1Department of Management, University of Isfahan, Isfahan, Iran. 2Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan,.
An Integrated Framework for Selecting Performance Indicators Based on ELECTRE Method and SMART Criteria With a Case Study in Single Level Bidirectional Service Supply Chain Arash Shahin1* Hoda Mehrparvar2 Mahdi Karbasian3
Abstract The aim of this study is to propose an integrated approach for selecting performance indicators in Service Supply chains (SSCs) and to offer a method for determining the performance of single level bidirectional services supply chains in three hospitals. For this purpose, performance indicators of SSC processes have been studied and selected based on SMART features using ELECTRE I. Final indicators have been classified in terms of inputs and outputs. The proposed approach has been examined in single level bidirectional SSCs of three public hospitals. Findings indicate that the chain loops in terms of output indicators have a good performance but in terms of input indicators have a weak performance. In addition, hospital A regarding input indicator and hospital C regarding output indicator do not have appropriate performance, and hospital B regarding both input and output indicators has appropriate performance. Thus, the main concern of hospital A is to focus on improvement; of hospital B is to focus on optimization; and of hospital C is to focus on waste of resources. Keywords: Service Supply Chain, Performance measurement, ELECTRE I, SMART, Fuzzy TOPSIS, Quadrant analysis
1Department of Management, University of Isfahan, Isfahan, Iran
2Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran 3Department of Industrial Engineering, Malek-e-Ashtar University of Technology, Isfahan, Iran
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1.Introduction In recent decades, the service sector has become extremely important in the world economies (Dong et al., 2012). The service economy has always been the driving force of economic growth of every developed nation (Giannakis, 2011). Services now account for two-thirds of the output of the advanced economies of the world. Furthermore, they represent the majority of employment in those nations (Shahin, 2010). However, services lag behind performance when compared to manufacturing. One of the reasons is that most of successful manufacturing organizations have an opportunity to achieve higher performance in pursuit of SCM, which is a common practice across manufacturing industries from both the practical and academic standpoints. The emphasis in SCM is still strongly skewed toward the manufacturing sector and therefore, service supply chain which with service at core is of great importance (Dong et al., 2012). Supply Chain Management (SCM) can bring reliability, responsiveness, consistency, flexibility, cost reduction and process efficiency. From academic and practical viewpoints, the emphasis on supply chain and operations management is still strongly on the manufacturing sector (Boonitt and Pongpanarat, 2011). However, with the development of service industry, some scholars realize that the application of the supply chain theory in the service industry can also achieve success. Jia and Gang (2006) show that service enterprises provide service for society not independently but relatively as a chain. Especially in the time which is oriented to service-economy and in the background of intense competition, it can help enterprises to improve their core competitiveness, reduce uncertainty in the business and improve service delivery and decision-making efficiency (Li et al., 2008). Because of intangible aspects of service, disability in storage and transportation due to production and consumption at the same time, labor intensive, difficulty in reselling, difficulty in automating and heterogenity, in most researches SCM has been studied in the manufacturing industry and there is a need for more research in the service field. Managers of service organizations have found that in order to promote productivity and profitability in their organizations, they need to appraise the quality performance of their SSC. As an indispensable management tool, Performance Measurement (PM) provides the necessary assistance for performance improvement in pursuit of SC excellence (Chan et al., 2003). Also, most service firms realize that in order to evolve an efficient and effective SSC, Service Supply Chain Management (SSCM) needs to be assessed for its performance (Dong et al., 2012). Also, a suitable Performance Measurement (PM) system helps the managers of supply chains to understand and improve the performance of their supply chain activities properly. Studies on PM of supply chains are widespread but using these studies in service industries is challenging regarding special and unique features of services. For this reason, little research has been done in this field. Hence, lack of an effective decision making system to select PM indicators in SSC is felt. For this reason, the aim of this study is to propose a comprehensive and standard framework for Service Supply Chain Performance Measurement (SSCPM) indicators and to propose an approach for studying the performance of a single level bidirectional service supply chain in three hospitals. Therefore, after identification of PM indicators through conducted studies, the maximum rate of indicators eligibility of SMART features is considered as the criterion of selecting indicators and an integrated framework based on ELECTRE I technique and SMART criteria suggested in order to select PM indicators, and Fuzzy TOPSIS technique and Quadrant Analysis (QA) applied in order to determine the desired chain performance. This survey allows service organizations to identify strong and weak points of their supply chain and to be able to improve their functions internally and externally and consequently, results in increased service delivery rate to customers, improved quality of services, lowered costs and finally profitability and access to a durable competitive advantage for organization.
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In the following, literature is briefly reviewed on SSC. Then, definitions, characteristics, types of SSCs and differences between SSCs and physical inventory supply chains are studied. In addition, the need for performance measures and metrics (measurement) in a supply chain is analyzed. Then, performance measures and metrics (indicators) in SSC are presented. Also, the SMART criteria, ELECTRE I technique and Fuzzy TOPSIS technique are described. In the research methodology section, a comprehensive framework is proposed for selecting indicators and a proposed approach has been analyzed to determine the performance status as a case study of single level bidirectional service supply chain (in hospitals).Finally, the findings are discussed and conclusions are made.
2. Literature review Considerable research has been done in the field of manufacturing supply chain and its performance measurement while less related studies have been done on supply chain in the service sector and particularly on single level bidirectional service supply chains. In the following, some of the important researches are addressed. Sampson (2000) states in his research that SC definition is quite easy for manufacturing organizations because participants in this type of chains receive the inputs from the suppliers and do the necessary processes on the inputs and then deliver outputs to a distinct set of customer. Although one of the primary suppliers of the process inputs are the customers themselves, this is a meaning of customersupplier duality which has been extended and developed as a component of SSCM. Kathawala and Abdou (2003) presented the theory of applying SCM on services industry as opposed to the manufacturing sector. The comparison resulted in the redefinition of SCM on services. Also, the characteristics and principles of the services industry were shown to be a hybrid of the different types of manufacturing sectors. Ellram et al. (2004) have referred to the growth and increase of the service importance and presented a suitable framework for SSC by comparing with three models of manufacturing SC named Global Supply Chain Forum Framework, SCOR model and Hewlett-Packard SCM model. For the first time, they called the information flow, demand management, capacity and skills management, Customer Relationship Management (CRM), Supplier Relationship Management (SRM), service delivery management and cash flow as SSC processes and finally stated that attention and distribution of information about the techniques and trends of SSCM may result in improving organizations’ competitive advantage, cost-cutting and value increase of service organizations. Baltacioglu et al. (2007) developed a general model of SC in service industries named IUE-SSC based on the existing knowledge derives based on the study of Ellram et al. (2004) and SCOR models. This model includes all elements of the supply chain and defines the managerial activities (information and technology management, demand management, CRM, SRM, capacity and resource management, order process management and service performance management) to be fulfilled for effective management of SSC. The model was studied in healthcare industries. The results indicate that implementation of effective SCM is regarded as a powerful means for dealing with the present challenges in this industry. Song et al. (2008) specially concentrated on professional SSC in a survey and based on the perspective of professional service outsourcing defined SSC they suggested a professional model of SSC based on literature review about SSC in which the process of developing the service delivery is the chain core. After analyzing the traits of the SSC, a comprehensive performance evaluation system was suggested for SSC based on AHP and DEA models and finally a case study was conducted to select one supplier out of five service suppliers in the field of outsourcing the human resources management of a firm to a counseling institute of professional human resources.
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Shahin (2010) introduced SSCM and its types using various examples; he described the meaning of customer-supplier duality to different level of SSC and also addressed particular elements and impacts of SSCM. It is necessary to point out that SSC seems more complicated than manufacturing supply chain. Also, SSCM is an analogous systems approach that is especially suitable for delivering mobile services. Generally, SSCM has significant advantages for service organizations. Haas and Hansen (2010) analyzed the concept of facilities management (FM) in an SSCM perspective by making a comparison of FM and SSCM and found the similarities between these two areas. They pointed out that definitions of FM include elements of SSCM processes considered by Ellram et al. (2004). The results indicate that adopting SSC perspective in FM may lead to a new role for FM and also helps the FM in defining core business processes. Giannakis (2011) explores the utility of the manufacturing biased supply chain operations reference (SCOR) tool in services and develops a reference model for service applications. The developed model conceptualizes the capacity of service firms as a resource inventory to build a service offering. This inventory-capacity duality that describes a service firm’s capabilities is applicable across a wide spectrum of the service sector. The reference framework that is developed is a proposition of how management in SSCs could be standardized. Boonitt and Pongpanarat (2011) applied the Q-sort technique to the scale development process in order to address the reliability and validity problems caused by subjectivity of SSCM. Four dimensions, including demand management, capacity and resource management, order process management and service performance management with limited scales, indicate that the scales need to be reviewed. Zailani and Kumar (2011) measured SSC practices based on SSC model proposed by Ellram et al. (2004) and Lin et al. (2009). In this study, only tailors the measurement for practices such as information flow, knowledge management, capacity and skills management and cash flow management were considered as strategic resources to the logistics service provider and proposed items were studied in the logistics industry. Lang and Chang (2012) proposed a conceptual framework in order to study the relationship between SSCM practices and public healthcare’s organizational performance that recognize the mediating effect of alliance integrated network in Malaysia. Information and technology management, demand management, CRM, SRM, capacity and resource management which were more repeated in the past studies were selected as SSCM practices. Findings indicated a certain link of SSCM practices with organizational performance. Dong et al. (2012) developed a hierarchical structure for SSCPM based on classified PM dimensions of Fitzgerald’s et al. (1991), Parasuraman’s et al. (1988) and the SCOR model. In addition, the framework was implemented in performance measurement of supply chain in a hotel and the indicators weights were obtained using Fuzzy AHP method. The results indicated that implementation of effective SSCPM in the hotel industry emerges as a powerful tool to cope with the new challenges in the field of service. Sakhuja and Jain (2012) proposed an integrated conceptual framework to make a connection between the core elements of SSC and key service operations. This general model can fit to different service industries and also provides the vision for the operational managers in service industries to do their service activities in a systematic and planned manner to achieve organizational objectives. As it is addressed, the SSC processes offered by Dong et al. (2012) seems more complete than other emerged processes.
3. Service Supply Chain (SSC)
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The study on SSC has started in recent years. Ellram et al. (2004) focused on the professionals in SSC. They proposed information, processes, capacity, service performance and funds from the earliest supplier to the ultimate customer as the only relevant definition of SSCM the management. The first definition about SSC was proposed by Baltacioglu et al. (2007) as the network of suppliers, service providers, consumers and other supporting units that perform the functions of transaction of resources required to produce services; transformation of these resources into supporting and core services; and the delivery of these services to customers. Cayama (2008) stated that SSCM is the science of adapting the organization, configuration, and capacity of the service delivery process according to demand variability, and establishing a network with the best resources and service providers to deliver a service that satisfies customers' expectations of quality. Shahin (2010) explained that SSCM is an analogous systems approach that is especially suitable for delivering mobile services such as parcel delivery and home health care. Boonitt and Pongpanarat (2011) considered SSCM as a tool for forecasting, planning, implementing, and controlling the process of the supply chain with the objective to satisfy customer requirements in an efficient manner.
3.1. Differences between SSCs and physical inventory supply chains Services differ from physical products in several ways. Pure services are intangible, labour intensive, difficult to resell, difficult to automate, heterogeneous, not able to be stored and transported due to production and consumption at the same time, are often perishable (unused capacity is capacity lost forever) and have a quality dimension that is difficult to evaluate (Arlbjorn et al., 2011). The SCM concepts and ideas have been traditionally associated with the logistics and transportation of manufactured goods between different stages of the chain from raw material to the final customer. The service industry presents particular characteristics that impede the direct application of the current body of knowledge. Among these differences are (Cayama, 2008): 1) Customer is a participant in the SSC: In SSCs, the tangible and intangible elements of services are directed towards the customer and his/her possessions, intellect, assets, or information. There are flows in both directions between the consumer and the service providers. Since the customer represents the first link in the chain, it is important to establish effective ways of receiving the inputs that he provides. 2) SSC delivers an intangible output: When a customer buys a manufactured product, she is able to see, feel, and test it before committing to buy. On the other hand, the customer cannot have that kind of experience with services. Because the flows within the SSC involve intangibles, they are different from physical inventory supply chains and present new challenges. 3) There are no inventories between the different tasks: In manufacturing, inventories serve as a buffer to absorb variations in demand. In SSC this buffer does not exist, and demand fluctuations are transmitted directly to the chain. In manufacturing, the different stages are joined by inventories of parts and goods in process. In the SSC, the buffers between different tasks are customers or customers' belongings waiting in a queue. The inventory size equivalency is queue length. 4) Time perishable capacity: Because services are non-inventoriable commodities, they cannot be stored for later use. Service capacity is lost forever when not used. This generates a challenge for SSCM that should balance capacity, facility utilization, use of idle time, and customer waiting. 5) The output quality is hard to assess before delivery: Because of the nature of the SSC, the output can hardly be inspected and measured before delivery. It can only be assessed after the customer experiences the service. When services have already been consumed, they cannot be serviced or reworked; and services that do not meet the requirements are difficult to replace.
3.2. Customer- supplier duality
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Service chains despite of the goods supply chains often involve the customer in production process as an active participant. Recent progresses in information technology involve the customer in production process completely and directly. The nature of the service makes a customer-supplier duality that affects the relations of SSC. Instead of manufacturing supply chain in which physical goods move from a nature into another nature, a service may be regarded as an act on the people’s minds (e.g. education, entertainment, psychology), people's bodies (e.g. transportation, lodging, funeral services), people's belongings (e.g. landscaping, dry cleaning, repair), people's information (e.g. insurance, investments, legal services) (Fitzsimmons and Fitzsimmons, 2006). All services act on something which is provided by the customer. In other words, customers are suppliers in all service businesses, which is the customer-supplier duality (Shahin, 2010).
3.3. Different types of SSCs Customer-supplier duality implies that production flows not only from suppliers to customers, but also from customers to suppliers. Therefore, production flow is bidirectional, which is a key factor in linking traditional supply-chain concepts to service process realities. The simplest form of a bidirectional supply chain is for the customers to provide their inputs to the service provider, who converts the input into an output which is delivered back to the customer. This single-level bidirectional supply chain is depicted in Figure 1.
Material or equipment suppliers
Service customer supplying inputs
Service provider
Service customer consuming output
Figure 1. Single-level bidirectional supply chain(Sampson, 2000; Shahin, 2010)
Things get more complicated when the service provider employs another service provider to assist with the processing of customer inputs. The result is a two-level bidirectional supply chain. Such a two-level bidirectional supply chain is depicted in Figure 2. In two-level bidirectional supply chains, the initial service provider is an interface between the service customer and the service supplier. Service supplier receiving customer inputs Service supplier providing output
Service customer supplying inputs Initial service provider
Service customer consuming output
Figure 2.Two-level bidirectional supply chain(Sampson, 2000; Shahin, 2010)
There is a third type of service supply chain that is not bidirectional, but incorporates the customersupplier duality. This is a class of service processes in which the customer provides inputs to the service provider, who processes the inputs and delivers them to an entity which is different from the customer. Even if the original customers never see the original delivered output, they do receive benefits from the delivery. This Unidirectional supply chain is depicted in Figure 3. An example is postal or package delivery, where customers deliver their documents or packages to the delivery service provider to be spatially transformed (i.e. moved) to a desired location (Sampson, 2000; Shahin, 2010). 6
The firm’s supplier’s supplier
The firm’s supplier
The firm
The firm’s customer
The firm’s customer’s customer
Figure 3.Unidirectional supply chain (Sampson, 2000; Shahin, 2010)
4. The need for performance measurement and metrics in an SSC Services are difficult to visualize and measure and the diversity of the services sector makes it difficult to develop a unified services framework (Ellram et al., 2004). To obtain objectives or ensure continuous improvement, the performance of the processes must be measured. Moreover, a process cannot be managed if its performance cannot be measured. Performance measures can be used to establish performance goals. This allows for a focus on the future. In order to identify the quality of a fulfilled practice or a sequence of activities, results must be measured. Target vs. actual performance value data should be analyzed (Dong et al., 2012). Hence, a PM system with a suitable design should help the managers of supply chain to understand and improve the performance of their supply chain activities properly (Seyedi and Gholamian, 2010). Thus, it is important to develop a framework of SSCPM to evaluate changes and to assess the performance of the service supply chain. SSCPMS not only provides feedback information to show improvement, reinforce motivation and communication and identify problems, but also promotes integration and coordination among SSC members. As a result, overall customer service level as well as competitiveness and profitability can be increased. The objective of SSCPM is to assess key SSC activities under different performance dimensions (Dong et al., 2012). Although, it is believed that service industry can benefit applying some best practices from manufacturing industry, the indifferences between service and manufacturing industries could create a need for constructs or scales reflecting SSC practices (Boonitt and Pongpanarat, 2011). Therefore, there has been little research to date on SSCPM and this issue demonstrates the importance of a new system for SSCPM.
5. Performance metrics and measurement indicators in an SSC In this section, the metrics and measures are identified and discussed in the context of SSC processes suggested by Dong et al. (2012). With regard to the performed studies on SSC processes, it was specified that the desired processes of Dong et al. (2012) seems more complete. Thus, eight processes of SSC proposed by Dong et al. (2012) have been used. Having investigated the performed researches, the performance indicators related to each SSC process have been identified as follows:
5.1.
Demand management
The focus of demand management, which is the preliminary function of SSCM, is on forecasting and managing customer requirements, with the objective of facilitating this information to shape SSC operating decisions (Dong et al., 2012). In a service flow, the functions of demand forecasting, determination and planning are needed prior to actual service delivery. Also, the supporting functions in the SSC, which are directly related to the product supply chain, should also be taken into 7
consideration (Baltacioglu et al., 2007). Indicators related to this process are forecast accuracy (Chopra and Meindl, 2004; Dong et al., 2012), forecasting customer requirements (Ellram et al., 2004) and demanding resources reliability (Boonitt and Pongpanarat, 2011).
5.2.
Capacity and resources management
Capacity management is the dedicated act to balance demand from customers and the capability of the service delivery system. The task of capacity management is to try to achieve a balance between too much and too little resource utilization, within the constraints of the networks and facilities of the operation (Dong et al., 2012). Service capacity is defined as the maximum level of value-added activity over a period of time that the service process can consistently achieve under normal operating conditions (Johnston and Glark, 2008). Resource utilization is a performance criterion which evaluates how efficiently resources are utilized in the delivery of services (Fitzgerald et al., 1991). Scheduling deals with the allocation of resources to tasks overtime to perform a collection of activities (Dong et al., 2012). Indicators related to this process include service capacity and capacity utilization (Johnston and Glark, 2008; Haksever et al., 2000; Dong et al., 2012); effectiveness of scheduling techniques (Giannakis, 2011; Haksever et al., 2000; Dong et al., 2012); the ability to manage intangible resources (e.g. skills, experiences, and knowledge) to operate at optimum service capacity; and the ability to manage tangible resources (e.g. facilities, labor, and capital) to operate at optimum service capacity (Boonitt and Pongpanarat, 2011).
5.3.
Customer Relationship Management (CRM)
CRM which includes the processes that focus on the interface between the firm and its customers, seeks to create customer demand and facilitate the placement and managing of orders (Chopra and Meindl, 2004). The aim of CRM is to integrate the many communication channels between an organization’s units and its customers (Dong et al., 2012). Managing customer relationships is about establishing, maintaining and enhancing relationships with customers for mutual benefit (Johnston and Glark, 2008). Indicators related to this process are customer retention (Bruhn and Georgi, 2006; Dong et al., 2012); the ability to develop long-term relationships with customers; the ability to communicate optimistic information to customers; the ability to establish effective relationships with customers to the benefit of the brand loyalty; and the ability to manage relationship with customer to create the impression before and after service (Boonitt and Pongpanarat, 2011). Customer relationship indicators include customer satisfaction; customer loyalty (Bruhn and Georgi, 2006; Dong et al., 2012); customer profitability; and customer value (Bruhn and Georgi, 2006).
5.4.
Supplier Relationship Management (SRM)
SRM’s basic aim is to arrange for and manage various supply sources for various goods and services (Baltacioglu et al., 2007). In service supply chains, suppliers contribute directly to the production of services and usually in direct contact with customers. Thus, suppliers play an important role in customer’s perception of services and customer satisfaction. A failure in the supply side may simultaneously turn into a failure in service delivery performance (Dong et al., 2012). Boonitt and Pongpanarat (2011) suggested indicators such as the ability to develop long-term relationships with suppliers, the ability to maintain close relationship with a limited pool of suppliers, the ability to focus on key supplier to improve the service chain quality, and the ability to develop a partnership program with suppliers for the benefit of the whole service supply chain. Buyer–supplier partnership level mentions the extent of partnership that exists between service firms and suppliers (Dong et al., 2012). Buyer–supplier partnership level (Doran et al., 2005; Hansen, 2009; Toni et al., 1994; Thakkar et al., 2007; vander Valk et al., 2009; Dong et al., 2012) indicators include extent of mutual understanding 8
and closeness for business growth-long-term perspective (Thakkar et al., 2007); level and degree of productive and logistic congruency (Toni et al., 1994); level and degree of information exchange (Toni et al., 1994; Gunasekaran et al., 2001); buyer–supplier risk/profit sharing initiatives (Thakkar et al., 2007; Dong et al., 2012); extent of mutual cooperation leading to continuous improvement (Doran et al., 2005; Hansen, 2009; Thakkar et al., 2007; Gunasekaran et al., 2001); level and degree of operative interaction between buyer and supplier (Toni et al., 1994); and extent of mutual assistance in problem solving efforts (Doran et al., 2005; Thakkar et al., 2007). Suppliers in service industries need more collaboration than those in manufacturing industries because they perform different activities consecutively in a whole service process and in order to impress customers consistently; they have to employ compatible interface management (Feng et al., 2011). Evaluation of suppliers indicators include supporting service delivery lead time (Feng et al.,2011; Dong et al., 2012); quality of supplier’s service level (Feng et al., 2011; Giannakis, 2011; Dong et al., 2012); cost saving initiatives, and supplier pricing against market (Feng et al.,2011; Dong et al., 2012); risk sharing initiatives; utilization of service facilities; equipment and staff; the delivery efficiency of supporting services; volume and specification flexibility and quality assurance methodology; ability in day to day technical representation; adherence to a developed schedule,; and ability to avoid complaints of service delivery (Dong et al., 2012).
5.5.
Order process management
Lambert et al. (1998) defined order processing as the function that entails the system which an organization has for getting orders from customers, checking on the status of orders and communicating to customers about them, and actually filling the order and making it available to the customer. Order processing has great importance in service businesses and improvements in this function are usually reflected in cost decreases (Baltacioglu et al., 2007). The order entry method determines the way and the extent to which the customer specifications are converted into useful information, and are passed down along the supply chain (Gunasekaran et al., 2001). Service order lead time refers to the time which elapses between the receipt of the customer’s order and the delivery of a service to the customer. Also, the customer service order path determines a series of activities that need to deliver a service (Dong et al., 2012). Managing the cycle times of the various supply chain processes is a crucial enabler of outstanding customer delivery performance (Stewart, 1995). Indicators related this process are the service order entry method and the customer service order path (Gunasekaran et al., 2001; Dong et al., 2012); service order lead time (Giannakis, 2011; Gunasekaran et al., 2001; Dong et al., 2012); total cycle time (Stewart, 1995; Dong et al., 2012); and the efficiency of process orders or reservation systems (Boonitt and Pongpanarat, 2011).
5.6.
Service performance management
Service performance management can be regarded as the key function in the service supply chain (Baltacioglu et al., 2007). Also, it plays a key function which manages the necessary activities for the delivery of a service to the customer in the service supply chain. Because of the nature of service businesses, the service delivery process requires both customer and producer to be present. In addition, service delivery and consumption occurs simultaneously. These have difficulty in measuring the performance of the service delivery process (Dong et al., 2012). Indicators related to this process are service flexibility (volume, delivery speed, specification) (Fitzgerald et al., 1991;Parasuraman et al., 1988; Dong et al., 2012); range of services (Johnston and Glark, 2008; Dong et al., 2012); total service delivery cost (Giannakis, 2011; Johnston and Glark, 2008; Dong et al., 2012); the customer query time (Gunasekaran et al., 2001); post process services (Bruhn and Georgi, 2006) and making promises to customers (Ellram et al., 2004). Silvestro and Cross (2000) provided indicators on service delivery performance evaluation. These included profit margin; productivity (Dong et al., 2012);
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service value (customer perception of service value); satisfaction (satisfaction with service quality, physical environment and management style); operating ratio of actual to planned working hours and average customer spend per visit per store (Dong et al., 2012); and employee loyalty (Dong et al., 2012) such as employee referral, employee turnover and employee absence.
5.7.
Information and technology management
SCM is to a large extent about the management of information flow (Baltacioglu et al., 2007), because it provides the basis on which supply chain managers make decisions. Information technology consists of the tools used to gain awareness of information, analyze this information, and execute on it to increase the performance of the supply chain (Dong et al., 2012). When the speed of information flow increases, it will improve the efficiency and effectiveness of the supply chain and help the organizations to respond to customer changing needs at a faster pace (Zailani and Kumar, 2011). Indicators related to this process is IT supply chain applications (Zailani and Kumar, 2011; Dong et al., 2012); level of information sharing (information on customer request, service planning, capacity allocation and other type of planning process); and information quality as the accuracy, timeliness and credibility of the information exchanges (Zailani and Kumar, 2011); demand estimation (Ellram et al., 2004); etc. Boonitt and Pongpanarat (2011) suggested indicators as using new technology for increasing channel to customers to contact the organization, the ability to create effective networks management to share information among internal functions, suppliers and customers, the ability to track accurate information and/or data within the supply chain by using information technology, and decision-making management based on updated information technology.
5.8.
SSC finance
Cash flow is a necessary process control which is needed in the SSCM (Ellram et al., 2004). Johnson and Mena (2008) have changed the term proposed by Ellram et al. (2004) from cash flow management to financial flow management to avoid the confusion between the cash flow management from finance context and cash flow management under SSC (Zailani and Kumar, 2011). Supply chain finance is related to optimizing the financial structure and the cash-flow within the supply chain. Its objective is to optimize financing across company borders in order to decrease the cost of capital and speed up cash-flow (Dong et al., 2012). The total cash flow time can be measured as the average number of days required to transform the cash invested in assets into the cash collected from a customer (Stewart, 1995). Once the total cash flow time is determined, it can readily be combined with profit with the objective of providing an insight into the rate of return on investment (ROI) (Gunasekaran et al., 2001). Indicators related to this process are total cash flow time (Giannakis, 2011; Stewart, 1995; Dong et al., 2012); rate of ROI (Gunasekaran et al., 2001; Dong et al., 2012); and flow of payments between parties (Ellram et al., 2004).
6.
SMART criteria
Each indicator should be based on criteria that make it suitable for further analysis. Reviewing the literature, it is found that the set of criteria most often referenced is SMART (Specific, Measurable, Attainable, Realistic and Time-bounded) (Shahin and Mahbod, 2007):
Specific: being special, definite and distinct, that is, the indicators should be comprehensive, precise, clear and simple so that a similar conception of definitions is made. Measurable: their appraisal is simply possible, that is, in addition to the quantitative performance; it should also have the capacity of qualitative performance definition within quantitative variables. Attainable: indicators should be reasonable and attainable in a specific time-bounded.
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7.
Realistic: attainability indicators should be realistic and be aligned with organizational goals. Time-bounded: indicators should be defined in a time frame for completion.
The ELECTRE method
The ELECTRE (Elimination and Choice Translating Reality) method is one of the most famous methods of ranking especially in Europe (Atai, 2010). It is a comprehensive evaluation approach which is used for ranking a number of alternatives, each of which is described in terms of a number of criteria. The main idea is the proper utilization of what is called outranking relations (Wang and Triantaphyllou, 2006). This method has a clearer view of alternatives by eliminating less favorable ones, especially convenient while encountering few criteria with large number of alternatives in a decision making problem (Triantaphyllou et al., 1998). The versions and types of mathematical problems that these methods are able to solve are different. ELECTRE I method particularly is used for solving selection problems; ELECTRE TRI method for allocation issues; and ELECTRE II, III, IV techniques for ranking issues (Atai, 2010).
8.
The fuzzy TOPSIS method
In classical TOPSIS method, to determine the criteria weights and alternatives ranking, exact and definitive values are used. In many cases, the human’s thoughts are with uncertainty and this affects making a decision. Therefore, in these cases, it is better to apply the methods of fuzzy decisions, which fuzzy TOPSIS method is one of them. In this mode, decision matrix elements either weights of criteria and/or both of them are evaluated using linguistic variables given by fuzzy numbers and therefore, the problems of classical TOPSIS method are removed (Atai, 2010). Fuzzy TOPSIS has the advantage of analyzing the qualitative and quantitative criteria simultaneously and evaluating different options with regard to various criteria that do not have equal units (Seyedi and Gholamian, 2010). In this paper, ranking of alternatives and the importance weights of each criteria is determined by the linguistic variables which are presented as triangular fuzzy numbers (Tables 1 and 2). Table 1. Linguistic variables for the importance weight of each criterion (Chen, 1997: 4) Importance
Fuzzy number
Very low
(0; 0; 0.1)
Low
(0; 0.1; 0.3)
Medium low
(0.1; 0.3; 0.5)
Medium
(0.3; 0.5; 0.7)
Medium high
(0.5; 0.7; 0.9)
High
(0.7; 0.9; 1.0)
Very high
(0.9; 1.0; 1.0)
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Table 2. Linguistic variables for the rankings (Chen, 1997: 4)
9.
Importance
Fuzzy number
Very poor
(0; 0; 1)
Poor
(0; 1; 3)
Medium poor
(1; 3; 5)
Fair
(3; 5; 7)
Medium good
(5; 7; 9)
good
(7; 9; 10)
Very good
(9; 10; 10)
Research methodology
In order to do this study, the following steps are performed: Step 1: Providing a general framework for performance measurement of SSC. Step 2: Selecting an SSC. )choosing one type of service supply chain) Step 3: Prioritizing the SSCs loops based on input and output indicators usingFuzzy TOPSIS technique. Step 4: Integration of the results of the previous step using quadrant analysis. Step 2 to step 4 have been examined in the case study (three case studies). In the following, the steps are described. With regard to providing a general framework for performance measurement of SSC, in section five, important criteria for measuring the performance of an SSC are studied and all of indicators related to any processes of SSC are addressed. Regarding extensive and numerous indicators and by considering this matter that performance measurement indicators should have SMART characteristics, indicators are screened based on SMART criteria to achieve a comprehensive and general framework, therefore ELECTRE I technique is applied. For integrating ELECTRE I technique and SMART criteria, an integrated approach is given in order to achieve a comprehensive framework for selecting SSC indicators that are more consistent with SMART criteria. Hence, ELECTRE technique is used among the techniques of decision making due to its high efficiency in solving the issues with large number of alternatives. Since the ELECTRE I method is used to solve the selection issues specifically, this version was chosen. Within the proposed framework, indicators include the SMART criteria and alternatives are SSCPM indicators. A questionnaire was adjusted to collect the data and delivered to some of the university scholars and experts in this field. In this questionnaire, indicators associated with any process of SSC have been expressed. Thus, in order to solve ELECTRE I method, a questionnaire has been designed for achieving final SSCPM indicators. This questionnaire in turn includes two questionnaires of paired comparisons of SMART criteria and choosing SSCPM indicators based on SMART indicators. The questionnaire is also researcher-made by Lickert's spectrum that has been sent to academic experts for approval. The details have been presented in Table 3.
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Table 3. Data related to the questionnaire of choosing SSCPM indicators Respondent
Number
PhD. PhD. Student
14 6
Number of returned questionnaires 9 4
In this questionnaire, the decision-making options are SSCPM indicators and criteria include the SMART. The weight of criteria has been calculated by paired comparisons questionnaire that has been completed by 13 experts. In order to obtain the paired comparisons matrix, the group decisionmaking concept in the hierarchical process has been used. The incompatibility rate of this matrix was equal to 0.012 which is lower than 0.1. Thus, the compatibility of this matrix is confirmed. In the questionnaire of choosing SSCPM indicators based on SMART criteria, the indicators related to each SSC process has been stated. Considering that the number of questionnaires is high (6 pages), only a sample of questionnaire has been presented in Table 4. Table 4. A sample of questionnaire for selecting SSCPM indicators based on SMART criteria Demand management Indicators Specific
SMART criteria Importance Ratio(1-5) Measurable Attainable Realistic
Time-bounded
Forecast accuracy Forecasting customer requirements Demand resources needs reliability
Finally, after forming the decision making matrix and passing the steps of the problem-solving by ELECTRE I method, a final table has been developed as addressed in appendix. Since this study is being conducted on supply chain, it is necessary to determine which indicators on the chain are inputs and which are outputs. Because the organizations steadily try to reduce inputs and increase outputs, selected indicators in this paper have been distinguished based on the nature of input and output on SSC. The framework developed is shown in Table 5.
Table 5. A framework on metrics for the performance measurement of an SSC Process
Performance metrics
References
Demand management
Forecast accuracy
Chopra and Meindl (2004), Dong et al.(2012)
Capacity and resources
Service capacity
Johnston and Glark (2008), Haksever et al. (2000), Dong et al.(2012)
management
Capacity utilization
Johnston and Glark (2008), Haksever et al. (2000), Dong et al.(2012)
Effectiveness of scheduling techniques
Giannakis (2011), Haksever et al. (2000), Dong et al.(2012)
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Input
Output
Process
Performance metrics
References
Customer
Customer satisfaction
Bruhn and Georgi (2006), Dong et al.(2012)
relationship management
Customer loyalty
Bruhn and Georgi (2006), Dong et al.(2012)
Supplier
Level and degree of productive and logistic congruency
Toni et al. (1994)
Supporting service delivery lead Time
Feng et al,(2011), Dong et al.(2012)
Quality of supplier’s service level
Feng et al. (2011), Giannakis (2011), Dong et al.(2012)
Utilization of service facilities, equipment and staff
Dong et al.(2012)
Delivery efficiency of supporting services
Dong et al.(2012)
Adherence to a developed schedule
Dong et al.(2012)
Order process
Total cycle time
Stewart (1995), Dong et al.(2012)
management
Service order leadtime
Giannakis (2011), Gunasekaran et al. (2001), Dong et al.(2012)
Service
Profit margin
Silvestro and Cross (2000)
performance
Productivity
Silvestro and Cross (2000), Dong et al.(2012)
management
Service value
Silvestro and Cross (2000)
Employ satisfaction
Silvestro and Cross (2000)
Making promises to customers
Ellram et al.(2004)
Operating ratio ofactual to planned working hours
Silvestro and Cross (2000), Dong et al.(2012)
Averagecustomer spend per visit per store
Dong et al. (2012)
Employee referral
Silvestro and Cross (2000)
Employee turnover
Silvestro and Cross (2000)
Employee absence
Silvestro and Cross (2000)
Service flexibility
Fitzgerald et al. (1991), Parasuraman et al. (1988), Dong et al.(2012)
Range of services
Johnston and Glark (2008), Dong et al.(2012)
Total service delivery cost
Giannakis (2011), Johnston and Glark (2008), Dong et al.(2012)
The customer query time
Gunasekaran et al. (2001)
Accuracy of the information exchanges
Zailani and Kumar(2011)
relationship management
Information And Technology
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Input
Output
Process
Performance metrics
References
management
Timeliness of the information exchanges
Zailani and Kumar(2011)
Credibility of the information exchanges
Zailani and Kumar(2011)
Using new technology for increase channel to customers to contact the organization
Boonitt and Pongpanarat(2011)
Decisionmakingmanagementbased on updated information managedIT
Boonitt and Pongpanarat(2011)
Total cash flow time
Giannakis (2011), Stewart (1995), Dong et al.(2012)
Rate of return on investment
Gunasekaran et al. (2001), Dong et al.(2012)
Flow of payments between parties
Ellram et al.(2004)
Service supply Chain finance
Input
Output
10. Case study and findings The public service sector is a major player in supply chains and is a part of the service SCM domain (Arlbjorn et al., 2011). In the new millennium, healthcare institutions faceadditional challenges such as increased complexity of processes, need for efficient utilization of resources, increased pressure to improve quality of services and need to control the workload of healthcare personnel (De Vries et al., 1999). Therefore, all these challenges prove that implementation of logistics and SCM have now become a hot issue for healthcare organizations (Baltacioglu et al., 2007). The effective SCM practices will reduce costs, boost revenues, increase customer satisfaction, and also improve service delivery (Lang and Cheng, 2012). Hence, one of the public hospitals in Iran is selected as the case study. Considering the point that more attention is paid to the issues related to the supply chain in private hospitals compared to the public hospitals, and the higher number of people referring to the public hospitals than those referring to the private hospitals, the need to investigate the supply chain performance in order to improve and increase the speed of offering healthcare services was felt. Thus, three hospitals of A, B, and C have been selected in Iran for examining the proposed approach. Step 2: Selecting an SSC (Choosing one type of services supply chain) In order to prioritize the SSC loops, a single-level bidirectional supply chain that corresponds to a non-emergency patient is selected. In fact, the hospital’s psychology process which is in accordance with the single–level bidirectional supply chain depicted in Figure 1 is selected for analysis. The supply chains loops, include admission and physician. Step 3: Prioritizing the SSCs loops based on input and output indicators using Fuzzy TOPSIS technique
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In this step before prioritizing, the resulted performance measurement indicators in Table 4 are corresponded to the desired chains. Regarding the fact that the psychological process has been considered in of the three hospitals, the performance indicators of single level bidirectional services supply chain are similar to each other. In the real world, due to incomplete and unavailable information, the data is not usually as certain but often as fuzzy. In this paper, in order to prioritize the loops of the chains, fuzzy TOPSIS method is utilized. Hence, the criteria are SSCPM indicators corresponding to the desired chains and the alternatives are the chains loops. To get a deep insight of the chain performance, prioritizing has been done based on both input indicators and output indicators. To determine the indicators weight and to form the matrix of fuzzy decision-making, the process owners and in some cases regarding the type of indicator, the patients related to the chain are interviewed. In order to compose a fuzzy decision matrix, an interview has been performed with the owners of the processes (in those loops which more than one owner of the process exist, the more experienced person was selected). For this purpose, they were asked about the indicators and they responded to the questions based on linguistic variables (Table 2). As an example about the indicator of employee’s satisfaction a question was developed as “to what extent you are satisfied with the service quality and physical environment and managerial method?” and in some cases the patients related to the process have also been interviewed. As a sample, the patients’ satisfaction was asked. Then, prioritizing of the chains loops is performed based on input and output indicators. Prioritizing chains loops based on input indicators In this section, fuzzy decision matrix and fuzzy weights, and also the final table that includes the relative closeness indicator and priorities of alternatives for input indicators related to each chain are presented in Tables 6 to 11. According to the results obtained from the present loops prioritizing in the process of psychology, it is observed that the physician loop based on input indicators has a better performance than the admissions loop. It has been observed that for all three hospitals’ chains the physician’s loop based on input indicators compared to the admission loop has better performance. Table 6. Fuzzy decision matrix and fuzzy weights (Hospital A)
Service order lead time
Using new technology for increase channel to customers to contact the organization
Employee absence
Service flexibility
(0.3; 0.6; 0.9)
(0.5; 0.8; 1)
(0.1; 0.6; 1)
(0.3; 0.7; 1)
(0.5; 0.8; 1)
Admission
(7; 9; 10)
(25; 25; 25)
(0; 1; 3)
(7; 7; 7)
(5; 7; 9)
Physician
(5; 7; 9)
(12; 12; 12)
(1; 3; 5)
(12; 12; 12)
(7; 9; 10)
Capacity utilization
Table 7. Prioritization of service supply chain loops (Hospital A) Alternatives
𝒅
𝒅−
𝑪𝑳𝒊
Rank
Admission
2.823
1.979
0.412
2
Physician
2.533
2.365
0.483
1
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Table 8. Fuzzy decision matrix and weights of fuzzy (Hospital B)
Service order lead time
Using new technology for increase channel to customers to contact the organization
Employee absence
Service flexibility
(0.3; 0.7; 1)
(0.5; 0.95; 1)
(0.3; 0.6; 0.9)
(0.7; 0.9; 1)
(0.5; 0.85; 1)
Admission
(7; 9; 10)
(20; 20; 20)
(3; 5; 7)
(8; 8; 8)
(7; 9; 10)
Physician
(7; 9; 10)
(15; 15; 15)
(3; 5; 7)
(10; 10; 10)
(7; 9; 10)
Capacity utilization
Table 9. Prioritization of service supply chain loops (Hospital B) Alternatives
𝒅
𝒅−
𝑪𝑳𝒊
Rank
Admission
2.019
2.052
0.504
2
Physician
2.027
2.091
0.508
1
Table 10. Fuzzy decision matrix and weights of fuzzy (Hospital C) Capacity utilization
Using new technology for increase channel to customers to contact the organization
Service order lead time
Employee absence
Service flexibility
(0.3; 0.6; 0.9)
(0.5; 0.8; 1)
(0.1; 0.5; 0.9)
(0.5; 0.85; 1)
(0.5; 0.7; 0.9)
Admission
(5; 7; 9)
(35; 35; 35)
(1; 3; 5)
(10; 10; 10)
(5; 7; 9)
Physician
(5; 7; 9)
(10; 10; 10)
(3; 5; 7)
(13; 13; 13)
(7; 9; 10)
Table 11. Prioritization of service supply chain loops (Hospital C) Alternatives
𝒅
𝒅−
𝑪𝑳𝒊
Rank
Admission
2.673
1.727
0.393
2
Physician
2.160
2.360
0.522
1
Prioritizing chain loops based on output indicators In this section, fuzzy decision matrix and fuzzy weights, and also the final table that includes the relative closeness indicator and priorities of alternatives for output indicators related to each chain are presented in Tables 12 to 17. According to the results obtained from the present loops prioritizing in the process of psychology, it is observed that the admissions loop based on output indicators has a better performance than the physician loop.
17
It has been observed that for all three hospitals’ chains, the admission loop based on output indicators compared to physician’s loop has better performance. Table 12. Fuzzy decision matrix and fuzzy weights (Hospital A) Forecast accuracy
Service capacity
Effectiveness of scheduling techniques
Accuracy of the information exchanges
Timeliness of the information exchanges
Credibility of the information exchanges
productivity
(0.5;0.8;1)
(0.3; 0.5; 0.7)
(0.3; 0.7; 1)
(0.7; 0.9; 1)
(0.9; 1; 1)
(0.7; 0.9; 1)
(0.5; 0.85; 1)
Admission
(7; 9; 10)
(5; 7; 9)
(7; 9; 10)
(5; 7; 9)
(5; 7; 9)
(7; 9; 10)
(10; 10; 10)
Physician
(5; 7; 9)
(5; 7; 9)
(3; 5; 7)
(9; 10; 10)
(9; 10; 10)
(7; 9; 10)
(1.71;1.71;1.71)
Table 12. (Continued) service value
employ satisfaction
customer satisfaction
making promises to customers
operating ratio ofactual to planned working hours
Range of services
(0.7; 0.95; 1)
(0.9; 1; 1)
(0.3; 0.8; 1)
(0.9; 1; 1)
(0.3; 0.7; 1)
(0.3; 0.75; 1)
Admission
(3; 5; 7)
(7; 9; 10)
(3; 8; 10)
(3; 7; 10)
(0.58; 0.58;0.58)
(5; 7; 9)
Physician
(7; 9; 10)
(0; 1; 3)
(7; 7; 7)
(1; 5; 9)
(0.66; 0.66; 0.66
(10; 10; 9)
Table 13. Prioritization of SSC loops (Hospital A) Alternatives
𝒅
𝒅−
𝑪𝑳𝒊
Rank
Admission
5.404
7.401
0.578
1
Physician
6.622
6.145
0.493
2
Table 14. Fuzzy decision matrix and weights of fuzzy (Hospital B) Forecast accuracy
Service capacity
Effectiveness of scheduling techniques
Accuracy of the information exchanges
Timeliness of the information exchanges
Credibility of the information exchanges
productivity
(0.7;0.95;1)
(0.7; 0.9;1)
(0.3;0.6;0.9)
(0.9; 1; 1)
(0.9; 1; 1)
(0.7;0.95;1)
(0.7;0.95;1)
Admission
(7; 9; 10)
(7; 9; 10)
(7; 9; 10)
(7; 9; 10)
(5; 7; 9)
(9; 10; 10)
(7.14;7.14;7.14)
Physician
(5; 7; 9)
(7; 9; 10)
(7; 9; 10)
(7; 9; 10)
(9; 10; 10)
(7; 9; 10)
(1.81;1.81;1.81)
Table 14. (Continued) service value
employ satisfaction
customer satisfaction
making promises to customers
operating ratio ofactual to planned working hours
Range of services
(0.7; 0.9; 1)
(0.9; 1; 1)
(0.5;0.85;1)
(0.7; 0.9; 1)
(0.5; 0.7; 0.9)
(0.5; 0.85; 1)
Admission
(5; 7; 9)
(7; 9; 10)
(3; 8.25;10)
(3; 6.5; 10)
(0.42; 0.42;0.42)
(7; 9; 10)
Physician
(9; 10; 10)
(5; 7; 9)
(5; 9; 10)
(3; 6; 9)
(0.82; 0.82; 0.82)
(9; 10; 10)
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Table 15. Prioritization of service supply chain loops (Hospital B) Alternatives
𝒅
𝒅−
𝑪𝑳𝒊
Rank
Admission
4.664
5.375
0.535
1
Physician
4.739
5.122
0.519
2
Table16. Fuzzy decision matrix and weights of fuzzy (Hospital C) Forecast accuracy
Service capacity
Effectiveness of scheduling techniques
Accuracy of the information exchanges
Timeliness of the information exchanges
Credibility of the information exchanges
productivity
(0.5;0.8;1)
(0.5;0.7; 0.9)
(0.3;0.5; 0.7)
(0.3;0.6;0.9)
(0.5;0.7;0.9)
(0.7; 0.9; 1)
(0.5; 0.85; 1)
Admission
(5; 7; 9)
(3; 5; 7)
(5; 7; 9)
(5; 7; 9)
(3; 5; 7)
(5; 7; 9)
(4.66;4.66;4.66)
Physician
(3; 5; 7)
(3; 5; 7)
(5; 7; 9)
(5; 7; 9)
(7; 9; 10)
(5; 7; 9)
(3.75;3.75;3.75)
Table16. (Continued) service value
employ satisfaction
customer satisfaction
making promises to customers
operating ratio ofactual to planned working hours
Range of services
(0.5; 0.7; 1)
(0.9; 1; 1)
(0.5;0.85;1)
(0.7;0.95;1)
(0.3; 0.6; 0.9)
(0.3; 0.7; 1)
Admission
(3; 5; 7)
(3; 5; 7)
(3; 6; 9)
(3; 6.5; 10)
(0.64; 0.64;0.64)
(3; 5; 7)
Physician
(5; 7; 9)
(0; 1; 3)
(3; 7; 10)
(1; 5; 9)
(0.66; 0.66; 0.66)
(9; 10; 10)
Table 17. Prioritization of service supply chain loops (Hospital C) Alternatives
𝒅
𝒅−
𝑪𝑳𝒊
Rank
Admission
6.142
5.859
0.488
1
Physician
6.347
5.701
0.473
2
Step 4: Integration of the results of the previous step using quadrant analysis After gaining the priority of loops based on input or output indicators in previous step, in order to merge the obtained results, informing the processes condition and making a decision about processes condition improvement, quadrant analysis has been applied. According to the obtained results of Tables 7 and 13(for the first hospital), physician loop in terms of input indicators and admissions loop in terms of output indicators have a higher optimal weight (0.483, 0.578). Thus, they were selected for quadrant analysis (other hospitals are the same) (Figure4).
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Concentration for improvement
Corresponding optimal output weight
0.578 0.535 0.5 0.488
Hospital A
Optimum
Hospital B
Hospital C
Weak
Waste of
0.483 resources 0.5 0.507 Corresponding optimal 0.522 input weight
Figure 4. Quadrant Analysis for determining the performance status of psychologyprocess
In quadrant 1, the corresponding optimal input weight is high and the corresponding optimal output weight is low, which indicates concentration on improvement status in the process. In quadrant 2, both the corresponding optimal input weight and the corresponding optimal output weight are high, which indicates optimum status and maintains performance in the process. In quadrant 3, both the corresponding optimal input weight and the corresponding optimal output weight are low, which indicates weak status in the process. Finally, in quadrant 4, the corresponding optimal input weight is low and the corresponding optimal output weight is high, which indicates waste of resources in process. Since the corresponding optimal output weight in this process is high and corresponding optimal input weight is low, the process performance status is concentration on improvement. Since for hospital A, the corresponding weight of optimal output is high and corresponding weight of optimal input is low, the statue of performance process is concentration on improvement. Similarly, the statue of performance process for hospital B is optimal, and the statue of performance process for hospital C is waste of resources.
11. Discussion and conclusions In this study, the main objective was to propose a comprehensive framework for selecting SSCPM indicators and to present a new approach using fuzzy TOPSIS technique and quadrant analysis to study the performance quality of single-level bidirectional SSC in three hospitals. Accordingly, after identifying metrics and performance measurement indicators in SSC, the indicators which were more SMART than the others were selected as desired indicators of this framework by using the technique
20
of ELECTRE I. The proposed framework would help the managers to identify the improvement opportunities in their SSC. The results of prioritizing the process loops of psychology using fuzzy TOPSIS technique indicate that physician loop based on input indicators has the first rank; admissions loop has the second rank; the admissions loop based on output indicators has the first rank; and the physician loop has the second rank. The psychological process was considered as single level bidirectional services supply chain in all the three hospitals. Then, by using Fuzzy TOPSIS based on input and output indicators, the loops of processes were prioritized. The results indicate that the physician's loop based on input indicators has the first priority in the three hospitals, and the admission loop based on output indicators has the first priority. In this study, the corresponding optimal input weight associated to the loop with first rank about input indicators as well as the corresponding optimal output weight associated to the loop with first rank about output indicators have the highest influence in determining the performance status of psychology processes. Hence, they were selected for quadrant analysis. Regarding the fact that the corresponding optimal output weight is high and the corresponding optimal input weight is low, the results of quadrant analysis shows that the performance status in this process is concentration on improvement. In hospital A the corresponding weight of optimal input is low, and the corresponding weight of optimal output is high. In hospital B, the corresponding weight of both optimal input and output is high and in hospital C, the corresponding weight of optimal input is high and the corresponding weight of optimal output is low. The results obtained from square analysis show that the performance statue in this process for hospital A is concentration on improvement; for hospital B is to focus on optimal; and for hospital C is to focus on the waste of resources. In comparison with the previous studies, performance measurement indicators suggested in this paper have been classified based on input and output quality on SSC while in a framework developed by Dong et al. (2012), indicators were distinguished based on being financial and non-financial. The advantage of indicators classification with respect to input and output status is that an organization may increase productivity through lowering inputs and trying to increase outputs and consequently create a competitive advantage for organization. Moreover, indicators given in this framework include the whole eight domain of SSC, while indicators given by Dong et al. (2012) have lack of indicators in the field of IT management. Therefore, the framework presented in this survey is possessed of a higher generality to evaluate the performance of SSC. With respect to the results of prioritizing the loops and quadrant analysis, managerial implications include: - Regarding the subject that both of the loops had no good performance about input indicators, these indicators especially the ones in which admissions loop had a low performance should be concentrated. - As the clinic of hospital performs weakly in applying new technologies, it is suggested that a section be established on the site of hospital with a title of “clinic service” in which the whole information required by the clients be included. For instance, schedule of the physicians and appointments for the clients. Even, the appointments may be made as internet through the site or a phone number except the number of answering machine of the hospital. If these services are delivered without the presence of the clients, the number of clients and the problems of overcrowd will be reduced. - Regarding the service order leadtime, it is suggested that if a physician has a delay or could not attend at all, the clients be informed as early as possible. For increasing flexibility, it is suggested that when the number of clients are a lot in admissions, with the help of another person, the service delivery may be done quickly.
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- Regarding this issue that the loops in contexts of output indicators had a good performance, they should be maintained in the same status. However, considering the fact that the physician has achieved a low priority, it may be recommended that the manager should make serious decisions about the physical environment that is available for the physician in order to attain the physician’s consent further. In addition, to increase productivity, the physician should decrease times of his/her delay and consequently useful workhours, hence productivity will be improved. Considering the results obtained from prioritizing chains loops and also square analysis, it is observed that hospital A compared to hospitals B and C does not have appropriate performance regarding the input indicators. Moreover, the statue of psychological process performance in this hospital is to focus on improvement, and it is suggested that the management concentrate more on improving the input indicators related to admission loop in order to achieve optimal condition. Hospital C compared to hospitals A and B does not have appropriate performance in output indicators. Moreover, the statue of psychological process performance in this hospital is the waste of resources and it is suggested that the management concentrate on improving output indicators related to physician's loop to achieve optimal condition. Considering the fact that hospital B has appropriate performance regarding both input and output indicators and it has appropriate performance, and is in optimal condition, it is suggested that the management first preserve the current condition, and then try to improve it continuously through focusing on improving input indicators related to admission loop and improving output indicators related to physician's loop. Studying the framework in an industry and also considering only one type of SSC, regarding nonemergency cases of hospital in terms of process selection and not using the other techniques of decision making for prioritization are the limitations of this study. Suggestions for future study include examining the proposed approach in other service industries; considering other types of SSC and comparing the obtained results with the results of this study; considering emergency cases in hospitals in selecting the process of SSC; applying other techniques of decision making in prioritizing the chain loops; and to compare the attained results with the results of this study.
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The dominate count
Final indicator
Forecast accuracy
46
Forecasting customer requirements
33
Demand resources needs reliability
29
Service capacity
57
Capacity utilization
42
Effectiveness of scheduling techniques
45
The ability to manage intangible resources to operate at optimum service capacity
7
The ability to manage tangible resources to operate at optimum service capacity
25
Customer retention
15
Customer satisfaction
55
Customer loyalty
52
Customer value
5
Customer profitability
8
The ability to develop long-term relationshipswith customers
7
The ability to communicate optimisticinformation to customers
17
The ability to establish effective relationshipswith customers to the benefit of the brandloyalty
12
The ability to manage relationship withcustomer to create the impression before andafter service
9
Performance metrics
Demand management
Capacity and resources management
Customer relationship management
25
Process
The dominate count
Performance metrics
Final indicator
Extent of mutual understanding and closeness for business growth – long-term perspective
5
Level and degree of productive and logistic congruency
41
Level and degree of information Exchange
21
Buyer–supplier risk/profit sharing initiatives
10
Extent of mutual cooperation leading to continuous improvement
5
Level and degree of operative interaction between buyer and supplier
27
Extent of mutual assistance in problem solving efforts
10
Supporting service delivery lead Time
70
Quality of supplier’s service level
40
Risk sharing initiatives
22
Cost saving initiatives
1
Supplierrelationship
Supplier pricing against market
34
management
Utilization of service facilities, equipment and staff
51
The delivery efficiency of supporting services
68
Volume and specification flexibility
31
Quality assurance methodology
30
Ability in day to day technical representation
23
Adherence to a developed schedule
56
Ability to avoid complaints of service delivery
22
The ability to develop long-term relationships with suppliers
32
The ability to maintain close relationship with a limited pool of suppliers
19
The ability to focus on key supplier to improve the service chain quality
35
The ability to develop a partnership program with suppliers for the benefit of the whole service supply chain
13
The service order entry method
22
The customer service order path
9
Service order lead time
70
Total cycle time
70
The efficiency of Process orders or reservation systems
35
IT supply chain applications
19
level of information sharing
30
Accuracy of the information exchanges
51
Timeliness of the information exchanges
64
Credibility of the information exchanges
47
Order process management
Information and technology management
26
Process
The dominate count
Final indicator
Using new technology for increase channel to customers to contact the organization
48
The ability to create effective networks management to share information among internal functions, suppliers and customers
28
The ability to track accurate information and/or data within the supply chain by using information technology
50
Decision-making management
30
Performance metrics
managementbased
onupdated
information
Demand estimation
35
Profit margin
59
Productivity
63
Service value
52
Employ satisfaction
65
Operating ratio ofactual to planned working hours
63
Averagecustomer spend per visit per store
64
Service
Employee referral
69
performance
Employee turnover
47
management
Employee absence
70
Service flexibility
62
Range of services
60
Total service delivery cost
66
The customer query time
54
Post process services
35
Making promises to customers
70
Rate of return on investment
70
Total cash flow time
49
Flow of payments between parties
48
SSC finance
27
