European Journal of Operational Research 152 (2004) 382–398 www.elsevier.com/locate/dsw
Design of an IT-driven decision support system for vehicle routing and scheduling Sotiris P. Gayialis *, Ilias P. Tatsiopoulos Section of Industrial Management and Operational Research, Faculty of Mechanical Engineering, National Technical University of Athens, 15780 Zografos, Greece Received 26 January 2001; accepted 23 October 2002
Abstract This paper presents the development of a decision support system used by an oil downstream company for routing and scheduling purposes. The studied problem refers to a complex delivery process of oil products from a number of distribution centers to all customers. The latest rapid advance of operations research (OR) applications, in the form of advanced planning and scheduling (APS) systems, has shown that OR algorithms can be applied in practice if (a) they are embodied in packaged information technology (IT) solutions, (b) the interface problems to mainstream ERP software applications are solved. In this study the utilisation of advanced IT systems supports effectively the planning and management of distribution operations. The combination of a supply chain management (SCM) application with a geographical information system (GIS) integrated with an enterprise resource planning (ERP) software resulted to this innovative decision support tool. The objectives of this new tool are: optimum use of the distribution network resources, transportation cost reduction and customer service improvement. The paper concludes with the benefits of the new system, emphasising at how new technologies can support transportation processes with the help of operations research algorithms embedded in software applications. 2003 Elsevier B.V. All rights reserved. Keywords: Decision support system; Supply chain management; Routing; Information systems integration; Oil industry
1. Physical distribution and vehicle routing Physical distribution, as a component of the supply chain, includes a set of activities executed to obtain the delivery of a product from the pro-
* Corresponding author. Tel.: +30-10-7722384; fax: +30-107723571. E-mail addresses:
[email protected] (S.P. Gayialis),
[email protected] (I.P. Tatsiopoulos).
duction location to the end customer [26]. Problems related to physical distribution are: selection of distribution channels, determination of customer service level, distribution centersÕ location planning, inventory management, transportation means selection, fleet composition, delivery scheduling and vehicle routing. Vehicle routing [23] refers to a broad group of problems that could be expressed as following: a finite set of customers at fixed locations with defined demand, must be supplied with goods by a
0377-2217/$ - see front matter 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0377-2217(03)00031-6
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number of vehicles having a finite capacity and predefined starting points and terminals. The vehicle routing and scheduling problem consists of two sub-problems: the customer grouping to routes (clustering) and the definition of the optimum tour for every route (cluster). Therefore, route is the total number of deliveries made by a single vehicle and tour is their sequence. The solution of these sub-problems results to the routes and tours that minimise the total transportation cost. Vehicle routing and scheduling performed in the supply chain context is a complex process. Alter [1] offers a simple definition of a systemÕs complexity in stating that it is a function of the number of differentiated components and the number and nature of their interactions. The characteristics that Alter describes can be compared with those to which Scuricini [31] refers, who identifies four attributes of complexity: numerosity of components, variety of components, type of components and organisation. The complexity of vehicle routing and scheduling process lies on the fact that there are many constraints and parameters to consider, concerning the available transportation means, the products, the depots, the customers and the road network [20]. A companyÕs dispatcher is usually responsible for the execution of the described complex process, which is focused on the effective resource planning of the distribution system so that the products are delivered to customers on time. The effectiveness of the process suggests a balance between the highlevel customer service and the corresponding total cost. The dispatcherÕs tasks that are managed by the designed system include customer allocation to distribution centers, customer order grouping and dispatching to the vehicles, as well as determination of the sequence of deliveries.
2. The integration of information technology and OR algorithms 2.1. Heuristics for the vehicle routing problem Many authors have suggested a number of solution methods for the vehicle routing problem. Heuristics have been developed and applied to
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many routing and scheduling case studies and several researchers have compared and evaluated these methods [5,15,17,21,22]. The advantage of the heuristics is their ability to handle efficiently a large number of constraints and parameters of the routing problem. They perform a relatively limited exploration of the search space and generally produce good quality solutions within modest computing time. Therefore, classical heuristics are still widely used in commercial software packages. Heuristics for the vehicle routing problem can be classified into two main classes: classical heuristics, developed between 1960 and 1990 and metaheuristics whose growth has occurred in the last decade [22]. The most well known classical heuristics are the Savings and Sweep algorithms. The most successful metaheuristic approach is the tabu search heuristics. In terms of the solution procedure, classical heuristics can be classified in sequential and parallel. Sequential heuristic algorithms solve the vehicle routing sub-problems (clustering and finding best tour) separately and consecutively. Parallel heuristics produce routes and tours concurrently and can be classified in construction and improvement methods. Clarke and Wright [7] developed a parallel heuristic, known as the Savings algorithm. Sweep algorithm, which is attributed to Gillett and Miller [16], is the most common sequential heuristic. Other classical heuristics include sequential improvement methods, insertion methods [24], exchange algorithms, petal algorithms, cluster first route second algorithms, nearest neighbour and simulated annealing methods. In metaheuristics, the emphasis is on performing a deep exploration of the most promising regions of the solution space. These methods typically combine sophisticated neighbourhood search rules, memory structures and recombination of solutions. The quality of solutions produced by these methods is usually much higher than that obtained by classical heuristics but the computing time is increased. 2.2. Advanced planning and scheduling systems: The OR practice using IT Modern information technology supports the definition and analysis of the factors influencing
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the routing process, a problem difficult to solve using empirical methods. Nowadays, there are software applications that manage supply chain processes at all three decisional levels of management (Table 1), including features of transportation planning and execution: • high level (strategic)––these applications offer transportation planning and fleet composition modules; • middle level (tactical)––these applications allocate resources to the general transportation plan derived from the previous level; • low level (operational)––at this level the applications are oriented to detailed scheduling of routes and tours on a daily basis. The mathematical technique most commonly applied to strategic transportation planning is linear programming and related algorithms like network optimisation and mixed integer programming. Linear programming based approaches model the current transportation business including revenues and costs. The models are extended to include decisions on the location of new facilities, acquisition of transport resources and implementation of transport strategies [32]. These applications date back to the 1970Õs and many companies
have taken this approach [14]. Transportation planning applications at the tactical level include linear programming based models similar to the ones mentioned above. Their functionality is found in most ERP systems. The operational level applications utilise mostly heuristic algorithms. These algorithms can find a near-optimum solution for complex and multi-parameter problems in short time. As early as the 1960Õs, papers were published which claimed to solve a problems of generating distribution schedules. The methodologies were seen by many computer manufacturers, as ‘‘programmable’’ [23]. Although serious efforts were done since the 1970Õs, to embody OR algorithms in computerised routing and scheduling systems, they were seen to fail due to insufficient consideration of additional features required (e.g. graphical interface, road network modelling, decision-making) and low computational capabilities of the systems. Today, the advanced computer programming languages and the powerful hardware supports the use of these algorithms in advanced planning and scheduling (APS) software systems. The ability to implement APS systems as a decision support tool directed in transportation, has been greatly enhanced by improvements in telecommunications, better supply chain management systems, more
Table 1 Supply chain processes and decisional levels Strategic
Tactical
Supply chain planning
Transportation planning
Shipment planning
Vehicle routing
Warehousing
Site location Capacity sizing
Site location Fleet sizing
Outsourcing Bid analysis
Sourcing
Distribution centersÕ allocation
Fleet sizing
Fleet sizing Service day balancing Frequency analysis
Warehouse layout Material handling design Control systems
Production planning
Transportation strategy
Routing strategy
Storage allocation
Sourcing
Network alignment
Consolidation strategy Mode strategy
Zone alignment
Order picking strategies
Vehicle dispatching
Order picking
Inventories planning Operational
Material requirements planning––MRP Distribution requirements planning––DRP Enterprise resource planning––ERP
Load consolidation Timing and volume of movements
Shipment dispatching
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comprehensive ERP implementations, and more powerful forecasting tools [32]. The 1990Õs have seen a revolution in computer and information technology that has had a significant affect on the OR practice [12]. Recently the field of operations research has greatly influenced by the electronic revolution, causing the practice of OR to be more widespread than in previous decades. The power of OR models and optimisation methods can be enhanced incorporating them within a decision support system, which takes advantage of modern information technology. Many case studies concerning OR computer applications in routing and dispatching problems, can be found in the literature. A problem of optimising routes and scheduling of freight operations solved by a combination of heuristics and integer programming [18]. A problem of delivering orders to customers homes and dispatch technicians to service customers is solved by a clusterfirst-route-second heuristic [36]. The problem of picking and delivering products to dairies is solved by a tour construction/improvement heuristic [28]. A DSS for a dairy companyÕ vehicle routing and scheduling includes the sweep algorithm of Gillet and Miller [16] to schedule the trips of the tank trucks and a farthest insertion algorithm to determine the sequence of the loading points that minimise the distance traveled for each trip [19].
3. Decision support systems construction The decision support system (DSS) has emerged as a computer-based approach to assist decisionmakers to address semi-structured problems by allowing them to access and use data and analytic models [34]. Such a system does not replace the decision-maker. It supports the decisions where part of the analysis can be systematised for the computer, so that the decision-makerÕs insight and judgement are improved [19]. It is often amenable to modelling, OR-techniques and graphical presentation for parts of the overall scenario. In the last 20 years, the availability of increasingly powerful computers at a decreasing cost, on one hand, and advances in operations research/ management science that have resulted in im-
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proved algorithms, on the other hand, have made it possible to solve larger and more complicated problems than ever before [11]. The need of an optimisation-based decision support system, which can also be viewed as an application-specific delivery system for an optimiser engine [13], arises when OR technology can be used to support highly repetitive decisions, such as daily vehicle routing problems. The process of constructing a decision support system requires developing complex mathematical models, using specialised algorithms to solve these models, handling large amounts of input data, and interpreting of output data. Because of the complexities and the different kinds of knowledge required, constructing this type of system tends to be a time-consuming and expensive software development project, which requires many hours of work of a team of computer programmers and operation researchers. Until now, however, there have been relatively few tools that have been proposed for supporting this process. Although important advances have been made in the development of modelling systems and modelling management systems [4], most of the tools that are in use are intended to support the operations research practitioner in modelling and solving the problem. Interesting works in the area of developing tools to support DSS development can be found in [2,25]. Relative work in the field of DSS development for dispatching purposes, can be found since 1987 in [6], where is described an integrated, automated, real-time computer system for centralised control of distribution to customers of light petroleum products. The implemented system includes a collection of integer programming methods used within a real-time, transaction-driven information management system and exploits the experience and knowledge of the human dispatchers. Another case study of a solution used at an oil downstream company is indicative of the approaches used for routing single and multi-compartment bulk movements [27]. The implemented dispatching system consists of models and algorithms, together with user interfaces for input and output sides, as well as databases and data management routines. Ronen [27] agrees that the rapid development of
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computing power, combined with the development of ever more efficient algorithms, has facilitated the recent emergence of optimisation models in transportation dispatching systems. The development of a system that dispatches truck shipments of heavy petroleum products, using nonhomogeneous fleet of companyÕs controlled and contract vehicles is described in [3]. A constraint-based vehicle scheduling system for an oil and bitumen company is described in [10]. The paper presents the development of an application using the modules of optimisation-specific software and it consists of a front-end data management system and a scheduling system. Our study targeted at the design of an IT-driven system for decision-making concerning routing and scheduling in oil downstream operations. The concept was not to develop a system from the beginning, as it would result to a lengthy and expensive software development project. Hence, the design of the new system utilises the advanced planning and scheduling capabilities of OR methods embedded in commercial software applications, the efficiency of a graphical user interface and the complementary geo-reference abilities of a GIS. The DSS design methodology emphasises process modelling and analysis in order to lay out the exact system specifications and evaluate the
functionality of the proposed applications. A special issue is the selection and integration of the overlapped functions of the software applications selected.
4. Case study: An oil downstream company 4.1. The considered distribution system The case study is an oil downstream company, which is the commercial subsidiary of a group of oil companies. Sales and distribution cover the full range of petroleum products (petrol, diesel, fuel oil, aviation fuel, liquid gas, lubricants and asphalt) both in the domestic market (petrol stations, retail and industries) and the transit business (aviation and marine). The enterprise supply chain (Fig. 1) is comprised of the vendors (refineries and third party oil companies), the fuel depots–terminals and the final customers. The products are distributed using chartered ships, pipelines, barges, tank trucks and rail. The majority of deliveries is accomplished with surface transportation means, especially tank trucks. A high percentage of these trucks (85%) is hired, including dedicated contract carrier trucks and a few common carrier trucks. The fleet is
Fig. 1. Enterprise supply chain.
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composed of 190 trucks of various types with different compartment capacities. The distribution responsibility regions are divided into northern, southern, central and western Greece. Each distribution center corresponds to one responsibility region and each region includes many storage facilities. Consequently, each distribution center is liable for the planning and routing of deliveries to regional customers. 4.2. Problem definition The oil company requires a decision support system for efficient distribution planning and control. The major requirement is the support of the dispatcherÕs decisions in scheduling the deliveries and routing all the tank trucks to the customers, which is a hard work especially if executed manually. The dispatcher must attend to many details concerning customer, vehicle fleet and product status. The cost of distribution is very sensitive to dispatching decisions and even small errors can disrupt daily operations. Apart from delivery scheduling and vehicle routing, the new system must include other distribution operations such as customer order processing, invoicing, bill of lading control, dispatch reporting and what–if scenario analysis. The objectives of the new system are to minimise the total transportation cost of deliveries, to balance the work load among the tank trucks, and to load the maximum weight on a truck while adhering to all rules of loading. These conflicting objectives must be met within the constraints of maintaining customer service level. As described previously, mainland distribution is divided into four regions and each one has a separate distribution center which manage several storage facilities. Each of the facilities may have different products available and the same product may have storage cost at each terminal. The various tank trucks are located to separate distribution centers and the cost structure for each truck differs between owned or hired. The hired trucks cost depends on the travelled distance, the delivered product and minimum charges restrictions. The trucks may have different capacities, and different numbers and sizes of separate bulk cargo com-
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partments. In addition, assigning orders as loads on trucks may require adjustment of ordered quantities of products so that they fit into truck compartments. An order usually consists of several products and may require special delivery equipment. The restrictions of the road network must be taken into account as well. The cited problem summary makes clear that dispatching decisions are too complex for manual optimisation. Dispatchers are usually looking for acceptable feasible solutions, guided by simple decision rules. In such an environment, dispatchers cannot be expected to look for very low-cost solutions. The IT-driven decision support system aims at assisting the oil companyÕs distribution operations at a lower cost. The vehicle routing and scheduling system is focused either at decentralised deliveries from distribution centers or at a centralised distribution system, which requires less personnel and accomplishes better administration and control.
5. Decision support system design methodology The methodology followed in the case study has similar phases to the information systems development specialised at a decision support system. Emphasis was given at system modelling and process analysis, as this is the basis for exact system requirements specification and successful decisions regarding the software selection and implementation. A combination of functions from the selected software applications are integrated in order to implement the decision support system. The main steps of the methodology used are: • Data collection. Firstly, all necessary data is gathered concerning process mapping, constraints, empirical optimisation rules, and the information systems infrastructure. • Distribution system modelling. This step deals with the AS–IS analysis utilising modelling tools like: flowcharts for the functions and activities, data flow diagrams for the database and the information flow, GRAI diagrams for the decisional view and the ARIS system architecture
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•
•
•
•
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for the overall system view. The same tools were used for the TO–BE analysis in order to conclude the systemÕs specifications. Software market research. International software market research is the next procedure in order to examine suitable solutions for distribution management and more specifically automation and optimisation of enterprise dispatching processes. System requirements specification. After the completion of the software market research, a list of features of the most specialised software concerning the current problem is deployed. From that list and the TO–BE model the system requirements are extracted in the format of a selection criteria catalogue. Software selection. The criteria catalogue is forwarded to pre-qualified software vendors as a request for proposal (RFP). The company receives all proposals and after evaluating them, the most suitable solutions are selected. Implementation of the new system. The selected software applications are integrated in order to implement the decision support system.
The case of fully customised development of the suitable software was examined thoroughly and it was found unacceptable due to the very high costs and lengthy time horizon. However, apart from the step of ‘‘software selection’’, all other methodological steps could be the same in the case of customised software development. The framework of the methodology followed is shown in Fig. 2 and is described analytically below. 5.1. Data collection All the information was collected by interviews held with employees at all management levels, corporate archives and past reports (historical data). The specifications of the existing information infrastructure (based on the answers received through structured questionnaires) focused on existing applications that support the distribution processes. The routing and scheduling parameters and constraints were identified, too. The basic parameters and variables of the problem are product
Fig. 2. Decision support system design methodology.
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availability, vehicle availability and capacities, order priorities, customerÕs location and road network structure. The basic constraints that increase the complexity of the OR problem are listed here: • multiple alternative loading points which may be different for some products within a trip; • transportation with a variety of vehicles and bulk compartments capacities; • order quantities adjustment according to vehicles compartments; • hired vehicle work balancing; • maximum use of the owned vehicles; • vehicle rotation to the customers; • several loading points for the same trip within the regional area of each distribution center; • complicated transport cost calculation which depends on factors such as: category of delivered product, category and number of customers per route, customersÕ geographical allocation per route regarding the loading location of the vehicle; • minimum transportation cost for last point delivery, regardless the delivered quantity; • unplanned orders subsumption to the delivery schedule. Emphasis was given at the collection of the empirical optimisation rules that are used by the dispatchers. All collected data was analysed and the results were used in the next phases of the study. 5.2. Distribution system modelling The second phase included the modelling of the existing situation (AS–IS) and the new situation (TO–BE) as well. The objective was to map in a structured way the distribution processes of the oil company. The modelling tools and methods applied in this phase were chosen in such a way so that all the aspects of the distribution processes were identified and analysed properly. The overall outcome of the distribution modelling was a set of complementary methodologies, each of which contributes to that objective from a different perspective. More specifically:
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Flowcharts were applied in order to identify the sequence of distribution activities, as well as the decisions to be taken in various steps of the distribution process. The flowchart models were initially designed so that the personnel involved in the distribution processes could review them. Their simplicity allowed accurate definition of activities and decisions in workflow among the different departments of the company. The data flow diagrams modelled the information flow in the distribution process. They were based on the flowcharts and they were enriched in order to define the different databases that should be used in order to store critical data for the decision support system. Data flow diagrams are an asynchronous network representation of a system and they are useful for modelling information flow and data transformation through the system. They are the cornerstone for structured systems analysis and design [35]. The second level data flow diagram in Fig. 3 depicts the data sources, destinations, flows, data stores and transformations of the studied system. The next modelling method that was applied was GRAI (Graphes a Resultats et Activites Interreliees), part of GRAI integrated methodology (GIM). This method proposed by Doumeingts [8] for handling the decisional view of a system with GRAI-Grids and GRAI-Nets as mapping tools. The GRAI-Grid provides a general view of the decisional model, from a high-level perspective. The main objective of the GRAI-Grid is to analyse the functional areas of the system and to identify the most important decision centers that are responsible for the decisions that are taken at all decision-levels and consequently affect the activities of the lower levels of the system. A GRAI-Net is associated to every decision center included in the GRAI-Grid and consists of the constraints, decision variables, performance indicators and other parameters that characterise every important decisional activity of a system. The input for these models was the flowcharts and the data flow diagrams. The flowcharts provided the raw decisional framework in order to model in detail the specific decisional parameters of the distribution system. Additionally, the data flow diagrams defined the information flow in the GRAI-Grids constructed
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Fig. 3. Second level data flow diagram.
and the support information in the GRAI-Nets. The GRAI-Net diagram in Fig. 4 depicts the decision variables, the objectives, the constraints and the other parameters of a decisional activity and refers to orders allocation to truck compartments. Finally, the ARIS system architecture [29] and the related tool set was applied in order to provide the overall distribution system model. In ARIS architecture, the modelling is done using a tool set instead of a language. Several sub-tools are available related to the examined problem. The information captured by the ARIS tool set [30] is stored in a database following the entity-relationshipmodel (ERM). This architecture distinguishes between organisation, function, information and control views of the system model. It focuses on the analysis and requirements definition phase during the design of information systems.
ARIS models used the flowcharts and data flow diagrams in order to develop the function trees, the organisation trees, the entity-relationship diagrams and the event-driven process chain (EPC) diagrams of the distribution system. The event-driven process chain of the ARIS methodology for a very small part of the process ‘‘Inventory status control’’ is seen in Fig. 5. This diagram used for representing the control view of the model and combines the function, information and organisation view in a single diagram. Function trees used for modelling the function view of the system. A part of a single function tree (without alternative trees) is shown in Fig. 6. The contribution of the ARIS tool set lies on the fact that it supports the implementation of the new ERP system of the company. It describes very accurately the information that is exchanged throughout the distribution system and provides the organisational view of
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Fig. 4. GRAI-Net diagram.
Fig. 5. Single step of an event-driven process chain diagram of ARIS methodology.
the company where the different departments interact in order to support the distribution process. To conclude with, the four modelling methodologies presented above, exhibit a complementary
added value in this step and are connected to each other. There is a gradual relation in the modelling process: the flowcharts and data flow diagrams are a first approach of analysis and the GRAI and
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with add-on software modules responsible for routing and scheduling, so that they function on a common database allowing querying, statistical analysis and user-friendly visual mapping of the transportation network. The market research was conducted by using the Internet and contacts with a large number of software vendors. Several packages were identified from the international market research that cover the two areas as shown in Fig. 7. Summary tables of the software packages functionality created in order to categorise, review and compare the abilities and features all of them. Fig. 8 depicts one of these tables. 5.4. System requirements specification Fig. 6. Part of a single function tree of ARIS methodology.
ARIS models are based on their input, describing the distribution system in more detail. After the modelling process was completed, it was shown that the distribution system involved many parameters that were never taken into consideration before, such as the delays caused from the interactions of the different departments in the distribution process. The existing situation (AS– IS) modelling resulted to identification of the weaknesses and problems of the studied processes. The results also included the detailed definition of the objectives, the constraints and the variables for the major decisions taken into account and the sizing of the required decision support tool. The TO–BE models included the solutions to the identified problems and they were considered as the basic resort for the systemÕs requirements specification and the software selection phases.
The next phase aimed at the extraction of specifications for the new decision support system. The applications surveyed in the previous step were analysed as far as their features are concerned and function maps were created for each software package. These function maps were matched to the results of the TO–BE analysis and appropriate indicators were assigned to the functions that corresponded to the requirements of the TO–BE model. The result of this phase was lists of functional and technical specifications broken down in four levels of details. A sample of the produced lists of specifications is shown in Fig. 9. The structured and detailed process analysis was critical for the success of the systemÕs analysis and requirements specifications. The detailed functional and technical specifications that resulted from this phase, showed the most suitable applications during the software selection phase. 5.5. Software selection
5.3. Software market research The software market research that followed had two goals. The first was the study of the supply chain management (SCM) applications and their efficiency in working out problems similar to ours. These applications support the supply chain activities of a company from the high level (strategic decisions) to the low level (routing and scheduling). The second one covered the geographical information systems (GIS) applications integrated
All data was gathered and put into tables for evaluation purposes. This process pointed out that no software application alone could cover the requirements, as determined through the systemÕs specifications. Consequently, it was obvious that a combination of different applications was necessary for the new system to work according to the companyÕs requirements. A detailed study of the functions of the available applications concluded that there are overlapped areas between ERP,
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Fig. 7. Software vendors resulted from market research.
Fig. 8. Software packages features review by vendor.
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Fig. 9. Two levels of system specifications.
SCM and GIS applications. A representation of the overlapped areas, at a high level of functionality, is shown in Fig. 10. The most suitable software solutions resulted from this phase, from the supply chain management area, were the applications of two software vendors that are specialised in oil industry and have plenty of successful stories. Besides an advanced optimisation tool was examined closely as it had full compatibility with the companyÕs ERP system. Two software vendors resulted from the GIS area research. Fig. 11 depicts the software applications that pre-qualified from SCM and GIS areas, during the software pre-selection phase. The next stage was the submission of a RFP to the pre-qualified software vendors, including the requirement for a presentation of their proposal. After that, the evaluation of the proposals took place based on criteria catalogues. The result was the selection of specific functions of the final applications in order to deliver the new decision support system to the company.
5.6. Implementation of the new system The final phase of the project includes the purchase of the software applications, the design and implementation of the interfaces with the companyÕs ERP system and the final implementation of the integrated system. Due to the overlapped areas between the functions of the companyÕs ERP system and the examined SCM and GIS applications, the careful selection of specific functions from each application, is the basic factor for the successful implementation of the new integrated solution. The selected GIS and SCM applications include digital maps and optimisation tools respectively. The combination of these features executes distribution scheduling and routing on electronic maps utilising geo-reference capabilities, advanced scheduling techniques and a friendly graphical representation of the result. Additionally, backoffice activities of the dispatching process, such as financials and reporting, are managed by the legacy ERP system of the company that serves as the
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Fig. 10. Overlapped areas between ERP, SCM and GIS applications.
Fig. 11. Pre-qualified SCM and GIS applications.
core database for the SCM and GIS modules. The chosen SCM application is oil industry specific, including planning and scheduling modules and the appropriate algorithms for multi-criteria routing and scheduling problems that can be adjusted to the companyÕs individual needs, as they were analysed during the modelling steps of our methodology. The architecture of the designed system includes a central database managed by ERP system of the company, but optionally it could be managed in-
dependently by the SCM application, fully integrated with the ERPÕs database management system. The main input to the planning and scheduling modules are the daily orders and the availability of the resources. The combination of the suitable algorithms, the geo-reference data and the user-identified parameters of the routes result to the schedules of deliveries and the appropriate routes of the vehicles. The dispatcher, who is actually supported to his decisions, can modify final schedules. The designed systemÕs architecture is shown in Fig. 12. The success of the designed new system is that it saves dispatchers time, creates valuable flexibility and improves the quality of decision-making. The system creates schedules, chooses plans between alternatives, creates routes for the vehicles, minimises the total distance travelled (which is a crucial factor for the transportation cost) and allows the dispatcher to modify the routes manually. The major scope at this phase is the evaluation of the algorithms and optimisation methods used by the selected applications as well as the effectiveness of the new system related to potential cost
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Fig. 12. Decision support system architecture.
savings and service level improvement. Since the cost of investment in software applications for the integrated solution is high (about 1.5 million euro), a budgetary analysis of the costs and benefits interceded the purchase and implementation of these applications. The estimation of the benefits was executed by using the activity based costing (ABC) technique [33], based on the REALMS methodology [9], supported by the European Esprit programme. This methodology resulted to the cost calculation and performance measurement in order to define the cost benefits of the new system. Using the ABC technique, specific performance indicators were identified, corresponding the objectives of the distribution process and cost drivers were set for the activities that have high cost for the oil company. The result was the following estimated benefits by the new system: • Reduction of the transportation cost for the hired vehicles 8%, due to the reduction of the total kilometres travelled and the more efficient schedules. This reduction means savings of 470.000 euro per year. In addition there is a better control of the charges of the hired fleet and prevention of overcharges. • Improved utilisation of the fleet 11% and consequently of the fixed costs of the vehicles. The
required number of hired vehicles is reduced which means less additional costs of the hired fleet. The estimated savings due to the better utilisation of the fleet are 40.000 euro per year. • The savings from the reduction of the personnel for order receiving and dispatching are estimated to 390.000 euro per year in the case of the decentralised distribution (45% less personnel) and 700.000 euro per year for a central system (80% less personnel). The personnel reduction is a result of the automated order receiving and dispatching processes and the reduced lead times. • More effective processes, reduced lead times for the dispatching process and fewer mistakes (
