Semisequicentennial Transportation Conference Proceedings
May 1996, Iowa State University, Ames, Iowa

The Impact of Supply Chain Management on Logistics Service and Productivity

Michael Crum and Miren Arango

Iowa State University,
Department of Transportation and Logistics,
334 Carver,
Ames, Iowa 50011.

This study investigates the impact of interfirm relationships among selected supply chain members on various customer service performance and logistics cost measures. The data were generated from a survey of firms in the food production industry. The food industry is generally viewed to be on the leading edge of logistics management. Two sets of dyadic relationships are investigated: (1) the food producer and its key motor carriers and (2) the food producer and its key customers. The analysis includes a comparison of the strength of the interfirm relationships previously described and the use of regression analysis to determine the impact of individual supply chain management factors on logistics performance. The hypothesized influencing factors include information technology connectivity (i.e., EDI), interfirm communications (i.e., types of information exchanged and timeliness of communications), and partnership characteristics (i.e., flexibility and extendedness). Two sets of dependent variables were examined: absolute and relative measures of performance. The absolute measures include a macro performance measure and scales for inventory, time, and cost. The relative measures include a macro performance measure and scales for total performance and the order process. The key findings are (1) the respondents perceive greater communications with customers than with motor carriers; (2) the respondents view their carrier relationships as being stronger in the noncommunications dimensions; (3) EDI is not used very extensively; and (4) there is some support for the hypothesis that a partnering relationship with customers and motor carriers results in better logistics performance. Of particular importance is the extent and timeliness of information exchange.


OVERVIEW

A key area of logistics management research during the 1990s has been interfirm relationships. Most recent corporate logistics and customer service initiatives, such as "supply chain management," "quick response logistics," and "efficient consumer response" entail developing or strengthening relationships among supply chain members as a means to improving customer service and reducing logistics costs. Though a number of logistics and marketing studies have addressed partnering relationships (1-8), there has been limited empirical testing of these factors, and the authors have not found any published articles that attempt to assess the impact of these relationships on firm performance. The objective of this study is to determine whether interfirm relationships influence logistics performance. Two sets of interfirm relationships in the supply chain are investigated: (1) between manufacturer and customer and (2) between manufacturer and motor carrier. Additionally, one "three party relationship" measure is included in the analysis. It should be noted that with few exceptions previous research has been directed at the supplier-customer relationship and has not included transportation service suppliers.

The sample was selected from one industry to control for contextual variables that might influence interfirm relationships, e.g., environmental uncertainty or line of trade of shipper (7). Food producers/processors were chosen because the grocery industry (i.e., the customer base) is noted for its Efficient Consumer Response (ECR) initiative, launched in 1993. ECR is a strategy in which the manufacturers and distributors work together to improve value for the consumer.

The paper is organized in the following manner. First, the model and its theoretical basis is presented. Second, the research methodology is described. Third, findings and implications are discussed.

THE MODEL

The general model postulates logistics performance (dependent variable) to be a function of interfirm relationships (explanatory variable) and firm management characteristics (mitigating or moderating variables) as depicted below.

Logistics performance = f (firm's relationship [1]
with customer, firm's relationship with motor
carrier, firm's management)

Previous studies from both logistics and marketing literature (7,9-11) have identified four components of interfirm relationships: extendedness (i.e., expectation of a long-term or continuing relationship), flexibility (i.e., the willingness of partners to make adjustments in the relationship as circumstances change), solidarity (i.e., commitment to achieving mutual benefits and sharing burdens), and communication (i.e., information exchange). Ellram and Hendrick (9) disaggregated communication to include the type of information exchanged, the level and frequency of information exchanged, and computer links between partners (or electronic data interchange, EDI).

There are a number of logistics performance measures that firms utilize to control and evaluate their operations. Firms are interested both in absolute measures and relative measures (i.e., performance relative to primary competitors). Absolute cost and service performance measures and relative service performance measures were adapted from Sterling (12) and Lambert (13).

The authors identified four aspects or characteristics of the firm and its logistics management to investigate as moderating variables for logistics performance: activity based management (ABM), total quality management (TQM), outsourcing, and firm size. ABM involves a detailed understanding and control of internal logistics processes. Pohlen and LaLonde (14) identified distinguishing features of ABM. Similarly, TQM requires a customer focus and firm commitment to improving its logistics processes and performance. Outsourcing logistics has increased in recent years as firms seek to focus on their core competencies. Gardner, Cooper, and Noordewier (7) identify firm size as a contextual variable that influences interfirm relations. Furthermore, firm size is likely to influence other contextual variables (e.g., market share, relative dependence between partners).

As a result of the preceding discussion, a more specific expression of the general model follows:

Logistics performance = f (extendedness of relationship, [2]
flexibility of relationship, solidarity of relationship,content
of information exchanged, temporal dimension of information exchange, EDI, ABM, TQM, outsourcing, firm size)

The authors hypothesize that a partnering relationship between the food producer and its key customers and key motor carriers will lead to better logistics performance. The methodology used to test this hypothesis is described below.

METHODOLOGY

A six-page survey instrument was developed and mailed to logistics managers/executives at 132 food producers and processors. Forty-seven usable responses were received for a 35.6 percent effective response rate. Survey items pertaining to interfirm relationship and performance variables were either adapted from previous empirical studies (when possible) or developed by the authors based on previous theoretical or conceptual studies. (A copy of the survey instrument and sources of its questions will be made available to interested readers.) The categorization schemes of previous research and factor analysis were utilized to group items for scale development. Multiple-item scales were developed and tested for reliability (using the Cronbach alpha). Two macro (i.e., nonfactored or aggregated) scales were identified for both the customer and motor carrier: RELC and RELM include all noncommunication relationship items and COMC and COMM include all communications items for customers and motor carriers, respectively. Reliable scales were found for the interfirm relationship constructs of extendedness (variable labels EXTC and EXTM for customer and motor carrier, respectively), flexibility (FLEXC and FLEXM), the content of information exchanged (INFXC AND INFXM), the temporal dimension of information exchange (TINFXC AND TINFXM), two-way EDI (EDIC and EDIM), and three-way EDI (EDIB).

With respect to the performance variables, all absolute measures (ABSOLUTE) comprised a reliable macro scale, and reliable absolute performance measures were developed for inventory (INV), order cycle time (TIME), and logistics cost (COST). Similarly, all relative measures (RELATIVE) formed a reliable macro scale, and reliable relative performance measures were developed for global or overall logistics performance (GPERF) and order handling (ORDER). The scale measures, their means, and their coefficient alphas are presented in Table 1.

Table 1 Descriptive Statistics for Scale Measures*
Relationship with customers (no. of items) Means a Relationship with motor carriers (no. of items) Means a
RELC (13) 3.182 .75 RELM (12) 2.84 .87
EXTC (3) 3.311 .70 EXTM (5) 2.96 .80
FLEXC (3) 3.142 .77 FLEXM (5) 2.64 .83
COMC (12) 2.633 .85 COMM (11) 3.33 .79
INFXC (5) 2.123 .88 INFXM (4) 3.06 .72
TINFXC (3) 2.672 .73 TINFXM (3) 3.03 .71
EDIC (5) 4.372 .78 EDIM (5) 5.14 .91
EDIB (5) 5.56 .85
Absolute performance Means a Relative performance Means a
ABSOLUTE (13) 3.56 .72 RELATIVE (7) 2.45 .76
INV (3) 3.42 .67 GPERF (3) 2.43 .80
TIME (3) 3.52 .83 ORDER (3) 2.49 .61
COST (4) 3.89 .70

* All measures on a 1 - 7 scale and expressed here such that the lower the rating, the stronger the relationship or the better the performance. n ranged from 45-47 for all scale measures except for EDI measures where n ranged from 25-30.

1 Difference between customer and motor carrier relationship means is significantly different at p> .10.

2 Difference between customer and motor carrier relationship means is significantly different at p> .05.

3 Difference between customer and motor carrier relationship means is significantly different at p> .01.

The moderating variables were defined in the following manner. A ten-item scale was developed for ABM (a = .94). TQM was a dummy variable based on the existence or nonexistence of a formal quality program. The natural logarithm of annual revenue was used to measure SIZE. Outsourcing (SOURCE) was a dummy variable based on the extent of outsourced warehousing (i.e., less than or equal to ten percent vs. greater than ten percent). Correlation analysis revealed ABM most likely to be the most influential moderating variable (this was confirmed later in the regression analysis).

Regression analysis was utilized to determine the existence of a relationship between the interfirm relationship variables and each performance variable. Given the sample size, the number of independent variables tested at any one time had to be limited. To test the general model, the four macro relationship scales (i.e., RELC, RELM, COMC, COMM) and one moderating variable (i.e., ABM) were selected. Because of the large number (i.e., ten) of factored relationship scales and a significant level of correlation among some of them, stepwise regression was employed to develop regression models. The stepwise regression technique inserts variables into a regression model until a satisfactory regression equation is reached.

Regression models for each performance measure are reported and discussed in the next section.

RESULTS

Table 1 reveals food producers’ perceptions of their relationships with key customers and key motor carriers and perceptions of logistics performance. There are some noteworthy findings. One, the respondents perceive greater communications with customers than with motor carriers. The means are significantly different for each of the four communications scales, indicating a greater sharing of information in a more timely fashion with customers. Two, the respondents view their carrier relationships as being stronger on each of the other dimensions. Third, each of the EDI means is greater than the theoretical midpoint (i.e., > 4.0), a somewhat surprising and disappointing result given the general recognition of the benefits of EDI. Finally, the respondents tend to view their firms’ performance relative to key competitors more favorably than they do in an absolute sense. Most of the absolute performance means are between 3.5 and 4.0. Table 2 provides the macro scales regression results for the five performance variables with significant, or nearly significant, F-statistics. The regression results tend to be “better” for the relative performance variables than for the absolute. Four interfirm relationship measures have significant (p > .10) coefficients; three are communications scales (two have the expected negative sign as the scale measure is reversed for these items) and three are customer relationship scales. Table 2. Regression Models for Macro Relationship Scales*

										
Perform.	ABM	RELC	COMC	RELM	COMM	CNST	R2	F	SIGF	

ORDER
    	.356	.086	.244	.149	.205	5.612 	.199	1.89 	.119     
   SE	.304	.193	.192	.178	.203	1.383
   SIGT	.249	.659	.212	.406	.317 	.000	
	
RELATIVE
   	.382	.123	.203	.164	.2664	.994	.338	3.89	.006
   SE	.233	.148	.147	.136	.1551	.060
   SIGT	.110	.409	.176	.236	.095 	.000	
	
GPERF
   	.469	.331	.114	.266	.274	4.195	.300	3.25	.015
   SE	.297	.189	.188	.174	.198	1.351
   SIGT	.123	.087	.546	.134	.174 	.004	
	
ABSOLUTE
   	.484	.090	.235	.148	.136	2.406	.191	1.70	.159
   SE	.226	.145	.139	.133	.149	1.020
   SIGT	.040	.540	.100	.271	.370	 .024
	
COST
   	.929	.002	.404	.288	.273	2.602	.210	2.02	.098
   SE	.378	.240	.239	.221	.252	1.720
   SIGT	.019	.993	.099	.290.	286	 .139
* The definition of each variable is included in the text. Results in bold print indicate negative values. Items in the communication scales (COMC and COMM) were reverse scaled on the survey instrument. Thus, the expected sign of is negative for the communication scales.

The results from the stepwise regression models are depicted in Table 3. Models were selected on two criteria: (1) the first regression model that included both one significant beta coefficient and a F-statistic significant at p > .05 and (2) the final regression model produced from the stepwise method. For three of the performance variables (GPERF, COST, and TIME) criterion one was met only by the final regression model. Table 3. Regression Models with Factored Relationship Scales*

										
PERFORM	ABM	TINFXC	EDIC	EXTM	FLEXM	INFXM	TINFXM	CONST	R2	F	SIGF	

ORDER            
    	.461	.235	.152		.268	.416		6.175	.529	4.04	.012
    SE	.306	.194	.122		.295	.148		1.565
    SIGT	.150	.242	.227		.377	.012		.001

    						.471		4.953	.313	10.00	.005
    SE						.149		.757
    SIGT						.005		.000	
	
RELATIVE
    		.332			.590		.360	7.526	.366	3.86	.025
    SE		.193			.295		.162	1.472
    SIGT 		.100			.059		.038	.000
       
    					.657		.383	6.192	.272	3.93	.036
    SE					.306		.168	1.310
    SIGT					.043		.034	.000		
			
GPERF
    	.877			.467				4.113	.367	       6.09	 .008
    SE	.378			.187				.531
    SIGT	.031			.021				.000	
			
ABSOLUTE
    	.588		.177					4.387	.309	4.69	.021
    SE	.283		.105					.394
    SIGT	.050		.106					.000

    	.701							3.799	.214	6.00	.023
    SE	.286							.194
    SIGT	.048							.417	
		
COST
    	.934							4.115	.153  	3.96	.059
    SE	.469							.318
    SIGT	.059							.000	
							
TIME
    			.351					4.573	.155	      4.05	 .057
    SE			.174					.673
    SIGT			.057					.000			
					
	
INV
    		.649				.199		0.928	.259	3.67	.043
    SE		.287				.217		1.570
    SIGT		.035				.368		.561

    		.711						.241	.229	6.54	.018
    SE		.278						1.378
    SIGT		.018						.863		
		
	
* The definition of each variable is included in the text. Results in bold print indicate negative values. Items in the communication scales(TINFXC, EDIC, INFXM, and TINFXM) were reverse scaled on the survey instrument. Thus, the expected sign of is negative for the communication scales. The results from the factored scale regressions and macro scale regressions are similar in that the communications scales appear to exert greater influence on logistics performance vis a vis the noncommunications dimensions of interfirm relationships. Unlike the macro scale model, however, the factored scale regressions indicate a greater importance of the motor carrier relationship.

CONCLUSION

The findings of this study provide some support for the hypothesis that a partnering relationship with customers and motor transport service providers results in better logistics performance. Of particular importance is the extent and timeliness of information exchange between supply chain members, a not surprising result given that logistics is an information-intensive set of activities. These variables generally appear to be more significant than the noncommunications dimensions of interfirm relationships. As Table 1 indicates, there is considerable room for improvement of communications with both customers and, particularly, motor carriers.

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