Improving Customer Service in the Distribution Logistics of Grocery Products in Brazil: a longitudinal study

Kleber Figueiredo
Rebecca Arkader
Cesar Lavalle
Maria Fernanda Hijjar

Abstract

This paper deals on the evolution of customer service practices in the distribution of grocery products in Brazil in the 1994-2001 period, based on a longitudinal survey of a large sample of grocery retailers in the cities of Sao Paulo and Rio de Janeiro. Data were collected on retailer expectations on four relevant dimensions of customer logistics service provided by manufacturers, as well as on their perceptions of logistics service performance, both of the typical supplier and the best supplier. The assumption of increasing customer satisfaction with service provided by suppliers in such dimensions was tested, as represented by decreasing gaps between retailer service expectations and manufacturer distribution performance. Results indicate a trend towards an adjustment of distribution performance to expectations and the provision in some cases of service above customer expectations. This is interpreted as a positive response of manufacturers to the structural and competitive changes in the market by the adoption of improved logistics practices that are tending to meet their customers' needs.

Keywords:

Customer Service; Customer satisfaction; Logistics practices; Grocery industry; Brazil

Introduction

The past decade was marked in Brazil by increased competition, due among other factors to deregulation and the opening of the market. Some aspects of this process were particularly relevant to the retail industry and its suppliers, mainly the competition from imported products, growing foreign investment and concentration through mergers and acquisitions. Following an international trend, local retailers have suffered from compressed margins as a result of intensified competition and consumer interest in low prices. This has been leading companies to cut costs and give more attention to service so as to increase value offered to customers. At the same time, peculiar characteristics of the local business and economic environment have been putting added pressure on retailers to reduce inventory. Indeed, having to cope with one of the highest costs of money in the world, Brazilian retailers have been forced to seek ways to reduce their inventory levels. As a consequence, the improvement in logistics practices throughout the distribution channel has become an imperative for competitiveness in the Brazilian grocery retail industry, by means of ECR and other efficiency enhancement initiatives.

In order to achieve improved operational efficiency, grocery retailers need to receive better logistics service on the part of their suppliers (Heskett, 1994). It is to be expected that the growing concentration in the retail industry is leading to increased bargaining power vis-à-vis manufacturers and allowing retailers to demand better customer service from suppliers, imposing stricter requirements (Schellhase et al., 1999). To manufacturers, improved logistics can thus be a strong differentiating factor in a situation of harsh market competition (Sharma and Lambert, 1994; Bookbinder and Lynch, 1997).

Against this background, this paper deals on the evolution of customer service practices in the distribution of grocery products in Brazil in the 1994-2001 period. Data on customer service provided by manufacturers to grocery retailers in the period were collected in a sample of 240 retailers located in the main Brazilian consumer markets, the cities of Sao Paulo and Rio de Janeiro. Yearly rounds of a comprehensive longitudinal survey were conducted with these retailers in the period, collecting data on their expectations of supplier performance in the distribution of manufactured grocery products and their perception of actual performance, both in terms of the typical supplier and of the best supplier, relative to customer service dimensions derived from a review of the relevant logistics literature. In addition to presenting the results on the evolution of the gaps between performance expectations and actual performance in variables operationalizing such dimensions, the paper tests the assumption of increasing customer satisfaction with service provided by suppliers in such dimensions, as represented by decreasing gaps between retailer logistics service expectations and manufacturer distribution performance.

This introduction is followed by a review of the theoretical background supporting the research. The third part of the paper describes the methodology. This is followed by the presentation and discussion of the results of tests conducted on the data. Finally, in the conclusion, the issue of service performance adjustments to customer needs as a response to market and business environment pressures is addressed in face of what is shown in this study.

Review of the literature

Customer service is increasingly seen as fundamental for retail companies (Ellram et al., 1999), constituting the main output of logistics systems in supplier companies as well as the "place" component of their marketing mix (Stock and Lambert, 1992; Lambert, 1994). Christopher (1992) defines "customer service" as the consistent provision of time and place utility. It has a strong strategic component (O'Laughlin and Copacino, 1994; van der Veeken and Rutten, 1998), and aims to "enhance 'value-in-use', meaning that the product has more worth in the eyes of the customer because service has added value to the core product. In this way significant differentiation of the total offer (that is the core product plus the service package) can be achieved" (Christopher, 1992:16).

For this differentiation to happen, it is necessary to adopt a more segmented marketing approach (Sharma and Lambert, 1994), so as to identify the value requirements of customers in terms of markets as a whole as well as the customer segments to be served, as the importance to different customers of different benefits obtained through customer service elements is not the same (Stock and Lambert, 1992; Christopher, 1992; Heskett, 1994; Collins et al., 2001). Therefore, "the importance of such benefit segmentation lies in the fact that often there are substantial opportunities for creating differentiated appeals for specific segments" (Christopher,1992:5). Thus customer service requirements command the structure of the supply chain, including manufacturing, marketing, and logistics, and to understand such requirements is a fundamental step for the design of a customer service strategy that meets customer expectations (O'Laughlin and Copacino, 1994). These expectations have been pointed out as the main competitive benchmarks for evaluating customer service, as merely comparing the performance of different suppliers does not lead to the identification of areas of potential improvement (Stock and Lambert, 1992). Knowing what are the expectations of their customers allows companies to establish customer service strategies which target the attributes that are actually important, so as to offer neither more nor less than customers expect (Lambert, 1994).

Schellhase et al. (1999) have pointed out that customer satisfaction in retail has not been extensively studied. They also indicate customer satisfaction as a difficult construct, which cannot be directly measured. Satisfaction would be the extent in which the results produced for the customer and the process this customer has gone through to obtain these results meet the customer's expectations; it is thus inversely proportional to the gap between customer expectations and perceptions (Harvey, 1998). Expectations are the internal patterns used by customers to judge the quality of a service experience. If customers perceive the actual delivery of the service as better or equivalent to what was expected, they will be satisfied; if on the contrary it is below expectations, they will be dissatisfied and will judge the quality according to their degree of satisfaction with the service (Lovelock and Wright, 1999). Studies on the formation of customer service expectations have pointed out the dominant role played by previous experiences the customer had with a specific service (Gummeson and Grönroos, 1997; Zeithaml et al., 1993). Zeithaml et al. (1993) have further considered the need the customer has in terms of performance in a certain service attribute as a major factor in the creation of expectations regarding this attribute.

The relationship between performance on certain customer logistics service dimensions and customer satisfaction has been the object of limited study so far (Mentzer et al., 1989; Emerson and Grimm, 1996). Since La Londe and Zinszer (1976), several studies have classified and identified the main customer service dimensions in logistics (among others, La Londe et al., 1988; Mentzer et al., 1989; Bowersox and Cooper, 1992; Christopher, 1992; Bowersox and Closs, 1996; Bookbinder and Lynch, 1997; Emerson and Grimm, 1996). Typical logistics customer service dimensions (as opposed to marketing dimensions) are associated with the order cycle (Mentzer et al., 1989), such as lead time and fill rate (Bookbinder and Lynch, 1997).  Collins et al. (2001, p. 7) have included in a list of customer logistics service dimensions most frequently mentioned in literature those of order cycle time; consistency and reliability of delivery; inventory availability; order-size constraints; ordering convenience; system picking, packing and labeling accuracy; delivery times and flexibility; ability to substitute; invoicing procedures and accuracy; claims procedure; condition of goods on arrival; post-sale support for the product; product tracing and order status information.

When inventory reduction is of relevance to customers, variables intervening in the calculation of safety stock levels in order to deal with supply uncertainty may be considered as decisive dimensions for determining customer satisfaction (Chopra and Meindl, 2001). Methodologies for calculating safety stock levels have taken into account variables such as lead time (Bowersox and Closs, 1996; Krupp, 1997), lead time variability (Bowersox and Closs, 1996), percent of demand met when order is taken (Chopra and Meindl, 2001) and fill rate (Chopra and Meindl, 2001).

 Methodology

The methodology for the customer service benchmarking survey from which this specific study draws was based on Christopher (1992). Since the original round in 1994 it has been repeated in 1995, 1997, 1998, 1999, 2000 and 2001. A structured questionnaire based on nine customer service dimensions was answered by executives responsible for supply management in about 240 supermarkets in the cities of Rio de Janeiro and Sao Paulo, half in each (there is a slight variation in sample numbers along the period). The dimensions were operationalized according to their respective distribution service attributes, based on the definitions in Bowersox and Cooper (1992), Christopher (1992) and La Londe et al (1988): product availability, order cycle time, order cycle consistency, delivery frequency, flexibility in the delivery system, failure recovery system, information support system, support to physical delivery and post-delivery support. On a Likert type scale from 1 to 5 respondents were asked to indicate the degree of importance they attributed to the nine distribution service dimensions. Variables in each dimension were measured according to three references: the minimum customer performance expectation; the typical market practice and the best supplier practice. The minimum expectation represents the performance below which the customer is dissatisfied; the best market practice reflects the best performance among all suppliers to that retailer, to be pursued as a benchmark; and the typical market practice represents the performance of a typical supplier among those serving each retailer included in the sample. Suppliers were manufacturing firms producing perishable food items, non perishable food items, paper products and hygiene and cleaning products.

The present analysis considers five service attributes, representing the first four customer service dimensions. The latter have been consistently ranked by surveyed retailers as the most important for their operations. As such, it may be assumed they exercise the strongest influence on satisfaction with supplier service in logistics. Furthermore, they have been shown above to be related to customer satisfaction when inventory reduction is a main performance objective to be pursued. For the first dimension the attributes considered were the percent of demand met when order is taken and the percent delivered of the total order. The 'percent of demand met when order is taken' (PERDEM) relates to the information the retailer receives when the order is placed and accepted as to the availability of the ordered items, and as such does not relate to actual order delivery. The attribute 'percent delivered of the total order' (PERTOT) relates to merchandise actually delivered. The attribute 'time between order and delivery' (TIME) operationalizes in days the order cycle time dimension. The attribute 'percent of late deliveries' (LATDEL) is relative to the order cycle consistency dimension, and has to do with the delivery being done after promised dates. Finally, the attribute 'number of deliveries per month' (NUMDEL) by suppliers operationalizes the delivery frequency dimension.

The main research question: "Has there been an improvement in the satisfaction of grocery retailers with the Customer Logistics Service provided by their suppliers (manufacturing companies) in Brazil?" has been addressed by showing the evolution of the gaps between performance expectations and actual performance in the five abovementioned attributes. In addition, the assumption of increasing satisfaction of retailers with service provided by their suppliers in the five attributes here considered was tested, as represented by decreasing gaps between retailer service expectations and manufacturer distribution performance. For each attribute the assumption was first tested considering the gap between expectations and perceived performance of the typical supplier (identified as "a" gaps) and then considering the gap for the best (benchmark) supplier (identified as "b" gaps) respectively for the attributes PERDEM, PERTOT, TIME, LATDEL, NUMDEL.

Increasing satisfaction was verified by ANOVA tests of linearity for each gap for a 5% significance level conducted in the SPSS for Windows 10.0 statistical package.

Results and Discussion

Product Availability When Order is Taken  

Gaps 1.a and 1.b refer to the variable PERDEM, the percent of demand met when order is taken. It is one of the attributes measuring the availability of products offered by manufacturers to retailers. The complementary measure in this case is the percent of stockouts on the side of the manufacturer informed to the retailer when an order is received from the latter. Gap 1.a represents the average of differences between the tolerance of retailers to non availability of products when ordered (expectation) and the percent of orders actually accepted by suppliers with a typical service performance in the market. Gap 1.b, on the other hand, represents the average of differences between expectation of retailers and the percent of orders actually accepted by the best suppliers.

Figure 1
Gap 1.a (expectation - perception of performance of the typical supplier in the attribute PERDEM, measured as a percentage)

ANOVA table for Gap 1.a on the attribute PERDEM - typical supplier

This variable began to be collected in 1997. In 1997 retailers were willing to put up, in average, with 5.5% more of product non availability when order was taken by manufacturers than the actual percent of non availability informed by the typical supplier (a negative gap). By 1999 this measure dropped to 1.6%. In 2001, however, the average gap was back to levels observed in 1997.

In accordance, no evidence of linearity was found for this variable related to product availability. The negative gap indicates that service is, in average, better than customer expectations, that is, it has been overspecified. In 1999 expectations and practice seem to have been better adjusted, reducing the absolute value of the gap. This has, however, increased again in 2000 and 2001.

Figure 2
Gap 1.b (expectation - perception of performance of the best supplier in the attribute PERDEM)

ANOVA table for Gap 1.b on the attribute PERDEM - best supplier

In the case of best practices, it may be seen that the average gap remained negative throughout the period. This means that in 1997 retailers would still be satisfied if there were more 14.1% of non available products when best suppliers took an order. This percent, however, has been decreasing along time.

In this case significance was evident in the test for linearity. The gap for the best practice has been reduced and service, that has always been better than expected, seems to be adjusting better to expectations.

Product Availability When Order is Received

Gaps 2.a and 2.b refer to the variable PERTOT, the percent delivered of the total order. It relates to the same dimension as the previously discussed variable PERDEM, that is, supplier product availability. PERTOT measures what percent of the quantity confirmed by the manufacturer when order is taken are actually delivered to the retailer. Gaps 2.a and 2.b represent therefore the difference between the minimum percentage of confirmed orders that manufacturers are supposed to deliver so that retailers are satisfied and the percentage of the order actually delivered.

Figure 2 - Gap 2.a (expectation - perception of performance of the typical supplier in the attribute PERTOT, measured as a percentage)

ANOVA table for Gap 2.a on the attribute PERTOT -  typical supplier

This gap did not test significantly for linearity at a 5% level. The negative gap indicates that, in average, service is better than expected by retailers. Even though no significant linearity could be found, a certain trend for better adjustment of expectations and market practice can be observed from the graph, where the gap line is indeed approaching zero.

Figure 4 - Gap 2.b (expectation - perception of performance of the best supplier in the attribute PERTOT)

ANOVA table for Gap 2.b on the attribute PERTOT -  best supplier

In this case there was significant linearity, and a trend along the period for a decrease in the gap between expectations and best practice can be observed, indicating better adjustment of service levels to customer needs. 

Order Cycle Time

Gaps 3.a and 3.b refer to the variable TIME, the number of days elapsed between the date the order is placed and that in which the products are received by retailers. This is a measure of the order cycle time and represents the sum total of time spent in the order processing, service and delivery activities that eventually get the ordered products to their destination at the retailer end. For gaps 3.a and 3.b a positive gap points to customer satisfaction, that is, the expected time is larger than the actual time.

Figure 5 - Gap 3.a (expectation - perception of performance of the typical supplier in the attribute TIME, measured in days)

ANOVA table for Gap 3.a on the attribute TIME -  typical supplier

Observation of the graph indicates this gap (time in days between order and merchandise delivery) has been oscillating around the zero value, mainly from 1998 on, indicating a trend to adjustment of performance to expectation. Considering the whole period, however, there was no evidence of significant linearity.

Figure 6 - Gap 3.b (expectation - perception of performance of the best supplier in the attribute TIME)

ANOVA table for Gap 3.b on the attribute TIME -  best  supplier

This gap tested significantly for linearity. In 1994 the best manufacturers used to deliver products to retailers in average 2.5 days earlier than expected, but now service seems to have adjusted to requirements, as the gap was in average one day. Still the best supplier was in average exceeding retailer service expectations. The trend was to a balance between expectations and actual performance, with gaps approaching zero days.

Consistency in Delivery Times

Gaps 4.a and 4.b relate to the variable LATDEL, the percent of late deliveries. In order to identify if promised delivery dates were being kept by manufacturers, retailers were asked about the percent of deliveries that arrived after the due dates. This is a measure of the consistency in the order cycle.

Gap 4.a represents the average of the differences between the percent of late deliveries considered as acceptable by retailers and the percent of deliveries practiced in the market. Gap 4.b indicates the average of differences between the levels of tolerance with delays in deliveries and the performance of best practice suppliers. For the attribute LATDEL, positive gaps indicate that in average customers are satisfied with the punctuality of deliveries by manufacturers.

Figure 7 - Gap 4.a (expectation - perception of performance of the typical supplier in the attribute LATDEL, measured as a percentage)

ANOVA table for Gap 4.a on the attribute LATDEL -  typical  supplier

The negative gap in this case indicates that service is worse than retailer expectations. This gap was negative throughout the survey rounds and linearity could not be verified in the test. However, the graph shows that since 1999 the gap has been narrowing, pointing to a decrease in dissatisfaction with this attribute.

Figure 8 - Gap 4.b (expectation - perception of performance of the best supplier in the attribute LATDEL)

ANOVA table for Gap 4.b on the attribute LATDEL -  best  supplier

Contrary to Gap 4.a, GAP 4.b has always been positive along survey rounds, indicating that manufacturers with best practices have had less late deliveries than retailers are willing to tolerate. Linearity could be verified at the 5% level, indicating a trend to the adjustment of expectations and performance.

Delivery Frequency

More frequent deliveries to retailers imply smaller buying lot sizes and less inventory and related costs. The variable NUMDEL refers to the number of times per month a supplier is asked to deliver, both in terms of the usual market practice and the best supplier practice.

Gap 5.a represents the difference between the expectation of retailers on the number of monthly deliveries and the actual delivery frequency typically practiced in the market, whereas Gap 5.b measures the average of differences between expectations on this attribute and the best supplier practice. For this variable, negative gaps are usually an indication of customer satisfaction, that is, suppliers deliver more frequently than the minimum acceptable to retailers. 

Figure 9 - Gap 5.a (expectation - perception of performance of the typical supplier in the attribute NUMDEL, measured in number of monthly deliveries)

ANOVA table for Gap 5.a on the attribute NUMDEL -  typical  supplier

The negative gap indicates that expectations are below actual delivery frequency performance in the market. In 1995 performance was a bit worse than expected, but since 1997 the average performance slightly exceeds expectations and, in general, is not far from zero. The gap tested significantly for linearity and the indication in this case is of customer satisfaction.

Figure 10 - Gap 5.b (expectation - perception of performance of the best supplier in the attribute NUMDEL)

ANOVA table for Gap 5.b on the attribute NUMDEL -  best  supplier

This gap has always been negative, that is, performance has always been better than expectations in terms of delivery frequency. No evidence of linearity was found, but from the graph it may be seen that there is a recent trend for the adjustment of service to customer expectations. It may be observed that for the first four attributes linearity was significant for the best supplier gap but not for that of the typical supplier. On delivery frequency, however, the reverse was observed.

Conclusions

Results from this study indicate that the gaps between expectations and performance of Brazilian grocery retailers with the customer logistics service manufacturers have been providing to them seem to be narrowing, that is, there seems to be a trend for customers to be more satisfied with the service they receive, especially, as could be expected, from their best suppliers. An interesting conclusion is that, in most cases, even for the typical or average supplier, service was in fact overspecified and performance was above customer expectations. Two implications are may be drawn from this overspecification. First, suppliers are possibly incurring in costs that are higher than they could have been if a better adjustment of customer demands and needs and service provided was reached. Second, former experiences with services, as seen, play a decisive role in building customer expectations on service. This means that if a service provider delivers service above expected levels, customer expectation for the next service situation will be adjusted to the former experience, creating an escalation in expected service levels along time. This may in fact be a trap to suppliers willing to exceed customer expectations in the distribution logistics of grocery products. If this is not achieved by any reason, the supplier's image may be harmed and customers once satisfied with a level of service below the one now provided may be dissatisfied and be lost.

The outstanding exception to the apparent trend to increasing retailer satisfaction with service provided by manufacturer is in the case of the percentage of late deliveries on the part of the typical supplier (though not of the best supplier). Even though there was indication of possible improvement by the end of the period, customers still seem to be far from satisfied with the percentage of late deliveries they are getting from their suppliers. Considering both the need to provide good service to consumers and the high cost of inventory holding in Brazil, this does not come as a surprise. It represents, therefore, a good opportunity for suppliers to differentiate themselves from competitors in terms of customer service by seeking to satisfy better their customers' expectations.

In all, this study seems to point to a positive response of manufacturers to the structural and competitive changes in the market by the adoption of improved logistics practices that are tending to meet their customers' needs.

A limitation to this study arises from the fact that the research deals only with the perceptions of retailers. In addition, it should be noted that some of the complexities of forging customer satisfaction and establishing adequate service levels might not be adequately captured by an expectation-actual performance gap analysis. As mentioned above, customer needs have been shown to influence expectations on performance of suppliers on a service attribute. If for example a manufacturer knows that a certain retailer is dependent on a high frequency of deliveries due to storage space constraints, the logistics system this supplier implements will seek to deliver frequently, even if this means partially fulfilling orders. Partial fulfillment of orders will in turn influence satisfaction on the product availability dimension and, if this is also relevant to the retailer, may create overall dissatisfaction despite efforts on the side of the manufacturer to satisfy the customer. Suppliers should be aware of these interrelationships and analyze with customers the trade-offs that arise in their specific logistics service relationship so as to establish service levels for the different dimensions that effectively meets customers needs and do not create unnecessary additional costs to suppliers. 

Another limitation is that none of the variables included in this study considers the volume of purchases by retailers. This means that it is possible that retailers are satisfied with the service performance of manufacturers in most of the attributes because they may be purchasing more than is necessary due to overestimated demand and, therefore, may still be keeping more inventory than would be necessary. If, due to ineffective inventory control and demand forecasting retailers are not adjusting inventories to their real needs in terms of future sales, they may be overvaluing the performance of their suppliers because they have not suffered stockouts. It is important to remember that for decades and until eight years ago, during the long inflationary period in the Brazilian economy, inventory holding was seen by most retailers as the key to their success in business. Today, holding excess inventory probably means the opposite. The supply chain efficiency effects of improvement in distribution logistics of grocery products in Brazil may therefore depend not only on the service provided by suppliers but on improved supply and demand management practices on the side of retailers.

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