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Food and grocery supply chains:a reappraisal of ECR performance

Bobby J. MartensDepartment of Logistics, Operations, and Management Information Systems,

Iowa State University, Ames, Iowa, USA, and

Frank J. DooleyDepartment of Agricultural Economics, Purdue University,

West Lafayette, Indiana, USA

Abstract

Purpose The paper aims to reappraise efficient consumer response (ECR) in the grocery and food

industry to determine whether financial and operating performance improves with ECR adoption.Design/methodology/approach The paper uses a time-series multiple regression model. Themethodology overcomes historical shortcomings in ECR and supply chain management researchrelated to small sample size, one-tier investigation, and short-longitudinal focus.

Findings ECR adoption has beneficial impacts for both financial and operational performance.

Research limitations/implications Two limitations exist. First, determining the actual time ofimplementation for supply chain management strategies by firms in the food industry is extremelydifficult. The method used to classify firms as ECR adopters in this paper is believed to be sound andunbiased, but errors may exist. Second, this analysis does not account for differences in theimplementation level for ECR. For simplicity, a binary variable is used to distinguish firms adopting ornot adopting supply chain management strategies (ECR). Further study is needed to determine howdifferences in the level of ECR implementation impacts firm performance.

Practical implications The paper overcomes historical shortcomings in ECR performanceresearch. The paper provides academics and practitioners in the food and grocery industry definitiveevidence that ECR has beneficial impacts for both financial and operational performance in the foodand grocery industry.

Originality/value By placing greater attention on overcoming historical shortcomings in supplychain management research related to small sample size, one-tier investigation, and longitudinalstudy, the paper improves upon previous evaluations of ECR.

Keywords Fast moving consumer goods, Supply chain management, Performance monitoring,Demand management

Paper type Research paper

IntroductionWal-Mart entered the grocery business in late 1980s with a supply chain managementstrategy of continual replenishment of products based on consumer purchasing habits.This approach allowed Wal-Mart to lower inventory levels, reduce the cost of goodssold, and lower its prices. To counter Wal-Marts entry into the grocery business, largesupermarket chains (e.g. Kroger and Safeway) and food manufacturers (e.g. Kraft andGeneral Mills) formed an industry task force that called for an industry-widecommitment to efficient consumer response (ECR) (Kurt Salmon Associates, 1993).The ultimate goal of ECR was for retailers and suppliers to work closely together toreduce costs within the supply chain and to bring better value to the grocery customer.

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0960-0035.htm

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Received December 2009Accepted April 2010

International Journal of PhysicalDistribution & Logistics ManagementVol. 40 No. 7, 2010pp. 534-549q Emerald Group Publishing Limited0960-0035DOI 10.1108/09600031011071993

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The four main ECR strategies are efficient store assortment, efficient replenishment,efficient promotion, and efficient product introduction (King and Phumpiu, 1996). Theaim of efficient replenishment focuses on cost control while the other initiatives promotedemand management. Successful implementation of ECR depends on numerous factors

such as the number of processes, demand patterns, product life cycles, and an ability tomaintain cooperative relationships with suppliers/vendors (Mejza and Wisner, 2001).

Early in the history of ECR, it was estimated that the food industry would realizeincredible benefits from adoption. Studies estimated that ECR adoption could save$30 billion in cost while reducing inventory by over 40 percent (Kurt Salmon Associates,1993; Kahn and McAlister, 1997). Suppliers were estimated to capture 70 percent of thesavings due to their higher supply chain costs (Adams, 1995). In 1994 and 1995, the FoodMarketing Institute projected that the industry would spend $7.5 billion implementingECR and predicted a payback period of less than three years (Walsh, 1995).

However, others question whether ECR investments have been profitable for the foodindustry (Walsh, 1995; Brown and Bukovinsky, 2001). Brown and Bukovinsky (2001)

suggested that grocers were dissatisfied with ECR because it failed to deliver thepromised cost savings or increased profitability. A related paper by Brown and Buttross(2007) reached a similar conclusion for apparel retailers. However, these findings seemcounterintuitive given the level of investment in supply chain initiatives by firms in thefood retailing and manufacturing sector, as well as the continuing reports of newinitiatives in the trade press. In contrast, other studies have found that adopting supplychain management systems such as information technology increase gross margin,inventory turnover, market share, return on sales (Dehning et al., 2007). The medianincrease attributable to investments in supply chain management was 1.78 percent forreturn on assets (ROA) and 1.44 percent for return on sales (ROS) (Hendricks et al., 2007).

In addition, Whipple and Russell (2007) stressed the importance of collaboration insupply chain performance. They argue that rather than being dissatisfied with supply

chain initiatives like ECR, firms involved in supply chains are seeking even greatercollaboration through participation in industry initiatives such as the voluntaryinterindustry commerce solutions (VICS). By implementing ECR strategies, firms in thefood supply chain aim to lower inventory levels, thereby decreasing cash conversioncycles (CCCs). In turn, lower CCCs should allow firms to increase liquidity and raiseprofitability.

Given the inconsistency of Brown and Bukovinsky (2001) with other work, the firstobjective of this paper is to determine whether food retailers and manufacturers thatadopted ECR improved their financial and operating performance compared to firmsthat did not adopt such a strategy. A second objective of this work is to considerGiunipero et al. (2008) who encourage creating a body of literature that is more heavilyinfluenced by a deeper analysis of the supply chain on a chain wide or network basis asopposed to the more popular dyadic studies. Specifically, the goal of this work is torevisit Brown and Bukovinsky (2001) in light of Giunipero et al.s recommendation toplace greater attention on overcoming historical shortcomings in supply chainmanagement research related to:

. small sample size;

. one-tier investigation; and

. longitudinal study.

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The next part presents a review of ECR literature and then considers Brown andBukovinsky (2001) in light of recommendations for supply chain research fromGiunipero et al. (2008). Next, variables and an empirical model are presented, followedby the empirical results. The paper ends by providing conclusions, implications, and

ideas for future research.

LiteratureECR strategies have been studied from multiple perspectives including ECRmeasurement (Aastrup et al., 2008), ECR adoption principles (Phumpiu and King,1997), knowledge creation (Soret et al., 2008), and retailer-supplier relationships (Corstenand Kumar, 2005; Lusch and Brown, 1996; Heide and John, 1988). Other investigationsassessed the impact of specific aspects of ECR on operational performance (Corsten andKumar, 2005; Dhar et al., 2001; Gruen and Shah, 2000; Stank et al., 1999). Stank et al.(1999) found that information technology solutions associated with ECR has improvedinventory management and shortened order cycles, while Corsten and Kumar (2005)

found that suppliers perceive positive economic performance from ECR adoption.However, none of these studies directly evaluated the effects of ECR adoption oninventory and financial performance.

A review of literature found only three studies that directly analyzed the effect ofECR adoption on inventory and financial performance. First, Phumpiu and King (1997)evaluated the effect of ECR initiatives on financial performance and inventory turnover(INVTURN) for 40 Minnesota grocery stores. By focusing on five aspects of the stores:

(1) store and manager characteristics;

(2) inventory management and ordering processes;

(3) store layout, shelf-space allocation, and product assortment;

(4) product pricing and promotion decisions; and

(5) key challenges facing managers and three store productivity measures (sales perlabor hour, sales per square foot of selling area, and INVTURN), the stores werecompared using organizational form and three ECR readiness groups (low,medium, or high).

The ERC readiness groups were based on the number of ECR practices implemented.Stores with a high ECR readiness index exhibited improved performance. When

compared to stores with a low ECR readiness index, stores with a high ECRreadiness index exhibited 59 percent higher sales per labor hour, 125 percent highersales per square foot of selling area, and 131 percent higher average INVTURN(Phumpiu and King, 1997). The authors could not determine whether the adoption ofECR practices leads to strong performance or whether strong performance facilitates

ECR practices. However, they concluded there was a strong association between theadoption of ECR practices and financial performance. Their results for organizationalform suggest that size is important for achieving a high level of financial and inventoryperformance.

A second study compared annual data for nine retail grocery chains from 1992 to1997 (Bowersox et al., 1999). Although ECR was not explicitly identified in this study,the results suggest that improved profit were obtained from procurement practicessuch as forward buying rather than through improved operating efficiency. The study

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The next section describes the methodology used to definitively determine whetherfood retailers and manufacturers that adopted ECR improved their financial andoperating performance compared to firms that did not adopt such a strategy.

MethodologyThis work builds on the approach of Brown and Bukovinsky (2001), yet differs in fourways as it strives to address the limitations in supply chain research identified byGiunipero et al. (2008). First, Brown and Bukovinsky (2001) is classified as a one-tierinvestigation in that it only considers one level of the supply chain, namely grocers(Table I). This is a chain level analysis in that it includes grocers, mass merchandisers,food wholesalers, and manufacturers of food and consumer product goods. The scopeof the analysis was expanded to include the entire food industry to reflect that ECR isan industry wide supply-chain initiative. Mass merchandisers were added because partof the intent of ECR was to develop strategies to compete with the entry of Wal-Mart.The product categories were also expanded beyond food to consider other consumer

products like health and beauty. Thus, the model in this study includes a binaryvariable distinguishing between the retail/wholesale sector and the food and consumergoods manufacturing sector.

Second, Brown and Bukovinskys (2001) regression models apparently were basedon one observation for 20 grocers in 1998 or 25 grocers in 1997 (Table I). In contrast,the regression analysis in this work was based on 1,785 observations from 119 firms,with annual observations from 1992 to 2007. Third, the longer time span allows us toconsider the effects of ECR from a longitudinal perspective as well, an additionalconcern of Giunipero et al. (2008). Finally, this analysis adds the variable AQUIRE tocapture disruptions in operations that might lead to variations in growth rates.AQUIRE is the expenditures on acquisitions by firm, as indicated by the Thomson OneBanker database, divided by the firms TA. This gives the relative impact of

acquisition activity. Because this study considers firms operating continuously since1992, the AQUIRE variable captures expenditures on acquisitions, which includesmergers and consolidations of the surviving firm.

Both studies use multiple regression, with a similar set of dependent andindependent variables, and both studies use an indicator variable to capture whether afirm adopted ECR. The result is that both studies suffer from the challenge ofclassifying firms as adopters of ECR. Brown and Bukovinsky (2001) had the firms

Supply chain researchlimitation criteriaa Brown and Bukovinsky Current work

Tier One tier: firm level

grocers

Multiple tier: chain level food

and consumer productmanufacturers, wholesalers,grocers and mass merchandisers

Sample size 20-25 grocers 1,785 observations from 119 firmsResearch method Multiple regression Multiple regressionYears of study 1992-1998 1992-2007Classification of ECR adoption Survey Factiva search

Source: aGiunipero et al. (2008)

Table I.Contrast of Brown andBukovinsky with currentresearch, byresearch limitation

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self-identify based on responses to a survey. In this work, firms were classified asadopters of ECR based on searches of the Dow Jones database, Factiva, by combiningthe search terms efficient consumer response with each company name. Results fromeach of 238 searches were then read to identify whether a firm had adopted ECR, and

when the action was first reported. Searching Factiva instead of using a survey askingfirms to self-identify their ECR strategy avoids the potential for self-selection bias.If one source was found that indicated a firm had employed some aspect of ECR, thefirm was classified as an ECR adopter. In this study, 61 firms were classified as ECRadopters, while there was no indication that the other 58 firms were using ECR.

A list of grocers was first developed from Hoover Company and Industry Reportsand Business Week, using SIC 5141 and 5144. The sample was then expanded beyondgrocers by identifying suppliers and customers of the grocers in the Mergent (formerlyFIS/Moodys) database. This extended the companies to include wholesalers (SIC 5149),mass merchandisers (5331), citrus farms (SIC 017), food manufacturers from SIC group20, as well as health and beauty manufacturers (SIC group 28), or household goods (SICgroups 30 and 34). The 10-K yearly financial report filed with the Securities and

Exchange Commission was obtained for each company from the database ThomsonOne Banker. The 10-K data included a balance sheet, an income statement, and a cashflow statement for 16 years for each company, beginning in 1992 and ending in 2007.Companies were divided into two sectors, grocers and food manufacturers. There were33 grocery firms and 86 manufacturers in the dataset.

Variables and modelAn excel spreadsheet model was designed to calculate several inventory, profitability,and growth measures (Table II). These measures were then used in the regressionanalyses. For the multiple regression models, data for all 119 firms for all years(1993-2007) were used to regress each DV on the independent variables. The statistical

software STATA was used to estimate the regression equations.The multiple regression model uses eight different DVs with a set of eightindependent variables. Four DVs (CCC, INVTURN, ItoA, and ItoS) measure inventoryperformance and four DVs (ROA, ROS, return on investment (ROI), and Gross margin(GM)) measure financial performance (Table III). Each of the eight DVs was regressedon seven independent variables. In general, form, the model is:

Variable Abbreviation Formula

Cash conversion cycle CCC Days inventory days receivables 2 days payableInventory turnover INVTURN Cost of goods sold/inventoryInventory to assets ItoA Inventory/total assets 100

Inventory to sales ItoS inventory/net sales

100Return on sales ROS Net income/net sales 100Return on assets ROA {Net income [interest expense (1 2 tax rate)]}

/(total assets 100Return on investment ROI ROS asset turnoverGross margin GM (Net sales 2 COGS)/net sales 100Asset growth AG [Total assetst 2 total assetst21]/total assetst21Sales growth SG [Net salest 2 net salest21]/net salest21Acquire ACQUIRE Acquisitions/total assets

Table II.Formulas to calculate

inventory, financial, andgrowth performance

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DV fTA; AG; SG; SECTOR; ECR; ACQUIRE; TREND 1

where:

TA total assets.

AG asset growth.

SG sales growth.

SECTOR firm is in retail/wholesale sector 1, Otherwise 0.

ECR adopted supply chain management strategies 1, Otherwise 0.

AQUIRE ratio of firm expenditures on acquisitions to TA.

TREND the time period 1993-2007 1-15.

Regression results for the inventory performance DVs (CCC, INVTURN, ItoA, and ItoS)are expected to generally be the same for CCC, ItoA, and ItoS and opposite forINVTURN. Since larger firms typically have greater access to capital than smallerfirms, TA is expected to have negative signs with CCC, ItoA, and ItoSales, while apositive sign is expected with INVTRUN. In turn, larger firms are expected to be moreefficient, thus leading to shorter CCCs and greater INVTURN. Firms can shorten the

CCC in one of three different ways:(1) reduce the inventory conversion period;

(2) reduce the receivables conversion period; and/or

(3) increase the payables deferral period (Moss and Stine, 1993).

ItoA and ItoS ratios should also be lower for larger firms because of these efficiencies.AG and SG measure the annual change in magnitude as measured by assets and

sales. AG is expected to have positive signs with CCC, ItoA, and ItoA and a negative

Mean values

VariableOverall

industry Retail and wholesale Food manufacturer ECR adopter Non-adopter

ROSa,b (%) 3.34 0.76 4.33 4.76 2.31ROAa,b (%) 6.36 5.28 6.77 8.07 5.13ROIa,b (%) 4.73 3.50 5.21 6.54 3.43GMa,b (%) 35.49 26.44 38.96 36.60 34.68INVTURNa 8.40 11.92 7.05 8.25 8.51CCCa,b 62.95 27.32 76.62 42.54 77.67ItoAa,b (%) 19.15 24.63 17.05 17.11 20.63ItoSa,b (%) 11.92 8.62 13.19 9.55 13.64AGa (%) 5.73 7.84 4.93 6.04 5.51SG (%) 5.87 7.22 5.36 4.93 6.56TAc,b 5.10 6.19 4.68 10.82 0.98ACQUIREb 129.10 85.34 145.88 274.69 24.08

Notes: aStatistical differences in mean values at the 95 percent level of confidence for sector;bstatistical differences in mean values at the 95 percent level of confidence for ECR adopter/non-adopter; cmeasured in billions

Table III.Mean values by overall

industry, sector, and ECRadopter/non-adopter

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The TREND variable is again used to measure trend effects for the period 1993-2007,and the sign of this variable is indeterminate.

Empirical resultsMean values were computed for inventory, financial, growth, and size measures andcompared for the overall industry, by sector, and for ECR adopters versusnon-adopters (Table III). A paired t-test found statistical differences by sector for theINVTURN, CCC, ItoA, ItoS, and AG variables. Mean values for ItoA were lower for themanufacturing sector than for retailers due to structural differences between the twoindustries. Manufacturing firms generally have larger amounts of capital invested inplants and equipment, whereas retail and wholesale firms mainly invest in stores andinventories. For CCC and ItoS, the mean values for the retail/wholesale sector werelower than the manufacturing sector. The structure of the retail/wholesale sectorfocuses more on turnover than the manufacturing sector, which focuses upon efficientproduction.

A paired t-test for the mean value of the financial measures ROS, ROA, ROI, andGM found statistical differences by sector, and in all cases this measure was lower forthe retail/wholesale sector than for the food manufacturing sector (Table III). Theseresults reflect differences in pricing and markup for firms in the manufacturing sectorversus firms in the retail/wholesale sector.

A paired t-test found the financial measures ROS, ROA, and ROI have statisticaldifferences for adoption or non-adoption of ECR (Table III). These results suggest thatfirms adopting ECR initiatives have higher profitability levels than non-adopters. ECRadopters also have statistically lower levels of inventory and shorter CCCs. The ECRadopters are also more than ten times larger, as measured by TA, than non-adopters onan average.

Tables IV and V report the results for the eight inventory and financial DVs,

respectively. F-tests for each of the eight regression models exceed the critical value2.41 at the 1 percent significance level, indicating that the independent variables usedin the regressions are jointly statistically significant at the 99th percentile. R2s wereused to test the goodness of fit. The results, R2s ranging from 0.10 to 0.22, are good fortime-series panel data and on an average similar to the results of Brown andBukovinsky (2001). Variance inflation factors (VIF) were used to test formulticollinearity. For variables that are unrelated to each other, VIF will approachone, but for related variables VIF will become large (SAS, 2000). Results of the VIFtests suggest the absence of multicollinearity.

Whites test was used to test for heteroskedasticity. Results from the White test(significant at the 1 percent level) rejected homoskedasticity in all models, suggestingthe presence of heteroskedasticity. Heteroskedasticity is the result of unequal variance

between random variables. When heteroskedasticity is present, ordinary least squarecoefficients are accurate, but the error variance is biased, which may result ininaccurate conclusions about the significance of parameter estimates (Breusch andPagan, 1979). To correct the potentially biased error variance, a robust varianceestimator developed by White, was used for each regression (White, 1980; MacKinnonand White, 1985). Tables IV and V are results reported using robust standard errors.Table IV reports the ordinary least squares estimation results for the four inventoryDV models (CCC, INVTURN, ItoA, and ItoS). The t-tests for TA, SG, ACQUIRE,

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DVs

Parame

terestimatesfortheindependentvariables(t-value)

CCC

INVTURN

ItoA

ItoS

INTERCEPT

84.8770

(23.8

0)***

7.2

150

(11.4

3)***

20.4

280

(30.2

4)***

14.7

631

(30.4

5)***

TA

20.00023

(23.9

8)***

2

0.0

0004

(25.2

2)***

2

0.0

0007

(24.2

6)***

2

0.0

0000

(20.7

8)

AG

0.3741

(7.0

1)***

2

0.0

445

(21.4

0)

2

0.0

355

(21.8

6)*

0.0

259

(2.2

3)**

SG

20.13188

(22.0

8)**

2

0.0

680

(21.2

8)

0.0

457

(2.8

6)***

0.0

019

(0.2

)

ACQUIRE

20.2876

(22.0

7)**

0.0

07

(0.3

7)

0.0

058

(0.1

7)

2

0.0

076

(20.4

1)

ECR

2

26.7226

(29.7

3)***

2

1.0

33

(22.5

5)**

2

3.6

048

(26.0

6)***

2

3.3

169

(29.1

5)***

SECTOR

2

44.9193

(220.6

0)***

5.3

68

(10.9

8)***

8.3

834

(11.7

1)***

2

4.0

228

(211.6

5)***

TREND

0.2857

(0.8

8)

7.2

150

(2.2

7)**

2

0.2

249

(23.2

6)***

2

0.0

550

(21.1

9)

R2

0.1880

0.1

804

0.1

276

0.1

045

F-value

79.43

30.5

2

30.1

9

32.6

5

Note:Statisticalsignificanceat:*90,

**95,

***99percentle

vels

Table IV.Empirical results

for inventory models

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DVs

Par

ameterestimatesfortheindependent

variables(t-value)

Independentvariables

ROA

ROI

ROS

GM

INTERCEPT

4.6

774

(4.2

7)***

2.7

053

(2.7

1)**

2.1

860

(2.6

7)***

38.5

069

(45.7

6)***

TA

0.0

0002

(1.7

6)*

0.0

0003

(1.9

5)**

0.0

0005

(4.6

5)***

0.0

0009

(3.7

4)***

AG

0.1

137

(1.0

6)

0.1

476

(1.3

9)

0.0

532

(1.7

3)*

0.0

2114

(1.9

0)*

SG

0.1

569

(1.6

7)*

0.1

488

(1.6

3)*

0.1

353

(2.7

2)***

0.0

113

(0.6

5)

ACQUIRE

2

0.0

687

(21.5

9)

20.0

971

(21.7

4)*

2

0.0

463

(21.8

8)*

2

0.0

376

(21.2

8)

ECR

3.4

542

(5.8

1)***

3.5

585

(5.8

3)***

2.9

073

(6.4

8)***

3.3

943

(4.3

0)***

SECTOR

2

2.8

173

(24.0

9)***

23.1

488

(24.5

5)***

2

4.6

046

(28.7

9)***

2

13.3

805

(218.4

7)***

TREND

2

0.0

644

(20.8

9)

20.3

334

(20.4

5)

2

0.0

024

(20.0

3)

2

0.1

608

(21.8

0)*

AdjustedR2

0.1

917

0.2

200

0.1

661

0.1

402

F-value

7.9

9

8.6

1

20.9

7

52.9

8

Note:Statisticalsignificanceat:*90,

**95,

***99percentlevels

Table V.Empirical results for theprofitability models

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ECR, and SECTOR were statistically significant in the CCC model and exhibitednegative signs, while AG was significant with a positive sign (Table IV). As expected,firms adopting ECR are able to lower CCCs. The negative sign on TA suggests largerfirms have shorter CCCs, while the positive sign for AG suggests that increases in AG

rates (as opposed to asset size) are related to longer CCCs. AG can be difficult tomanage. SG and ACQUIRE also decrease CCCs as days of receivables fall with SGwhile ACQUIRE is driven by an increase in days payable or a delay in payment tovendors. The negative sign on SECTOR was as expected, suggesting retail/wholesalefirms experience lower CCCs due to immediate customer payment.

In the INVTURN model, TA and ECR were negative and significant, while SECTORand TREND were positive and significant. ECR adopters actually lower theirINVTURN, a surprising result (Table IV). Furthermore, the negative sign on TAsuggests that larger firms have lower inventory turns, contrary to expectations.These surprising ECR and TA results are discussed in the conclusions and implicationssection of this paper. The positive sign for SECTOR suggests higher inventory

turns among retailers than manufacturers, while the positive sign for TREND suggestsan underlying increase in inventory turns.

For the DV ItoA model, all independent variables had significant signs, exceptACQUIRE (Table IV). TA, AG, ECR, and TREND were negative, and these signs wereall as expected. SG was positive, suggesting that as firms increase sales, inventorylevels rise. As expected, SECTOR exhibited a positive sign, likely because the retailsector generally has fewer assets than the food-manufacturing sector. The negativeECR sign shows that ECR adopters are able to decrease their ItoA ratios. Theindependent variables AG, ECR, and SECTOR were statistically significant in the ItoSmodel. The sign for AG was positive, and as expected, the signs were negative for ECRand SECTOR. Once again, ECR adopters are able to lower their ItoS ratios.

Table V shows the results for the profitability DV models (ROA, ROI, ROS, andGM). In the ROA model, the signs of the independent variables TA, SG, and ECR werestatistically significant and positive as expected, while the sign for SECTOR wasnegative (Table V). Hence, it appears that firms who adopt ECR supply chainmanagement strategies are likely to improve returns on assets. The positive sign forTA reinforces the sense that larger food industry firms are able to enjoy economies ofsize. The independent variables TA, SG, ACQUIRE, ECR, and SECTOR werestatistically significant in the ROI model (Table V). The signs for TA, SG, and ECRwere positive as expected, and ACQUIRE and SECTOR exhibited negative signs. Asanticipated, acquisitions may have an immediate negative effect on ROI due to thelarge capital investment.

Results for the ROS model indicate all independent variables were statistically

significant (Table V). TA, AG, SG, and ECR signs were positive; suggesting that AGcan increases ROS. Once again, ACQUIRE decreases ROS, suggesting that firmsstruggle incorporating newly acquired business into their operations. TA and SGexhibited expected signs. In the GM model, the independent variables TA, AG, ECR,SECTOR, and TREND were significant (Table V). The positive signs on TA and ECRsuggest that larger firms and firms with ECR supply chain management strategies areable to increase margins. The negative sign for SECTOR in all four profitability modelssuggests a difference in pricing power between retailers and manufacturers.

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Conclusions and implicationsBefore discussing the conclusions, it is important to acknowledge two limitations to thisstudy. First, it is virtually impossible to determine the actual time of implementation forsupply chain management strategies by firms in the food industry. The method used to

classify firms as ECR adopters is believed to be sound and unbiased, but errors mayexist. Second, this analysis does not account for differences in the implementation levelfor ECR. For simplicity, a binary variable is used to distinguish firms adopting or notadopting supply chain management strategies (ECR). With these limitations in mind,five conclusions for inventory measures and six conclusions for financial performancemeasures can be drawn from this study.

First, for inventory performance measures, three models (CCC, ItoA, and ItoS)exhibited highly significant and beneficial results for adopters of ECR supply chainmanagement strategies, implying that ECR adopters do have a distinct advantage inmanaging CCCs and inventory over non-adopters (Table IV). The regression resultssuggest that ECR adopters reduce their CCC by 26.7 days, while reducing their ItoA and

ItoS ratios by over three points each. The results for the INVTURN model suggest thatECR adopters actually have a lower rate of INVTURN. On the surface this result issurprising, but an explanation is available. INVTURN is defined as cost of goods solddivided by inventory. ECR adopters realize a lower cost of goods sold than non-adopters,decreasing INVTURN. Additionally, cost of goods sold arelikely lower for ECR adoptersbecause of pecuniary economies of scale. The INVTURN result is similar to the findingsof Brown and Bukovinsky (2001).

Second, larger firms in terms of TA have lower CCCs and lower ItoA ratios comparedto smaller firms. Once again, however, larger firms have a lower level of INVTURN. LikeECR adopters, larger firms may enjoy purchasing power, resulting in decreased cost ofgoods sold, and effectively increasing the INVTURN. Third, as AG occurs, CCCs becomelonger, implying that growth is difficult to manage. The ItoA ratio decreases with AG,suggesting that firms are adding physical assets such as plants and buildings asopposed to inventory. Fourth, as SG increases, the ItoA ratio increases, implying firmsare adding inventory to meet growing sales. SG also results in a lower CCC, as firms mustbe demanding payment or managing days payable more diligently.

Fifth, inventory performance is different between the retail/wholesale sector and thefood-manufacturing sector. Operating in the retail/wholesale sector results in lowerCCCs, higher INVTURN ratios, and lower ItoS ratios, all expected advantages for theretail sector. Because firms in the retail/wholesale sector normally have less capital tiedup in plants and equipment than food manufacturers, ItoA ratios are higher.

For the financial performance measures, the results first suggest that ECR adoptersexhibited positive results for all four profitability financial measures (ROA, ROI, ROS,

and GM), as anticipated by its proponents. These results imply that ECR adopters enjoysuperior financial performance over non-adopters, which differs starkly from thefindings of Brown and Bukovinsky (2001). ECR adopters had an average of 4.76 percentfor ROS compared to 2.31 percent for non-adopters. Adopters also had higher averageROA and ROI (8.07 and 6.54 percent) than non-adopters (5.13 and 3.34 percent). Thus,profitability is nearly double for ECR adopters.

Second, TA (firm size) increase all four performance measures (ROA, ROI, ROS, andGM). Larger firms have the advantage economies of size. Third, increases in ROS and GM

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accompany AG, suggesting that the AG is related to new plants or buildings rather thaninventory. Fourth, SG is important for the financial measures of ROA, ROI, and ROS.

Fifth,firmsinvolved in acquisitions (AQUIRE)havea negative impact on their ROI andROS. This is not to say that acquisitions in the food industry have a long-term negative

impact on profitability measures. Acquisitions may lead to economies of scale andincreased profitability over time. Furthermore, it might be that the firms in the sample aremore stable than others given that only firms with data for all 15 years were used in theanalysis. Finally, firms in the retail/wholesale sector exhibited negative signs for all fourperformance measures (ROA, ROI, ROS, and GM), implying that retail and wholesalefirms margins are less than food manufacturers.

The results of this study are important because they suggest, contrary to the results ofBrown and Bukovinsky (2001) that the adoption of ECR has led to growth in profit.The growth in profit appears to come from improved performance for inventory measures(ItoA and ItoS ratios), butthe driving force behind these improved financial measuresmaybe attributed to the CCCs. By shortening CCCs, firms in the food industry can improveprofits. Longer CCCs create a need for costly external financing (Moss and Stine, 1993).

The difference in the financial performance results in this study from those found byBrown and Bukovinsky (2001) may arise from two sources. First, some improvementmight be attributed to having a larger sample size, by including food manufacturers aswell as grocers, along with lengthening the time period involved to 1992-2007. Giuniperoetal. (2008) suggest that the advantages from supply chain strategies were more prevalentin more recent years. Second, the inclusion of the variable AQUIRE was an attempt tocapture deviations in variability in growth for assets and sales in the model. The variablemight allow disruptionsfromacquisition and mergeractivityin the industry to explainthedifference in findings, but were typically statistically insignificant.

The results of this analysis strongly support the proposition that the adoption of anECR strategy pays off. Thus, the time spent in developing close relationships with buyers

or suppliers and the investments in information technology for firms in the food industryhas led to shorter CCCs and lower ItoA and ItoS ratios, thereby improving financialperformance. The use of information technologies, such as electronic data interchange,changes the traditional processes for purchase orders, invoices, shipping notices,and funds transfer. Thus, the need for clerical, mailing, and other costs associated withpaper-based information can be eliminated, while time delays and errors can be reduced.Size matters; ECR is more effective due to economies of scale and information technology.However, this may lead to more consolidations because all firms do not have the capital toinvest in these initiatives. In short, to remain competitive ECR strategies should stronglybe considered by firms that are lagging in implementation.

Several areas of future research should be considered. First, developing improvedmethods to measure the effects of merger, acquisition, and closure activities would be

useful. This could help to determine whether ECR activities decrease the likelihood of aclosure or increase the likelihood of a merger or acquisition. Second, a more detailed studycould evaluate the level of ECR adoption. This study limited the ECR adoption to a binaryvariable, but, in reality, ECR supply chain practicesmay be adopted slowly or quickly, andat various levels of detail. Capturing these implementation differences would enhance thecurrent model. Finally, ECR is one of several supply chain strategies adopted in the foodindustry. Future work could evaluate the effects of collaborative planning, forecasting,and replenishment or VICS.

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Corresponding authorBobby J. Martens can be contacted at: martens1@iastate.edu

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