Canada Transportation Act Review
Relationship between e-business, Advanced Transportation Logistics, and Canadian Industrial Economic Performance
FINAL REPORT
REFERENCE NUMBER: T8080-00-1335
Submitted by:
HLB Decision Economics Inc.
Ottawa, Ontario
May 2001
HLB Reference: 6658
2 Benefits Estimation Framework
3 Meta-Analysis of Logistics Response
4 Implications for the Canada Transportation Act
Appendix A. Micro-Economic Model of Benefits
Appendix E. Logistics Elasticities
Summary Figure 1: Effect of transport improvements on logistics costs.
Figure 2: Relationship between total logistics cost and number of warehouses.
Figure 3: Generalized cost trade-offs for transportation services.
Figure 4: Basic inventory cost trade-offs.
Figure 5: Inventory levels under a fixed order quantity-variable order interval policy.
Figure 6: Transportation and the economy.
Figure 7: Demand curves for transportation.
Figure 8: Logistics savings as a function of travel time improvements.
Figure 9: Minimum logistics cost versus transit time for various levels of reliability.
Figure 10: Optimal fill rate as a function of delivery variation.
Figure 11: Logistics savings and benefits as a function of transport cost reductions.
Figure 12: Micro-economic framework model structural logic diagram
Table 1 - Empirical elasticities of logistics cost with respect to travel time improvements.
Table 2: Logistics costs as a result of 1% transport cost savings.
Table 3: Elasticities of total logistics cost with respect to transit time
Table 4: Elasticities of total logistics cost with respect to travel time variation.
Table 5: Elasticities of fill rates with respect to travel time.
Table 6: Elasticities of fill rates with respect to travel time variation.
Logistics has moved to centre stage as a result of trading agreements such as NAFTA, a continued explosion of computer and information technology, the further development of global markets resulting in a large number of companies with operations worldwide, and a corporate emphasis on quality and customer satisfaction.
This paper explores the relationship between e-business, advanced transportation logistics and growth in industrial productivity in the Canadian economy. It examines the direct and indirect effects of investment in transport infrastructure and info-structure on productivity growth. To do this, we provide a framework for measuring both direct and indirect economic benefits from improvements in Canada's transportation system.
Conventional cost-benefit analyses consider direct savings from transportation improvements. As a result of substituting various logistics inputs, so-called re-organization effects can occur within a supply chain at the level of the firm. Re-organization benefits might arise from road improvements due to redesign of logistics systems including relocation of plants, warehouses, changes in stocking policies, and different markets. Traditional cost-benefit analysis has not captured the value of these indirect benefits.
The paper concludes as follows:
· Indirect benefits are real. Previous case studies have clearly documented the effects of transportation improvements on logistics and service levels. By recognizing trade-offs between transportation, inventory, warehousing, and information costs, it is possible to lower logistics costs and improve logistics productivity. The benefits of re-organization are incremental to direct savings in vehicle operating costs and value-of-time savings.
· Benefits are now measurable. A micro-economic framework within which to measure freight related economic benefits of transportation improvements has been developed. It has recently been peer reviewed by an expert panel and has gathered support within the academic community. The framework is an expression of the theory, concepts and principles of developments in transportation economics.
· Benefits are significant. As shown at Summary Figure 1, transport system improvements, changes in policies and/or regulations resulting in 10% reduction in transportation costs to shippers could yield an estimated 14.5% reduction in overall logistics costs. This estimate demonstrates that significant benefits from logistics re-organization can be realized beyond gains from lower unit transportation costs.
Summary Figure 1: Effect of transport improvements on logistics costs.
· Benefits vary across industries. Since industries are structured differently with different cost structures, the scale of potential gains from transport system improvements and policy changes vary. Logistics intensive, and in particular transport intensive industries with low inventory turnover rates, poorly integrated supply chains, and large market areas likely have the most to gain from transport improvements.
HLB is currently engaged in conducting a survey of Canadian firms to provide further insights on the importance of transportation system performance on logistics and productivity across various industries.
The level of capital investment required to generate significant and system-wide transport cost reductions is not fully known. What is known however is that transportation efficiency results in compounded logistics efficiency. Although the degree of compounding depends on the propensity of firms to take advantage of transportation improvements by optimising their supply chain, these dynamics may not be fully considered in policy decision making. Therefore, without this consideration, there is likely to be under-investment in transportation in Canada.
"Supply and transport stand or fall together; history depends on both"
Winston Churchill
Logistics has moved to centre stage as a result of trading agreements such as NAFTA, a continued explosion of computer and information technology, the further development of global markets resulting in a large number of companies with operations worldwide, and a corporate emphasis on quality and customer satisfaction.
To what extent have recent developments in e-business and advanced logistics contributed to industrial productivity growth? Are investments in transport infrastructure and info-structure sufficient to eliminate barriers to such productivity growth? In this paper, we explore these issues and provide a framework for measuring benefits as a result of improvements in Canada's transportation system.
With remarkable growth of e-business and advances in supply chain management, there is a need to better understand their effects on the Canadian economy. Demands for new and improved transport services and capabilities are emerging as a result of supply chain integration and new business arrangements between suppliers and customers. Not only does Canada have to offer increasingly better products at competitive prices, it must have the means to deliver and distribute these products faster and more reliably across the supply chain, both domestically and internationally.
Since 90% of domestic freight and 70% of CA-US trade (by value) depends on truck transportation, we focus in this paper on benefits of highway improvements. The concepts however extend naturally to all modes of transport either singly or through inter-modal connectivity. This study is not an in depth analysis of costs and productivity. The report takes a strategic level view of the relationship between transportation and productivity by developing concepts illustrated with examples.
The report is organized as follows. Following this introduction discussing trends and situating the problem, Section 2 describes a micro-economic framework for quantifying benefits from changes in transportation system performance including the effects of logistics response. Section 3 examines previous work attempting to measure such benefits. Using best available data, aggregate benefits are estimated under various scenarios. The report concludes with an assessment of logistics effects and potential productivity gains as they relate to a renewed Canada Transportation Act. Supporting and technical material can be found in the appendices.
1.1 Developments in Supply Chain Management
According to a survey of more than 100 logistics service providers conducted by McKinsey, the best performers are investing in highly integrated systems across internal and external supply chains [1]. The survey confirmes that IT expenditures alone cannot create or maintain competitive advantage. High performing firms use planning and scheduling systems known as enterprise resource planning systems (ERP). These systems provide functions for optimizing warehouse management, order and inventory management and transport load planning. IT investments have enabled significant increases in inventory turns, resulting in reduced overall costs and better service. Potential future gains provided by ERPs are now increasingly limited by capabilities of transportion systems.
A case can be made for supply chain design as a foundation for business success. Supply chain management is a discipline that focuses on the integration of suppliers, factories, warehouses, distribution centres, and retail outlets so that items are produced and distributed to the right customer, at the right time, at the right place, and at the right price. The goal is generally to reduce costs while at the same time improving service levels. There are many components involved - all of which reflect on cost and service level. The focus is not on a specific cost component such as reducing inventory, but rather on minimizing system-wide costs at given service levels.
Supply chain integration is key to reduce costs and increase service levels. From this perspective, the info-structure can be seen as important as the infrastructure. Given that there are many parties with different and sometimes conflicting objectives, finding the optimal strategy across the whole supply chain is a huge challenge. The overall objective of integration is to reduce cost much more than would be possible by allowing each party in the supply chain to find their optimal policy. There is a shift away from "sequential" optimization to "joint" optimization. Strategic partnering between suppliers and buyers has enabled strategies such as Quick Response, Continuous Replenishment; Collaborative Planning, Forecasting, and Replenishment (CPFR); and Vendor Managed Inventory (VMI). The value of each of these strategies, based on information and collaboration to better match supply and demand is difficult to estimate. Here, we focus on one element of logistics and the supply chain - transportation.
The Council of Logistics Management defines logistics as "the process of planning, implementing and controlling the efficient, cost-effective flow and storage of raw materials, in-process inventory, finished goods and related information from point-of-origin to point-of-consumption for the purpose of conforming to customer requirements". Large sums of money are tied up in logistics-related activities. Unfortunately, there are no official statistics of the logistics industry [2] and widely differing estimates of the industry's size exist. McDonald (195) cites 1992 government estimates that the total cost of logistics in Canada was $40.7 billion or 7.3% of GDP (a conservative estimate). Of this, freight transportation accounts for approximately 40% with the remainder mostly associated with inventory costs and warehousing. A small reduction in transportation costs will not only generate transportation cost savings but may also alter the balance of inventory, warehousing, and distribution. There is clearly much at stake in effective management of the supply chain.
Our main question is therefore how does transportation play into supply chain effectiveness and in particular overall economic performance. Benefits realized as part of transportation improvements go beyond conventional savings, something we set out to estimate in this paper.
Some six to seven years ago, transportation executives would have led supply chain design. Companies now recognize that a much broader involvement is required. Operations executives monitor inventory, transportation, and production. Sales and marketing are also involved by forecasting demand and can analyse impact of service level on sales and, therefore, on revenue. When we seek information and data on the effects of transportation improvements, the influences can be propagated at many levels. Since each firm has its own logistics and supply chain network, we must seek information at the firm level on potential benefits that can be realized. It is not possible to fold production, inventory, and transportation into one equation for anything but the firm or specific segments of the supply chain.
At the strategic level, firms will design their logistics network as a function of markets and transport infrastructure. They must determine how many warehouses should be maintained, where should they be located etc. Some of these decisions will be influenced by inter-modal facilities and capabilities.
Not all industries will respond to transportation system improvements in the same way. In the soft-drink industry, where transportation costs are huge but production costs are not that high, they may focus on minimizing transport distances. Reductions in transport costs may allow them to consolidate warehouses and reduce inventory. In PC manufacturing, where production costs are high and matching supply and demand with higher service levels is a priority, timeliness and reliability are more critical. For these reasons, any study on logistics productivity must be done systematically at the level of the firm.
The logistics services industry strives to ensure an efficient flow of products through the supply chain [4]. With deregulation, technological change and the greater integration of production and distribution across national boundaries, logistics services continue to grow in importance. With logistics costs accounting for a significant share of production costs, firms have much to gain in controlling these costs. In the manufacturing sector, domestic logistics costs as a share of production costs vary from 2% to 10.5% [5]. Logistics costs for food products in Canada accounts for nearly $3 billion. Significant leverage is provided by logistics cost reductions. For instance, if net profit on sales is 2.0 percent, then a savings of $2,000 in logistics costs is equivalent to a sales increase of $100,000.
Changes in Logistics Network Infrastructure
A firm could re-organize its logistics in many ways as a result of lower transportation costs. For one, it could reduce the number of warehouses and thereby increase the use of transportation services. Four factors influence the number of warehouses a firm chooses to maintain: cost of lost sales, inventory costs, warehousing costs, and transportation costs.
Cost of Lost Sales. The cost of lost sales is the most difficult to quantify. It would generally decrease with number of warehouses and would vary by industry, company, product, and customer. The remaining cost components are more consistent across firms and industries.
Inventory Costs. Inventory costs increase with the number of warehouses because firm maintain a safety stock of all (or most) products at each facility. More total space is required overall.
Warehousing Costs. More warehouses mean more space to be owned, leased or rented. Fixed costs across many facilities are larger than the marginal variable costs of fewer locations.
Transportation Costs. Transportation costs initially decline as the number of facilities increases due to proximity. Costs eventually increase for too many warehouses due to the combination of inbound and outbound transport costs.
A firm seeking to minimize total costs (the sum of the above components) could balance all cost components by solving a multi-facility location problem. As transportation costs decline however - possibly due to highway infrastructure investment, the minimum total cost will in general occur for fewer warehouses. The nature and timing of re-organization will occur at different points for each firm. Sufficient potential gains will need to be realized before an investment hurdle rate is exceeded.
Figure 2: Relationship between total logistics cost and number of warehouses.
A simpler more rapid response to lower transportation costs, improved transit times and reduced delivery time variability is a change in a firms' inventory policy. To demonstrate the direct relevance of travel time and travel time variability on total logistics costs, consider a simple example where a firm has a central production plant and a single warehouse located within its market area. Total costs are related to component costs, notionally, as shown at Figure 3.
Figure 3: Generalized cost trade-offs for transportation services.
As direct transportations costs decrease, the minimum total logistics cost point moves to the right. A profit maximizing firm would increase the demand for transportation services.
An increase in travel time and transit variability can be costly. Most obviously, money tied up in inventory isn't earning interest. The longer it takes to ship perishable goods (e.g., fresh fruit and vegetables, newspapers and magazines, high-fashion clothing), the more they depreciate. It's the near elimination of travel-time variability that makes just-in-time inventory management possible.
Figure 4: Basic inventory cost trade-offs.
Figure 5: Inventory levels under a fixed order quantity-variable order interval policy.
Stockout and backorder costs1 are a function of the lead time distribution for supply. Lead times are in turn a function of travel time and variability. Reductions in either travel time and/or travel time variability will directly impact various logistics cost components and may trigger re-organization at the level of the firm. Shorter and more predictable lead times can enable firms to reduce their reorder points and average stock levels while maintaining the same level of service. This in turn reduces logistics carrying costs.
Inventory Measures
Inventory turns is a good metric as a supply chain performance indicator. It is simple to measure and understand, companies track it, and there are benchmarks that allow comparisons across industries. The Monthly survey of Manufacturers in Canada shows a steady decline in inventory-to-shipment ratios. This suggests that manufacturers are benefiting from improved logistics systems in the form of efficient just-in-time production and distribution processes.
Sectors with lower average annual inventory turnover have much to gain since there is an inverse relationship between inventory turns and inventory carrying costs [6]. In the manufacturing sector, this includes textiles, wood, machinery, and metal products. Manufacturing turnover growth was highest in electronic products, beverages, and petroleum and coal products.
Business-to-business (B2B) e-commerce represents over 80% of all e-commerce and will likely be the driving force in the near future. Although Canada lags behind the US in B2B e-commerce, the compound annual growth rate between 1999 and 2004 has been estimated at nearly 68% [7]. There is evidence that the nature of transport demand is changing with the adoption of e-business practices.
According to Chow [8], e-business has enabled enhanced supply chain integration and places new requirements on transport firms with respect to technology, services and other capabilities. By facilitating the disintermediation of participants in the supply chain, e-business is changing the nature of transport demand and the types of services demanded. The sharing of information along the supply chain diminishes the need for large safety stock inventory. Given improvements in transport reliability, the role of intermediate warehouses in both industrial and consumer markets will diminish. This can result in consolidation of warehouses, reduced overall inventories and increased demand for fast reliable transport.
According to Chow, the growth of online B2C has increased demand for long distance delivery of small packages direct to consumers 7 days a week. By moving to a pull system, firms are more responsive to actual consumer demand which results in the movement of smaller quantities more frequently.
As supply chain integration continues to seek minimum total supply chain costs, there will be increased truck movements from centralized hubs. The extent of traffic and infrastructure implications along urban roads and inter-modal hubs is not yet fully known. With continued trends in e-commerce, disintermediation, and JIT delivery, there will be a point where the transport system may not be able to cope with demands placed upon it and may act as a barrier to productivity growth.
Summary
The benefits of transportation improvements go beyond direct cost reductions. Firms have the opportunity to optimize their logistics by re-organizing to take advantage of these lower costs. The extent and timing of substitution of transportation for inventory and warehousing will vary by firm. Benefits of transportation improvements include:
· Direct savings from reduced vehicle operating costs and time savings
· Indirect savings from reduced logistics costs through rationalized inventory levels, locations, and service levels
The systems approach has always been a critical concept in logistics. Logistics is, in itself, a system. It is a network of related activities with the purpose of managing the orderly flow of materiel and personnel within the logistics channel. In the next section, we describe an economic framework for estimating benefits of transportation system improvements. This framework is such that benefits are all-encompassing yet not double counted.
2 BENEFITS ESTIMATION FRAMEWORK
HLB Decision Economics has developed a micro-economic framework within which to measure freight related economic benefits of transportation improvements. A key objective was to ensure that the analytical framework recognizes the gains in economic welfare (efficiency) that follow from the propensity of industry to adopt productivity-enhancing "advanced logistics" in response to transportation infrastructure improvements. The framework is structured to quantify separately both conventional and indirect re-organization benefits.
The most significant effort to date to study effects of highway capital stock on costs and productivity of businesses is the work by Professor Ishaq Nadiri [9] of New York University, sponsored by the U.S. Federal Highway Administration (FHWA). Nadiri's work showed those effects to be significant. His analysis is concerned with the impact of all highway improvements (on a fairly high-level system) on all businesses. Although not focused on freight movement as such, freight carriage is clearly one of the effects that Nadiri captures, but not the only one, since improvements in highway passenger travel also have effects on business performance. In any event, Nadiri's work does not provide a basis for estimating benefits of future improvements in highway freight-movement. An entirely different approach is needed, one founded on the precepts of benefit-cost analysis, which is designed to estimate benefits of proposed investments.
2.2 Framework for Economic Analysis
Standard approaches to transportation cost-benefit analyses have not included potential re-organization benefits. Analyses of highway investment usually take account of benefits to immediate highway users. These include time savings, reductions in operating costs, and reduced accident costs. Benefits from savings in truck travel time are calculated using estimates of driver wages. In the context of freight, these are benefits to carriers, but do not include benefits to shippers as owners of the cargo.
Reductions in truck operating costs do not provide a sufficient means of estimating all benefits; the impact on shippers must be included as well. In industries with relatively high logistics costs, the greater share of gains from freight improvements may well go to the shipper rather than the carrier. Time-cost reductions-reductions in transit time and increases in reliability-have substantial value for shippers. These values need to be estimated and added to the benefits treated in the standard analysis.
This is, by no means, the whole story of bringing freight into a complete benefit-cost framework. There are significant effects of freight improvements beyond the immediate cost reductions for carriers and shippers. Improvements in the freight-movement system may allow companies to change their modes of operation in ways that lead to further gains in productivity. In a paper written over thirty years ago, Mohring [10] referred to this as the "reorganization effect." In this seminal paper, Mohring describes the process of minimizing total costs by determining the plant output that minimizes average manufacturing plus direct distribution costs. This work has since been generalized to include all logistics costs [11].
Improved transportation may let firms realize economies of density or scale, for example, by building bigger plants, warehouses, or stores, because a single facility can serve, or draw supplies from, a larger area owing to improved transportation. Much of a firm's response to transportation-cost reduction will be reorganization of logistics. It will respond to the lower costs by moving goods longer distances, using fewer warehouses, and carrying less inventory for a given level of sales. It will buy more transportation and realize gains from improved logistics. Firms can also make other changes in the ways they do things; for example lower costs might lead to product improvements. This in turn can increase exports and sales.
Figure 6: Transportation and the economy.
Supply and demand for freight carriage can be viewed from the standpoint of freight- transportation consumers, i.e., shippers. Cost reductions caused by an improvement can be considered as a change in freight-transportation supply to shippers. Their responses to these changes in supply are determined by the conditions of their demands for freight transportation. These demands reflect both market demands for their products and the ways in which they use freight transportation as an input to their production and/or distribution processes.
Demand conditions are embedded in a "demand curve." In our context, this curve shows the amount of freight transportation a firm will buy at various "prices," including, shipper-borne costs. The positions of the demand curves, D1, D2, and D3, in Figure 7 reflect changes in transport demand as shippers respond through time to a transportation improvement. The vertical line, D1, illustrates conditions immediately after the improvement. Then, shippers receive only first-order benefits. With time, they can modify their operations to take advantage of the improvement by adopting more transportation-intensive production and distribution processes. Schedule D2 describes conditions operational changes over a moderate time period-a year, say. Schedule D3 depicts further changes during, perhaps, the ensuing for years.
Shippers' demands for freight transportation reflect both market demands for their products and the ways they use freight transportation as an input. Freight improvements do not directly affect market demand for products, nor, at the first instance, the way in which shippers use freight transportation. Transportation use depends on basic logistical arrangements, especially the number and locations of warehouses. Warehousing changes require appreciable time. In a shorter time, though, increasing the frequencies of some shipments in order to carry "optimal" inventory levels would be feasible.
Figure 7: Demand curves for transportation.
Net benefits generated from transport improvements are represented by areas a, b, and c at Figure 7. These correspond to direct short run benefits, medium run benefits such as for inventory adjustments, and long run benefits such as for logistics network re-organization respectively. Instead of an often postulated uniform shift in the demand curve, there is an extension of demand made possible by re-organization, product price reductions, and increased product demand over time. With this approach, it is possible to estimate total benefits with and without re-organization. The great advantage of considering a simple demand curve is that it embodies all benefits.
The micro-economic framework, described mathematically at Appendix A, has undergone peer review and scrutiny by a panel of economists at the FHWA. There was definite support on the theoretical basis of the framework. although empirical measurement was acknowledged as technically challenging. One issue is accurately estimating the long run demand curve.
The computation of benefits as represented by the change in consumer surplus is derived at equation 2. The mathematical model provides the means to fully quantify benefits of any transport system improvement, whether it is infrastructure, info-structure, or as a result of a change in policy or regulation.
Information Requirements
In order to quantify overall economic benefits of transportation infrastructure improvements or policy changes, it is necessary to consider the following:
· Generalized logistics costs per unit of transportation used. This generalized cost includes all components including transportation, warehousing, inventory, transaction, and other costs;
· Reductions in generalized cost as a result of infrastructure improvement and/or policy change;
· Present transportation demand. This can be expressed by mode, by corridor, by industry, or aggregated at various levels. Sample measures of transport demand include km driven, ton-km, or product shipments.
· The elasticity or response of transport demand to changes in logistics costs. This quantity embodies both the short run effects and the longer term substitution effects from logistics optimization.
· Given present demand and elasticity, a new long run demand can be derived.
Full quantification of benefits by mode and sector cannot be achieved within the scope of this study. An attempt was made to illustrate its applicability to one sector - food - in the manufacturing industry. The food sector has the highest logistics cost among manufacturing industries (2.8B$) and is in the top 10 sectors with the highest logistics intensity, that is logistics cost as a share of production cost. Unfortunately, data was not available to carry out a sector-based estimation of benefits as a result of future transport improvements.
Measurement Issues
As previously mentioned, there are no official statistics on the logistics industry in Canada. Logistics industry statistics are difficult to isolate because logistics services are offered by companies assigned to an array of industrial classifications, the basis for most data collections. According to government estimates, the total cost of logistics in Canada in 1992 was over $40 billion or 7.3% of GDP. Although some recent estimates place the logistics service market at over $100 billion per year [12], we take the more conservative estimate of 7.3% of GDP in this paper.
Investment in the transportation system could take on various forms. On highways, flow capacity could be increased by the addition of new lanes, increases in speed limits from wider and safer roads, limited access highways, new interchanges, and operational/ITS2 type improvements. There may also be relaxed restrictions on truck weights, improved bridge clearances etc. Inter-modal improvements could be made to ports/customs thereby smoothing and increasing net system traffic flow. All these improvements potentially result in travel time savings and increased reliability.
In a survey of 1,200 California-based and large US-based for-hire and private trucking companies, Golob [13] examined freight industry attitudes towards policies to reduce road congestion so as to provide swift and reliable goods movement. The paper identified six classes of congestion mitigation policies: dedicated truck facilities, improved traffic management, improved operational efficiency, enhanced urban priorities, increased road capacity and congestion tolls. In this paper, we do not make distinctions between specific initiatives. We are primarily concerned with the net effects of improved travel times, enhanced reliability and lower transport costs.
In summary, the strategy pursued by most firms in their efforts to restructure will be to minimize their costs for a specified level of service. Given basic information about their response, it is possible to estimate the benefits that result from highway improvements, both direct and indirect. In the next section, we examine research carried out to obtain necessary information to estimate aggregate benefits.
3 META-ANALYSIS OF LOGISTICS RESPONSE
There are very few economic studies that have been conducted to measure the relationship between transportation improvements and logistics productivity. Using an inventory control process model, Tyworth [14] estimated the effects of carrier transit-time and reliability performance on logistics cost and service. In separate studies, HLB conducted extensive surveys with industry to arrive at empirical estimates of logistics changes due to highway improvements.
3.1 Changes in Logistics Costs
The single most comprehensive empirical study to quantify the relationship between logistics benefits and transport improvements was carried out in 1994. In NCHRP 2-17(4) Measuring the relationship between freight transportation and Industry productivity [16], HLB used surveys and case studies to estimate benefits from prospective travel time improvements.
A sample of US firms, chosen across 5 industries, were presented with hypothetical improvements to the transportation system and asked about their business response. Restructuring of logistics was assessed to be economically advantageous as a result of 15-30% reduction in travel time for most firms. Below such thresholds, direct gains were still realized; they could adjust their inventory holdings and shipment frequencies while benefiting from lower transport costs. Figure 8 shows the logistics cost effects for one firm in the sample.
Figure 8: Logistics savings as a function of travel time improvements.
Findings of this study, summarized in the table below, show estimated elasticities of logistics costs with respect to travel time as well as anticipated restructuring thresholds. One general observation is that elasticities of logistics cost with respect to travel time savings appear positively correlated with logistics costs as a proportion of sales. This result is consistent with intuition whereby logistics intensive firms will seek and obtain higher gains from transport cost savings through restructuring. Re-structuring thresholds were found to be similar across industries at the 15-30% level.
Table 1 - Empirical elasticities of logistics cost with respect to travel time improvements
In contrast, Tyworth estimated the effects of carrier transit time performance on logistics cost and service using a stochastic inventory model. Based on an optimal continuous review inventory system, he demonstrated the sensitivity of total logistics cost and service levels to transit time and transit time variability. His case study is representative of leading edge firms in the auto and related parts industry.
Logistics costs are shown to be highly dependent on both transit time and variability. The sensitivity to transit times increases significantly for higher values of variability. The same can be said for service levels. These results are reported at optimum values of logistics costs so provide the best case possible case. The sensitivity of logistics costs to travel time and variability reductions are illustrated at
Figure 9. Improvements in achievable service levels are made possible by a reliable and fast transportation system..
Benefits under reduced transit variability
Just as important as travel time reductions, improvements in reliability affect both logistics costs and service levels. For Tyworth's auto parts distribution example, optimal achievable fill rates are shown to be sensitive to transit time variation. Curves at Figure 10 correspond to mean delivery times of 1, 2, 3, ..., 10 days. Service levels are highly sensitive to delivery time variation, especially for longer transit times.
Figure 9: Minimum logistics cost versus transit time for various levels of reliability3.
Figure 10: Optimal fill rate as a function of delivery variation.
Aside from technical issues associated with the optimization of logistics processes in a stochastic environment, reduced variability allows for far greater gains in scheduling and routing of transport resources. Increased competitiveness as a result of improved service levels may translate into higher sales and increased demand for both products and transport services. These effects are considered implicitly in the framework.
The last entry in Table 1 above is taken from work by Tyworth [14]. Based on the case study treated in his paper for the auto parts industry, a comparable elasticity is reported for a mean travel time of 2 days and variability of 10% of the mean. Elasticities for the complete table of travel time and travel time reliability values have been computed and are shown at Appendix E. For service levels (fill rates), elasticities are negative, indicating that travel time and variability increases result in reduced fill rates for products normally held in inventory. This last elasticity estimate is representative of medium-run adjustments firms may make in adjusting inventory.
Value of time for freight transport
Congestion contributes not only to making travel times longer, but also more unpredictable. This unpredictability can hinder Just-in-Time inventory management and even hinder freight dependent production processes. As a result, shippers attach a dollar value to predictability and speed. An HLB study carried out for NCHRP developed a valuation of travel time savings and reliability [15]. The study was based on a survey of 20 carriers in California within 5 industry groups. Using a stated-preference model, the value of travel time was estimated at between $145 - $192 USD per hour. In contrast, scheduled delay late was estimated at $371 USD per hour. In addition to value-of-time savings, there would also be vehicle operating cost (VOC) savings from higher and more reliable speeds. Although these results are based on a small sample, they indicate the magnitude of savings. It is interesting to note that time late was valued at twice the rate of delivery time.
3.2 Changes in Transport Demand
The micro-economic framework requires the long run response in transport demand as a result of transport system improvements and hence costs. The most extensive review of elasticities of transport demand and their empirical estimates is by Oum et al [17], [18], and Goodwin [19]. Over sixty studies were surveyed from academic journals covering research conducted over a 10 year period.
Elasticity estimates show a wide range of values, not only across different commodity groups, but also for the same group using different functional forms. The authors conclude that across the board generalizations about transport demand are difficult to make. In addition. the authors identify a number of issues for future research such as inter-modal competition, time horizons, and the effects of aggregation.
These studies report on what can be termed conventional own-price elasticities of demand for transport. There have not been to our knowledge any work done on long run elasticities as a result of logistics changes and re-organization effects. Nonetheless, we use an indicative value of 0.93 as a nominal elasticity for comparison purposes. According to past studies, estimates for the elasticity of transport demand with respect to cost could be as high as 1.3 and as low as 0.69 . For illustration purposes, we use a value of 1.5 instead of 0.93 for the case of logistics re-organization.
Estimates of changes in transport demand as a result of re-organization were also derived as part of NCHRP 2-17(4). For a travel time improvement of 25% which prompted re-structuring for most firms surveyed, there was a 13% average increase in transport demand. This would indicate an elasticity of transport demand with respect to transport times of 0.52 (13/25). Unfortunately, transport costs are not directly proportional to transit times so we cannot compare this with the previous result. Additional research is required to quantify these relationships.
Having recognized logistics re-organization effects brought about by transportation improvements, the magnitude of such gains are estimated in the Canadian context using our framework.
Sample Model Results
To illustrate the concepts described in this paper, a simple model was developed based on the micro-economic framework using data aggregated at the national level. The model is based on the value of logistics costs as a fraction of GDP for the Canadian economy. Logistics costs are apportioned4 to inventory, freight transportation and other logistics costs as shown at Table 2. Within freight transport, distinction is made between trucking costs and costs for other modes. In this scenario, it is assumed that, as a result of a transport improvements, 10% of firms carry-on with business as usual, and 80% optimize their inventory to take advantage of the improvement.
Table 2: Logistics costs as a result of 1% transport cost savings5.
In the simple model, the direct effect column shows cost components as a result of a transport cost reduction. The subsequent columns represent cost allocations under three categories. These are for costs (by value) apportioned according to three categories of firms: those that will not change their logistics practices and realize only conventional savings, those that lower their inventory levels and purchase additional transportation, and finally those that re-structure their logistics network to take advantage of enhanced transport services. At each stage, there are different scales of possible savings. The actual proportion of firms (by value) in each category needs to be quantified for various improvement levels. A 1% reduction in transport costs was assumed to trigger 1 in 100 firms to re-organize, 79% to re-optimize their inventory, and 20% make no logistical changes. Based on this adaptation rate and sample elasticities obtained as part of a meta-analysis of the literature, a 1% reduction in transport costs would result in a 1.45% logistics cost savings above transportation cost reduction. Based on a GDP of 986 billion, this amounts to nearly $220 million.
This aggregate model has many simplifying assumptions. The results obtained are to be treated as illustrative of the dynamics. An extensive survey of firms would have to be conducted to obtain more accurate estimates of benefits to transportation improvements. Although the model has been applied to the total Canadian economy, gains for urban transportation will be different from gains for regional transport.
Applying the model for various transport cost reductions to estimates of the Canadian Logistics GDP, we obtain estimates of the conventional and re-organization benefits of hypothetical transport improvements.
Assuming firms do not adjust their logistics, a 1% reduction in transportation costs would lead to approximately $100M in conventional benefits. With most firms adjusting their inventory levels to take advantage of such improved lead times, overall logistics cost savings would amount to 1.5% of total logistics costs. At the extreme, with transport cost savings of 20% and associated improvements in travel time and reliability, logistics cost savings beyond transport cost reductions are estimated at 24%. The mark-up effect is due to long run logistics re-organization.
Results for various transport improvements
As transportation costs decrease, there is increased substitution of transportation for inventory and warehousing. The indirect benefits increase relative to direct benefits. Due to logistics "optimization", overall logistics savings are greater than transportation input cost reductions. Figure 11 portrays a direct savings reference line to compare with overall logistics savings.
Figure 11: Logistics savings and benefits as a function of transport cost reductions6.
Overall logistics savings are estimated as a function of cost reductions from transport system improvements. These can be compared to a direct benefit reference line. On a secondary axis, indirect benefits as a proportion of direct benefits are shown to level off due limitations on capacity to re-organize.
Although benefits estimation was carried out for illustrative cases involving congestion relief and reductions in overall road freight transportation costs, the same concepts would apply to all modes of transportation.
A survey of shippers was developed to reveal specific concerns about transport system inadequacies or policies that may be limiting the ability of firms to adopt e-commerce, advanced logistics and other productivity enhancing business models. The objective of the case study survey was to allow inferences to be made on what industries would benefit most from transport system improvements. Key characteristics of firms having the potential to realize significant benefits were to be identified.
A sample of firms invited to participate in the survey can be found at Appendix D. These companies cover four important sectors of the Canadian economy: retail food, automotive parts, telecommunications equipment, and agricultural products and chemicals. The case survey - directed to senior executives - focused on identifying strategic level issues in supply chain management, recent developments, evolving demands for transport services, barriers to business productivity growth, and business response to transport improvements. Proprietary and/or confidential information was not expected and therefore specific performance data was not requested.
The interviews were to provide supporting evidence on what are perceived to be barriers to productivity growth related to transportation and to guide the analysis. Short surveys at the executive level are not expected to provide specific quantitative information needed to populate the benefits model. Rather it provides the context under which such analysis can be done and can provide the necessary focus for empirical measurement.
Survey Design
The survey was designed around two primary branches in the framework's information/data map shown at Appendix C. First, present and future supply chain management practices are sought as well as links between e-commerce and the ability of the firm to implement new and improved distribution strategies. At question 2, the survey attempts to discover how e-commerce and evolving supply chain management practices affect the demand for transportation. It is important to understand both changes in scale as well as the nature of new demands. At question 3, we explore barriers to efficient logistics delivery, either as a result of congestion or other limitations of the transport system. At question 4, possible solutions are discussed and prioritized. Finally at question 5, we attempt to determine the key factors firms would consider in developing a business case for improvements in their logistics network and what effects these would have in their overall productivity.
Overview of Findings
Unfortunately the survey response rate was low, even after follow-up calls. Limited discussions with firms have confirmed results reported in previous studies [13, 24].
· Carriers are more concerned about highway system performance although shippers likely have most to gain. Carriers have to deal with associated problems on a day-to-day basis and act as a buffer for shippers who do not deal with delivery problems. Transportation is but one cost element in their supply chain.
· Delivery windows are narrowing. Some case studies report delivery windows of 15 minutes. In general, operating standards are increasing.
· Late deliveries are expensive and are mostly caused by highway congestion. Specific problem areas noted include highway links in Southern Ontario where congestion is growing. Local delivery appointments in the Greater Toronto Area are difficult to guarantee. As a result, carriers make provision for traffic congestion in scheduled transit times. This can be as high as 1.5 hours on a 5.5 hour trip.
· Inventories are getting smaller. Shippers report not being able to implement Quick Response (QR) because of inconsistent delivery times.
· Highway performance influences location decisions.
· Congestion can create more congestion. Due to increased transit times, more vehicles may be required to move the same quantity of product. This places more trucks on the road creating yet more congestion.
· Highway congestion could become a larger issue in the future. Since inventories are getting smaller and more distant, evolving demands for increased service levels will likely result in increased replenishment frequency.
Since highway performance cannot be directly controlled by shippers (other than scheduling departure/arrival times), firms take a constrained and short term view of the benefits of logistics performance. There are however some notable exceptions one of which is described here.
Sample Case of Logistics Reorganization
In the late 1980s Polaroid decided to centralize its European inventories by substituting transportation for warehousing; a large number of warehouses were closed7. Estimated annual gross savings were $6.9 million, broken down as follows:
· warehousing personnel-$2.5 million;
· inventory carrying costs-$2.2 million;
· warehouse rental costs-$1.0 million;
· facilities and offices-$0.6 million;
· internal transportation between dealers and subsidiaries-$0.5 million; and
· insurance premiums-$0.1 million.
Net annual savings were $6.3 million after subtracting $0.6 million per year for increased costs resulting from computer system maintenance and increased warehouse personnel at headquarters. A capital investment of $3.0 million for new computer equipment was required.
Besides these savings that Polaroid could quantify, there were other gains that were not measured. Prior to centralizing inventory, 69 percent of orders could not be filled at the location that received them, so that items were backordered until they could be filled from other locations. This required significant internal transportation among dealers and subsidiaries to reposition inventory. Polaroid also achieved unspecified freight cost savings based on volume discounts for consolidated (truckload) shipments to centralized warehouses as well as reduced freight rates that reflected truck cost savings from reduction of border crossing inefficiencies.
Polaroid's case illustrates the point that is at the heart of the analytical framework-that businesses will increase expenditure on freight transportation, buy more freight service, and thereby achieve a reduction in total logistics costs because of significant savings in inventory and warehousing. This is done in ways that also improve customer service within the supply chain.
4 IMPLICATIONS FOR THE CANADA TRANSPORTATION ACT
This paper has described a framework for considering and measuring the benefits of transportation improvements and policy changes and the effects on industrial productivity. Although the scale of possible benefits have been estimated for the whole Canadian economy, it is clear that regional and/or sectoral estimation of benefits would be of value.
Although the discussion has focused primarily on road freight movement, the basic concepts and overall conclusions apply to all modes of transportation. New capabilities related to improved system integration dealing with inter-modal movements such as improved port access could have positive effects on total logistics delivery.
With a move to supply chain integration and resulting increases in transport demand, firms will seek the most efficient means of transport. The balance of demand by mode must be well understood if policy makers are to facilitate optimal gains. For instance, the benefits of direct rail access in some manufacturing sectors such as in automobile manufacturing, may result in efficiency gains and at the same time alleviate road congestion.
Given that significant productivity gains can be generated by transportation improvements, the question remains as to where these investments are best directed to realize maximum benefits. Significant improvements in one transport mode may result in mode switching. If service levels could be matched, air freight might be shipped by truck based on new transport costs, speeds and enhanced reliability. While this paper has focused on previously un-measured benefits, costs and externalities should also be addressed in national transportation investment analyses.
Conventional cost-benefit analyses consider direct savings from transportation improvements. As a result of substitution of various logistics inputs, so-called re-organization effects can occur within a supply chain at the level of the firm. Traditional cost-benefit analysis has not captured the value of these indirect benefits.
The main findings of this paper are as follows:
· Indirect benefits are real. Previous case studies have clearly documented the effects of transportation improvements on logistics and service levels. By recognizing trade-offs between transportation, inventory, warehousing, and information costs, it is possible to lower logistics costs and improve logistics productivity. The benefits of re-organization are incremental to direct savings in vehicle operating costs and value-of-time savings. Policy makers must recognize these re-organization effects as legitimate benefit to industry.
· Benefits are now measurable. A micro-economic framework within which to measure freight related economic benefits of transportation improvements has been developed. It has recently been peer reviewed by an expert panel and has gathered support within the academic community. The framework is an expression of the theory, concepts and principles of developments in transportation economics.
· Benefits are significant. As shown at Figure 11, transport system improvements, changes in policy and/or regulations resulting in 10% reduction in transportation costs to shippers yield an estimated 14.5% reduction in overall logistics costs. This estimate - although speculative - demonstrates that significant benefits from logistics re-organization can be realized in addition to gains from lower unit transportation costs. To understand re-organization benefits, a total logistics view of transport as an integral part of the supply chain must be adopted. Investments in transportation can yield gains well beyond reductions in transport costs.
· Benefits vary across industries. Since industries are structured differently with different cost structures, the scale of potential gains from transport system improvements and policy changes vary. Logistics intensive, and in particular transport intensive industries with low inventory turnover rates, poorly integrated supply chains, and large market areas have the most to gain from transport improvements.
The revolution in supply chain management and accompanying productivity gains are based on a high-performance transportation system. It is acknowledged that the level and types of investments required to generate significant and system-wide transport cost reductions are not fully known. What is known however is that transportation efficiency can result in compounded logistics efficiency. Although the degree of compounding depends on the propensity of firms to take advantage of transportation improvements by optimising their supply chain, these dynamics are not considered in traditional policy decision making. There is likely to be under-investment in transportation in Canada as a result.
As a result of this study, it is clear that improved economic analysis for the measurement of benefits and costs of transport infrastructure investment are needed for Canada. Although this analysis is based on the best available data for elasticity estimates, additional empirical research is required before benefits of improved freight movements can be quantified to a higher degree of resolution across various industries at the program and project levels.
The federal government may have a role to play in increasing the awareness of provincial and municipal planners and decision makers with regards to potential benefits of improved transportation as they relate to logistics productivity growth. The government could facilitate joint transportation planning across various jurisdictions so that logistics and supply chain barriers and specific industry needs are considered. This, in turn, will help maximize benefits and productivity gains for the Canadian economy.
[1] Best practice in logistics, The McKinsey Quarterly 2000, no. 3.
[2] Bess, I., McKeown, L. The emergence of logistics services: measurement issues, Analytical Paper Series, Statistics Canada Aug 1998.
[3] McDonald, R.J. "Canada leads in logistics", Canadian Business Review, Autumn 1995, pp29-32.
[4] D A. Quarmby, "Developments in the Retail Market and their Effect on Freight Distribution," Journal of Transport Economics and Policy, Volume 23, Number 1, January 1989.
[5] Logistics and Supply Chain Management - Overview and Prospects, Service Industries and Capital Projects Branch, Industry Canada, 2000.
[6] Lambert, D.M., Stock, J.R., Ellman, L.M. Fundamentals of Logistics Management, McGraw Hill, 1998.
[7] International Data Corporation (IDC) Canada Ltd. Canada: The state of e-business when compared to the U.S., IDC presentation, Oct 2000.
[8] Chow, G. E-business and the Future of the Canadian Transport Industry, paper to the Canada Transportation Act Review Panel, Sept 2000. (draft)
[9] Nadiri, M.I.; Mamuneas, T.P. Contribution of Highway Capital to Industry and National Productivity Growth, Research Report prepared for the US Federal Highway Administration (US DOT), Sept 1996.
[10] Mohring, H., Williamson, H.F. "Scale and Industrial reorganization economies of transport improvements", Journal of Transport Economics and Policy, Sept 1969.
[11] Cost-benefit analysis of highway improvements in relation to freight transportation: micro-economic framework, Federal Highway Administration White Paper, Final Report, HLB Decision Economics, Mar 2001.
[12] Tausz, A. "A shot in the arm for full-service logistics: Wal-mart deal legitimizes outsourcing", Modern Purchasing, Aug 1995.
[13] Golob, T.F., Regan, A.C. Freight industry attitudes towards policies to reduce congestion. Transportation Research Part E 36 (2000) p55-77.
[14] Tyworth, J.E., Zeng, A.Z., Estimating the Effects of Carrier Transit-time Performance on Logistics Cost and Service, Transportation Research-A, Vol. 32, No.2, pp. 89-97, 1998.
[15] NCHRP Report 431. "Valuation of Travel-Time Savings and Predictability in Congested Conditions for Highway User-Cost Estimation", University of California (Irvine) and HLB Decision Economics Inc.,1999.
[16] NCHRP 2-17(4) "Measuring the Relationship between Freight Transportation and industry Productivity", Final Report, HLB Decision Economics Inc., June 1995.
[17] Oum, T.H., Waters, W.G., Yong, J-S., Concepts of Price Elasticities of Transport Demand and Recent Empirical Estimates, Journal of Transport Economics and Policy, May 1992.
[18] Oum, T.H., Alternative demand models and their elasticity estimates, Journal of Transport Economics and Policy 23 (2), 163-87, 1989.
[19] Goodwin, P.B., A review of new demand elasticities with special reference to short and long run effects of price changes, Journal of Transport Economics and Policy, May 1992.
[20] NCHRP 342 "Primer on Transportation Productivity and Economics Development", HLB Decision Economics Inc., Sept 1991.
[21] Roberts, P.O. Logistics Supply Chain Management: New Directions for Developing Economies, on behalf of the World Bank, Feb 1999.
[22] Baumol, W.J., Vinod, H.D. "An inventory theoretic model of freight transport demand", Management Science, Vol. 16, No. 7 Mar 1970.
[23] Button, K.J., Pearman, A.D. The Economics of Urban Freight Transport, Macmillan Press, 1981.
[24] Simchi-Levi, D., Kaminsky, P. Designing and Managing the Supply Chain, Irwin/McGraw Hill, 1999.
APPENDIX A. MICRO-ECONOMIC MODEL OF BENEFITS
The benefits resulting from transportation improvements can be derived from the change in consumer surplus as a result of cost reductions and increased transportation demand. In general form, it is possible to write:
In this case, price 
is the generalized cost of transportation per vehicle mile at a level of demand 
. This general expression encapsulates the net benefit of the transport improvement in the absence of marginal cost pricing.
The general form at equation one is decomposed into the re-organization benefit (first two terms) and conventional benefit (last term).
One approach to evaluating the above would be to assume a constant elasticity of demand near the present demand level. In most cases, this assumption is more sensible than a linear demand curve. A general expression for a constant-elasticity-of-demand schedule is
Q = a /P b , where Q is the quantity that would be sold at a price P and where a and b are constants. It is a simple matter to evaluate the integral.
Solving, we obtain, with constant elasticity demand curve:
where 
.
Monopoly
For the case of monopoly, it can be shown that net benefits involve both a consumer's gain as well as a producer's gain. This is true not just for transportation, but also for other markets.
The consumers gain 
is as above. The producer's gain 
has the exact same form except that the price is replaced by marginal revenue 
. The new price which maximizes the monopolist's profit can be approximated as 
. The net benefits expression is:
APPENDIX B.
Figure 12: Micro-economic framework model structural logic diagram
Covering letter - Survey of Shippers
2 Mar 2001
Dear Executive;
Competition in today's global markets, shorter product life cycles, and heightened expectations of customers has resulted in focus and significant investment in the supply chain. As a result, there is increasing evidence that innovative forms of supply chain integration are creating demands for new and improved transportation services and capabilities, both domestically and internationally.
As part of its mandate, the Canada Transport Act (CTA) Review Panel is considering the extent to which the current act supports the efforts of Canadian transport players to adapt to the new e-business environment and to meet global logistics requirements. E-commerce provides opportunities for and imposes requirements on all sectors of the transport industry to become more efficient and competitive. In submissions describing the status of transportation in Canada, the panel has heard few specific concerns about system inadequacies or government policies that are particularly inhibiting e-commerce.
A survey of leading firms is being conducted to provide insights into barriers to productivity growth, whether from legislation, regulations, policy, or infrastructure issues. The survey addresses three main areas: how e-commerce and advanced logistics affect demands for transportation, what are the effects on transport infrastructure requirements, and what factors would drive a business case for adopting productivity enhancing supply chain practices.
The results of this research will be aggregated by industry and made available to the CTA review panel. If you agree to participate, you may rest assured of complete confidentiality with survey responses. All participants will receive a copy of the survey summary results.
If you agree to participate in this important survey, a short interview can be scheduled at your convenience by contacting us by email at hlb-econ@hlb-econ.com or by phone at (613) 234-7575. Alternatively, completed surveys may be submitted directly by fax at (613) 238-6096.
This research is being conducted by HLB Decision Economics Inc. on behalf of the CTA Review Panel. Should you have any questions, please do not hesitate to call us. We hope to have all responses by April 21, 2001. Thank you for your assistance.
Sincerely,
David L. Lewis
Project Director and CEO
HLB Decision Economics Inc.
PROTECTED WHEN COMPLETED
Survey of Shippers
for the
Canada Transportation Act Review
Relationship between e-business, logistics, and productivity
Questionnaire
This survey should take approximately 15 minutes to complete. Please note that only aggregate information will be presented in any reports from the survey, with no identification of responses from individual firms.
This research is being conducted by HLB Decision Economics Inc. on behalf of the CTA Review Panel. Should you have any questions, please do not hesitate to contact us at (613) 235-7575.
Relationship between e-business, logistics and productivity
for the Canada Transport Act Review
Section A - General Information
1. Please provide the following general information:
Name ( ) Mr ( ) Mrs ( ) Ms |
Title | ||
Company Name | |||
Address | |||
City |
Province/Territory |
Postal Code | |
Telephone ( ) |
FAX ( ) | ||
2. How many employees are in your company's Canadian operations?
__ 1-49 |
__ 50-99 |
__ 100-199 |
__ 200-499 |
__ 500-999 |
__ 1,000-9,999 |
__10,000+ |
__ Dk/Na |
3. Which are the industries to which your company belongs? (check all that apply)
__ Agriculture and related services |
__ Clothing products |
__ Transportation equipment |
__ Mining, quarrying and oil well |
__ Wood |
__ Electrical and electronic products |
__ Food products |
__ Furniture and fixtures |
__ Non-metallic mineral products |
__ Beverage |
__ Paper and allied products |
__ Refined petroleum and coal products |
__ Rubber products |
__ Printing, publishing and allied industries |
__ Chemical and chemical products |
__ Plastic products |
__ Primary metals |
__ Wholesale trade |
__ Leather and allied products |
__ Fabricated metal products |
__ Retail trade |
__ Primary textile and textile products |
__ Machinery |
__ Other Specify: _______________ |
4. Please indicate the number of production, distribution and/or retail facilities your company operated in Canada during 2000 by province or territory.
Section B - Logistics and Transportation
Q1. Current Logistics Practices.
· Has your firm implemented e-business and/or integrated supply chain management processes? Please explain.
· Do you have plans for further developments and how might these affect your distribution strategies?
Q2. Transport Demand.
· Do you expect demands for transportation (km driven, number and type of shipments) to change as a result of recent developments in e-business or logistics? Please explain.
· Do you operate your own transport fleet or is this service contracted out?
Q3. Barriers to Transportation Efficiency.
Recent case studies have found that firms can realize significant gains when substituting transportation for large inventories and multiple distributed warehouses.
· How would rate congestion as a significant barrier to re-organizing your logistics? (please rate on a scale of 1-5)
· Are there other significant barriers (such as policies, regulations, infrastructure) to logistics efficiency beyond your control? Please explain.
1.
2.
3.
Q4. Improvement Priorities.
If congestion or other constraints are barriers,
· What types of transportation improvements or policies do you see as having the most significant impact for your firm? Please list in order of priority.
1.
2.
3.
Q5. Business Case for Re-organization.
· How quickly would you act on the above improvements?
__ less than 1 year |
__ 1-3 years |
__ greater than 3 years |
__ Dk/Na |
· What factors would you consider in a decision to re-organize your logistics ?
· What effects might these have on your total logistics costs and service levels?
Q6. Do you have any additional comments on any of the questions or other points of interest?
(please list the mode of transport (if applicable) for each comment)
Thank you for taking the time to complete this survey.
Please return to:
HLB Decision Economics Inc.
99 Bank St, Suite 400
Ottawa, ON K1P 6B9
Fax (613) 238-6096
Email: hlb_can@achilles.net
APPENDIX E.
A. Retail Food Sector
Campbell Soup Company Ltd
National Sales Director
60 Birmingham Street
Toronto, ON M8V 2B8
Coca-Cola Bottling Company
Vice President, Operations
42 Overlea Boulevard
Toronto, ON M4H 1B8
Coca-Cola Ltd.
Vice President, Sales & Category Management
42 Overlea Boulevard
Toronto, ON M4H 1B8
Dole Foods of Canada Ltd.
Logistics Manager
100 York Blvd., Suite 510
Richmond Hill, ON L4B 1J8
General Mills Canada, Inc.
V.P. Operations/Customer Services
2845 Matheson Blvd. East
Mississauga, ON L4W 5K2
Grantham Foods Ltd.
General Manager
1388 Cliveden Avenue
New Westminster, BC V3M 6K2
H.J. Heinz Company of Canada Ltd
Manager Logistics
Erie Street South
Leamington, ON N8H 3W8
Hershey Canada Inc.
Credit Manager
2350 Matheson Blvd. East
Mississauga, ON L4W 5E9
Kellogg Canada Inc.
Supply Chain Business Partner
6700 Finch Avenue West
Etobicoke, ON M9W 5P2
McCain Foods(Canada)
A Division of McCain Foods Limited
Director of Transportation & Distribution
107 Main Street
Florenceville, NB E7L 1B2
Nabisco Ltd.
Distribution Manager
255 Chrysler Drive
Unit 1
Brampton, ON L6S 6C8
Pepsi-Cola Canada Ltd.
V.P. Market Development
5205 Satellite Drive
Mississauga, ON L4W 5J7
The Pepsi Bottling Group (Canada), Co.
Vice President, Retail Sales
5205 Satellite Drive
Mississauga, ON L4W 5J7
Pillsbury Canada Limited
Vice President, Sales
675 Cochrane Drive, Suite 700
Markham, ON L3R 0M7
Redpath Sugars, A Division of Tate & Lyle North American Sugars Ltd.
V.P. Distribution & G.M. Quebec Operations
7400 Trans Canada Highway
Ville St. Laurent, QC H4T 1A5
Tetley Canada Inc.
Vice President of Sales
6725 Airport Rd., Ste. 704
Mississauga, ON L4V 1V2
Unilever Canada Limited
V.P. Customer Business Development
160 Bloor Street East, Suite 300
Toronto, ON M4W 3W3
Weston Bakeries Limited (George Weston)
Director Sales
1425 The Queensway
Etobicoke, ON M8Z 1T3
B. Automotive Parts
Magna International Inc.
337 Magna Drive
Aurora, Ontario L4G 7K1
Stelco Inc.
Corporate Office
P.O. Box 2030
Hamilton, Ontario L8N 3T1 Canada
Burlington Technologies Inc.
Corporate Office
2380 South Service Road West
Oakville, Ontario
Canada L6L 5M9
Tel: (905) 847-8112
Fax: (905) 847-3748
Email: burltech@burltech.com
Website: www.burltech.com
C. Telecommunications Equipment
Nortel Networks
Corporate Headquarters
8200 Dixie Road, Suite 100
Brampton, Ontario L6T 5P6
JDS Uniphase Corporation
Corporate Offices - Canada
570 West Hunt Club Road
Nepean, Ontario K2G 5W8
D. Agricultural Products and Chemicals
Agrium Inc.
Corporate and Wholesale Head Office
13131 Lake Fraser Drive
Calgary, Alberta T2J 7E8
Pacific Ammonia Inc.
Vancouver Head Office
Suite 660, 1380 Burrard Street
Vancouver, B.C. Canada V6Z 2H3
APPENDIX G.
APPENDIX H. LOGISTICS ELASTICITIES
The effects of changes in transit time and transit time variability have been estimated in a particular auto parts case study conducted by Tyworth et al [14]. This work, based on an optimal continuous review inventory model, demonstrates the effects of transport system performance levels on logistics costs and service levels.
Representative elasticities have been calculated based on this work. This model only looks at inventory control for the case of a single production and single distribution centre. It does not consider logistics network re-organization and multiple warehouse consolidation.
Since each firm has its own logistics configuration, cost savings would need to be obtained at the level of the firm and scaled up to some corridor, region, or industry.
Table 3: Elasticities of total logistics cost with respect to transit time
Coefficient of variation of T | |||||
|
0 |
0.2 |
0.4 |
0.8 |
1.6 |
1 |
0.027857 |
0.032169 |
0.044914 |
0.072255 |
0.10711 |
2 |
0.054203 |
0.062332 |
0.085967 |
0.134773 |
0.193495 |
3 |
0.079159 |
0.090672 |
0.123636 |
0.189396 |
0.26464 |
4 |
0.102832 |
0.11735 |
0.158324 |
0.237533 |
0.32425 |
5 |
0.125319 |
0.142506 |
0.19037 |
0.280272 |
0.37492 |
6 |
0.146706 |
0.166268 |
0.220065 |
0.318475 |
0.418522 |
7 |
0.167071 |
0.188749 |
0.247659 |
0.352826 |
0.456438 |
8 |
0.186488 |
0.21005 |
0.273367 |
0.383881 |
0.489711 |
9 |
0.20502 |
0.23026 |
0.297376 |
0.412092 |
0.519146 |
10 |
0.222726 |
0.249462 |
0.319849 |
0.437832 |
0.54537 |
Table 4: Elasticities of total logistics cost with respect to travel time variation.
Coefficient of variation of T | |||||
|
0 |
0.2 |
0.4 |
0.8 |
1.6 |
1 |
0 |
0.033649 |
0.074179 |
0.174319 |
0.432274 |
2 |
0 |
0.032556 |
0.072751 |
0.174921 |
0.447274 |
3 |
0 |
0.033332 |
0.074926 |
0.181873 |
0.469886 |
4 |
0 |
0.033617 |
0.075846 |
0.185313 |
0.482708 |
5 |
0 |
0.033084 |
0.074877 |
0.184114 |
0.484618 |
6 |
0 |
0.031936 |
0.072479 |
0.179392 |
0.478234 |
7 |
0 |
0.030198 |
0.068722 |
0.171342 |
0.464104 |
8 |
0 |
0.028094 |
0.064093 |
0.161027 |
0.444213 |
9 |
0 |
0.025682 |
0.058721 |
0.148732 |
0.419008 |
10 |
0 |
0.02286 |
0.052377 |
0.133882 |
0.386931 |
Table 5: Elasticities of fill rates with respect to travel time.
Coefficient of variation of T | |||||
|
0 |
0.2 |
0.4 |
0.8 |
1.6 |
1 |
-0.0002 |
-0.000401 |
-0.000902 |
-0.002407 |
-0.006452 |
2 |
-0.000401 |
-0.000802 |
-0.001805 |
-0.004827 |
-0.012988 |
3 |
-0.000601 |
-0.001203 |
-0.002711 |
-0.007257 |
-0.01961 |
4 |
-0.000802 |
-0.001605 |
-0.003617 |
-0.0097 |
-0.026318 |
5 |
-0.001003 |
-0.002007 |
-0.004526 |
-0.012154 |
-0.033116 |
6 |
-0.001203 |
-0.00241 |
-0.005436 |
-0.014621 |
-0.040004 |
7 |
-0.001404 |
-0.002812 |
-0.006348 |
-0.017099 |
-0.046985 |
8 |
-0.001605 |
-0.003215 |
-0.007261 |
-0.01959 |
-0.05406 |
9 |
-0.001806 |
-0.003619 |
-0.008176 |
-0.022093 |
-0.061231 |
10 |
-0.002007 |
-0.004023 |
-0.009093 |
-0.024608 |
-0.0685 |
Table 6: Elasticities of fill rates with respect to travel time variation.
Coefficient of variation of T | |||||
|
0 |
0.2 |
0.4 |
0.8 |
1.6 |
1 |
0 |
-0.00534 |
-0.01278 |
-0.03532 |
-0.12276 |
2 |
0 |
-0.00492 |
-0.01178 |
-0.03263 |
-0.11349 |
3 |
0 |
-0.00451 |
-0.01081 |
-0.02986 |
-0.10313 |
4 |
0 |
-0.0041 |
-0.00982 |
-0.02713 |
-0.09365 |
5 |
0 |
-0.00366 |
-0.00874 |
-0.02403 |
-0.0822 |
6 |
0 |
-0.00316 |
-0.00754 |
-0.0207 |
-0.07035 |
7 |
0 |
-0.00272 |
-0.00649 |
-0.01775 |
-0.05971 |
8 |
0 |
-0.00217 |
-0.00515 |
-0.01401 |
-0.04672 |
9 |
0 |
-0.00157 |
-0.00372 |
-0.01012 |
-0.03355 |
10 |
0 |
-0.00116 |
-0.00272 |
-0.00722 |
-0.02309 |
1 Stockout periods occur when a product is not available. A key element of customer service, these periods can lead to out-of-stock costs incurred when an order is placed but cannot be filled from inventory. These costs can be classified as lost sales costs and back-order costs. Back-orders often generate additional order processing as well as transportation costs when they are not filled through the normal distribution channel.
2 ITS - Intelligent Traffic Systems
3 Transit reliability is expressed using the coefficient of variation CV. CV is defined as the ratio of the standard deviation of transit time over its mean.
4 Based on Industry Canada staff paper.
5 Transport demand (truck-km) is from the Canadian Vehicle Survey, Quarter 4-1999 (km driven by trip purpose), Transport Division, Statistics Canada, Oct 2000.
6 Overall logistics savings are estimated as a function of transport cost reductions from system improvements. These can be compared to a direct benefit reference line. Indirect benefits as a proportion of direct benefits are shown on a secondary axis.
