GIS DEVELOPED FOR BACKLOADING ANALYSIS

As mentioned above, a GIS is mainly composed of: spatial data and attribute data. The integration of these two types of data in the GIS is fundamental to the backloading analysis. Spatial data

Because some of the post-code data in the KPI database was of poor quality, several levels of geo-coding are required to ensure that the post-code entered into SAS/GIS was as accurate as possible. Most of the postcodes (81% of journey legs with postcode information) had been geo-

coded with full post-code. Post-codes for which there was no match in the Code Point database were converted into Eastings and Northings using a centre of gravity approximation. The trip patterns of the sample fleets are displayed in Figure D.1 in Appendix D.

Attribute data

The other relevant information is retrieved from the Access database and stored in CSV format. This includes vehicle data, composition of trips and information about loads and delivery time windows. These files were imported into SAS using codes written in SAS programming language. The spatial data is then integrated with the attribute data using the interface provided in SAS/GIS.

4.4.1Features of the Food Transport GIS 4.4.1Features of the Food Transport GIS

The interface of the GIS for backloading analysis is shown in Figure 4.9. This shares common features with other applications developed in windows environment, such as object(s) selection, enlargement, and drag and move. It also has all the common features of a GIS, such as showing location and distance, and providing feedback on selected areas or lines (i.e. journey legs in this case).

As shown in Figure 4.9, trips of different companies are displayed in different colours. Each company’s distribution network can also be displayed separately by clicking in the corresponding check boxes at the top.

An interactive query interface is also available so that journey legs or trips of certain types (for example, of a specific company or over a certain distance) can be selected. Various actions relating to different levels of screening can then be taken for selected journey legs, as shown in Figure E.2 in Appendix E. The screening results are stored in table format and can be subject to further analysis if required.

Figure 4.9. Features of the GIS

4.4.2Screening of Potential Backloads 4.4.2Screening of Potential Backloads

Screening was undertaken at four levels: location / distance, vehicle compatibility, capacity, and delivery time window.

Location and distance

The assessment of backloading potential is not only constrained by the distance between the original collection and delivery points, but also by the extent to which vehicles must deviate from the direct return route.

(a) The straight line (‘crow-fly’) distances used in this analysis were converted into approximate road network distances using a standard wiggle factor of 1.28.

(b) Allowance was made for major geographical barriers, such as rivers, estuaries and mountains (Table 4.2). Movements between twelve zones affected by these barriers were routed via eleven ‘way points’ to prevent, for example, the routing of a vehicle across the Wash. (This method was previously applied by a member of LRC research team for an online backloading service called the backload.com). Most of the parameters have been tested previously in practice with reasonably high accuracy. A graphical representation of the estuary zones and way points is shown in Figure D.3 in Appendix D. This system ensures, for example, that traffic moving between the Lothians and Fife passes a waypoint at the Forth Bridge.

Table 4. 2 . Estuary Zones and Way Points

Way Points ID _Routes Major Estuary Zones Dartford river crossing WP1 M25 Kent↔East Anglia

Sleaford WP2 A17 East Anglia↔ Lincolnshire

Humber bridge WP3 A15 Lincolnshire↔East Yorkshire

Tyne Tunnel WP4 A17 East Yorkshire↔Borders East

Forth Road Bridge WP5 A90 Borders East↔Fife

Tay Road Bridge WP6 A92 Fife↔Grampian

Inverness WP7 A9 Grampian↔Highland

Glasgow WP8 M74 Highland↔Strathclyde

Gretna WP9 M74 Strathclyde↔North West

Warrington WP10 M6 North West↔Wales

Severn river crossing WP11 M48/M4 Wales↔South West

A future refinement of the backloading analysis could be the linking of a road network database to the GIS to provide more accurate distance measurements.

Vehicle Compatibility

Previous research (McKinnon, 1996) has shown that the incompatibility of upstream and downstream flows can severely limit the scope for backhaulage, particularly for firms in process industries. The impact of vehicle incompatibility on back haulage in the food sector has not, however, been explored. In the food sector, whether a vehicle can take a load or not is first of all restricted by its temperature-control capabilities. Secondly, the temperature range of a

compartment and the time required to vary its temperature in order to meet the requirements of different products is another limiting factor, affecting the compatibility of a vehicle with potential backloads.

Vehicle Capacity

Vehicle capacity is another important factor that determines whether a vehicle can pick up a back load. A simplifying assumption has been made in this modelling exercise that a backload will only be collected where there is sufficient capacity in the empty vehicle to carry the full load. In a future version of the model it may be possible to relax this assumption and allow the collection of part loads.

Delivery Time Window

The prime objective of transport management is to meet requirements for fast and reliable outbound delivery to customers. Any delay in the return of delivery vehicles and its cumulative effect can compromise this objective. At corporate level, the benefits of return loading are often considered to be relatively minor when set against the marketing advantages of gaining and maintaining a reputation for on-time delivery. It can be important therefore to accommodate any backloading within existing schedules and ensure that vehicles are returned in time for their next outbound delivery. As the KPI database contains information on the timing of follow-on trips, it is possible to check whether backload opportunities could be realised within scheduling constraints.