A data envelopment analysis (DEA) study of the logistics capabilities of Mexican states [ ]

Transcription

1 A data envelopment analysis (DEA) study of the logistics capabilities of Mexican states [ ] David Güemes-Castorena Center for Quality and Manufacturing, Monterrey Tech Neale R. Smith. Center for Quality and Manufacturing, Monterrey Tech Monterrey, Nuevo León, México

2 Abstract In this paper we present the results of a data envelopment analysis (DEA) study of the logistics capability of the states of. Publicly available data obtained from government agencies and well regarded private research centers is used as the input for the study. The primary objective is to characterize, within the Mexican business environment, the relationship between logistics capabilities and the economic performance of each state. The results may be used to identify alternatives for investment in logistics capability development, which may include both services and infrastructure. Keywords Data envelopment analysis, logistics,, economic indicators, GDP 1. Introduction and literature review With increasing globalization logistics capability becomes an increasingly important competitive factor. Several recent studies support this viewpoint. Bianchi [1] points out that Latin American countries need to improve their logistics systems to attract foreign investment and mentions that Brazil is promoting the privatization of ports and railways as a way to expand the national transportation infrastructure. King and Keating [3] identify the 100 most logistics- friendly cities in the US. Most of the best cities for logistics are seen to be strong in at least three of the four major transportation modes: road, rail, air, and water, indicating the great importance of including these four modes of transportation in any study of logistics capability. Oum and Park [4] identify, among other factors, transport linkage, port, airp ort and intermodal transport facilities among the most important factors that attract foreign direct investment in the northeast Asian region. Given the importance of four major transportation modes, we have focused on them in our study. Just as logistics capability is important for international competitiveness it is also important for individual states within a country that are competing for investments to boost their economic growth. In this paper we use a DEA model to examine the efficiency of the states of with respect to several indicators of logistics capability and several indicators of economic performance. The DEA study helps to identify states that may serve as role models for less efficient states. The organization of this paper is the following: section 2 describes the data and methodology and present s the DEA results, section 3 contains a discussion of the results, and section 4 provides concluding remarks. We assume the reader is familiar with the map of but if such is not the case, a map with each state s abbreviated name is included in an appendix. 2. Data The data used in this study comes from official publications by agencies of the Mexican government. These sources are the Communications and Transportation Secretariat (SCT) Statistical Year Book for 2002 [6], and the National Statistical Office (INEGI) XII Census for 2000 [2]. All of the data was gathered for each of the 31 states and for the Federal District. The data was grouped by major transportation mode infrastructure: road, rail, air and water. In this case water includes only ocean transportation, excluding river and canal transportation. For each mode, statistics were selected to represent three different aspects of the transportation mode. The three aspects were named absolute, density, and flow. Absolute refers to the total amount of infrastructure. For instance, in the case of roads, absolute refers to the total number of kilometers of roads in the state. Density refers to the amount of infrastructure per area in the case of roads and railroads, per length of coast in the case of ports, and per city of more that 100,000 inhabitants in the case of airports. In the case of airports, the number of relatively large cities was used instead of area because airports are built to serve population centers independently of the surface area of the state. Flow refers to the use given to the transport infrastructure. Since flow is not directly reported in the published statistics a proxy for flow was selected in each case from among the available statistics. For instance, in the case of roads, the number of cargo vehicles in urban locations was used as the best available proxy for flow on roads. In the case of rail transportation, no published statistic was found that could serve as a

3 proxy for flow. Table 1 details each of the statistics used for each of the groups and it also identifies the source. Road Rail Water Air Table 1. Logistics capabilities statistics Source Absolute Total roads (km) SCT [6], p 24 Density Highway length per 100 km 2 SCT [6], p 215 Flow Cargo vehicles in urban locations SCT [6], p 215 Absolute Total railroads (km) excludes private railroads SCT [6], p 66 Density Railroads length per 100 km 2 SCT [6], p 215 Flow Not Available Absolute Total number of ports (Calculated using the density times the length of the shore) SCT [6], INEGI [2] Density Ports per 100 km of shoreline SCT [6], p 215 Flow Total tons of flow (incoming and outgoing) including both international and cabotage. SCT [6], p Absolute Total number of airports (Calculated using the airport density times the number of locations of 100,000 hab. or larger) SCT [6], INEGI [2] Density Airports per location of 100,000 hab. or larger SCT [6], p 215 Flow Number of comercial aircraft SCT [6], p 93 The eleven statistics identified in Table 1 were used as inputs in the DEA models. The main outputs used are the total, primary, industrial, and service sector gross domestic products (GDP) of the states. This GDP data were obtained from INEGI [2]. The primary sector includes activities such as agriculture, agribusiness, livestock, fishing, forestry, and mining; the industry sector includes activities such as manufacturing, construction, and energy; the services sector includes activities such as trade and commerce, education, health, communications, transportation, financial services, leisure, and touris m [2]. The last variable included in the study was the number of persons employed in transportation, mail, and warehousing activities reported by the INEGI in the XII General Census in 2000 [2]. This last data was used as an input in one DEA model and as an output in a second DEA model. In cases where a statistic did not apply to all states, only the states with applicable data were included in the model. For example, not all states have railroads (surprising) so the model for rail included only the states with railroads. 3. Models The data was analyzed using DEA models of the CCR type [7]. Each transportation mode was analyzed separately in order to keep the models number of inputs and outputs in a reasonable range consistent with the number of available data points. For each transportation mode, two models were developed. The first model strives to represent the point of view of the Mexican government and views number of persons employed in transportation, mail, and warehousing activities (PE) as an output. This reflects the government s desire to increase employment. The second model strives to represent the point of view of industry and views PE as an input. This reflect industry s desire to increase efficiency by reducing headcount. We will re fer to the first type of model as the Model (GM) and to the second type as the Model (IM). The GM nominaly has three inputs and five outputs. The inputs are the measures for absolute, density, and flow described previously. The outputs are total GDP, primary GDP, industrial GDP, service sector GDP, and PE. In the case of rail transportation, where we were unable to obtain a measure of flow, the GM has only two inputs. The IM nominally has four inputs and four outputs, PE now being treated as an input rather than as an output. Only three inputs are included in the case of rail transportation due to a lack of flow data. The GM and IM models are shown in Figure 1.

4 Figure 1. Graphical representation of the Governement and Industrial Models. Absolute Density Flow Model Primary GDP Secondary GDP Tertiary GDP Total GDP Employment Absolute Density Flow Employment Model Primary GDP Secondary GDP Tertiary GDP Total GDP In order to discriminate between states that perform efficiently and those that do not, the number of DEA variables should be kept to a minimum. In this study, each of the two mo dels is applied to each of the four main transportation modes. This gives us eight different combinations of model type and transportation mode: GM & road, GM & rail, GM & water, GM & air, IM & road, IM & rail, IM & water, and IM & air. 4. Analysis of results Each of the eight data envelopment models was solved and the res ults were analyzed. The analyses will be presented in five sections. The first four sections that follow discuss the findings for each of the four major modes of transportation, road, rail, water, and air, in that order. The fifth section discusses findings that apply across the four modes of transportation and the differences between the GM and the IM. Since DEA is only one of many tools used to measure efficiency, the results of this study should be used with caution. 4.1 Road The GM & road and the IM & road models identified nearly the same states as efficient, the IM selecting 14 states and the GM 15. The state of was considered efficient by the GM but not by the IM. In fact, the IM ranks the state of as the least efficient. Undoubtedly this is because the state of employs a large number of persons (considered good by the government), comparatively more than necessary, to be considered efficient by the IM. Table 2 shows a summary of the findings grouped by GM and IM, showing the 100% efficient states and the five least efficient states. 100 % Efficient Bottom % Efficient Bottom 5 Table 2. Road DEA results for the GM and IM. AGS, BCS, CAMP, COAH, COL, CHIH, DF, DGO, GRO, MEX, MOR, NAY, OAX, QROO, ZAC PUE, YUC, SLP, GTO, HGO AGS, BCS, CAMP, COAH, COL, CHIH, DF, DGO, GRO, MOR, NAY, OAX, QROO, ZAC VER, HGO, JAL, GTO, MEX A graphical representation is useful to visually represent the similarities in results between the two models. Figure 2 shows the graphical representation. In the figure, a large cluster of most-efficient states (darker states) is seen in the northern part of the country. A cluster of less efficient states (lighter states) is also seen in the center of the country, which is represented better by the IM. The northern and southern peninsulas show both extremes in the efficiency continuum for the GM and IM.

5 Figure 2. Road DEA results for the GM and IM. - Road 100% efficient (15) Less efficient (7) Least efficient (10) - Road 100% efficient (14) Less efficient (4) Least efficient (14) 4.2 Rail The GM & rail and IM & rail models also give similar results but the IM scores are slightly higher (on average) that those for the GM. The IM identifies nine efficient states and the GM identifies six. The case of the state of is worth mentioning again since it does not appear in the GM bottom 5 ranking, but is identified as the least efficient state by the IM. Again, this is due to the PE variable, which results in a low efficiency from the point of view of. Table 3 shows a summary of the findings grouped by GM and IM, showing the 100% efficient states and the five least efficient states. Table 3. Rail DEA results for the GM and IM. 100 % Efficient AGS, BC, CAMP, COL, DF, GRO Bottom 5 VER, PUE, HGO, SLP, MICH 100 % Efficient AGS, BC, CAMP, COL, DF, DGO, GRO, NAY, ZAC Bottom 5 GTO, HGO, PUE, VER, MEX A graphical representation is shown in Figure 3. The map of the GM results a mostly white country and a scattered selection of states being the most efficient. The IM map shows a cluster of four efficient states (Zacatecas, Durango, Nayarit and Aguascalientes) in the western part of the county and some grey in the north and mostly white south, and it keeps the same scattered states as the GM. Since Quintana Roo and Baja California Sur do not have railroads, they were not considered for the rail analysis and are shown in white in the maps. Figure 3. Rail DEA results for the GM and IM. - Rail 100% efficient (6) Less efficient (3) Least efficient (23) - Rail 100% efficient (9) Less efficient (5) Least efficient (18)

6 4.3 Water The GM & water and IM & water models included only 17 states (BC, BCS, CAMP, CHIS, COL, GRO, JAL, MICH, NAY, OAX, QROO, SIN, SON, TAB, TAMPS, VER and YUC). All the other states were not considered for this study since they do not have ports. Due to the reduced number of states the DEA model is less able to differentiate efficient and less efficient states. The GM identified nine 100% efficient states and the IM identified 11. In this case, we report only the bottom 4 states s ince the efficiency of the best bottom 4 state is 65%, which is not a very bad performance. See table 4 for a summary of the results. In general, the water models show most of the states working at comparable levels relative to the other state s. Yucatan is an exception, since it has fewer people working in logistics-related areas, a measure considered better by the IM but not by the GM. The graphical representation for the water-dea is shown in Figure 4. Table 4. Water DEA results for the GM and IM. 100 % Efficient BC, CAMP, COL, CHIS, JAL, MICH, NAY, SON, VER Bottom 4 OAX, GRO, TAB, YUC 100 % Efficient BC, BCS, CAMP, COL, CHIS, JAL, MICH, NAY, QROO, SIN, SON Bottom 4 VER, OAX, YUC, TAB Figure 4. Water D EA results for the GM and IM. - Water 100 % efficient (9) Less efficient (4) Least efficient (19) - Water 100 % efficient (11) Less efficient (5) Least efficient (16) 4.4 Air The only state that was not included in this study was Tlaxcala, since it does not have an airport (Tlaxcala is the smallest state in, besides the Federal District). In this case, both the GM and the IM show differences in the rankings, with nine 100% efficient states for the GM and 12 for the IM. On the other hand, both the GM and IM ranked as worst performers the states of Oaxaca and Chiapas. A summary of the rankings is shown in Table 5. Table 5. Air DEA results for the GM and IM. 100 % Efficient AGS, CAMP, DF, DGO, GTO, MEX, MOR, QRO, ZAC Bottom 5 SON, BC, BCS, OAX, CHIS 100 % Efficie nt AGS, BCS, CAMP, DF, DGO, MEX, MOR, NAY, NL, QRO, QROO, ZAC Bottom 5 MICH, VER, PUE, OAX, CHIS The graphical representation of the results is shown in Figure 5. Neither the GM nor the IM results clearly identify a single cluster of similarly performing states but the concentrations of flights to the Federal District, Morelos, Querétaro, and the state of makes them a logical cluster to a considerable concentration of people in the Valley of. The second logical cluster consists of the states of

7 Aguascalientes, Zacatecas and Durango, scored similarly by both the GM and IM. Amazingly, Nuevo León appears as an efficient state for the first time in the analysis in the IM & air model. This is surprising since Nuevo León is considered to be one of the main industrial engines of the country. Figure 5. Air DEA results for the GM and IM. - Air 100% efficient (9) Less efficient (11) Least efficient (12) - Air 100% efficient (12) Less efficient (12) Least efficient (8) 4.5 Cross observation To summarize all the available information provided by the eight DEA analyses is a genuine challenge. We will try to provide a summary of the best-performing states as well as the worst-performing states. A single state, Campeche, was identified as 100% efficient in all eight of the analyses. Taking into account that not all the states have ports, the states of Aguascalientes and the Federal District were also among the most efficient, being identified as 100% efficiency by all six non-water models. Zacatecas and Durango were identified as 100% efficient in five of the non-water models. Also among the most efficient are Nayarit and Colima (which do have ports), identified as 100% efficient in six of the models. On the other end of the spectrum, the states of Yucatan, Tlaxcala, Tamaulipas, Tabasco, San Luis Potosi, Puebla and Hidalgo did not obtain a 100% efficiency score in any of the 8 simulations. Data Envelopment Analysis provides a large quantity of information for each of the states, and such information includes: The magnitude of the potential improvements that may allow a state to reach the 100% efficiency compared to peer states, the peer states being those to which the analyzed state has been most directly compared when calculating its efficiency rating. The comparison of the contributions of each reference state to the rating of each state on an individual input and output basis. The contributions information displays the extent to which each reference state has contributed to the efficiency rating given to a state in question. This contribution helps to judge the information provided by the reference comparison results. If a state had little influence in rating a state in question, then it is probably not a good reference with which to compare the inefficient state. However, if a reference state has great influence on just one or two inputs or outputs, then it can prompt an investigation of those inputs or outputs. The input/ output contribution information presents information about the emphasis that the analysis has placed on each input/output variable. This is a useful indicator of which inputs and outputs have been used (and which have been ignored) in determining a given efficiency score. In some cases, this helps to validate the score. The reference set frequency information shows how many times an efficient state has been part of an inefficient state s reference set. The higher the frequency with which an efficient unit appears in reference sets, the more likely it is that it is an exemplar of good performance. Efficient states which appear in few reference sets are likely to have an unusual combination of inputs and

8 outputs and as such are not likely to offer the best operating practices for inefficient states to emulate. It is beyond the scope of this paper to put the DEA model to use in the ways just described. In general, this additional information would be useful on a case by case basis, for example, when a particular state wishes to analyze its situation to determine ways to improve its logistics capabilities. 5. Conclusions This study presented a snapshot of the logistics capabilities of the states in using Data Envelopment Analysis. An analysis for each state can be performed and analyzed in order to identify their strengths and weaknesses and set realistic goals for improvement. In this paper we presented the results of a data envelopment analysis (DEA) study of the logistics capability of the states of. The publicly available data and their sources are described and the DEA results are presented for each of the eight developed models. Some of the most noteworthy observations are the changes that the PE variable produces in the government models as compared to the industrial models, in some cases changing the efficiency score of a state from 100% to the lowest on the list. The results may be used to identify alternatives for investment in logistics capability development, which may include both services and infrastructure. As in many DEA studies, more questions are raised than answered. The authors hope is that the reported findings will lead to further research that can be of use to states of wishing to improve their logistics capabilities. Acknowledgements This research was partially supported by the ITESM Extended Enterprise for Mass Customization Research Chair in Industrial Engineering. References [1] Bianchi, M., 2003, Global watch: Latin America, seeking to improve logistics, Chemical Market Reporter, 264, p. 24. [2] INEGI, 2000, XII Censo General de Población y Vivienda, INEGI, < [Accessed January 4, 2005]. [3] King, B. and Keating, M., 2002, America s 100 most logistics -friendly cities, Transportation and Distribution, October, p. 14. [4] Oum, T.H. and Park, J., 2004, Multinational firms location preference for regional distribution centers: focus on the northeast asian region, Transportation Research Part E, 40, p [5] SCT, 2001, Programa Sectorial de Comunicaciones y Transportes , < GjnTJtnv6h11kkv7mC3gk9nRy! ! ?_nfpb=true&_pageLabel=P38003>, [Accessed January 4, 2005]. [6] SCT, 2002, Anuario Estadístico del Sector Comunicaciones y Transportes, 2002, Secretaría de Comunicaciones y Transportes, < [Accessed January 4, 2005]. [7] Charnes, A., Cooper, W.W., and Rhodes, E., 1978, Measuring the efficiency of decision making units, European Journal of Operational Research, 2, p

9 Appendix States of BAJA CALIFORNIA SONORA CHIHUAHUA COAHUILA DE ZARAGOZA SINALOA DURANGO NUEVO LEON BAJA CALIFORNIA SUR ZACATECAS TAMAULIPAS NAYARIT SAN LUIS POTOSI AGUASCALIENTES GUANAJUATO HIDALGO JALISCO QUERETARO COLIMA DISTRITO FEDERAL TLAXCALA MEXICO MICHOACAN E DF PUEBLA VERACRUZ MORELOS TABASCO GUERRERO OAXACA YUCATAN CAMPECHE QUINTANA ROO CHIAPAS States acronyms AGS Aguascalientes GRO Guerrero QROO Quintana Roo BC Baja California HGO Hidalgo SLP San Luis Potosí BCS Baja California Sur JAL Jalisco SIN Sinaloa CAMP Campeche MEX México SON Sonora COAH Coahuila de Zaragoza MICH Michoacán de Ocampo TAB Tabasco COL Colima MOR Morelos TAMPS Tamaulipas CHIS Chiapas NAY Nayarit TLAX Tlaxcala CHIH Chihuahua NL Nuevo León VER Veracruz de Ignacio de la Llave DF Distrito Federal OAX Oa xaca YUC Yucatán DGO Durango PUE Puebla ZAC Zacatecas GTO Guanajuato QRO Querétaro