the demand associations of different fruits and vegetables and fine-tuning their operations (Dasari, and Kurhekar, 2011)

 

This study examines how brand and stock-keeping-unit assortments affect category sales across a large number of categories over two time periods in which brand and/or stock keeping units assortments were changed in two retail outlets. It uses store range records and sales data from a retailer who primarily added or deleted entire brands in one outlet and primarily added or deleted SKUs within brands in a second outlet. Categories with a greater number of SKUs achieved relatively higher sales revenue (Tan et al., 2011).

 

OBJECTIVES OF THE STUDY:

•       To identify, understand and analyze interrelations of key drivers of back end operations such as sourcing, procurement and logistics.

•       To determine the relation between various factors influencing margin and weighted average margin.

•       To create framework for supply chain optimization model aligned to consumer preference.

 

RESEARCH METHODOLOGY:

This study was conducted on two major organized retail companies operating in the convenience stores format in Bangalore catering to about seventy outlets and having distribution centres. The same was derived using the probabilistic multi stage sample design, on the population size of urban Retailers operating in the convenience stores format in the city of Bangalore. The research is an exploratory one. Primary data was collected through observation schedules using cost templates for key drivers to capture present costs. In- depth interviews with experts was conducted to collect data on supply chain and other Drivers. Secondary data was collected through, Journals, newspaper reports and articles, Government policy declarations, internet and other consultancy firms, Industry association reports. Data was analyzed using Multiple Linear regression as inferential model, Linear programming for simulation and optimisation.

 

HYPOTHESES :

Back end grocery significant influence of factors:

Hypothesis-1

H0: There is no significant influence of stock keeping units, sales total, sales quantity total, cost of procurement, warehouse cost and secondary freight on weighted average margin

H1 There is significant influence of stock keeping units, sales total, sales quantity total, cost of procurement, warehouse cost and secondary freight on weighted average margin

 

Back end fruits and vegetables significant influence of factors:

Hypothesis--2

H0: There is no significant influence of stock keeping units, cost of procurement, net sales value, operations cost on weighted average margin.

H1 There is no significant influence of stock keeping units, cost of procurement, net sales value, operations cost on weighted average margin.

 

 

DATA ANALYSIS AND DISCUSSION:

Following is the summarized result from analysis of:

Table 1:Model summary of regression model fit for back end grocery variables

a. Predictors: (Constant), Secondaryfreight1, stock keeping units, Cost of procurement Total, Sales Quantity Total, Warehousecost1, Sales Total

b. Dependent Variable: Weighted average contribution margin per unit

 

The table depicts that there is significant influence of various independent variables over dependent variable being weighted average contribution margin per unit as is depicted in the model summary which accounts for 99.7% of the variance. Adjusted R square at.994 establishes the overall significance of the regression model.

Back end grocery significance of relation between variables:

 

Table 2: Influence of backend grocery variables- secondary freight, stock keeping units, cost of procurement, sales quantity, warehouse cost and sales total on weighted average margin contribution per unit

a. Dependent Variable: Weighted average contribution margin per unit

b. Predictors: (Constant), Secondary freight1, Stockkeeping units, Cost of procurement Total, Sales Quantity Total, Warehousecost1, Sales Total

 

As clearly depicted by anova there is significant linear relation between independent variables and dependent variables and hence rejects the null hypothesis.

 

Back end grocery significant predictors:

 

Table 3: Significant predictors of backend grocery- Stock keeping units, Sales Total, Cost of procurement Total and Secondary Freight

a. Dependent Variable: Weighted average contribution margin per unit

 

The table shows that Stock keeping units, sales total, cost of procurement and secondary freight are significant and accordingly the equation of impact of change in any one unit on weighted average margin is derived below.

 

Weighted average contribution margin per unit = -.023 +.007(Stock keeping units) + Sales Total (1.587E-005) – (5.621E-007) (Sales Quantity Total) - (1.587E-005)(COGS Total) -.337 (Warehousehousecost1)- 3.855(Seondaryfreight1) + e1

 

Table 3.1: Variables excluded by the model on account of collinearity:

Excluded Variables^a

a. Dependent Variable: Weighted average contribution margin per unit

b. Predictors in the Model: (Constant), Secondaryfreight¹, Category, COGS Total, Sales Quantity Total, Rent1, Sales Total

 

The above table 3.1 depicts the variables such as manpower, inventory and processing which has been excluded by the model on account of collinearity.

 

Following is the summarized result from analysis of:

 

Table Error! No text of specified style in document.:Model summary of Regression model fit for backend fruits and vegetables variables.

a. Predictors: (Constant), Operationscost1, Stock keeping units, cost of procurement, Net Sales value

b. Dependent Variable: Weighted average contribution margin per unit

 

The table depicts that there is significant influence of various independent variables over dependent variable weighted average contribution per unit as is depicted in the model summary which accounts for 98.2% of the variance. Adjusted R square at.981 establishes the overall significance of the regression model.

 

Back end fruit and vegetables significance of relation between variables

 

Table 5:Influence of backend fruits and vegetables variables- operations cost, stock keeping units, cost of procurement and net sales value on weighted average margin.

a. Dependent Variable: Weighted average contribution margin per unit

b. Predictors: (Constant), Operationscost1, Stock keeping units, Cost of procurement, Net Sales value

 

As clearly depicted by ANOVA (there is significant linear relation between independent variables and dependent variables and rejects the null hypothesis.

 

Back end fruits and vegetables significant predictors:

Table 6: Significant predictors of backend fruits and vegetables-constant, Cost of procurement, Net sales value and Operations cost.

Dependent Variable: Weighted average contribution margin per unit

 

Cost of procurement, net sales value and operations cost are significant and the equation of impact of change in any one unit on weighted average margin is derived below.

 

Weighted average contribution margin=

Constant(-.003)+ Cost of procurement (-2.780E-006)+ Net Sales(2.800E-006) + Operationscost1(-6.297E-005)+e1

Following is the summarized result from analysis of linear programming performed for various optimisation simulation:

 

Backend grocery simulation original mix

Table below depicts the original sales mix of critical category in grocery and staples which reflects the present preference of customers due to variety of reasons including price points, stock keeping units range, and quality. Taking this as base, further optimisation of margin through optimisation of sales and category mix is generated through linear program under different optimisation scenarios.

 

Table 7: Back end grocery original category mix

 

The above table depicts the seven main categories viz., atta, cereals, dry fruits, edible oil,  pulses, salt and sugar and spices in the grocery and staples segment which comprises the sales composition of this business and reflects the customer preference at the front end and procurement in the back end i.e. a total of 1900 line items have been categorized thus. First row indicates the category sold in quantity, Second row depicts the value of sales and the third row the cost of items sold within the category. Percentage of sales depicts the percentage of contribution of each category to the total sales. Profit depicts the combined impact of margins due to the sales mix. Total sales is the sum total of all sales in the individual seven categories.

This original mix will be the base for simulating optimum mix scenarios.

 

Backend grocery simulation one on original base.

Keeping cost of goods sold as constraint the optimum mix sales revenue is generated. Contribution of each category in terms of percentage to the whole still remains closer to the original mix and therefore is viable as the present preference of customer is intact.

 

Table 8 :Back end grocery optimisation solution one

 

Simulation Solution one depicts that 21 % increase in overall sales as compared to original sales increases the profit by 17..97%.Further change in the sales mix i.e. reduction in contribution of atta and pulses and marginal increase in contribution of other categories also contribute to a higher margin as compared to original mix.

 

Backend grocery simulation two on original base.

In this there is substantial increase in many categories. This indicates that supply chain factors have to be improved particularly procurement cost as the cost of goods sold is to be kept at a particular point while the top line improves. However another aspect is that since the mix of many key categories is increasing, promotion and price strategies in the front end to be considered.

 

Table 9: Back end grocery optimisation solution two

 

Simulation Solution two depicts that 22 % increase in overall sales as compared to original sales increases the profit by 18.29%.Further change in the sales mix i.e. increase in contribution of all the seven categories and pulses and substantial increase in contribution of edible oil also contribute to a higher margin as compared to original mix.

 

But the increase generated in edible oil has to be correlated with customer preference.

 

Backend grocery simulation three on original base.

This simulation generates ideal mix for all categories. But considering cost of goods sold as a constraint may not be appropriate as the cost cannot be kept at a constant level. However this is a good indicator probable ideal mix.

 

Table 10: Back end grocery optimisation solution three

 

Simulation Solution three depicts that 6 % increase in overall sales as compared to original sales increases the profit by 5.18%.Further change in the sales mix i.e. increase in contribution of all the categories except salt and sugar increase the margin as compared to original mix. But zero sales generated in salt and sugar is not a viable proposition in correlation with customer preference.

 

Backend grocery simulation four on original base.

Table 11: Back end grocery optimisation solution four

 

Simulation Solution four depicts that 16.62 % increase in overall sales as compared to original sales increases the profit by 13.11%.Further change in the sales mix i.e. increase in contribution of all the categories except salt and sugar increase the margin as compared to original mix. This is a good indicator of ideal mix. But keeping cost of goods sold as a constraint may not be appropriate as the procurement cost can also increase.

 

Backend fruits and vegetables simulation original mix.

Table below depicts the original sales mix of critical category in fruits and vegetables which reflects the present preference of customers due to variety of reasons including price points, stock keeping units range, and quality. Taking this as base further optimisation of margin through optimisation of sales and category mix is generated through linear program under different optimisation scenarios.

 

Table 12: Back end fruits and vegetables original category mix

 

The above table depicts the six main categories viz exotic, fruits, onion, potato, tomato (OPT), sprout and vegetable in the fruits and Vegetable segment which comprises the sales composition of this business and reflects the customer preference at the front end and procurement in the back end ie a total of 145 line items have been categorized thus. First row indicates the category sold in quantity, Second row depicts the cost of item sold in each category and the third row the value of sales. Percentage of sales depicts the percentage of contribution of each category to the total sales. Profit depicts the combined impact of margins due to the sales mix.

This original mix will be the base for simulating optimum mix scenarios.

 

Backend fruits and vegetables simulation one on original base.

Keeping demand as constraint optimisation mix is generated keeping the same closer to present mix as it reflects the customer preference.

 

Table 13: Back end fruits and vegetables global optimisation solution one

Increase in sales is the only driver for optimisation and is suggested at 33% to increase profit by 35%. Original mix is retained in line with customer preference

 

Backend fruits and vegetables simulation two on original base.

Table 14 :Back end fruits and vegetables global optimisation solution two

 

Increase in sales is the only driver for optimisation and is suggested at 32.95% to increase profit by 31.72%. Original mix is retained in line with customer preference.

 

Backend fruits and vegetables simulation three on original base.

This simulation is heavily skewed towards one category that is vegetables and hence will not be a viable proposition. It would be imperative to look at other costs such as operations cost and dump.

 

Table 15: Back end fruits and vegetables global optimisation solution three

 

Increase in sales is the only driver for optimisation and is suggested at 32.99% to increase profit by 33.75%. Original mix is retained in line with customer preference

 

FINDINGS:

1.        The study indicates that there is significant linear relation between independent and dependent variables and stock keeping units, sales total, cost of procurement and secondary freight emerge as significant predictors for backend grocery.

2.        Similarly for backend fruits and vegetable cost of procurement, net sales value and operations cost emerge as significant predictors.

3.        Stock keeping units and category mix plays an important role in the back end grocery.

4.        Independent variables stock keeping units, cost of goods sold, secondary freight has significant inter play and establishes them as key drivers and factors in the back end.

5.        In case of fruits and vegetables sales revenue plays a major role. However operations cost also need to be monitored for better margin particularly dump cost.

6.        In the front end sales revenue is the key drivers to understand customer preference.

7.        In case of fruits and vegetables, it is substantially driven by changes in sales.

8.        Backend grocery optimisation solution suggests an increase of overall sales by 21% and thereby profit by 17.97% and seems to be the right mix as the original mix as per consumer preference is intact. However it requires marketing efforts to increase sales.

9.        Optimisation solution two for backend grocery ignores the edible oil in terms of correlating to customer preference and hence may not be viable.

10.     Optimisation solution three at increased sales of 6% is ideal, However this is viable only if the cost of goods is constant. Moreover it ignores important category like salt and sugar.

11.     Optimisation for backend fruits and vegetables can happen only on optimising sales and is the only driver of ideal mix.

12.     In the back end grocery and staples supply chain efficiencies in terms of cost of goods and secondary freight emerge as significant in addition to optimising category mix in line with customer preference.

13.     If the optimisation of back end grocery can be applied to improvement in share of grocery business in line with consumer preference as depicted by current sales the overall supermarket business can also improve the margin.

 

MANAGERIAL IMPLICATIONS :

The study highlights the imperativeness of evolving efficient supply chain models, backward and forward integration of supply chain and front end operations in organised retail. It also brings to the fore the need for creating separate modules for supply chain and front end operations which can be monitored separately and then integrate them into efficient value chain. This could help in enhancing the capability of Indian organised retail companies to gear up to counter stiff competition and sustain in the long run.

 

CONTRIBUTIONS TO THE BODY OF KNOWLEDGE :

Though this study is confined to only the convenience stores discount format in the organised retail, it has opened up a new vista of research into the subject. Further research can be conducted in creating integrated business model framework and expanding them to other formats of organised retail and can throw up newer ideas, innovations and efficient models. This research will hopefully initiate more exhaustive research which will have a cascading effect on the industry as a whole.

 

CONCLUSIONS:

India's retail market is expected to grow at 7% over the next 10 years, reaching a size of US$ 1.3 trillion by 2020. Traditional retail is expected to grow at 5% and reach a size of US$ 988 billion (76%), while organized retail is expected to grow at 25% and reach a size of US$ 312 billion by 2020.The overall conclusion which can be inferred from this study is that creation of a supply chain optimisation model framework aligned to consumer preference for the convenience stores format will not only give a direction for the strategy to be adopted but will also stream line the processes and systems which will enable Indian organised retail to capitalise on the growth of organised retail and equip them to adapt to differing marketing dynamics in view of impending foreign direct investment in multi brand and create sustainability

 

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Accepted on 26.12.2016          © A&V Publications all right reserved