The application of decision-making support systems for fleet management in small and medium-sized transport enterprises in Russia

Transcription

1 The application of decision-making support systems for fleet management in small and medium-sized transport enterprises in Russia Aleksey Dorofeev National Research University Higher School of Economics Moscow, Russia Abstract Currently, problems of optimization of logistic tasks attract a lot of attention. The widespread use of modern information technology in the management of transport enterprises has become nearly ubiquitous, including the Russian Federation. At the same time, due to sharp increase in the volume of information, there appear certain problems regarding the effective use of this information for decision-making. The use of general BI-systems in small and medium-sized Russian transport companies is not very attractive due to their high costs and difficulties in setting up and training of staff as well as due to the nature of business problems. This paper proposes and discusses the main features of the architecture of the support system for decision-making in the small and medium business segments of the freight. Finally, an example of the developed application and of its implementation in the logistics of dairy products is presented. Keywords: decision support system, BI-system, fleet management I. INTRODUCTION In the current economic environment it is now almost impossible to conduct business without a thorough analysis of data from various sources [1]. In the transport industry, decision making process depends on multitude of both internal and external factors, which include: balance of supply and demand on the freight market, regulating legislation, technical condition of vehicles, road and weather conditions, etc [2]. These factors can be known in advance as well as occur unexpectedly, which requires a rapid and operative response from a dispatcher and an intervention in the transportation process. At that time, the situation can often be complicated by additional collisions. For example, in case of a sudden failure, or a car crash it is necessary to replace the vehicle. In case if the vehicle has already been loaded, then the discharge of the vehicle with the subsequent loading of the substituting vehicle is fulfilled. The complexity of these works depends greatly on the cargo itself, the manipulation of which may require a crane or other equipment. Obviously, all this leads to time delays and breaking of transportation schedule. At the same time, the probability of a sudden failure of any component or unit of the vehicle or an accident to a certain extent depends on the technical condition of the vehicle, the driver's skills and responsibility, as well as the state of the road, traffic, weather and climate conditions. Management of technical condition of vehicles, selection and training of driving staff is entirely the responsibility of the owners or managers of transport enterprises, which must ensure them in compliance with existing standards, methods and recommendations [3]. II. MODERN INFORMATION TECHNOLOGY AND FLEET MANAGEMENT Direct transportation planning, routing and monitoring is being carried out with the help of geo information systems, wireless telecommunications RFID, GPS / GSM, WI-FI, which in a significant extent expands the possibilities for optimization of the transportation process. Thus, the use of GIS, public as for example, Google.Maps [4], as well as commercial, for example, ESRI ArcGIS, allows to generate the shortest possible routes of vehicle movements, and also acquire information about traffic jams, traffic density. Determination of the location of the vehicle during the transportation, as well as its operation settings (speed, fuel consumption, gas stations) taking into account state of the traffic gives opportunity to make prompt decisions on the correction of the route, and also to monitor the transportation schedules [5]. Subsequently, the data obtained from the monitoring system can be used for the analysis of the performance and profitability of the vehicle. It is known that in the supply chains material flow is always accompanied by the flow of information. An integration and speed of data transmission under modern conditions is so high that we can talk about the actual supply chain management under online mode [6]. Such development of information and telecommunication technologies has opened up new opportunities for logistics services operators to satisfy customer needs more effectively, including reduction of transportation costs, minimizing risks and uncertainties, adaptation in frequently changing conditions, resolving non-standard tasks, ensuring the supplies Just-In-Time [7]. At the same time it should be noted that the volume of electronic documents and operational data of supply chain providers can reach quite considerable size, which assumes the use of powerful computational resources, as well as specialized software for information analysis and processing. III. LOGISTIC INFORMATION SYSTEM In practice, the management of the logistics company is carried out on several levels - operational, tactical, strategic, whereupon every level managers require their own analytical reports, charts, diagrams and data marts of various extents of

2 detailing and consolidation of information for the study of retrospective and future planning [8,9]. For example, at the operational level the vehicles are distributed by routes, customers, management and correction of vehicle routes is made in case of changes of the conditions of the road or the receipt of new applications for transportation. At the tactical level, scheduling, planning of technical maintenance of the vehicle is implemented. At the strategic level, the performance of the transportation process as a whole is analyzed, decisions on purchasing vehicles, selection of their type, capacity, cost efficiency of vehicle exploitation, and profitability of transportations in general are made. In such a way, data from information logistics system is a basis, allowing defining the strategy of transport company development, as well as a tool to increase efficacy of its operation and get competitive advantages [10]. However, during practical application, it is a problem to obtain information from the database of the logistics system, which is required for the support of management decisionmaking. Moreover, based on the experience in the implementation and operation of such systems in various transport companies in Russia, it is possible to identify a number of difficulties faced by business analysts. A variety of requests necessary to be formed to a database system can be quite large, since business needs require research of an enterprise activity from different perspectives. Architecture of information logistics typically consists of tens or hundreds of internal tables that can be used in various combinations and quantities in analytical queries depending on business tasks. At the same time, an ordinary manager of the transport company, as a rule, does not possess great knowledge in programming, so he needs a tool with which he could easily build ad hoc queries against the database and receive a variety of analytics [11]. IV. DECISION SUPPORT SYSTEM FOR FLEET MANAGEMENT As of now, in various industries are widely used BIsystems, functionality of which includes a multi-dimensional data analysis (OLAP), forecasting, the system of key performance indicators, Data Mining, Data Marts and etc [12]. These systems have wide opportunities, however, in the real situation there is a problem with the lack of time to prepare preliminary analytical queries, even with the application of special masters or query builders that exist in the BI-systems. In practice, the user wants to get the results he needs in one or two clicks without studying the of additional software, model construction in accordance with logistics business processes of the company, or the description of the business problem he wants to solve. It often happens that the manager does not know methods of solving business problems that his company is currently facing [13]. Moreover, often there is no system of performance indicators that would demonstrate the current state of the company. It should be noted that the transport and logistics sector activity requires special knowledge and high professionalism of the management staff. However, the level of training of managers at enterprises is often not high enough. Therefore preliminary settings of BI-system prior to the analysis should be minimized, i.e. the system must already have a business model specially for the transportation industry. The peculiarity of Russian conditions is that the fleet (of commercial vehicles) is the most diverse, including the most modern models of trucks and buses, manufactured in the USA and Western Europe, as well as heavily out-of-date models of trucks and buses of Russian and foreign manufacturers. Integration with information logistics applications with onboard electronic systems of cargo trucks is still poorly developed. In general are used set systems GLONASS / GPS, by means of which occurs control over the location, time of operation of the vehicle, as well as fuel consumption. Diagnostic information on the operation of on-board systems of a vehicle or a bus, as well as data about the actions of the driver is still hardly used for operational management. Less commonly can be met electronic meters of passengers in buses and taxis, although the information from them is very important for the evaluation of passenger flow. In those areas where they are used in municipalities, for example, in Moscow, by means of analytical applications are planned the optimal routes, schedules of buses taking into account the dynamics of vehicles movement, passenger flow changes etc. Thus, an application of BI-systems in Russia in vehicles logistics and management is still at an early stage of development. Practically, they are not used in Russian small and mediumsized transport companies, as BI-general-purpose systems are quite expensive for such companies. Thus, the actual task is the development of specialized decision support system for fleet management of small and medium-sized enterprises, which would include the multidimensional data analysis (OLAP), a system of key performance indicators for vehicles operation, module of transportation cost analysis, vehicle technical maintenance planning module, charts, and data marts. The architecture of this system should have a unified database containing electronic documents concerning operational activity of transport-logistics company [14]. These documents include requests for transportation, cargo bills, route sheets, information from the monitoring system and on-board diagnostic system, the acts of performed repair works, etc [15]. In particular, OLAP-module should be able to build hyper cubes on fuel consumption according to the transport operation, technical maintenance of the vehicle, total transportation costs. For example, for the operational management the controller needs to know the daily fuel consumption for each vehicle, as well as cost overruns or fuel economy. It is well known that fuel consumption can be affected by various factors - the skill of the driver, failure of any system of the truck, road and climate conditions, insufficiently inflated tires, bad aerodynamics, etc. With the help of multi-dimensional tables manager is able to analyze the factors affecting the change in fuel consumption and make timely decisions about their elimination. In such a way the dependence of fuel consumption on the drivers' qualification is determined by the construction of the corresponding hyper cube s section. To evaluate the transportation efficiency a hypercube with measurements "route", "customer", "cargo", "vehicle, "date / month" is built [16]. The analysis, as a rule, is usually carried out in terms of tonnage and ton-kilometers (Fig. 1)

3 Figure 1. OLAP-analysis of cargo transportation with measurements "customer", "cargo", "vehicle, "date / month" Figure 2. Freight performance measures system V. PERFORMANCE INDICATORS AND PLANNING To evaluate the effectiveness of work of transport - logistics company system of performance indicators is required. In general, for cargo transportation enterprises are used common indicators, characterizing the work of cargo transport - time for loading and unloading, the average daily mileage, mileage with cargo, etc [17]. Also for the analysis a ratio of mileage with cargo to total mileage, ratio of transported tonnage to vehicle lift capacity is used, etc. For example, during cargo transportation the customer pays only for the shipping from the initial to the final point of destination, and does not pay for the return route. Accordingly, in order to inease profitability, the owner must find a work for the return route. Often, trucks go around several cities in sequence, performing transportation for various customers in order to get back and minimize an empty run [18]. In this case, the total mileage is calculated according to the data from the monitoring system or route sheet and a run with the load is calculated according to the invoices. The more a mileage with the load approaches the value of the general run, the more effective is the use of the vehicle. During the analysis of this and other indicators it is visually clear to show the dynamics of indicators change in the form of the corresponding diagrams (Fig. 2) Another important task is technical maintenance planning. In Russian Federation is approved and operates planningpreventative maintenance system, which is also widely used in

4 other countries. Planning is performed based on the standards set by the plants - producers of vehicles, on average daily vehicles mileage as well as operating conditions. As a result of planning process, the planning manager gets forecast values of the number of days for vehicle technical maintenance, the amount of normative operations and hours necessary for its performance. These data are the initial information for the calculation of necessary personnel, equipment necessary to perform technical maintenance, and production areas. Further forecasted number of days of downtime on technical maintenance can be used to calculate the size of the fleet required to implement the necessary volume of transportation. Planning the types and number of vehicles of each type, as well as the analysis of the cost of their operation is also the subject of close attention from transportation company s directors and managers [18-19]. On the basis of historical data, as well as current requests for transportation is built a forecast of transportation volume for the future - the total tonnage and ton-kilometers. On the basis of the average daily mileage, vehicle lift capacity and size of bodies it is possible to calculate the required amount of vehicles. In general, the problem of selection of the optimal number of vehicles is quite complex, where much depends on the routes, seasonality and the fact that fleets can often be heterogeneous. To solve the problem special algorithms are used. But for small and medium-sized enterprises with a uniform transport means, constant routes and orders, one can apply more simplified calculations. As a result of planning process is also calculated the cost of operation of the vehicle fleet, which consists of the cost of fuel, spare parts, oil and consumables, depreciation, repairs and technical maintenance, wages for the drivers, overhead charges. VI. DECISION-MAKING SUPPORT SYSTEM «AUTOBASE- EXPERT» We developed a decision-making support system «Autobase-Expert» for transportation and logistics companies, in which is implemented the subsystem of multidimensional data analysis (OLAP), the module of calculation and visualization of key performance indicators, subsystem of maintenance planning, module of calculation of cost production and cargo transportation profitability. Small and medium-sized Russian enterprises involved in cargo transportation on the road, pretty much felt the effects of the global economic crisis, which led to the closure of many of them. One of the reasons for such outcome was insufficient use of IT-management solutions for vehicle management. However, there are examples which show that the effective development of the transportation company, stable position in a highly competitive market, and sustained growth are the result of a competent and successful use of information logistics system. Thus, on a strategic level the IT solutions as well as BI-system are that asset that provides a competitive advantage. In particular, one of the segments of cargo transportation with stable situation is the distribution of food, milk-based products [20]. One of the largest distributors of milk, milk products and other food products in the Moscow region is the company "East-Milk", in which was implemented a system «Autobase- Expert». The company has more than 100 vehicles with carrying capacity from 3.5 to 20 tons per day and serves more than 2,500 customers daily. For managers of the company it was necessary to analyze the dynamics cargo transportation, evaluate fuel consumption taking into account work of refrigerating machines, assess the cost of repairs and spare parts to optimize the size of the fleet and the cost of its exploitation, which became a prerequisite for the introduction of BI-systems. CONCLUSION Use of application «Autobase-Expert» has shown that specialized decision support system positively affects the efficiency of fleet management. BI-systems allow managers to receive visual indicators of the activities of the enterprise for the current moment instead of "raw data" as well as to receive forecasted information for the future periods of time. Currently, due to the use of the built-in services in the transport industry, offered by manufacturers of trucks like SCANIA, VOLVO, Daimler and others, one expects further increase of interest in the BI-systems. It is clear that the emphasis in their application will be shifted in order to meet the challenges of operational and tactical management on the strategic level as well. Besides, information related to road safety also became available for analysis, for example, data on the performance of the driver in various maneuvers on the road, information on traffic conditions etc. The development of the WEB 3.0 technology (RFID, web-services, semantic web) opens up wide perspectives for further «intellectualization» of fleet management systems. REFERENCES [1] Tamio Shimizu, Marly Monteiro de Carvalho, Fernando Jose Barbin Laurindo. Strategic alignment process and decision support systems : theory and case studies. IRM Press, 2006, p. 357 [2] Teodor Gabriel Crainic, Michel Gendreau, Jean-Yves Potvin. Intelligent freight-transportation systems: Assessment and the contribution of operations research. Transportation Research Part C 17 (2009) [3] Alfonso J. Pedraza-Martinez, Luk N. Van Wassenhove. Transportation and vehicle fleet management in humanitarian logistics: challenges for future research. EURO J Transp Logist (2012) 1: [4] Luís Santos, João Coutinho-Rodrigues, Carlos Henggeler Antunes, A web spatial decision support system for vehicle routing using Google Maps. Decision Support Systems 51 (2011) 1 9 [5] Jaime Barceló, Hanna Grzybowska, Sara Pardo. Vehicle routing and scheduling models, simulation and city logistics. Dynamic Fleet Management Operations Research/Computer Science Interfaces Series Volume 38, 2007, pp [6] Daniel Prajogo, Jan Olhager. Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration. Int. J. Production Economics 135 (2012) [7] Andrea Attanasio, Jay Bregman, Gianpaolo Ghiani, Emanuele Manni. Real-Time Fleet Management At Ecourier Ltd. Dynamic Fleet Management Operations Research/Computer Science Interfaces Series Volume 38, 2007, pp [8] Reza Zanjirani Farahani, Shabnam Rezapour, Laleh Kardar. Logistics Operations and Management: Concepts and Models. Elsevier, 2011, p. 469 [9] Öykü Işık, Mary C. Jones, Anna Sidorova. Business intelligence success: The roles of BI capabilities and decision environments. Information & Management 50 (2013) [10] Anabel Gutierrez, Alan Serrano. Assessing strategic, tactical and operational alignment factors for SMEs: alignment across the organisation's value chain. Int. J. of Value Chain Management, 2008 Vol.2, No.1, pp

5 [11] Ralph Kimball, Joe Caserta. The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley, 2004 p. 491 [12] Larissa T. Moss, Shaku Atre Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications, Addison Wesley, 2003, p. 576 [13] Dorian Pyle. Business Modeling and Data Mining. Morgan Kaufmann Publishers, 2003, p. 650E.W.T. Ngai, T.K.P. Leung, Y.H. Wong, M.C.M. Lee, P.Y.F. Chai, Y.S. Choi. Design and development of a context-aware decision support system for real-time accident handling in logistics. Decision Support Systems 52 (2012) [14] E.W.T. Ngai, T.K.P. Leung, Y.H. Wong, M.C.M. Lee, P.Y.F. Chai, Y.S. Choi. Design and development of a context-aware decision support system for real-time accident handling in logistics. Decision Support Systems 52 (2012) [15] Hanna Grzybowskaa Jaume Barceló. Decision support system for realtime urban freight management. Procedia - Social and Behavioral Sciences 39 ( 2012 ) Jaume Barceló. Decision support system for real-time urban freight management. Procedia - Social and Behavioral Sciences 39 ( 2012 ) , Hanna Grzybowskaa Jaume Barceló. Decision support system for real-time urban freight management. Procedia - Social and Behavioral Sciences 39 ( 2012 ) [16] Erik Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley p. 688 [17] Francesco Paolo Deflorio, Guido Perboli, Roberto Tadei. Freight distribution performance indicators for service quality planning in large transportation networks. Flex Serv Manuf J (2010) 22:36 60 [18] Johan Woxenius. Directness as a key performance indicator for freight transport chains. Research in Transportation Economics 36 (2012) [19] Arild Hoff, Henrik Andersson, Marielle Christiansen, Geir Hasle, Arne Løkketangen. Industrial aspects and literature survey: Fleet composition and routing. Computers & Operations Research 37 (2010) [20] Niklas Arvidsson. The milk run revisited: A load factor paradox with economic and environmental implications for urban freight transport. Transportation Research Part A 51 (2013)