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5. Chapter 5 – Data Analysis

    5.1 Introduction

   This chapter presents the results of the questionnaire. A respondent profile is analysed, followed by descriptive results that sum up respondents' answers to the questions used in the analysis. It also represent  some trends like the extent of usage of 3PL services, the reasons of outsourcing or not outsourcing, the most recurrent outsourced logistics functions plus the potential and the impact of the usage of 3PL services. Then a number of statistical results from independent-test, ANOVA and Chi-square are presented to provide in-depth study of the relationships between different variables. This chapter presented empirical findings received.

   5.2 Respondent profile

   As this questionnaire is anonymous, there is no company-specific information on which companies responded and which companies did not. However the questionnaire did gather some general statistics about the size and the industry of the respondents. This research has attained replies from 92 companies and out of these companies, there are 52 companies currently using 3PL services.


 5.2.1 Company employee numbers

    The first question of the questionnaire deals with the number of employees in each responding company. This was used to decide respondent size. The results are shown  below in table 5.1. It reflects the different categories provided as well as the responses received, with regard to the number of 3PL users and non-users of the respondent companies. Of the respondents to this question, 61% of the respondents indicated that their employee numbers were more than 20 while the remaining of the respondents had the employment characteristics ranging between 0-20 employees. The details of employee numbers of each group are available in table 5.1. As the respondents were small in group between ( 6-10 ), (11-15), and 16-20, these groups were re categorized into small (0-20) and large (20+) companies.

Table5.1: Details of company and number of 3PL users and non-users in each  

    Category.   .


Use of 3PL Not use of 3PL

Categories No. Percentage

No. Percentage No. Percentage

Number of Employee

0-5     16 17% 5 10% 10 25%

6-10 9 10% 8 15% 2 6%

11-15 6 7% 2 4% 5 11%

16-20 5 5% 3 6% 1 3%

21+ 56 61% 34 65% 22 55%

Turnover in 2015 ($millions)

0-5    27 29% 12 23% 14 36%

6-10 7 8% 4 8% 3 8%

11-15 3 3% 2 4% 0 0%

15-25 6 7% 4 8% 2 6%

26-100 13 14% 8 15% 6 14%

100+ 36 39% 22 42% 15 36%

Business Category

Automotive 10 11% 8 15% 2 5%

Retail 9 10% 6% 5 13%

Chemicals/Healthcare 2 2% 0 0% 2 6%

Hi-tech/Electronics 3 3% 3 6% 0 0%

Fashion/Textiles 5 5% 3 6% 2 6%

Food & Beverage 18 19% 15 29% 2 6%

FMCG 5 6% 4 8% 2 6%

Industrial 11 12% 10 19% 1 3%

Others 29 32% 6 11% 24 61%

5.2.2 Company turnover

    The company size of the respondents was also considered by asking respondents to point out the level of annual turnover of the company. Turnovers of the respondent companies ranged from $0 million to more than $100 million, representing from very small to very large firms in the sample. Thirty nine percent of respondents were in excess of $1 billion. The results also indicated an increasing relationship between the number of employees and level of turnover. Further results are revealed above in table 5.1.

5.2.3 Business category

    The responding companies were in a broad cross-section of the industry. The following categories were provided and responses were received. Of the respondents to this question, 19 percent were from food and beverage industry, 12 percent were involved in industrial, 11 percent in the automotive industry, 10 percent in retail, 6 percent in FMCG, and 5 percent in fashion/textiles. The respondents from hi-tech/electronics industry and healthcare were 3 percent and 2 percent respectively. Respondents that had indicated ‘Others' were mainly from industrial (construction / building,) industries. From this research, it was not possible to connection trends and practices specifically to the different industry sectors as some of the responses were too small. This can be seen from table 5.1: the small number of 3PL users and non-users in each industry.

5.3 Reasons for not outsourcing logistics activities

   The main reasons why companies are not presently outsourcing logistics activities was ascertained by  asking respondents to select the reason(s) leading them not to outsource logistics activities from a list. The results in figure 5.1 showed that up to seventy two percent of the companies that did not outsource logistics had considered themselves to have enough skills and resources instead. Some other concerns were the loss of control over the logistics function (42%), losing touch with important information (25%), uncertainty in service levels provided (17%), lost of customer feedback, hidden true cost of outsourcing (11%) and lack of shared goal (6%). Interestingly, difficulty in obtaining organization support was not selected by any respondent. With respect to the 25 percent of respondents that indicated that there were reasons in addition to the list as followings: Company has capital investment in terms of warehousing and this is quite similar to company has adequate skills and resources; company concerns about transport damage; specialized product services not available; and a few of respondents were not aware of 3PL services

.Figure 5.1: Reasons for not outsourcing logistics activities

Notes: N=40 for each reason.

Therefore, companies seem to be disinterested in logistics outsourcing mainly because they are satisfied with their own logistics competence and apparently do not want to give up the control of logistics function in this survey. This may be due to a lack of the advantages of outsourcing and/or marketing by providers; and/or a lack of good experience of logistics function outsourcing. In addition, the majority of respondents did not see difficulty in obtaining organization support and lack of shared goal as important reasons not to undertake the outsourcing of their logistics activities.

Then an investigation on whether the size of a company has an impact on whether they choose a particular reason for not using 3PL services was conducted. This yielded meaningful information for 3PL providers to improve awareness by targeting specific groups of customers. Companies with less than 20 employees are categorized as small companies, whereas the rest are formed as large companies. Table 5.2 exhibits the results of the cross tabulation and Chi-square test for the size of companies and whether they have selected a particular reason of not using a 3PLs services.

Table 5.2 Reasons for not outsourcing logistics activities; Small companies vs. large companies:

Whether select the Reason




Type of Reasons Percentage Yes No (df) p-value

Loss of control over the Small   6 (33%) 12 (52%) 1.286 .257

logistics function Company (1)

Large 11 (67%) 11 (48%)

Hidden true cost of Company .406*

Small 1 (25%) 17 (47%) .689

Outsourcing Company (1)

Large 3 (75%) 19 (53%)

Losing touch with important Company 1.00*

Small 4 (44%) 13 (44%) .000

Information Company (1)

Large 6 (56%) 17 (56%)

Uncertainty in service levels Company .764*

Small 3.5 (50%) 14 (43%) .090

Provided Company (1)

Large 3.5 (50%) 19 (57%)

Company has adequate Company .056*

Small 10 (35%) 8 (70%) 3.662

skills and resources Company (1)

Large 19(65%) 3 (30%)

Lost of customer feedback Company .192*

Small 3 (75%) 15 (41%) 1.702

Company (1)

Large 1 (25%) 21 (59%)


Difficulty in obtaining N/A N/A

Small 0(0%) 18 (44%)

organization support Company

Large 0(0%) 22 (56%)

Lack of shared goal Company

Small 1 (50%) 17 (44%) .026 .871*

Company (1)

Large 1 (50%) 21 (56%)

Others Company .121*

Small  7 (67%) 11 (37%) 2.400

Company (1)

Large   3 (33%) 19 (63%)


Notes:* represents cross tabulation has cells that have expected count less than 5

Significant Pearson Chi-square results are found on the reason of company has adequate skills and resources (χ2=3.662, df=1, P two-tailed <0.1). Where one cell has an expected count of less than 5, the results may not prove reliable. However, it is noticeable that compared to small company, the percentage of large company choosing this reason as not using 3PL is twice of that of small company, indicating a large difference on the selection. This may be that large companies are big enough to have their own logistics departments, which are equally effective at meeting expectations as outside logistics provider (Sanders, et al., 2007). By comparison, small companies tend to rely more on 3PL providers due to limited resources.

Another noticeable fact to company size was 67 percent of the large companies and 33 percent of small companies have selected loss of control over the logistics function as a reason for not outsourcing. It reveals that large companies have more concerns about selecting a 3PL provider than small companies do. Such caution of large companies may also reflect lack of confidence over management of 3PL providers and unawareness of the benefits of outsourced logistics function.

5.4 Reasons for outsourcing logistics activities

To determine why user companies employed 3PL providers, respondents were asked to identify their reasons for outsourcing logistics functions. The following categories were based on the benefits and advantages of outsourcing. Of the respondents to these questions, 52 percent of the respondents outsourced logistics activities due to the pressure to logistics cost reduction; 48% of them wanted to use the 3PL service providers so that they can focus on their core competencies; 44 percent of them due to the reduction in capital investments; 33% aim to improve the logistics process and customer services; and 15 percent due to expansion to unfamiliar markets and access to emerging technologies. Other reasons are reported in figure 5.2.

Figure 5.2: Reasons for outsourcing logistics activities

Notes: N=48 for each reason

Therefore the majority of respondents consider logistics outsourcing to be a tool for achieving reduction in logistics cost and capital investments and focusing on their core competence. Similar result on logistics cost reduction has been ranked top from other studies made in USA, Australia, Western Europe, Singapore, Saudi Arabia, Malaysia and India (Lieb & Randell, 1994; Millen et al., 1997; Bhatnagar et al., 1999; Sohail & Sohal, 2003; Sohail & Al-Abdali, 2005; Sahay & Mohan, 2006). Unlike previous studies, improving customer service is not very high on the list.

Moreover, a cross tabulation is produced between small and large companies, and reasons for outsourcing logistics activities. This is to detect whether the size of a company based on the number of employees has effects on whether they choose a particular reason of using 3PL services. Categorization of company according to the number of their employees is applied following the same rule as discussed before. Table 5.3 shows the results of the cross tabulation and Chi-square test.

Table 5.3: Small companies vs. large companies: reasons for outsourcing logistics activities

Whether select the Reason



Size of value

Type of Reasons Company Yes No (df) p-value

Focus on core competence Small 4(17%) 14(52%) 6.273 0.12

Company (1)

Large 21(83%) 13(48%)


Logistics cost reduction Small 6 (24%) 12(48%) 2.973 .085

Company (1)

Large 21 (76%) 13(52%)


Access t o Emerging Small 5 (57%) 14 (32%) 1.691 .193*

Technologies Company (1)

Large 3 (43%) 30 (68%)


Improving customer service Small 11 (63%) 8 (22%) 7.696 .006

Company (1)

Large    6 (37%) 27 (78%)


Improve the logistics Small 10 (56%) 9 (25%) 4.554 .033

process Company (1)

Large 7 (44%) 26 (75%)


Reduction in capital Small 8 (33%) 11 (37%) .071 .790

investments Company (1)

Large 15 (67%) 18 (63%)


Productivity improvements Small 1 (50%) 18 (35%) .194 .660*

Company (1)

Large 1 (50%) 32 (65%)


Increasing inventory turn Small 4 (80%) 14 (30%) 4.850 .028*

Company (1)

Large 1 (20%) 33 (70%)


Expansion to unfamiliar Small 3(43%) 15 (34%) .198 .656*

markets Company (1)

  Large 5(57%) 29(66%)


Others Small 0 (0%) 19 (40%) 3.061 .080*

Company (1)

Large 5 (100%) 28 (60%)


Notes:* represents cross tabulation has cells that have expected count less than 5

Significant Pearson Chi-square results are detected on reasons including logistics cost reduction (χ2=2.973, df=1, P two-tailed <0.1), improving customer service (χ2=7.696, df=1, P two-tailed <0.05), improve the logistics process (χ2=4.554, df=1, P two-tailed <0.05). The results indicate that there is an association between the size of the company in terms of the number of employees and whether they were selected using these reasons via 3PL services.

For logistics cost reduction, it is observed that the percentage for small company is more than triple of the large company (24%, 76% respectively). The results may imply that larger companies tend to focus more on logistics costs due to better experiences with logistics reduction (Harrington, 2007). However, logistics cost reduction may not be the top priority for small companies to address, due to their greater flexibility. This can be explained by looking at the “improving customer service” column.

It is also noticeable that small companies tend to use 3PL services to gain improvement of their customer services rather than logistics cost reduction when compared with large companies (63% and 37% respectively). For reasons of improving the logistics process, the percentage of selecting it between small and large company does not make a big difference (56% and 44% respectively), indicating that significant results are not due to the difference between them.

In addition, other reasons such as access to emerging technologies, productivity improvements, increasing inventory turn, and expansion to unfamiliar markets and others are not discussed due to insufficient frequencies, which cannot produce reliable Chi-square results.

5.5 Extent of use of the third party logistics services

5.5.1 Current level of logistics outsourcing

The current levels of logistics outsourcing among respondent companies were identified by inquiring them to state whether or not their company had employed 3PL services. Companies which are current 3PL users were asked to complete the relevant questionnaires regarding their logistics outsourcing practices, trends, and opinions. Companies which are not current 3PL users were given a chance to explain the reason for not employing this service.

Among the respondents, the numbers of current users were 57 percent, while 43 percent are non-user firms. This indicates that a number of companies have realized the importance of employing third party logistics providers. For an overview of how the answers were divided among the different firms by number of employee, turnover, and business category see table 5.1.

Third party logistics play a significant role in Indian organizations as a whole. However, when considering international statistics such as the following: USA (79%) (Lieb & Bentz, 2004), Mexico (78.7%) (Arroyo, et al., 2006), Australia (75%) (Sohal, Millen, & Moss, 2002), Saudi Arabia (63.5%) (Sohail & Al-Abdali, 2005), Malaysia (63%) (Sohail & Sohal, 2003) and Singapore (60.3%) (Sohail, et al., 2006), this survey result may indicate that logistics outsourcing level in India is behind as compared to these countries.

5.5.2 Geographical coverage

Table 5.4 shows that about 46 percent of 3PL users in India use 3PL providers to perform both domestic and international operations. The other 23 percent use such services for international operations only and 31 percent of users only use 3PL services domestically.

Table 5.4: The purpose of 3PL services used by geographical coverage

The purpose 3PL Service Used No. Percentage

Pure International 12 23%

Pure Domestic 16 31%

Both 24 46%

Total 52 100%

This survey showed that the respondents in India mainly used both domestic and international operations, similar to the study in USA, Western Europe, Singapore and Malaysia. These countries have used 3PL services for both domestic and international purpose (Lieb & Bentz, 2004; Millen, et al., 1997; Sohail, et al., 2006; Sohail & Sohal, 2003). India is a country where businesses incline to be more orientated towards imports and exports of  goods and services. More specifically, many respondents of this survey have identified themselves as 3PL users while using outsourced logistics services in multiple geographies such as Australia, Asia, Western Europe, and North America. This was the same as the study in America (Lieb & Bentz , 2005). The results had shown that 69 percent of the respondents used 3PL services in Australia, followed by 63 percent in Asia (excluding China), 44 percent in China, same percent (41%) in both Western Europe and North America. Further coverage is shown in Table 5.3.

Figure 5.3: Use of 3PL services outside India

Notes: N=32 for each geographicial region, with 1 missing value.

Furthermore, the size of a company may have an important impact on the purpose of the usage of 3PL. The size of a company can be represented in terms of their turnover. Bagchi and Mitra (2008) revealed that there was a significant association between turnovers and the degree of globalization in their research. Therefore, the investigation undertaken was to see if the company's size impacts on the purposes of usage of 3PL service, which are categorized by international purpose and domestic purpose here. Table 5.5 produces a cross tabulation results between them.

Table 5.5: Cross tabulation between turnover and the purpose of 3PL Service

3PL Service used for

Pure International and Both Pure Domestic

Group of Turnover in 2008(in millions)

0-25 18(49%) 5(33%)

>25 18(51%) 11(67%)

Total  36(100%)  16(100%)

Notes: χ2=0.962, df=1, ptwo-sided =0.327

For this cross tabulation, a looser criterion was applied for defining a smaller company (with less than 25 millions). The results in table 5.5 show an almost similar percentage using 3PL service for both small and large companies (49% and 51% respectively) relatively to international purpose, whereas the difference is more than doubled for domestic purpose. Moreover, Pearson Chi-square is not significant (p>0.1, χ2=0.962). Therefore, the results reveal that there is no association between company's turnover and the purpose of using 3PL services in this study.

5.5.3 Length of experience in using 3PL services

Of those respondents who are currently outsourcing logistics activities, 71 percent of them indicated that their firms have been using 3PL for more than 3 years. Only 4 percent of respondents presented that have been using 3PL services less than one year. This indicates a significant amount of experience with 3PL services in Indian companies. It is also similar to the study done in Australia with 66 percent of the users outsourced logistics for more than 3 years (Sohal, et al, 2002), in both USA and Singapore with 84 percent of users outsourced logistics services for more than 3 years (Lieb & Bentz, 2005; Sohail, et al., 2006). Table 5.6 also shows how the length of experience with 3PL services was spread in the category of annual turnover. Interestingly, only 2 small size companies with annual turnover between 0-10 $millions have been experienced with 3PL for less than 1 year. Moreover, 75 percent of respondents with annual turnover more than 100 $millions have been experience with 3PL services for more than 3 years. These may due to the larger companies with longer company history and more experiences.

Table 5.6: Length of experience with 3PL services and Annual Turnover

The Length of Using 3PL(s)

Annual Turnover < 1year 1-3 years 4-5 years >5 years Total

in 2008 ($millions)

0-5 1(9%) 4(36%) 0(0%) 7(55%) 12(100%)

6-10 1(25%) 0(0%) 2(50%) 1(25%) 4(100%)

11-15 0(0%) 0(0%) 0(0%) 2(100%) 2(100%)

16-25 0(0%) 1(25%) 0(0%) 3(75%) 4(100%)

26-100 0(0%) 2(29%) 0(0%) 6(71%) 8(100%)

100+ 0(0%) 5.(23%) 6.(27%) 11(50%) 22(100%)

Total 2(4%) 12(25%) 8(15%) 30(58%) 52(100%)

Chi-square test is performed to test whether the size of a company in terms of turnover has an impact on the length of using a 3PL. However, a large number of frequencies are less than 5 due to a small sample size. Therefore, the assumption of Chi-square's test is violated which can create problems on reliable results (Field, 2005, p.686). Therefore, companies that have turnover less than 25 millions are combined as small size company, whereas the others are formed as the large company. The results can be seen on table 5.7. It shows that Pearson Chi-square is not significant (χ2=0.314, df=1, ptwo-tailed >0.1). The results indicate that there is no association between company size and the length of experience with 3PL.

Table 5.7: Cross Tabulation for the Length of use of 3PL and Annual Turnover

The Length of Using 3PL(s)

Annual Turnover 0-3 years >3 years

in 2014 ($millions)

0-25 8 (50%) 15(41%)

>25 8 (50%) 21(59%)

Total 16(100%) 36(100%)

Notes: χ2=0.314, df=1, ptwo-tailed =0.575

5.5.4 Percentage of total logistics budget allocated to 3PL providers

Table 5.8 shows the survey results about the percentage of total logistics budget allocated to 3PL providers by five categories. About 39 percent of respondents allocated more than 40 percent of their current annual logistics budgets to 3PL providers and the remainder of the firms allocated less than 40 percent of their logistics budget. This is quite similar to the survey findings of Australian firms, which indicated that 24 percent of the firms allocated more than half of their total logistics budget to contract providers and 61 percent of the firms allocated less than 30 percent of their logistics budget (Sohal, et al, 2002). European (Wilding & Juriado, 2004) and Singaporean firms (Sohail, et al., 2006) were in similar positions.

Table 5.8: Percentage of total logistics budget allocated to 3PL providers

Percentage of Total Logistics

Budget Allocation No. Percentage

0-19% 15 29%

20-39% 17 32%

40-59% 12 23%

60-79% 4 8%

80-100% 4 8%

Total 52 100%

5.5.5 3PL services used, trends and satisfaction level

The activities listed in Figure 5.4 show the outsourcing trends in India with respect to the logistics services most outsourced. They are ranked from most frequently outsourced to least. Companies are using a wide range of 3PL and the typical user buys multiple logistics services from their provider(s). Of the respondents to these questions, the most frequently outsourced logistics services were domestic transportation (82%), freight forwarding (64%), warehousing (62%), international transportation (62%), and customs clearance and brokerage (47%). Those most outsourced logistics activities in the US, Western Europe, Asia Pacific, and Latin America were also domestic transportation (83%), international transportation (70%), warehousing (69%), customs clearances and brokerage(67%), and freight forwarding (51%) (Cap Gemini, 2007). In India, it seems freight forwarding was more popular than in other countries around the world; international transportation was a little bit lower.

However, less than 30 percent of the respondents outsourced other logistics activities in India. Despite the extensive movement of many 3PL providers into non-traditional services during the past few years (Cap Gemini, 2007), very few respondents reported using their 3PL providers for LLP/4PL services, customer service, consulting service etc. This perhaps indicates that a few primary logistics services are popular and more sophisticated and integrative services have yet to gain widespread acceptance in Indian 3PL industry.

Figure 5.4: Most frequently used 3PL services (percentage of responding users)

Notes: N=49 for each 3PL service, with 3 missing value.

In the follow-up question, respondents were asked to indicate the level of their satisfaction with the 3PL services (1 = very dissatisfied; 5 = very satisfied). The response of using 3PL services and the level of satisfaction is presented in table 5.9. These figures are in general relatively even and the average rate of satisfaction is 3.64 with maximum 4.14 and minimum 2.89. The result of the surveys demonstrated that respondents were overall satisfied with their 3PL service providers. The service which the respondents were most satisfied with was international transportation which scored 4.14 with a relatively small standard deviation of 0.448. The 3PL service with the lowest satisfaction level was reverse logistics (2.89 with standard deviation 1.364). It can imply the potential need for this area. The satisfaction level for domestic transportation (3.86 with standard deviation 0.585) and warehousing (3.71 with standard deviation 0.60) were relatively low compared with other frequently used 3PL services such as international transportation, freight forwarding, customs clearance and brokerage. Furthermore, the levels of satisfaction with customer service, shipment consolidation, order entry, processing and fulfillments, inventory management are less than 3.72 with a relative small standard deviation. There might be improvements that can be made in these aspects as well.

Table 5.9: The satisfaction level of outsourced logistics services

Type of 3PL Services N Mean Std. Deviation

International transportation 28 4.14 0.448

Freight Forwarding 29 4.07 0.799

Other 1 4.00 N/A

Customs clearance and brokerage 21 4.00 1.049

Product Labelling, Packaging, Assembling,

Kiting 13 4.00 0.000

Domestic Transportation 37 3.86 0.585

Carrier Selection 9 3.78 0.667

Cross Docking 9 3.78 0.441

Warehousing 28 3.71 0.600

Consulting services 6 3.67 1.033

Freight bill auditing and payment 6 3.67 1.033

Customer service 6 3.67 0.516

Shipment consolidation 12 3.67 0.778

Order entry, processing and fulfilment 8 3.50 0.535

Inventory management 13 3.38 0.768

Operation of IT systems 6 3.33 1.033

LLP/4PL services 1 3.00 N/A

Fleet management 2 3.00 0.000

Reverse logistics(Defective, Repair, Return) 9 2.89 1.364

Notes: Each respondent rated nineteen types of 3PL service based on a 5 point scale, where 1=very dissatisfied; 2= dissatisfied; 3=Neither; 4=satisfied; 5= Very satisfied.

Furthermore, it is interesting to investigate the relationship of whether the length of employing 3PL services has any impact on the number of services used. An ANOVA was conducted at first. However, it is difficult to have enough respondents for each group on the length of using a 3PL due to a small sample and this is designated to have four groups. Thus, an independent T-test was conducted instead of the initial proposed ANOVA. Companies which used a 3PL service for equal or less than three years are considered as a short-term group. Other companies employing a 3PL service for equal or greater than 4 years are formed as a long-term group. The results of T-tests are shown on table 5.10.

Table 5.10: Independent samples T-test for Mean of Number of 3PL Service used on the length of using 3PL(s)

Levene's Test

for Equality of




Length of Std. t value p-value (2-

Using N Mean Deviation F Sig. (df.) tailed)


In groups

Mean of the number <=3 year 12 4.0 1.76 1.171 .285 -1.815 .077

of 3PL Service used >3 year 38 5.9 3.47

From this table, it is observed that Levene's test for equality of variances is not significant (p value >0.05). With equal variances on each group, the two tailed T-test results are slightly significant (t=-1.815, p value <0.1). It indicates that within a subgroup analysis, there is a significant difference on the number of 3PL services used by users with different length experience with a 3PL provider. In this research, the reason for application of a p-value at 0.1 as the criteria for significant finding is the small sample of this research. The small sample was mainly caused by limited funding and time. Also, target samples are companies rather than individuals. In addition, Field (2005, p.31) pointed out in his study that choosing a lower significant level of accepting an effect will also increase the probability to reject the genuine effect. Therefore, it cannot be stated that choosing a lower significant value as the cutting point is better than choosing a higher significant value.

Moreover, an ANOVA has been used to test whether there are differences among the number of 3PL services used and the different level of logistics budget allocation to a 3PL company. Because of the small sample size, four groups of budget allocations have been combined into 3 groups: low-budget users (1-39%), medium-budget users (40%-59%) and high dedicators (over 60%). The ANOVA statistic results are shown in table 5.11.

Table 5.11: ANOVA for Number of 3PL Services used

Sum of Squares df Mean Square F Sig.

Between Groups 47.328 2 23.664 2.463 .097

Within Groups 403.472 42 9.606

Notes: Levene's Test for Homogeneity of variance results's p-value is .337, with F=1.117

In order to meet the assumption of an ANOVA test, a test of Homogeneity of variance is also conducted. The results with an insignificant p value (p value >.05) shows that each group is homogeneous in their variance. From table 5.11 the ANOVA statistic with a slightly significant value (F=2.46, p value <.1) indicates that there are differences on the number of 3PL services used across different logistics budget allocation groups. In order to investigate where the difference lay between groups, a Turkey test is also conducted (see table 5.12).

Table 5.12: Multiple Comparisons for Number of 3PL Services


Logistics Budget Group Comparison Difference Std. Error Sig.

Low Budget Vs Medium Budget -.806 1.19 .777

Medium Budget Vs High Budget -.1.94 1.51 .408

Low Budget Vs High Budget -2.75 1.25 .081

Notes: Low Budget =0-39%; Medium Budget = 40%-59%; High Budget =60%-100%

Table 5.12 shows that a significant difference exits only between low budget-allocated company and high dedicator (p value<.1). The non-significant results were detected between low and medium groups and medium and high groups, probably due to the small number of respondents in this research. The mean of the number of 3PL services used among the three groups indicates a trend that companies that allocate higher logistics budget to 3PL providers tend to buy multiple logistics services.

5.5.6  Number of 3PL providers used

The number of logistics service providers currently used was established by asking respondents to indicate how many providers they currently employed. Of the respondents to this question, 60 percent indicated that they used between two and five different logistics service providers, 15 percent used only one, and 25 percent used 6 or more providers.

Therefore, 85 percent of respondents employed the services of more than one 3PL provider. This was similar to most of the countries mentioned in the literature, such as USA (Lieb & Bentz, 2005), Australia (Sohal, et al., 2002) and Singapore (Sohail et al., 2006). Furthermore, the majority of the respondents use between two and five logistics service providers. This is probably because the 3PL users want to have a close relationship with their logistics providers. However, this result differs from the result of Malaysian companies, which do not rely on one or two logistics providers but employ various logistics providers to enhance their services (Sohail & Sohal, 2003).

Table 5.13 provides an analysis of the size of the firm based on annual turnover with the number of 3PL providers used. The most frequent users are companies with an annual turnover of more than $100 million; approximately 84 percent of them employ more than one 3PL service provider. There has been a trend of hiring more than one logistics provider for large global companies, in some instances this is due to the necessary geographic coverage, future expansion, and the need to ensure competition, flexibility, optimum costs and service (Song, Maher, Nicholson, & Gurney, 2000). Moreover, 82 percent of the users with annual turnover between 0-5 million employ two to five service providers. There has been another trend by companies to outsource to as few providers as possible in the US and UK, thus concentrating on a smaller numbers of two to five more reliable providers (Waters, 2003).

Number of different 3PL Provider

Annual Turnover 1 2-5 >=6 Total

in 2014 ($millions)

0-5 1(9 %) 11(82 %) 1(9 %) 13(100 %)

6-10 1(25 %) 2(50 %) 1(25 %) 4(100 %)

11-15 0(0 %) 1(50 %) 1(50 %) 2(100 %)

16-25 0(0 %) 2(50 %) 2(50 %) 4(100 %)

26-100 2(29 %) 6(71 %) 0(0 %) 8(100 %)

100+ 3(15 %) 10(48 %) 7(37 %) 20(100 %)

Total No. 7(14 %) 32(63 %) 12(23%) 51(100 %)

Notes: N=51, with 1 missing value.

According to the above findings, an independent t-test was employed to test the mean numbers of 3PL providers used with groups of turnover. The initial design was to divide companies into six groups by turnover. This is mainly because the majority of Indian companies are small companie. However, it is difficult to carry out an ANOVA test across turnover groups due to a very small sample being allocated to each group. Therefore, companies with a turnover of less than 10 millions  are grouped together, whereas the others (more than 10 millions) are formed as another group. Then an independent t-test was performed. The results are shown in table 5.14.

Table 5.14: Independent Samples T-test for Mean of Number of 3PL Provider used on groups of Turnover

Levene's Test for Equality of Variances



Std. t value p-value (2-

Turnover N Mean Deviation F Sig. (df.) tailed)

groups (in


Mean of the number 0-10 17 3.07 2.43 .420 .520 -1.303 .199

of 3PL Provider used >10 34 4.03 2.33 (45)

Notes: the figures shown in turnover groups represent millions. r=.191

From the table, it shows that, on average, small companies (turnover less than 10 millions) use a lesser number of 3PL providers than large companies (turnover more than 10 millions). The similarity of standard deviation of the two groups (2.43 vs. 2.33) shows that the average of the two groups is more comparable with each other. However, the results of p value (p – value > 0.1 with t = -1.303) from the t-test indicate that there are no significant differences for the number of 3PL providers used, no matter if the company is small or large in terms of turnover. However, it reflects a small size of effect (r = 0.191). The results imply that a large company may not use more providers because they may want to have a closer relationship with a small number of 3PL providers. It also reflects that they do not have the need to outsource logistics so much because they may have adequate resources and skills by themselves. In the USA and UK it has also been found that larger companies often seek to use a smaller number of providers, so that they can maintain control and closely monitor their outsourcing (Waters, 2003).

5.5.7 Length of third party contracts

Table 5.15 shows that 69 percent of the respondents had signed contracts with their 3PL service providers. From the results of contract durations long - term relationships are most common, in 82 percent of all cases the average duration of the contracts used is one to three years. There were similar results in the USA (Lieb & Bentz, 2005), Australia (Sohal, et al., 2002) and Western Europe (Wilding & Juriado, 2004). Malaysian and Singaporean companies however tend to have longer relationships with more than 5 years contract duration (Sohail, et al., 2006). Further considerations are in Table 5.15.

Table 5.15: Whether have contracts with 3PL and the length of 3PL contracts

Whether Have Contracts with 3PL Provider

Yes No

No. %age No. %age

Length of Contracts 36 69 % 16 31 %

<1 year 3 9 % N/A N/A

1-3 years 30 82 % N/A N/A

4-5 years 2 6 % N/A N/A

>5 years 1 3 % N/A N/A

5.5.8 Information sources used for finding 3PL service providers

3PL users become aware of their logistics services provider(s) in a variety of ways. They use various methods to discover the appropriate third party logistics service companies to meet their needs. Figure 5.5 shows that the most common ways were discussions with other logistics professionals (40 percent) and sales calls by representatives (36 percent). 21 percent of respondents mentioned other and added sources such as: local knowledge, previous experience, personal contact, partnership with a key freight center, response to request for tender or registration of interest and the logistics outsourcing decisions made before the employment of logistics managers. Similar findings are also reported from Australia, USA, Western Europe, Singapore and Saudi Arabia (Millen et al., 1997; Bhatnagar et al., 1999; Lieb & Randell, 1999; Sohail & Al-Abdali, 2005).

Figure 5.5: Most frequently cited ways those using 3PL services became aware of such services

Notes: N=51 for each information source, with 1 missing value.

5.6 Critical success factors of selecting and evaluating 3PL providers

The  key success  factors  used  when  selecting  logistics  service providers  were established by asking respondents to indicate the extent to which the factors listed were important to the company when selecting a service provider and also to rate the provider performance with these key success factors. The list of 14 key success factors was compiled from literature review.

Table 5.16 shows the means for both the factor importance and the company rating for selection and evaluation of a 3PL. Each respondent was asked to rate fourteen types of factors for both importance and company performance based on a 5 point scale. The factor importance ranges from 1 (very unimportant) to 5 (very important). Likewise, for company rating the ranges are also from 1 (very low) to 5 (very high). The average rating of the factor importance is 3.99 with maximum 4.63 and minimum 3.24 and company rating is 3.68 with maximum 4.00 and minimum 3.31. It is obvious that all means in this table are more than three. Therefore, the result of the survey demonstrated that respondents believe these factors are important and they are satisfied with their providers' performance in general.

Table 5.16: Factor importance vs. company rating (mean)

Factor Importance Company Rating

(N=50) (N=49)


Factors for Evaluation and Selection of a 3PL Mean Deviation Mean Std. Deviation

Breadth of service offerings 3.89 0.674 3.67 0.853

Price 4.37 0.645 3.84 0.878

Quality of logistics services 4.46 0.721 3.80 0.842

Flexibility to meet unanticipated customer needs 4.26 0.773 3.73 0.720

Financial stability 4.00 0.843 3.58 0.583

Length and depth of 3PL relationships 3.70 0.662 3.69 0.633

Experience as a 3PL provider 4.00 0.760 3.89 0.745

Investment in quality assets 3.72 0.688 3.51 0.661

Investment in information systems 4.15 0.729 3.67 0.640

Skilled logistics professionals 4.15 0.788 3.76 0.830

The size of logistics provider 3.24 0.736 3.31 0.668

Company reputation 3.78 0.758 3.62 0.777

Focus on specific industries 3.57 0.910 3.40 0.780

The speed of delivery 4.63 0.645 4.00 0.905

Note: N=50 for importance rating, with 2 missing value; N=49 for company rating, with 3 missing value.

It is noticeable that the service speed of delivery has the highest mean in the factor importance and has the lowest standard deviation when compared to the rest of the factors (mean = 4.63, standard deviation = 0.645). This result indicates that

respondents place more consideration on the speed of delivery. It also reflects the respondent's concern on the transportation function of 3PL service over other 3PL services such as price and quality of logistics services in India.

It is also clear to see the trend and ranking from the bar charts (figure 5.6 ) which rank the mean from the highest to the lowest for importance factor. Quality of logistical services (mean = 4.46, standard deviation = 0.721) and price (mean = 4.37, standard deviation = 0.645) are ranked as the second and the third important factors with relatively low standard deviation. A number of studies have revealed the importance of price and quality as well (La Londe & Maltz, 1992; McGinnis et al., 1995; So, et al., 2002; Sohail & Sohal, 2003). Thereafter the important factors are flexibility to meet unanticipated customer needs, investment in information systems, skilled logistics professionals, the size of logistics providers and company reputation according to the average rating.

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