Hypotheses Case Study 3: Theory-testing research: testing a deterministic relation

varied both dimensions of time window pressure see Table 6.2. The time window length varies from a 6-hour period between 6:00am and noon in subscenarios A 1-6 , via a 4.5-hour period from 6:30am to 11:00am in subscenarios B 1-6 , to the third series of subscenarios C 1-6 with a time window length of only 3 hours, from 7:00am to 10:00am see rows in Table 6.2. The number of time window restricted areas varies from the shopping centres in the five largest Dutch municipalities in scenario 1 to the shopping centres in the 250 largest municipalities in scenario 6 see the columns in Table 6.2 for the differences in number of time restricted areas. For each retailer we formulated the following two hypotheses. Hypothesis 1: For a given time window length A, B, or C the values of all four dimensions of distribution costs will be ranked in the perfect order according to the increasing number of time window restricted areas 1–6. Hypothesis 2: For a given number of time window restricted areas 1–6 the values of all four dimensions of distribution costs will be ranked in the perfect order according to time window length A–C.

6.2.8 Measurement

We have generated the values for distribution costs by calculating the real- istic effects i.e. based on current distribution activities of different real- istic but not actual levels of time window pressure on these 14 Dutch retailers’ costs. In order to be able to do that we first collected data on the actual distribution activities of the 14 retailers for a period of one week. Table 6.2 Scenarios of time access window pressure Number of time window restricted areas Time window Only 5 Only 10 Only 25 Only 50 Only 100 Only 250 length largest largest largest largest largest largest cities in the cities in the cities in the cities in the cities in the cities in the Netherlands Netherlands Netherlands Netherlands Netherlands Netherlands 6:00am–noon A1 A2 A3 A4 A5 A6 6:30am–11:00am B1 B2 B3 B4 B5 B6 7:00am–10:00am C1 C2 C3 C4 C5 C6 The measurement process followed the same procedure for all cases, and consisted of four steps: ■ open interview with the distribution or logistics manager to get familiar with each retailer’s operations and urban freight transport activities and the current or likely retailer’s reaction on time window pressures; ■ a questionnaire to collect detailed data on each retailer’s oper- ational level; ■ company documents and additional information with infor- mation on each retailer’s entire transport planning for one week; ■ e-mail andor telephone contact for additional information needed. Collected data were put into a mathematical model that generated the distribution costs in all four dimensions, for a given time access win- dow pressure. In this model we needed to solve a number of vehicle routing problems with time windows. The number of extra vehicles was kept to a minimum. To plan the new roundtrips we used the vehicle routing software SHORTREC 7.0, developed by Ortec Consultants. From the new calculated retailers’ roundtrip planning, we derived the values for the dimensions of distribution costs. For a detailed descrip- tion of the collection of actual retailers’ distribution data as well as of the model we refer to Quak and De Koster, 2007.

6.2.9 Data presentation

We filled all 18 cells of Table 6.2 for each retailer and for each of the four dimensions of distribution costs, resulting in 56 4 ⫻ 14 tables. The tables can also be represented in graphs, as is shown in Figure 6.2 for one of the 14 retailers case 8. The two time window pressure dimen- sions are represented in Figure 6.2 as follows: the x-axis represents the number of time window restricted areas resulting from each scenario for this retailer. The different values of time window length are represented by a line for each scenario A, B, and C.

6.2.10 Data analysis

Hypothesis 1 states that for each of the 14 retailers and for each of the four dimensions of distribution costs the six values 1–6 in each of the