Benefits assessment of investments in research projects is increasingly becoming important to funding agencies and stakeholders in order to demonstrate the effectiveness and benefits of funded projects and to provide guidance for future project appraisal and selection. This paper describes the development of a multi-criteria benefits assessment framework that is based on systems approach and developed using empirical data from rural road improvement projects funded by the UKAid. The framework is a scoring system where the outputs are presented as a report card or scorecard with scores and ratings assigned to the various subsystems. The scores represent the levels of benefits derived from conducting the research and implementation of products generated thereof. The framework is flexible enough to estimate the benefits for each subsystem independent of the other subsystems. The system can also be used as a decision support tool, providing quantitative information to validate funding and management decisions.
Policy, management, and technical decisions affecting highway infrastructure, truck operations, and regulatory enforcement, among other factors affecting the trucking industry require information on the types and operating characteristics of heavy vehicles. This information is contained in the set of regulations governing the sizes and weights of these vehicles and is traditionally provided in print medium. The results of a novel attempt to computerize these regulations and store them in a data base are presented. The platform used for the data base is user friendly, flexible, easily accessible, and interactive; sketches of vehicles can be included and information can be protected through definition of user passwords. It is demonstrated that even though industry regulations are complex, specific to jurisdiction, and varied in a number of respects, it is possible to capture the most important variables in a data base. It is also possible to enhance the utility and scope of the data base by interfacing with a geographic information system and to include other variables, such as the dynamic and operational performance attributes of heavy vehicles. The data base has limitations, among them that it cannot capture all descriptive details of the set of regulations and that it serves as an information base only and cannot be used as a legal document.
Recovery of agency costs in the provision of highway infrastructure is achieved through the process of highway cost allocation. Cost responsibilities are usually assigned to various highway users in terms of the relative consumption of the infrastructure. The implications of knowledge of dynamic wheel load for assigning cost responsibilities is presented. Pavement and vehicle characteristics are shown to have noticeable effects on the cost shares between different vehicle configurations and operating weights. Heavy vehicles equipped with road friendly air suspension systems are responsible for joint pavement costs that are about five percent less than those equipped with steel spring suspensions when operated at similar speeds, payload and on identical pavement conditions. Given that road and bridge–friendly suspension systems are becoming increasingly popular, cost allocation in terms of dynamic loads provides a fair basis for assigning costs to heavy trucks.
An improved methodology for evaluating infrastructure impacts and trucking productivity of different truck types operating under alternative truck weight limits and enforcement strategies is presented. The procedure accounts for the effects of enforcement on weight distributions in an objective manner allowing the consequences of adopting alternative weight control schedules to be determined. The methodology resolves some major uncertainties surrounding pertinent input variables required in evaluating truck weight regulatory policies. Key words: weight limit, enforcement, evaluation, pavement loading, payloads.
The paper addresses an interesting issue: providing a means of selecting routine-maintenance options based on the rough ness-progression profiles. The discusser discusses some short comings relating to the roughness modeling and maintenance effectiveness indices. In the paper, maintenance effect is modeled in terms of roughness as a function of age, traffic loading, and environ ment based on field data. Modeling roughness is much more complex than presented in the paper; moreover, some im portant variables that determine the roughness of a pavement are not included. In capturing all the contributing mecha nisms, the structured empirical approach includes the inter active terms of structural, surface-defect, and environmental effects in which roughness is modeled as a function of (I) structural deformation (function of modified structural num ber, traffic loading, etc.); (2) surface defects (function of changes in cracking, patching, and potholing); and (3) en vironmental and nontraffic-related mechanisms (function of pavement environment, time or age, and roughness) (Pater son 1987). In the more common generalized-structural-per formance model, roughness is expressed as a function only of traffic loading, pavement structural number, age, and en vironment. The surface-distress component is excluded. In either case, the structural capacity of the pavement and the supporting subgrade indicated by the modified structural number is one of the independent variables, because it influ ences pavement performance to a considerable extent. The authors presented a model that is similar to the gen eralized structural model but excludes the modified structural number of the pavement. Although it is stated that all pave ment sections used in the analysis are of the same type (flex ible pavement), it is not explained that the structure of the pavements (the thicknesses of and material types for the var cious pavement layers) and the subgrade conditions in all the
Policy, management, and technical decisions affecting highway infrastructure, truck operations, and regulatory enforcement, among other factors affecting the trucking industry require information on the types and operating characteristics of heavy vehicles. This information is contained in the set of regulations governing the sizes and weights of these vehicles and is traditionally provided in print medium. The results of a novel attempt to computerize these regulations and store them in a data base are presented. The platform used for the data base is user friendly, flexible, easily accessible, and interactive; sketches of vehicles can be included and information can be protected through definition of user passwords. It is demonstrated that even though industry regulations are complex, specific to jurisdiction, and varied in a number of respects, it is possible to capture the most important variables in a data base. It is also possible to enhance the utility and scope of the data base by interfacing with a geographic information system and to include other variables, such as the dynamic and operational performance attributes of heavy vehicles. The data base has limitations, among them that it cannot capture all descriptive details of the set of regulations and that it serves as an information base only and cannot be used as a legal document.
Purpose The purpose of this paper is to use empirical data to examine the impacts of integrating sustainability elements on the performance of supply chains of manufacturing small and medium scale enterprises (SMEs). Design/methodology/approach The conceptual framework was based on the systems theory and the triple bottom line concept. Purposive sampling approach was used to collect data from a cross-section of manufacturing SMEs. Partial least square (PLS) structural equation modeling (SEM) approach was used to explore the relationships among the constructs. Findings The results indicate strong statistically significant positive relationships between each of the three sustainability elements and integration constructs. Sustainability integration is a mediating variable that explains a significant variance in performance of a supply chain. Supply chain performance is determined by the degree of integration of the three sustainability elements. Research limitations/implications The research focused on SMEs in the manufacturing industry in a less developed economy. An extension of the findings to the service industry and larger manufacturing firms and different operating environments may be limited. Practical implications Sustainability integration enhances supply chain performance and can be a competitive tool for manufacturing SMEs. The research emphasizes the value of sustainability integration into supply chains of manufacturing SMEs in less developed countries. Originality/value This is an original research that examined the impacts of sustainability integration on performance of supply chains of manufacturing SMEs in a developing economy. This research used empirical data to establish that integration of the three sustainability elements collectively acts as a critical mediating variable that determines the performance of a supply chain. The research also demonstrates the use of PLS-SEM to analyze supply chain attributes that cannot be directly measured.
The relevance and important implications of regulations governing the sizes and weights of heavy vehicles in highway infrastructure management are presented. A procedure for evaluating the infrastructure impacts, trucking productivity, and highway cost-allocation implications of alternative truck weight limits and enforcement options is presented. The procedure uses a weight-prediction methodology that resolves some major uncertainties from the regulatory standpoint respecting input variables required to provide the technical basis to support policy regulatory and infrastructure management decisions. The regulations represent the core of transport policies relating to trucking productivity, infrastructure provision, and management. Any revisions in the size and weight limits are reflected in truck fleet, operating weights, and volumes, which in turn affect the infrastructure geometric requirements, loadings, maintenance, and rehabilitation intervention levels. The proposed evaluation approach allows regulatory and weight-control policies that are compatible with the existing infrastructure capabilities to be developed.
Analysis of trends in truck fleet mix and the relative productivity and operational characteristics of the five– and six–axle tractor–semi–trailers (342 and 3–S3) are presented. Marked increases in the use of 3–S3 at the expense of the 3–S2 are observed. The percentage of 342 in the heavy truck fleet dropped from about 70% in 1991 to about 50% in 1994, while the percentage of 343 increased from 9% and 20% over the same period. These changes could be explained by: better operating efficiency measured by the potential pavement damage per unit payload; flexible payload handling capability; and higher productivity indicated by the potential payload capacity actually utilised. Current trends in fleet mix changes suggest that the rate of increase in the percentage of 343 in the truck fleet is likely to be maintained in the next few years. Possible implications for trucks operations under North American Free Trade Agreement is that the 3–S3 offers a clear productivity advantage over the 3–52 and therefore suitable for long haul operations.
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