Key Performance Indicators of Distribution in the Automotive Industry

Transcription

1 Final Report Key Performance Indicators of Distribution in the Automotive Industry Professor Alan McKinnon Duncan Leuchars Logistics Research Centre May 2002 School of Management, Heriot-Watt University, Edinburgh, EH14 4AS

2 Contents 1. Introduction 2 2. Stage I: Consultation with the Industry Industry liaison Company interviews Current concerns about freight transport Level of interest in KPI initiative Fleet sizes KPIs currently used Scheduling of the survey Problems anticipated Particular concern Conclusion 8 3. Stage II: Adapting the KPI Survey to the Automotive Sector Advice on the customisation of the KPIs and survey method Response to the think-tank recommendations Modifications to the software Revision of the manuals Briefing sessions Date of the survey Survey statistics Analysis and benchmarking of the data Results of the KPI Survey Vehicle fill Empty running Time utilisation Deviations from schedule Energy efficiency Trips with more than four legs Concluding remarks 38 Appendix 40 Acknowledgements 41 1

3 1. Introduction Following the success of the 1998 KPI survey of transport operations in the food supply chain 1, a decision was made to extend the KPI initiative into another sector. Several sectors were identified as potentially benefiting from a transport benchmarking exercise. The automotive industry was finally selected. As in the food industry, just-in-time / quick response replenishment was exerting a downward pressure on vehicle load factors. At the same time, competitive pressures in the car market coupled with excess capacity in the industry were tightly squeezing margins. The high value of the pound was also forcing UK component suppliers and assembly firms to cut costs in an effort to remain internationally competitive. Many companies were, therefore, keen to find ways of cutting their transport bill. Numerous studies had been done to benchmark inventory levels, cycle times and productivity across the automotive sector. No similar research had been done on the transport operation. There were some important similarities between distribution operations in the food and automotive sectors. As mentioned above, both are subject to strong quick response / JIT pressures. Much of the freight moved in the automotive sector, like grocery products, is of relatively low density, 'cubing out before it weighs out'. Loads of automotive products also tend to be volume-constrained because of their low stackability. Another similarity is the high propensity of companies in the two sectors to outsource their logistics. The two sectors differed in several important respects: 1. Supply chain structure: the configuration of the inbound automotive supply chain differs quite markedly from the sections of the food supply investigated in the earlier KPI surveys 2. Handling units: a much wider variety of handling units is used in the automotive sector and a substantial volume of inbound freight is not unitised. 3. International haulage: although cross-border freight movements were not excluded from the earlier food KPI survey, only a tiny proportion of journey legs fell into this 1 A.C.McKinnon 'Benchmarking Vehicle Utilisation and Energy Efficiency in the Food Supply Chain : Full Report of the Key Performance Indicator Survey' School of Management, Heriot-Watt University, Edinburgh, (available at 2

4 category. A much larger proportion of component deliveries to factories and dealerships come from outside the UK and this proportion is increasing. 4. Temperature-control: many of the freight deliveries in the food sector are refrigerated. This does not apply to the automotive sector, removing the need to monitor energy consumption in refrigeration equipment. It was recognised at the outset that the first two differences would present new challenges and require significant modification to the existing KPI methodology and software. As in the food industry, a bottom-up approach was to be used involving wide consultation with companies in the automotive sector on the customisation of the survey to the particular needs of this industry. Unlike in the food industry, however, the automotive KPI initiative was not developed collaboratively with a major trade body. There was no equivalent to the Cold Storage and Distribution Federation, which had made a major contribution to the 1997 and 1998 food KPI surveys. Responsibility for stimulating interest in transport benchmarking within the automotive industry therefore rested with the project team. The project was divided into two stages. Stage I publicised the KPI initiative among automotive companies, canvassed their views and assessed their willingness to participate in a survey. At the end of this stage it was felt that there was sufficient interest to justify proceeding to Stage II, which involved customising the methodology and software, briefing the companies, running the survey and analysing the data. This final report summarises both stages of the project and outlines the main results of the KPI survey. It concludes with a brief critique of the project and discussion of the options for pursuing the KPI initiative in the automotive sector. 3

5 2. Stage I: Consultation with the Industry 2.1 Industry Liaison The key objectives of Stage I of the project were to publicise the transport KPI initiative among companies in the automotive sector and maximise the level of industry participation in KPI survey. Industrial support for the initiative was generated in several ways: 1. A data-base of automotive companies was compiled using several published and on-line directories. Companies were classified into four categories (car assembly companies, component suppliers, aftermarket distributors, logistics companies carrying automotive products) and as much information as possible collected on sales, employment and product type. 2. Letters were sent to a mixed sample of 150 of the largest companies in the data-base, outlining the initiative and inviting senior managers to a workshop. Thirty of the larger companies were also contacted by telephone. 3. A total of twelve companies participated at the workshop, with a further eight indicating that they would like to have been represented. 4. Following the workshop, twenty face-to-face interviews and a further twenty telephone interviews were held with managers in companies that had been identified as 'good prospects' through earlier contacts, attendance at the workshop or discussions within the industry. These interviews were semi-structured, eliciting information on a standard set of questions but also giving the interviewee the opportunity to talk freely about his / her distribution operation. All but two of the companies interviewed indicated a strong interest in the transport benchmarking project. (These two companies had no direct responsibility for transport and therefore had no transport operation to benchmark.) 5. Discussions were also been held with the Society of Motor Manufacturers and Traders. which expressed a strong interest in the initiative. The 38 companies expressing an interest in the KPI initiative during Stage I of the project covered all the main sectors of the automotive industry: Car assembly companies Component suppliers Aftermarket distributors Logistics service providers Car transporter companies. 4

6 2.2 Company Interviews The interviews had four objectives: To outline the KPI project in detail To encourage the companies to participate To assess the level of interest in the project To collect information about the nature of the transport operation and current concerns. There was therefore a two-way flow of information. companies is summarised below. Information gleaned from the 2.3 Current Concerns about the Transport Operation Companies were asked to indicate on a scale 1 (low) - 5 (high) their degree of concern about transport costs, service quality and vehicle utilisation trends. There was a relatively high level of concern over all three, with transport costs getting the highest rating (Figure 1). All but eight of the 40 companies gave transport costs a 5 rating. Concern about service quality was only slightly lower. Approximately two-thirds of the companies rated their concern about the trend in vehicle utilisation at 4 or 5. Several of the component suppliers and assembly companies assigning a lower score to this variable outsourced their transport and therefore had little direct experience of vehicle loading. It was the responsibility of their contractors to maximise vehicle fill. Index Car assembly companies Component suppliers Aftermarket distributors Logistics service providers 1 0 Cost Service Vehicle utilisation Interest in KPIs Car transporter companies Figure 1: Current Concerns and Level of Interest in Transport KPIs 5

7 2.4 Level of Interest in the KPI Project The preponderance of 4 and 5 scores for concerns about costs, service and utilisation trends help to explain the high level of interest in the transport KPI project. Companies were again asked to indicate their level of interest on a 5-point scale. All but two of the companies recorded a 4 or 5 score. This was taken to indicate that they were likely to participate in a transport benchmarking survey Fleet size This ranged from 2 vehicles to many hundreds. Twenty-one firms indicated the number of vehicles they owned or contracted on dedicated basis. Collectively this represented a total fleet of 1125 vehicles. This figure, however, excluded the fleets of many large operators. It was not known at this stage what proportion of their total fleets companies might wish to commit to a KPI survey. 2.6 KPIs Currently Used Many companies stated that they already used performance measurement systems. The most commonly used measures related to quality of service rather than operational efficiency, with delivery reliability the dominant criterion (Table 1). Only six of the companies claimed to monitor vehicle loading on a systematic basis. Table 1: Transport Performance Measures Currently Used Performance measure No. of Companies % of sample % of late / on-time deliveries 17 43% Cost-based 7 18% Vehicle loading 6 15% Fuel efficiency 3 8% % of 'failures' 3 8% Revenue-based 2 5% No. of drops per day 1 3% Time utilisation 1 3% Damage to products 1 3% Distance per vehicle / day 1 3% 6

8 2.7 Scheduling of the Survey The survey enquired about the timing of a possible transport KPI survey. Two thirds of the companies that expressed a view on this subject counselled against holding the survey in the summer, though definitions of the 'summer break' varied from June-Oct to three weeks in August. Only isolated objections were raised to holding the survey at other times of the year. 2.8 Problems Anticipated Managers were asked what problems they envisaged in the extension of the transport KPI initiative into the automotive sector. The following problems and constraints were highlighted by the survey: Lack of staff and management time Variable sizes and shapes of packaging and handling equipment Clients granting logistics service companies permission to participate Getting haulage sub-contractors to take part Car assembly firms thinking their logistical systems are all different Differing 'philosophies' of the car assembly companies Exercise would be dominated by car assembly companies and results used to put added pressure on component suppliers Need to distinguish between different delivery cycles (esp. 1 day and 3 day) Confidentiality 2.9 Particular Concerns It was recognised that in customising the KPIs to the automotive sector account would have to be taken of the concerns of transport / logistics managers. To gain an initial impression of these concerns, managers were asked to list concerns which might be addressed by the KPI initiative. This question generated a long list of responses, many of which were outside the scope of the KPI study. After vehicle utilisation, empty running / return loading and fuel efficiency have been excluded, the following list remains: 7

9 Environmental issues Inter-company load consolidation Potential for shared-user services Vehicle tracking systems Reliability and flexibility Urban Congestion Waiting times at suppliers' premises Delivery windows Availability of drivers Need for localised stockholding around car plants Returnable packaging Maintenance agreements Wage levels Lorry weights issues Road tolls Benchmarking against rail Stackability of products Reducing cost of European transport Advice on optimal fleet size Conclusions. Consultations with senior transport and distribution managers in the automotive sector suggested that there was healthy demand for a standardised performance measurement and benchmarking system. Just-in-time pressures within the industry had forced companies to sacrifice transport efficiency in an effort to cut inventories. In a renewed drive to cut costs, however, many companies were reviewing their transport operations in the hope that further savings might be achieved from improved vehicle loading. For example, in the summer of 2001, it was reported that 'in the latest restructuring, Ford (was) to cut its $1 billion logistics costs in Europe by 20% partly by pooling component deliveries with Ford brands such as Volvo' (Financial Times, 21 August 2001). Following discussions with the project sponsors, it was decided that there was sufficient interest across the automotive industry to proceed to Stage II of the project. 8

10 3. Stage II: Adapting the KPI Survey to the Automotive Sector 3.1 Advice on Customisation of the KPIs and Survey Method A 'think-tank' session was held with industry representatives to examine ways in which the KPI and survey method should be adapted to the needs of the automotive sector. This discussion focussed on eight key issues: 1. Nature of the vehicles to be surveyed: Advice was sought on three issues: (i) Should the survey monitor tractor units as well as trailers? There was general consensus that it was desirable to monitor tractor as well as trailer activity. In the food surveys only trailer activity had been surveyed. (ii) What should be the minimum weight / size of vehicle surveyed? This should remain at 3.5 tonnes, as in the food surveys. (iii) How should vehicles be classified for survey purposes? It was felt that the classification used in the food surveys, based on body type and weight class, was appropriate though with the addition of separate category of double deck vehicles. 2. Nature of the products / delivery operations to be surveyed: It was recognised that automotive products are sometimes combined other industrial products in the same load. Third party logistics companies operating network services typically consolidate a range of products. Furthermore, several of the suppliers distributed products to non-automotive as well as automotive customers. A large foam manufacturer, for instance, might deliver orders to a car plant and bed factory on the same journey. Given these interdependences it was clearly going to be very difficult to confine the survey to automotiverelated products and premises. It was decided that once companies had identified a group of vehicles for inclusion in the survey, all of their activities and loads carried over the 48 hour period would be included, regardless of product type and destination. 3. Measurement of vehicle loading: In the food KPIs surveys, all but a small proportion of loads were unitised (in pallets, rollcages or dollies). The situation is much more complicated in the automotive sector. While many loads are unitised, a much greater of variety of units is used. One industry estimate 9

11 suggested that around 80% of the volume of automotive parts was moved in seven different types of handing unit. A significant proportion of products was also transported 'loose' in a non-unitised state. According to the industry representatives, the shape, dimensions and weight of these products varied enormously. A single load might comprise four or five different types of handling unit and an assortment of non-united products. It might also contain empty handling units being returned, sometimes nested or 'collapsed'. The situation was further complicated by variations in the degree to which individual handling units were loaded. Stillages, for example, might be only partially filled or pallets loaded to only a fraction of the available height. It was considered impractical to measure the internal utilisation of rigid units such as stillages. The height of pallet-loads on the other hand could be estimated in the general assessment of cube utilisation. Several of the large companies claimed that they already recorded information on the dimensions of loads and would thus be able to estimate cube utilisation. For companies lacking this data two other options were considered: 1. Collect data on the dimensions (and weights) of the most commonly used types of handling unit used and record the number of each of type of unit carried on each trip. In this way it would be possible to 'reconstruct' the load and express its cubic volume as a percentage of the available space in the vehicle. This was considered impractical for several reasons: (a) it excluded loose product (b) it greatly increased the burden of data collection, especially where loads were typically composed of several different types of handling unit. (c) it did not adequately address the problem of variable pallet height. 2. Get the driver / traffic clerk to make a subjective assessment of the degree of vehicle fill within 10% brackets. While this was inevitably a fairly crude form of measurement, it was probably at least as accurate as method 1. During the think-tank session there was also a discussion of how the utilisation of car transporters might be measured. This was complicated by the fact that the car transporters can be reconfigured to carry differing combinations of cars of varying length and height. 10

12 Both the maximum carrying capacity and the average size of the car can vary significantly. It was decided to simplify the data collection in the trip audit by having companies indicate the proportion of available car slots occupied within the particular transporter configuration used on each trip. (As none of the car transporter companies participated in the pilot survey, the effectiveness of this approach was not tested.) 3. Measurement of Empty Running and the Return of Empty Handling Equipment Industry representatives argued that great care should be taken in the definition and measurement of empty running as press reports on this subject often portrayed companies unfairly in a poor light for running vehicles empty on a significant proportion of trips when in practice this was often an operational necessity. The definition of empty running used in the food survey was generally endorsed, i.e. where the vehicle was completely empty and offered the opportunity of collecting a load. The only possible content would be a load of empty wooden pallets considered an integral part of the vehicle. The return of other types of empty handling equipment would be separately monitored, where this was the only load carried. Where empty handling equipment was being carried as a part-load with other products it would not be separately recorded. 4. International Deliveries: A large and increasing proportion of automotive parts are sourced from outside the UK. Several car assembly companies reported that the efficiency of deliveries from other European countries was a matter of concern to them. It was important, therefore, that the survey accommodate cross-border movements. The companies argued that this would not create any particular problems. The only added complication would be the ferry / Eurotunnel crossing, which could simply be treated as another journey leg. 5. Deviations from Schedule: It was agreed that companies should indicate scheduled and actual start and end times for trips. The software would then automotically calculate the delay. Companies could over-ride this calculation and directly enter a value for delay. The industry representatives also decided to adopt the same list of causes of delay as used in the food KPI survey. 11

13 6. Types of Premises: The classification of premises used in the Food KPI surveys was considered inappropriate for the automotive sector, given important differences in the structure of the logistical systems. Following deliberations with company representatives, it was decided that the following list of premises be used: a Factory of component supplier b Car assembly plant c Premises of dealership / large fleet customer d Haulage depot e Cross -dock / consolidation Centre (for inbound flows to assembly plants) f Sequencing centre (usually adjacent to assembly plant) g National parts distribution centre (aftermarket) h Regional distribution depot/cross-dock (aftermarket) i Distribution centre for finished vehicles 7. Fuel Consumption: Advice was sought on the feasibility of measuring fuel consumption during the 48 hour period. This was considered to be impractical. The think-tank members decided to follow the example set by the food KPI survey and use companies' average fuel efficiency values for particular classes of vehicle based on data accumulated over the previous year. In the food KPI surveys, energy-intensity was measured in terms of milli-litres of fuel consumed per (outbound) pallet-km. This required all loads to be expressed in terms of industry-standard pallets or equivalent. Given the diversity of handling units used in the automotive sector and large quantity of loose product, this would not be possible. As an alternative it was decided that the denominator of the energy-intensity fraction should be expressed in terms of both volume (cubic-metre / kilometres) and weight (tonne / kilometres). 8. Timing of the Survey Advice was sought on three issues: (i) Over what period should the survey be conducted? There was a general consensus that the period of 48 hours adopted in the food surveys would also be appropriate in the automotive sector. 12

14 (ii) Should all companies monitor their transport operations simultaneously: The industry representatives felt that companies should endeavour to survey over the same two day period. This would ensure greater consistency in the benchmarking as all companies would be exposed to the trading and traffic conditions. It would also have the effect of 'concentrating the minds' of distribution managers and encourage them to schedule the survey more formally into their work programmes. (iii) On what days of the week / weeks of the month should the survey be done? Unlike in the food industry where Thursday / Friday was considered the most representative period, Tuesday / Wednesday were considered more appropriate for the automotive industry. No strong preferences were expressed for a particular week in the month. Industry representatives counselled against running the survey in July or August because holiday closures across the automotive industry could reduce participation and distort results. 9. Method of Data Collection Industry representatives stressed the need to make the KPI survey compatible with companies existing systems of data collection and referencing. It was evident that the companies represented differed widely in the amount of data collected and the means of recording and storing this data. Broadly speaking, the larger companies tended to have more sophisticated systems. It was felt that, rather than simply design the software to cater for the needs of companies with the most basic systems (i.e. adopting the lowest 'common denominator' approach), the software should be made sufficiently flexible to interface with systems of varying degrees of sophistication. This would allow the survey to take full advantage of the richness and accuracy of the operating data currently collected by companies with the most advanced IT systems. Companies were also asked if the logistical differences between the various sectors of the automotive industry were great enough to justify using several spreadsheet templates rather than one. The general response was that the differences were probably no greater than in the food industry and therefore, as in this industry a single template should be developed. Concern was expressed by some delegates, however, about the difficulty of collecting legspecific data for multiple-drop and collection rounds comprising numerous legs, in many cases in excess of twelve. It was recognised that this could impose an excessive burden on these companies and deter them from participating. 13

15 3.2 Response to the Think-Tank Recommendations Most of the issues were resolved during the think-tank session. Two fundamental problems remained: 1. How to measure cube utilisation of vehicles 2. How to ease the burden of data collection in the trip audit for companies operating multiple drops comprising large numbers of legs. 1. Measurement of cube utilisation: After much deliberation, it was decided that cube utilisation data would be collected in two ways: Companies which already collected volumetric data could input actual load dimensions. Where companies lacked access to this data, the driver / traffic clerk would make a subjective estimate of the degree of cube utilisation within 10% bands. No attempt would be made to record data on the number of handling units carried or quantity of loose product as this was considered impractical. 2. Treatment of multiple-drop / collection trips: Leg-specific data was a fundamental requirement in the food KPI surveys. The journey leg was the basic unit of data entry and analysis. Data collection at the leg level ensured a high degree of accuracy in the measurement of vehicle utilisation and energy-intensity. It was desirable therefore to collect as much leg-specific data as possible. Rather than risk the withdrawal of companies operating large numbers of multi-leg trips, it was decided to divide trips into two categories: Trips with fewer than four legs Trips with four or more legs For trips in the former category the standard trip audit applied with data collected on a legby-leg basis. For those in the latter category, data would be collected for the trip as a whole. Vehicle utilisation would be measured at the point of maximum loading. Empty running would be recorded as a percentage of the total journey length. No data would be collected on 14

16 the locations or type of premises visited at intermediate points on the journey. Table 2 lists the types of data collected for the two types of trip. A new Excel workbook had to be constructed for this latter category of trips and new algorithms used to calculate KPI values. Given the fundamental differences in the way data for the two categories of trips was collected and analysed, each trip type would have to be benchmarked separately. This 'solution' was less than ideal, but under the circumstances was the best that could be achieved. It met with the unanimous approval of companies at the subsequent briefing sessions. Table 2: Data Requirements for the Two Categories of Trip. Trips with 1-3 legs (leg-specific data) day, trip and leg numbers/identifiers vehicle type weight and volume utilisation postcodes of collection and delivery points land use at collection and delivery points classification of leg distance travelled start, end times and delay details Trips with 4 or more legs (trip data) day trip numbers/identifiers number of legs delivery, collection or 'mixed' trip vehicle type weight and volume utilisation at point of maximum loading classification of trip number of legs on which no load was carried distance travelled with no load total distance travelled on the trip start, end times and duration delay details - worst delay encountered on the trip 3.3 Modification of the Software Substantial changes had to be made to the KPI software to incorporate the various recommendations emerging from the think-tank session. Theses changes were made to the version of the software that was revised between July and September 2000 for repetition of the KPI survey in the food supply chain. This enabled companies to calculate most of the KPI values themselves. (This had not been possible in the 1998 food KPI survey.) The new changes that had to be made to the software, therefore, not only related to data input but to the 'macros' working behind the scene to calculate the KPI values. As part of this reworking of the software, further improvements were made to internal consistency checks and to the interfacing of the software with companies' existing IT systems. One of the main changes to 15

17 the software involved developing two separate Excel workbooks for trips with 1-3 legs and trips with 4 or more legs. 3.4 Revision of Manuals The manuals prepared for the earlier Food KPI survey were substantially revised to incorporate all the changes made to the software. Three manuals were prepared and distributed to participating companies: Manual 1: overview of the KPI Survey Manual 2: explanation of the data collection process Manual 3: advice on inputting and outputting data to and from other data bases 3.5 Briefing Sessions Two briefing sessions were held for participating companies. At these sessions companies were given detailed advice on how to collect the data and enter it into in the Excel workbook. Guidance was also given on how to calculate and interpret the KPI values and how to link the workbook to companies' existing IT systems. 3.6 Date of the Survey The survey was run on the Tuesday 2 nd and Wednesday 3 rd October Survey Statistics A total of seven companies took part in the survey and returned data. Some of the seven companies did not return complete data sets. The most serious omission is a lack of trip data for one company. This is reflected below in the variable sample size for different KPI estimates. A total of 143 tractor units, 343 trailers and 50 rigid vehicles were covered by the survey. Over the 48 hour period they made 679 trips, comprising 2557 journey legs and travelling just under 180,000 kilometres. The split between trips with 1-3 legs and 4 or more legs was as follows: no of trips % of trips no. of legs % of legs kms travelled Trips with 1-3 legs Trips with 4 or more legs Total

18 Trips in the 1-3 leg category comprised an average of 1.7 legs, while those in the 4 or more leg category contained an average of 6.3 legs. The latter trips were also around 64% longer (336 kms as opposed to 207 kms for trips with three or fewer legs.) Figure 2 shows the distribution of trips by number of legs. This distribution is heavily skewed to the left, with the majority of trips comprising fewer than four legs. Leg-specific data was available for 56% of all the trips surveyed, though these trips represented only around 43% of the total distance travelled by the vehicles during the survey period. Figure 2: Distribution of Trips by Number of Legs (total sample of trips) Number of trips Number of Legs 3.8 Analysis and Benchmarking of the Data The data returned by the companies was transferred to a Microsoft Access database for checking and processing. The query and report facilities in Access allowed flexible aggregation and comparison of individual fleet data. In addition, ad hoc reporting was used to identify potentially anomalous data for revision or exclusion from the final reports. Much more detailed analysis was possible on those trips for which leg-specific data had been collected. Most of the results reported in the next section relate to these trips. 17

19 The main benchmark tabulations were produced using complex Access reports which allow side-by-side comparison of the KPIs for an individual fleet with those of the benchmark group. A specimen benchmark report is included in Appendix 1. The reports were set up to allow flexible benchmarking against any desired sub-groupings of companies. As only seven companies took part in the pilot survey, however, there was little scope for subdividing the sample for benchmarking purposes. 18

20 4. Results of the KPI Survey The KPI results have been aggregated for the companies participating in the survey. Most of the results relate to trips comprising 1-3 legs, for which leg-specific data was available. Analysis of KPI values for trips with more than four legs are separately reported. It is not possible to directly compare the KPI values for the two categories of trip as a result of differences in the method of calculation. The KPI results for 1-3 leg trips have been compared with the KPI statistics obtained during the 1998 survey of transport operations in the food supply chain. 4.1 Vehicle Fill Vehicle fill was measured both in volume and weight terms, with separate averages calculated for articulated vehicles and rigids. (The average values quoted below were calculated for all the vehicles surveyed in each type and are not simply the mean of the company average values for each company fleet.) In the case of articulated vehicles, approximately 74% of the available vehicle cube was filled with a load. When expressed in weight terms, the level of utilisation was much lower at 42%. This reflects the fact that average load density is relatively low in the automotive sector with a large proportion of loads 'cubing out' (i.e. occupying all the available space or deck area) before the maximum weight limit is reached. Figure 3: Volume Utilisation on Laden Journey Legs. Utilisation 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% a b c d e f g Fleet Articulated vehicles Artics, all fleets Rigid vehicles Rigids, all fleets 19

21 In terms of volume utilisation, the five companies providing vehicle fill data for articulated fleets split into two categories. Three achieved fill percentages of around 80%, while the other two had values in the range 50-60% (Figure 3). The weight utilisation averages for these five companies showed much greater diversity, spreading quite evenly across the range 63% to 33%, and mainly reflecting differences in the nature of the products carried (Figure 4). Figure 4: Weight Utilisation on Laden Journey Legs. 70% 60% 50% Utilisation 40% 30% 20% 10% 0% a d c b e g f Fleet Articulated vehicles Artics, all fleets Rigid vehicles Rigids, all fleets The vehicle fill estimates for rigid vehicles were substantially lower than for artics. Rigids also differed from artics in having a higher level of weight utilisation than volume utilisation. The cube utilisation average of 11% and weight utilisation average of 17% seem extremely low and may be the result of rigids being used primarily to deliver small quantities on a justin-time basis. Only two of the companies provided data on rigid vehicles and their utilisation ratios varied by a factor of two (Figure 5). 20

22 Figure 5: Volume and Weight Utilisation for Individual Fleets 100% 80% Utilisation 60% 40% 20% 0% a b c d e f g Fleets Volume utilisation, artics W eight utilisation, artics Volume utilisation, rigids W eight utilisation, rigids Comparison with the Food KPI survey: It is possible to compare the weight utilisation averages with those calculated for the food supply chain in 1998 as the method of data collection was very similar. At 56% the average for food industry was 14% higher. This comparison is not very meaningful, however, as both automotive and food products have a relatively low density and most loads are volume- rather than weight-constrained. The comparison of cube utilisation averages is of greater interest, though one must exercise caution in making this cross-sectoral comparison as the methods of assessing volumetric fill differed. In the 1998 food KPI survey deck area coverage was measured separately from average pallet height. The former was estimated to be around 78% while the latter represented around 66% of the available load height. Multiplying these two figures suggested that, on average, approximately half of the available cubic capacity was occupied by a load. In the automotive survey, companies estimated cube utilisation directly. The average figure of 74% for articulated vehicles carrying automotive products is roughly 50% higher than the equivalent cubic utilisation estimate made for the food supply chain. 4.2 Empty Running Approximately 12% of journey legs and 13% of vehicle-kms were run empty. The fact that these proportions are almost identical indicates that empty and loaded trips had similar 21

23 average lengths. The % of vehicle-kms run empty was roughly half the average figure calculated by the government s Continuing Survey Road Goods Transport (CSRGT) for goods vehicles with gross weights over 3.5 tonnes. Further investigation of the automotive survey reveals why the figure is so much lower than the national average. As Figure 6 shows, two of the companies reported virtually no empty running. These were companies which achieved almost full utilisation of backhaul capacity by returning handling units. The return of these units was an integral part of the distribution operation and prevented the collection of any other backhaul loads. The other three fleets had a different pattern of working and did generate a significant amount of empty running. On average, 28% of their journey legs and 34% of their vehicle-kms were run empty, figures much more closely aligned with the CSRGT data. Figure 6: Level of Empty Running % of empty running 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% a b c d e f Fleets % empty legs % empty km Comparison with the 1998 Food KPI survey: This corresponding empty running figures for this survey were 22% of journey legs and 21% of total distance travelled. This survey highlighted the importance of return flows of empty handling equipment in determining the overall degree of empty running. The collection of unit load data in this survey revealed a strong inverse correlation between the 'return of empties' and empty running. This has been demonstrated once again in the KPI automotive survey, admittedly across a much smaller sample of fleets. 22

24 4.3 Time Utilisation Weekly Pattern Although the survey was only conducted over a 48 hour period, companies were asked to record the number of trips made by the audited vehicles over that week. This was done for two reasons: to see if the two survey days were representative of the working week as a whole and to permit aggregation of the results to a weekly level. The total number of trips made each day by the sample vehicles fluctuated only marginally, within the range As predicted by participants in the think-tank session, the amount of delivery work undertaken on the two survey days (Tuesday and Wednesday) was very typical of the working week. The level of activity dropped by just over 50% at the weekend. Daily Pattern Time utilisation was analysed separately for tractor units, trailers and rigid vehicles. This revealed significant differences in the pattern of useage (Figures 7, 8 and 9). In the case of articulated vehicles, tractor units spend more than twice as long running on the road as trailers (55% as opposed to 24%). This partly reflected the 2.4: 1 articulation ratio (i.e. ratio trailers to tractors) and widespread practice of trailers being left at factories / depots for loading / unloading. A surprisingly large proportion of trailer time was, nevertheless, spent 'awaiting unloading / loading'. This figure of 40% seemed very high relative to the proportion of time actually undergoing loading / unloading (7%) or pre-loaded awaiting departure (6%). Rigid vehicles spend a similar proportion of their time running on the road as tractor units. Very little of their time was spent awaiting or under-going loading and unloading (7%). As they were primarily engaged on short-distance, local collection and delivery work, the drivers statutory breaks may be accommodated in the normal scheduling of the vehicle. Under-utilisation of the vehicle asset can be measured by the proportion of time spent idle and empty. This KPI was much higher for rigid vehicles than for artics. For both tractors and trailers it was around 20% (5 hours per day), whereas for rigids it was 36% (8.6 hours per day). One possible explanation of this difference is that rigids are mainly engaged in the local delivery operations which are confined to the working day, in contrast to artics which undertake longer distance trunking operations, often overnight. Analysis of the time utilisation profile for the different classes of vehicle over the 48 hour period indicated, however, that this was not the case (Figures 10, 11 and 12). Half the small fleet of rigid vehicles surveyed undertook deliveries between 2100 and

25 Figure 7: Rigid Vehicles: time utilisation over 48 hour period. idle (empty & stationary) 36% maintenance / repair 0% awaiting unloading / loading 0% running on the road 57% pre-loaded, awaiting departure 4% loading / unloading 3% on the road daily rest 0% Figure 8: Trailers: time utilisation over 48 hour period. idle (empty & stationary) 18% maintenance / repair 3% running on the road 24% on the road daily rest 2% loading / unloading 7% awaiting unloading / loading 40% pre-loaded, awaiting departure 6% Figure 9: Tractors: time utilisation over 48 hour period idle (empty & stationary) 5% maintenance / repair 2% unhitched, stationary 16% awaiting unloading / loading 5% pre-loaded, awaiting departure 7% running on the road 55% loading / unloading 5% on the road daily rest 4% 24

26 The utilisation of trailer capacity was much more stable over the 48 hour period than that of tractor capacity (Figures 11 and 12). The proportion of trailers running on the road peaked between 1000 and 1600, though the degree of peaking was much less than for tractor units. The large proportion of trailers awaiting loading / unloading was fairly constant during the day. There was no evidence that waiting times were longer during the day than during the night or that they were affected by the pattern of shift working in factories or warehouses. Figure 10: Rigid Vehicles - time utilisation profile over 48 hour period Rigid vehicles :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 running on the road (including rest) loading / unloading pre-load, awaiting departure awaiting unloading / loading maintenance / repair idle (empty & stationary) Figure 11: Trailers - time utilisation profile over 48 hour period Trailers :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 1:00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 running on the road (including rest) loading / unloading pre-load, awaiting departure awaiting unloading / loading maintenance / repair idle (empty & stationary) 25

27 Figure 12: Tractor Units: time utilisation profile over 48 hours :00 3:00 5:00 7:00 9:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 1:00 3:00 5:00 7:00 9:00 11:00 13:00 Tractor units 15:00 17:00 19:00 21:00 23:00 running on the road (including rest) loading / unloading pre-load, awaiting departure awaiting unloading / loading maintenance / repair idle (empty & stationary) unhitched, stationary Comparison with the Food Sector In the 1998 food KPI survey, only the time utilisation of trailers and rigid vehicles was monitored. No attempt was made to monitor the activities of tractor units. Table 3 compares the time utilisation of the vehicles surveyed in the food and automotive supply chains. Table 3 Utilisation of Trailer / Rigid Vehicle Capacity over 48 Hour Period % of vehicle time Automotive sector Food sector Running on the road Daily rest period 2 6 Loading / unloading 7 16 Pre-loaded awaiting departure 6 12 Awaiting loading / unloading Maintenance / repair 3 6 Idle (empty / stationary) The description of this category was changed in the automotive survey. In the 1998 food KPI survey the category was called 'delayed or otherwise loaded and inactive'. Some of the discrepancy may therefore be the result of companies differently interpreting these terms. 26

28 Two major differences are apparent: Trailers in the automotive sector spend about 50% less of their time running on the road Trailers in this sector spend much more time awaiting loading and unloading, but much less time actually being loaded and unloaded. This suggests that although the amount of idle time is broadly similar, trailer capacity is much more intensively used in the food sector than in the automotive supply chain. The daily pattern of trailer use was much more variable in the food sector, with the proportion of time spend running on the road showing a much more pronounced peak. This peak also began around several hours earlier in the food supply chain than in the automotive industry (around 0600), mainly as a result of the need deliver stocks of fresh product to supermarkets prior to opening time. 4.4 Deviations from Schedule A third of the journey legs on trips with 1-3 legs were subject to a delay. The main cause of these delays was identified as traffic congestion, accounting for just under a third of the total (Figure 13). This next most important cause was a delay at the collection point, which was responsible for 18% of delays. Delays at delivery points were significantly less common (13%) and roughly equivalent to equipment break-downs and 'own company actions' in their frequency. Figure 13 understates the importance of the congestion problem partly because it makes no allowance for the length of the delay. Delays attributed to congestion averaged around 35 minutes, five minutes longer than the mean delay for all causes (Figure 14). Although lack of driver and equipment breakdown were responsible for longer average delays, they occurred much less frequently. Overall, congestion accounted for roughly half of the total delay to transport operations over the 48 hour period. Even this estimate is unlikely to measure the full impact of congestion on delivery efficiency as companies normally take traffic conditions into account in planning their delivery schedules. 27

29 Figure 13: Causes of Delay: % of journey levels affected by delay collection point problem 6% delivery point problem 4% traffic congestion 10% no delay 67% delays 32% equipment breakdown 4% own company actions 4% lack of driver 5% Figure 14: Average Duration of Delay by Cause minutes lack of driver own company actions collection point problem delivery point problem traffic congestion equipment breakdown Comparison with Food KPI Survey Only the schedule deviation data for the 1-3 leg trips is comparable to that collected in the 1998 Food survey. The proportion of journey legs subject to a delay was 7% higher in the automotive survey (32% as opposed to 25%). The proportion of delays blamed on traffic congestion was also 8% higher at 31% (Table 4). These differences in the relative importance of traffic congestion are statistically significant and may be attributable to differences in the 28

30 geographical pattern and scheduling of deliveries and the increase in traffic congestion between 1998 and Table 4: Percentage of Journey Legs Delayed by Cause. Automotive (2001) Food ( ) Traffic congestion Delivery point problem Collection point problem Own company action 4 13 Lack of driver 16 2 Equipment breakdown Energy Efficiency: Table 5 shows the mean and range of average fuel efficiency values for the seven fleets, differentiating four vehicle types. Table 5: Mean Fuel Efficiency (km per litre) Vehicle class mean km per litre range of values small rigid, <=7.5 t, 2 axles medium rigid, <=18 t, 2 axles tonne articulated tonne articulated There was a much greater variation in fuel efficiency values for rigid vehicles than for artics (Figure 15). This is possibly due to the classification of these rigid vehicles into broader weight classes (small rigid tonne / medium rigid tonnes) than the artics. The mean values for each class of vehicle are broadly in line with the average fuel efficiency figures compiled by the CSRGT 3. Only in the case of small rigid vehicles did the average fuel efficiency deviate significantly from the CSRGT mean, being around 20% higher (Table 6). 3 Department of Transport, Local Government and the Regions Transport of Goods by Road in Great Britain London,

31 Table 6: Average Fuel Efficiency: comparison of KPI survey and CSRGT data Vehicle class (KPI) km per litres Vehicle class (CSRGT, 2000) km per litre Small rigid <=7.5 t 2 axles 5.4 <= 7.5 t 4.5 Medium rigid <=18 t 2 axles t tonne articulated 3.1 <= 33 tonnes tonne articulated 3.0 > 33 tonnes 2.7 Figure 15: Average Vehicle Fuel Efficiency by Fleet kilometres per litre small rigid <7.5 t medium rigid <18 t 32 tonne artic 38 tonne artic Fuel efficiency, expressed in terms of litres per km travelled, is only a partial measure of energy use in the freight transport system. It is, after all, possible for companies to run their vehicles fuel efficiently but make poor use of their carrying capacity, with the result that relatively large amounts of energy are consumed per unit of freight movement. Energy intensity is a composite measure of fuel efficiency and vehicle loading, usually expressed as milli-litres of fuel consumed per tonne-km. Given the low density of loads in the automotive sector, it is more meaningful to express the amount of freight movement in terms of volume rather than weight. For this reason a new energy intensity index has been developed for this survey: millilitres of fuel consumed to move 1 cubic metre of goods a 30

32 distance of 1 km. Tonne-km based energy intensity indices have also been calculated for each company. These energy intensity indices are calculated as follows: 1. Weight-based measure: the energy intensity is the amount of fuel consumed to move one tonne of goods a distance of one km. For a collection of legs, Energy Intensity (weight) = Sum of fuel used in each leg / sum of tonne-kms on each leg 2. Volume-based measure: the energy intensity is the amount of fuel consumed to move 1 cubic metre of goods a distance of 1 km. For a collection of legs, Energy Intensity (weight) = Sum of fuel used in each leg / sum of freight m 3 -km on each leg Table 7 shows the mean energy intensity values for the four vehicle types. Figures show the variations in energy intensity values between the fleets surveyed, again differentiated by vehicle class. One operator of a 32 tonne articulated fleet achieved a volume-based energy intensity value approximately twice that of the other. When measured with respect to tonne-kms the difference was much smaller but still significant. Benchmarking other fleets of similar vehicle type also revealed significant differences in energy intensity (based on both weight and volumetric measures), with the exception of the two fleets of medium-weight rigids which used almost identical quantities of fuel per tonnekm. Table 7: Energy Intensity Values for Four Main Vehicle Classes Vehicle class Energy intensity (volume) ml fuel per m 3 -km Energy intensity (weight) ml fuel per tonne-km small rigid medium rigid tonne artic tonne artic

33 Figure 16: Volume-based Energy Intensity Values for Rigid Vehicles ml fuel to move 1 cubic metre 1 km y z Fleets medium rigid small rigid Figure 17: Weight-based Energy Intensity Values for Rigid Vehicles 600 ml fuel to move 1 tonne 1 km x y z Fleets medium rigid small rigid Figure 18: Volume-based Energy Intensity Values for Articulated Vehicles ml fuel to move 1 cubic metre 1 km q s p t Fleets 32 tonne artic 38 tonne artic 32

34 Figure 19: Weight-based Energy Intensity Values for Articulated Vehicles ml fuel to move 1 tonne 1 km p q r s t Fleets 38 tonne artic 32 tonne artic By graphing energy-efficiency and fuel efficiency values for each fleet it is possible to assess the extent to which companies with good fuel economy also achieve high load factors (Figures 20 and 21 - these figures have a logarithmic scale). Given the very small sample size it is not possible to analyse this relationship statistically. If, however, one examines the two 32 tonne artic fleets in Figure 20, you can see that the one with the higher km per litre figure also has a much lower energy intensity value. It appears to be reinforcing it fuel efficiency advantage by also attaining higher levels of vehicle fill. (It should be noted that to increase fuel efficiency the km per litre figure should rise, while to improve the energy intensity, the fuel consumed per freight m 3 - km index should fall.) Comparison with the Food KPI survey. The mean fuel efficiency of articulated vehicles in the 1998 food and 2001 automotive surveys was very similar (Table 8). For 32 tonne articulated vehicles, the ranges were also similar. As in the automotive survey, inter-fleet variations in fuel efficiency were much greater for rigid vehicles than for artics. Table 8: Comparison of Mean Fuel Efficiency (litres per km): 32 tonne articulated 38 tonne articulated mean range mean range Automotive survey (2001) Food survey (1998)

35 Figure 20: Relationship between Vehicle Fuel Efficiency and Energy Intensity (volume-based) ml fuel per cubic metre-km (log scale) kilometres per litre 5.7 small rigid 3.5 medium rigid tonne artic tonne artic Figure 21: Relationship between Vehicle Fuel Efficiency and Energy Intensity (weight-based) ml fuel per tonne-km (log scale) kilometres per litre 5.7 small rigid medium rigid tonne artic tonne artic 34

36 It is not possible to compare the energy intensity estimates made by the two surveys because of differences in the measurement of freight loads. As explained earlier, the 1998 food KPI survey collected data on the numbers of (industry-equivalent) pallets carried over the 48 hour period. Energy intensity was therefore measured in terms of fuel consumed per pallet-km. As no account was taken of the height of the pallets in this index, this was essentially a twodimensional measure of vehicle loading and quite different from the freight cubic metre index adopted in the automotive survey. As no basis exists for converting pallet-kms into cubic metre kms or vice versa, the two sets of energy intensity values cannot be compared. 4.6 Trips with More than Four Legs Around 45% of the trips monitored over the 48 hour period comprised four or more legs. No leg-specific data was collected for these trips. This seriously limited the scope for analysis. The only two KPIs for which meaningful values can be calculated for these trips are vehicle utilisation and deviations from schedule. Differences in the methods of calculating the KPIs for the two categories of trips makes it impossible to aggregate the two data-sets and very difficult even to compare the two sets of values on a consistent basis. Vehicle Utilisation Instead vehicle utilisation (by weight and volume) was measured only at the point on the trip where the maximum load was being carried. For delivery rounds, maximum loading generally occurred on the initial leg outbound from the factory or warehouse, while on collection rounds it was on the final leg. Very few of the multiple-drop trips with four or more legs combined deliveries and collections. In the absence of leg-specific data, no indication is given of the size and weight of load collected / delivered at each intermediate point on the route or of the distances between these points. Under these circumstances, average volume and weight utilisation can be caculated in two ways: 1. Estimate the % of available carrying capacity (by volume and weight) used at the point of maximum loading (corresponding to the initial leg on delivery rounds and the final leg on collection rounds). For trips with 4 or more legs, this yielded an average volume utilisation value of 54% and average weight utilisation of 40%. For peak load values, these averages appear relatively low. It should be noted, however, that the loading of vehicles on multiple drop/collection rounds is often restricted by the driver's daily shift and scheduling constraints at the delivery points. More of the driver's time is spent 35

37 loading and unloading the vehicles and liaising with staff at reception bays. Some delivery rounds adhere a regular routes, with the loading on particular days determined by the size of orders requested by or from the various premises on the route. The vehicle must have enough capacity to accommodate the maximum combined order likely to arise on a particular day. Depending on the degree of order variability, the average utilisation can fall well below the maximum. The adoption of more flexible route planning can help to raise this level of utlisation. 2. Assume that the initial load for delivery is dispersed evenly among the various points on the route and that these points are equidistant. (For collection rounds, equal amounts would be collected at each point). On this basis it is possible to estimate the average utilisation across all the legs on the trip. This calculation produced an average volume utilisation figure of 24% and average weight utilisation value of 15%. In practice, orders vary in size and weight, and larger / heavier consignments are often dropped off early in the round. Where this occurs, method 2 will tend to over-estimate the level of utilisation. This raises an important issue about the measurement of vehicle utilisation. To maximise the % use of vehicle carrying capacity, it would be desirable to deliver the largest / heaviest loads last. This would ensure that the vehicle travelled the maximum distance with the maximum load. It would, however, be the least energy efficient option. Great care must therefore be exercised in interpreting these utilisation statistics and ensuring that the chosen indicators do not encourage 'perverse' route planning. Vehicle utilisation levels are likely to be intrinsically low for multiple drop operations, as the lorry is gradually emptying as the trip proceeds. Only by combining collections and deliveries on a single round is it possible to raise the utilisation level substantially. As noted earlier, there was little evidence of this in the sample of trips with four or more legs. Deviations from Schedule This KPI had to be redefined for trips with four or more legs. The incidence of delays was measured at a trip rather than leg level and the length of delay recorded at the point on the trip at which the delay was at its greatest. (On trips with 1-3 legs, the actual delay on each leg as measured). Approximately a quarter of the 302 trips with four or more legs were subject to a delay and the average length of this delay 'at its worst point' was 43 minutes. 36

38 This figure cannot be directly compared with delay estimates for 1-3 leg trips, because it is not known how the average delay was distributed among legs or how much time could be 'made up' on subsequent legs. It is possible, however, to compare the relative importance of the various causes of delay (Table 9). The prevalence of the two main causes of delay (collection / delivery problems and traffic congestion) is similar for the two samples of trips. There are marked differences in the relative incidence of other causes of delay. The comparison is distorted, however, by the large proportion of delays to '4 or more' leg trips attributed to 'multiple causes'. Where delays occur at a trip level, they can be caused by a variety of factors, many of them interacting. It can be very difficult to isolate particular causes and assess their relative importance. As both collection/delivery problems and traffic congestion are likely to be implicated as 'multiple causes', their individual percentages are likely to under-estimate their disruptive effect on multiple drop / collection rounds in the automotive sector. Table 9: Main Causes of Delays: Comparison of Different Types of Trip Cause % of trips delayed (trips with 4 or more legs) % of legs delayed (trips with 1-3 legs) Collection / delivery problem Traffic congestion Multiple causes 24 - Own company actions 4 13 Vehicle breakdown 3 13 Lack of a driver 0 16 % of trips / legs delayed 25% 32% 37

39 5. Concluding Remarks Several important lessons have been learned from this pilot KPI survey in the automotive sector. 1. The benchmarking of vehicle utilisation and energy intensity in the automotive sector is inherently much more difficult than in the food sector because of the diversity of handling equipment and large volume of non-unitised product. Most loads comprise relatively low density products, making volumetric measurement is much more important than weightbased measures. As relatively few companies record accurate data on the volume of consignments, there is heavy reliance on subjective assessment of cube utilisation. The accuracy of the KPI estimates is therefore likely to be significantly lower than in the food industry, where unitised load data was much more readily available. 2. The decision not to collect leg-specific data for trips with four or more legs should be seriously reconsidered in any future KPI survey. The absence of this data severely constrains the analysis and makes it difficult to compare companies' transport operations on a consistent basis. The concession on more complex multi-drop/collection trips was made to secure the involvement of several companies. There is a clear trade-off between the desire to maximise sample size and the richness of the survey data. If it is decided in future KPI surveys to allow companies to submit non-leg-specific trip data, it will be advisable to set the maximum number of legs for trips with leg-specific data at a higher level. In the present survey it was set at four. The average number of legs on trips with more than four legs was only 6.3, however. As shown in Figure 2 (page 17), the distribution of trips by number of legs is heavily skewed to the left, with only 7% of trips comprising eight or more legs. Had the threshold been set at six legs rather than four, 77% of the trips surveyed, as opposed to 55%, would have yielded leg-specific data, permitting much more detailed analysis and much more effective benchmarking. 3. It may be necessary to provide companies with more advice on how to organise the survey internally. Based on the experience of companies contributing to the pilot survey, one could devise an outline procedure that companies might follow. A key element in this procedure would be the briefing of the staff actually undertaking the data collection 38

40 4. This pilot survey in the automotive sector was significantly smaller in terms of the number of fleets and vehicles than the equivalent survey which launched the KPI initiative in the food sector. A full survey in this sector, of comparable size to that undertaken in the food industry in 1998, will require stronger backing from trade bodies and the firm co-operation of several major car assembly companies and logistics providers active in this sector. 39

41 Appendix 1: Sample benchmark report Automotive KPI 2001 Report :Trips with 1 to 3 legs - Summary 17/03/02 Fleet Test1 Benchmark Group: Group1 Number of legs: Number of trips: Total distance: km km (Avg) Legs per trip: (Avg) kms per leg: 98.5 km km (Avg) kms per trip: km km Utilisation Empty running - number of legs: 23 [ 20% ] 282 [ 43% ] - total km: 1278 [ 11% ] [ 28% ] Volume utilisation*: 51% 74% Weight utilisation*: 50% 41% * For laden legs, utilisation = [carried capacity] / [maximum capacity], weighted by distance i.e. for volume = SUM(m³ x km) / SUM(max m³ x km), for weight = SUM( tonne x km ) / SUM(max tonne x km ) Low Utilisation running - number of legs where: volume carried < 50% max 48 [ 42% ] 97 [ 15% ] weight carried < 50% max 77 [ 68% ] 322 [ 49% ] Low Utilisation running - number of km where: volume carried < 50% max 4181 [ 37% ] [ 15% ] weight carried < 50% max 6286 [ 56% ] [ 61% ] High utilisation running - number of legs where: volume carried > 90% max 1 [ 1% ] 140 [ 22% ] weight carried > 90% max 8 [ 7% ] 9 [ 1% ] High utilisation running - number of km where: volume carried > 90% max 427 [ 4% ] [ 30% ] weight carried > 90% max 790 [ 7% ] 933 [ 1% ] Energy Intensity 1. ml fuel used per 2. ml fuel used per tonne-km delivered cubic metre-km delivered Fleet identifier Benchmark Group: Fleet identifier Benchmark Group: Test1 Group1 Test1 Group1 small rigid: small rigid: 86.3 medium rigid: medium rigid: large rigid: large rigid: draw bar: draw bar: city semi-trailer: city semi-trailer: 32 tonne semi: tonne semi: semi: semi: All vehicle types All vehicle types

42 Acknowledgements We are very grateful to all the companies which participated in this pilot survey and to the individual managers who attended the various meetings and took part in interviews during the period of the survey. The Society of Motor Manufacturers and Traders was also helpful in stimulating interest in the project among its members. Special thanks go to Bob Markham, now with the Transport Development Group, who acted as our KPI 'ambassador' championing the cause of KPIs across the automotive industry, Pete Dance of Nissan who gave us the benefit of his accumulated knowledge and experience of transport KPIs in this industry and to Andrew Davis of Future Energy Solutions (AEA Technology) who acted as project manager. Finally, we acknowledge the support of the Department of Transport, Local Government and the Regions which funded the project as part of its Energy Efficiency Best Practice Programme. Professor Alan McKinnon Duncan Leuchars 41