Case selection Case Study 4: Theory-testing research: testing a probabilistic relation

7.2.7 Hypotheses

The propositions state that for a specific change of time access window pressure differences between retailers with different levels on the dimen- sions of distribution strategy will be observable. For the present test, we decided to keep time window length scenario A, B, or C constant and to change only the number of time access windows levels 1–6. The following hypotheses were formulated, which must be tested separately for each scenario A, B, and C. Hypothesis 1: Retailers with a higher number of stops per roundtrip have, on average a steeper rise in total distribution costs with an increase in the number of time windows from level 1 to level 6 than retailers with a lower number of stops per roundtrip. Hypothesis 2: Retailers with a vehicle fleet with higher capacity per vehicle have, on average a steeper rise in total distribution costs with an increase in the number of time windows from level 1 to level 6 than retailers with a fleet of lower capacity per vehicle. Hypothesis 3: Retailers with longer stopping times have, on average a steeper rise in total distribution costs with an increase in the number of time windows from level 1 to level 6 than retailers with shorter stopping times. Hypothesis 4: Retailers with longer distances from the distribution centre to their shops have, on average a steeper rise in total distribution costs with an increase in the number of time windows from level 1 to level 6 than retailers with shorter distances from the distribution centre to their shops. Hypothesis 5: Retailers with less strict self-imposed time windows have, on average a relatively higher increase in total distribution costs that occur with an increase in the number of time windows from level 1 to level 6 than retailers with stricter self-imposed time windows.

7.2.8 Measurement

We could make use of the same data that we used in the study reported in Case Study 3. Time access window pressure was determined by the number of shops that are affected by time access windows and the time-window length. Total distribution costs in euros per week were determined by convert- ing our data on the weekly number of vehicle kilometres, the total time used including the loading and unloading times as well as driving and waiting time, the number and types of vehicles used, and the number of roundtrips, into a monetary value. The variable costs are mainly based on costs per hour and cost per kilometre. We validated the costs with all retailers, and adapted them slightly in case the retailers felt this would give a better representation of the actual costs. The costs for overtime are higher per hour than in the normal situation. The five dimensions of distribution strategy were determined as follows. ■ Stops per roundtrip. This was measured by calculating the aver- age number of stops per vehicle roundtrip during a week. This equals the average number of different shop deliveries that are combined in one vehicle. This can vary from full- truckload FTL deliveries, in which a vehicle only makes one stop per roundtrip, to less-than-truckload LTL deliveries, implying that a vehicle makes more than one delivery per roundtrip Stock and Lambert, 2001. ■ Vehicle capacity. Based on McKinnon et al. 2003 we distin- guished six different vehicle types. We sorted these types on increasing load factor, starting with the smallest capacity and ending with the largest vehicle capacity see Table 7.1. We calculated each retailer’s average vehicle fleet capacity based on the number of vehicles in each category. ■ Stopping time per vehicle. Stopping time can be split into two parts: a fixed stopping time per stop and a variable stopping time per Table 7.1 Vehicle types sorted on capacity Value Type Characteristics Example 1 Small rigid 2 axles, ⬍7.5 tons 2 Medium rigid 2 axles, ⬎7.5 and ⬍18 tons 3 Large rigid ⭓2 axles, ⬎18 tons 4 City semi-trailer articulated, 3 axles 5 Articulated vehicle articulated, ⬎3 axles 6 Drawbar combination combination, ⬎3 axles