Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.
We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.
To purchase hard copies of this book, please contact the representative in India:
CBS Publishers & Distributors Pvt. Ltd.
www.cbspd.com
|
customercare@cbspd.com
Current study provides a performance measurement system (PMS) based on Supply Chain Operations Reference (SCOR) adapted for the service sector. A systematic literature review was conducted on 16 performance measurement systems, which included 44 performance indicators and various performance metrics. Performance indicators and metrics were sorted according to conceptual similarities and then related to the five management processes intrinsic to SCOR model (plan, source, make, deliver and return). Indicators researched in a review of the literature were classified into six groups, namely, financial, velocity, sustainability, quality, resource utilization and customer services. The proposed PMS fills a gap in literature by forwarding a tool for the evaluation of the supply chain performance within the service sector.
*Address all correspondence to: talitafloriano@pet.coppe.ufrj.br
1. Introduction
Although the supply chain (SC) is viewed as an extension of a company, several organizations do not evaluate any effective performance to integrate their supply chain members. The evaluation of supply chain performance is quite crucial for the company’s operations since the primary aim of the supply chain is to maximize the generated overall value [1].
The evaluation of performance does not merely select but also assesses indicators to provide an appraisal of the company’s situation and identify possible improvements [2]. Supply chain performance is mainly related to the functioning of the company under analysis, with special focus on its core processes interconnected to other supply chain members [3].
After the selection of a set of indicators that may be used to manage organizations, a performance measurement system (PMS) is established to assess improvement opportunities for the organization. Conceptually, the performance measurement system is defined as a set of metrics used to quantify the efficiency and effectiveness of actions [4].
Consequently, the design of an effective PMS is basic since its usability is crucial for controlling the company’s operation processes [2, 5, 6, 7].
Despite the current importance given to performance measurement systems, several shortcomings are still extant, such as those related to non-financial indicators and to the behavior of organization members who fail to make the system function properly [2]. PMS is a widely discussed topic, albeit rarely defined. It may be casually defined as the process of quantifying action in which measurement comprises the process of quantification and action leading toward performance [8].
The study of performance measurement systems is challenging since it is still unclear how they enhance the effectiveness of an organization [9]. Moreover, when it comes to the service sector, there is a major gap in the supply chain field and the literature on this topic is still scarce [7, 10], similar to what happens with the evaluation of performance [11].
Service supply chain is a broad concept that encompasses companies dealing with spare parts supply, outsourcing, finance, insurance, retails and government services. Due to the service sector’s several peculiarities and the gaps previously mentioned, there is a need for better understanding what exactly makes service performance measurement problematic [12].
Supply Chain Operations Reference (SCOR) is a model which supports a PMS development, since it is not only able to measure and improve the company’s internal and external business processes [13], but also presents a cross-functional framework which integrates business processes re-engineering, benchmarking and performance measurement [14].
Owing to the lack of research publications related to service supply chain management (SCM) and performance measurement, the chapter structures a Performance Measurement System based on the SCOR model to assist the service sector’s supply chain management, especially at the operational level. Even though specialized literature presents a framework for measuring the supply chain performance [11], there is only a dearth of studies that associate performance indicators to every process of the SCM model. Consequently, a theoretical framework on supply chain management and performance measurement system is presented foremost. Secondly, we establish the methodological procedures and the results achieved by the proposed PMS, followed by the concluding remarks on the overall topic. The chapter foregrounds the theoretical constructs developed in the dissertation by [15]. Furthermore, the literature presents a practical application of the PMS in a service company, ratifying the feasibility of the proposed PMS [16].
The supply chain is a continuous process—ranging from the purchase of raw material up to the final product—with several functions, such as sales forecasting, purchasing, manufacturing, distribution, sales and marketing [17, 18, 19], with three major flows, namely, materials, information and money [20]. Supply chain management has represented a new frontier to obtain competitive advantages [21].
Mentzer et al. [22] define supply chain as the systemic and strategic coordination of traditional business functions and the tactics across the latter within a particular company and across businesses within the supply chain, to improve the long-term performance of individual companies and the supply chain as a whole.
Even though most of the definitions involving supply chain refer to the traditional concept (physical SC), there are some implications which differentiate physical SC from service SC. This is precisely the aim of the chapter. Ellram et al. [23] define service supply chain as the management of capacity, demand, customer relationship, supplier relationship, service delivery, cash flow and information flow. Moreover, the efficiency of a service SC depends on how to handle the items previously presented, whilst traditional SC demands more process standardization [23, 24].
Due to its dynamism, supply chain management requires mandatory decisions to improve its performance [25], framed into three categories or levels: strategic, tactical and operational. At the strategic level, it must be decided how to structure the chain, its configuration and the processes that will accompany each stage. Decisions made during this phase are also known as strategic decisions [26].
At the tactical level, planning includes decisions about which markets and locations must be supplied, the construction of inventories, outsourced manufacturing, policies of refueling and storage and frequency and size of marketing campaigns. Tactical-level measures include time efficiency of purchase order cycle, reserve procedures, quality assurance methodology and flexibility of capacity. Furthermore, the tactical level assures the achievement of specifications made at the strategic level [27].
Periodicity is weekly or daily at the operational level and during this phase firms make decisions on customers’ individual orders. The supply chain settings are fixed at this phase and planning policies should be already defined. Operations aim at implementing operational policies in the best suitable way, or rather, with a reduction of uncertainty and the optimization of performance, while constraints set by configuration and by planning policies are taken into account. Measures at the operational level comprise day-to-day capacity, ability to perform failure-free deliveries and the capacity to avoid complaints [26].
Despite advances in SCM and improvement in organizational effectiveness and efficiency, some challenges still need to be coped with, among which functional integration, collaboration with suppliers and, particularly, the alignment of a performance measurement system, may be mentioned. In fact, most organizations will never achieve an effective improvement without such alignment [28].
In the case of the alignment mentioned above, the concept of supply chain integration (SCI) must be introduced. According to Flynn et al. [29], SCI is the degree of collaboration among manufacturers and their partners to achieve effectiveness and efficiency on the flow of materials and information, including money and goods. Silvestro and Lustrato [30] underscore the importance of integrating financial supply chain and physical supply chain, due to the complexity of current supply chain networks. Furthermore, the highly complex supply chain networks tend to improve their performance as long as they are integrated, corroborating the benefits of SCI [31].
Besides the SCI issue, sustainability is also the chapter’s main topic. Green supply chain management (GSCM) is increasingly becoming an important strategy for the sustainable development of enterprises, since it is a sort of modern management method on environmental protection [13]. GSCM consists of social responsibility, from purchasing, manufacturing, distribution and marketing to reverse logistics [32, 33], which is an extended concept of SC [34]. Consequently, its aims toward a decrease in negative impacts, such as energy consumption, emissions and solid wastes [35].
In order to cope with such complexity, specific models are extant to assist the three management levels. According to the literature, there are two main models for managing a supply chain, namely, the Supply Chain Operations Reference (SCOR), developed by the Supply Chain Council, and the Global Supply Chain Forum (GSCF), developed by [36].
SCOR is the first reference model which has been built to describe, communicate, evaluate and identify opportunities for the improvement of work and information flow. Since the model uses standard measures for processes and activities, the former may be measured, managed, controlled and redesigned to achieve a certain purpose [37]. In the case of GSCF, it presents a more systemic view, highlighting the importance of balancing, physically aligning and managing technical aspects within the administrative management for a successful SCM [38].
The SCOR model provides a framework that relates performance metrics, processes, best practices and human labor within a single structure that enhances better communication among SC members and increases its efficiency, as well as promoting improved technology. Since the chapter aims at proposing a measurement system for service supply chain, SCOR has been preferred to GSCF due to its focus on performance metrics.
SCOR is actually more suitable for measuring service supply chain because of the relevance of human labor’s impact on its performance. Since it is somewhat complicated to control human performance in service operations, the employment of a measurement system for controlling human performance will contribute significantly to service SC.
Consequently, SCOR is a useful tool to ensure, document, communicate, integrate and manage key business processes along the SC, helping companies to conduct a systematic analysis and promoting communication among members at the firms´ internal and external milieu [39].
After foregrounding the supply chain management, that is, service supply chain and models of managing SC, it is important to investigate the concept of the performance measurement systems and their importance for the supply chain management.
Wong and Wong [40] discuss the importance of evaluating the supply chain’s performance to achieve adequate efficiency by providing the best possible usage of combined resources among chain members and by offering products at competitive prices. Performance evaluation is thus an important tool for managing supply chains [41, 42], since organizational performance always exercises a considerable influence on the companies’ activities [43].
Discussion on performance evaluation started in the late 1970s due to dissatisfaction with traditional accounting systems. Henceforth, this field of study has been developing in the literature [44], in terms of supporting its implementation and monitoring strategic levels [17], as well as identifying deficiencies and pointing toward pre-established goals [45]. Currently, the issue is widespread in the industrial and service sectors [46].
Regarding the supply chain, performance assessment has become increasingly important, especially in the manner the benefits of integration with suppliers improve performance [47, 48]. Furthermore, Cousins et al. [49] state that close contact with suppliers and customers are increasingly mentioned as a differentiating factor in the performance of supply chains. In fact, Gunasekaran et al. [50] highlight information sharing, communication and trust as being the essential factors to improve the performance of companies and integrated supply chains.
According to Gunasekaran et al. [27], although they have been in the limelight, measurement and performance metrics pertaining to the supply chain management are not receiving adequate attention in the literature due to lack of empirical findings and case studies on measures of performance metrics within the supply chain.
According to Kuo et al. [5], in general, if the measurement system is applied to distribution centers, six categories have to be considered, namely, financial, operational, quality, safety, employee and customer satisfaction. However, these categories may be generalized for several different scenarios. In addition to the criteria of cost and quality, Chan [51] insists that other performance measures may be used, such as resource utilization, flexibility, visibility, reliability and innovation. Moreover, Gunasekaran et al. [26] suggest that the performance measurement system may either be classified according to company levels (strategic, tactical and operational) or classified as financial and non-financial.
Among the researched papers (by Otto and Kotzab [52], Lohman et al. [47], Schmitz and Platts [53], Bremser and Chung [54], Folan and Browne [43], Rao [41], Giannakis [55], Bhagwat and Sharma [56], Gaiardelli et al. [57], Akyuz and Erkan [58], Naini et al. [59]), it has been noted that the Balanced Scorecard (BSC) is a state-of-the-art model, with widespread usage.
Balanced Scorecard combines financial and non-financial metrics. Objectives and metrics hail from the company’s strategy and vision, focused on four perspectives, namely, finance, customer, internal business processes and learning/growth [60]. Further, Tezza et al. [61] presented 140 approaches on performance measurement systems for the 1980–2007 period and papers were divided into four classifications: corporate, supply chain, service and individual.
The following section tackles the methodology used, especially with regard to papers’ selection and research on performance indicators.
So that the proposed objective could be accomplished, the following steps were established: (1) definition of the theoretical conceptual framework; (2) indicators survey (compilation) and performance metrics; (3) definition of performance indicators; (4) definition of performance metrics; and (5) proposal of a PMS directed toward the service sector. Figure 1 details all the methodological steps used in the current study.
Figure 1.
Methodology for a performance measurement system in a service supply chain.
4.1. Definition of the theoretical conceptual framework
The steps for defining the theoretical conceptual framework are (1) definition of the topic under analysis, or rather, the supply chain associated with the evaluation of performance; (2) database selection, or rather, choosing a relevant database within the academic environment; (3) selection of keywords for searching articles; (4) selection of articles in scientific journals, excluding articles from conferences and patents; (5) analysis of abstracts to identify research problem and work justification, methodology used and results; and (6) defining texts that will foreground the review of the literature.
The Web of Science (ISI) has been adopted for its journals’ impact factor, presenting important papers in terms of their topics and being academically relevant for the purpose. In fact, this database indexes the most important literature in the world [62].
So that a systematic review of the literature could be undertaken, keywords were defined and software Endnote® was used to support the entire process. The papers were sorted according to the following keywords: Performance Measurement, Supply Chain, Supply Chain Performance, Service, Performance and Supply Chain Service. Repeated papers, books, book sections and patents were excluded and only articles from scientific journals remained, particularly from the International Journal of Operations & Production Management.
4.2. Indicators research and performance metrics
Based on the articles defined in the previous section, indicators and performance metrics that compose a PMS service sector were highlighted. Fourteen approaches related to performance evaluation were found, plus two relevant applications, resulting in 16 approaches, as shown in Table 1.
4.3. Definition of the performance indicators
Indicators and metrics were defined for each basic process of the SCOR model (planning, supply, make, deliver and return) so that a PMS could be prepared. All indicators retrieved from the review of the literature were listed and separated by author. Forty-four indicators were provided. A respective concept was tagged to each indicator, according to the authors. Based on their definitions, a group was formed as Table 1 reveals (Section 5.1).
Related to use of physical resources needed to serve customers
Bb, Ch, Ho, V, SM, C, BS, B
Customer services
Empathy, privacy, security, growth and learning, innovation, measurement and customization production, efficiency, tangibility.
Related to customer service itself and to the factors affecting this service
Pa, Ch, P, C, BS, BB, SG, V, Pa, Ch
Table 1.
Summary of indicators, their respective definitions and suggested indicator.
Caption for 16 authors: P: Parasuraman et al. [63], S: Stewart [64], BB: Brignall and Ballatine [65], B: Beamon [66], C: Cravens et al. [67], Ho: Holmberg [68], L: Lambert and Pohlen [69], V: van Hoek [70], H: Hausman [71], Bi: Bititci et al. [72], Pa: Parasunaran et al. [73], SG: Shepherd and Günter [74], SM: Sellitto and Mendes [75], BS: Bhagwat and Sharma [56], SCC: Supply Chain Council (2010) and Ch: Cho et al. [11].
The above step has been necessary: a PMS requires various approaches from different authors. Although these authors use different tags for the same indicators, their definitions converge and thus a grouping process is required.
A few examples may be required. Since the indicators ‘Assets’ and ‘Sales’ are somewhat related to the cash flow of the company or of the supply chain, a group tagged ‘Financial’ was proposed for indicators with the same relationships (cash flow).
The group ‘velocity’ was related to the timing of the supply chain for fulfilling market or corporate demands upstream or downstream. ‘Sustainability’ was related to supply chain environmental issues; ‘quality’ was related to the fulfillment of norms, meeting customers’ needs; ‘resource utilization’ was related to the actual use of physical resources needed to serve customers; and ‘costumer service’ was related to the customer service itself and the factors affecting this service.
In other words, the procedure unified the indicators found in the literature. Only six groups were formed, namely: financial, quality, velocity, resource utilization, sustainability and customer service.
4.4. Definition of performance metrics
Metrics for the selected indicators were developed for performance according to the basic processes of the supply chain. The step involves the consolidation of a measurement system suited to the needs of the company’s performance. These metrics were focused on the service sector, foregrounded on the review of the literature.
Similarly, all metrics/measures have been surveyed according to the review of the literature. Separated by supply chain processes (plan, source, make, deliver and return), metrics were classified according to the most appropriate indicator (defined in the previous step), based on the definition of each.
For instance, the metric “full time cash flow” was related to the indicator on the Financial Planning process, since all metrics related to the financial indicators are somehow related to costs. In fact, “total time cash flow” represents somewhat the planning of the supply chain. The above scheme was undertaken for each metric discussed.
In addition, all metrics were divided into strategic, tactical and operational levels. Some of the metrics have been classified in the work of Gunasekaran et al. [26]. Metrics with no level definition were classified and justified into each level.
Another example is the ‘Inventory Costs’ metric related to the Financial indicator and directed to the ‘source’ process. The item ‘Inventory Costs’ was classified at the operational level since cost information may be obtained in a shorter period of time and such information reflects what has already been planned, where operating policies have already been defined at strategic or tactical levels.
Since the main goal of the current research is the construction of a PMS at the operational level, the metrics classified at strategic and tactical levels were disregarded, and only metrics were taken into account for the operational level.
4.5. Proposal of a PMS for the service sector
Since indicators were placed in groups (defined in the previous step), the metrics were separated into each process (plan, source, make, deliver and return). Separated into processes, the metrics were classified according to the most appropriate indicator (defined in the previous step), based on the proposed definition of each indicator. Subsequently, the metrics with no definition of levels (strategic, tactical and operational) arranged in the literature were classified and justified for each level. The metrics classified at strategic and tactical levels were disregarded, that is, only metrics for the operational level were taken into account for the consolidation of PMS.
5. Proposal of a PMS directed to the service sector
This proposal includes the following sequential steps.
5.1. Research of performance indicators
Table 1 shows indicators separated by authors, their definition as stated in the literature and the basis or justification for grouping them into one of the six proposed groups (financial, velocity, sustainability, quality, utilization and customer service)
The next section discusses the construction of a system of performance measurement focused on the service sector.
5.2. Definition of a PMS for the service sector
Figure 2 presents the metrics sorted by indicators focusing on the operational level for each SCOR process. Further, five metrics were excluded—percentage of manufacturing of main product costs (F), economic order quantity (F), percentage of erroneous artifacts (Q), percentage of manufacturing orders in US dollars fulfilled within the deadline (V), percentage of manufacturing orders in units (tons, parts, etc.) within deadline (V)—due to their non-applicability to the service sector. In fact, there were mostly related to manufacturing.
Figure 2.
Indicators related to SCOR processes.
Finally, Figure 3 shows the metrics defined for the PMS according to the proposed model and to the following criteria: (F) financial, (Q) quality, (V) velocity, (U) utilization service, customer service (C) and (St) sustainability.
Figure 3.
Performance measurement system at operational level in the service sector.
The article “Performance measurement system in supply chain management: application in the service sector” [16] is an application that presents a practical application of the PMS in a service company, using the Analytic hierarchy process (AHP) that indicates which metrics is more important to the supply chain in service sector.
The PMS proposed has as one of the objectives measuring the importance of processes and metrics established. For this, the Expert Choice software was used. Two research instruments were developed. The first aims at comparing the processes and the second compares performance metrics. To synthesize the processes weights, an arithmetic mean of responses between the focus-firm and suppliers was calculated resulting in the Table 2.
Order
Process
Degree of importance (%)
1
Source
25.29
2
Plan
21.55
3
Deliver
19.50
4
Make
17.20
5
Return
16.47
Total
100
Table 2.
Mean for processes importance degree.
Table 2 shows that the most important process between the two firms is source. It is the process in which the focus-firm receives supplies from supplier, to supply the firm focus needs good planning of its inputs. The second most important is the plan process: the focus firm, this process consists of planning to meet the demand in convergence with the supplier; on the other hand, in the supplier it consists of planning the purchase of inputs to meet the clients, including the focus-firm.
The third most important process between companies is delivering. In the focus firm consists of the customer contact to deliver products/services. On the other hand, in the supplier, the “delivery” process is the action of delivering the products required to the supplier firm. The fourth most important process is make, which is associated with tender care to the client in focus firm to understand its needs, while the supplier (for being an importer/distributor) consists of the charge separation step. Finally, there is the return process.
The second part of the ranking is to sort the PMS metrics. Thus, Table 3 lists the metrics by order of the processes and in descending order by degree of importance.
Process
Indicator related
Metrics
Degree of importance (%)
Plan
Quality
Fulfillment of perfect order
38.50
Financial
Cost information
32.08
Speed
Lead time of order
29.40
Source
Financial
Stock costs
30.73
Speed
Percentage of deliveries in dollars on the due date
25.03
Speed
Percentage of deliveries in units (tons, parts, etc.) on the due date
19.03
Quality
Percentage of deliveries in units (tons, parts, etc.) accepted on the first time
15.03
Quality
Percentage of supplies in dollars accepted on the first time
10.18
Make
Speed
Average response time to a service request
31.88
Financial
Number of pending orders
18.78
Customer service
Number of services per employee
16.38
Customer service
Lead time of customer service
17.28
Financial
Production cost
15.68
Deliver
Quality
Deliveries without defects
45.05
Financial
Delivery costs
28.15
Quality
Number of deliveries on time
26.75
Return
Quality
Customer complaints
30.18
Sustainability
Percentage of recycled materials returned, in units
26.53
Speed
Response time to customer for warranty
24.08
Sustainability
Percentage of recycled materials returned in dollars
19.20
Table 3.
Summary of the degree of importance of metrics.
According to Table 3, the most relevant indicator in the planning process is the fulfillment of perfect order, which indicates if what was planned to meet the demand was executed on the purchase orders, that is, if the expected was achieved, being a quality indicator with 38.5%, followed by the metrics on cost information (financial) with 32.08% and lead time order (speed) with 29.40%.
In the source process, metrics were prioritized in this order: stock costs (financial) percentage of supply in dollars on the due date (speed), percentage of deliveries in units on the due date (speed), percentage of deliveries in units accepted on the first time (quality) and percentage of deliveries in dollars accepted on the first time (quality). the stock cost is the amount one incurs to have goods in stock, involves storage costs, opportunity cost, cost of capital employed, cost of obsolescence and others. Despite being focused company and supplier of services, they need equipment to perform such services and also supplies to sell to customers.
The make process has the metrics “Average response time to a request for service” as the most important in this process, related to the speed indicator. It is related to the average time needed to answer a request from a client, after it has already been answered.
In the deliver process, the metric “Detect-free Deliveries” was listed as most important, followed by the “cost of delivery” and finally, “the number of deliveries at the right time.” The metrics “Detect-free Deliveries” is related to the quality indicator and means the number of deliveries in which the product or service were delivered as expected by the client.
In the return process, the metric of customer complaint was considered the most important. This metric is an indicator of quality that can express customer satisfaction with the company.
According to the results, it is possible to settle the most important metrics between focal company and supplier, expecting the relationship’s performance to be improved.
Theoretical research on the evaluation of performance reveals that, although the topic has been researched for years, yet there is no consensus in the literature on its application. One approach on the evaluation of performance is related to the performance measurement system. Consequently, a review of the literature based on the supply chain and on the service sector was performed to elucidate this gap. performance measurement systems under analysis demonstrated lack of consensus on the topic, especially with regard to indicators. Among the 16 approaches, 44 indicators were raised, reduced to six groups on account of their definition and application, namely, financial, velocity, sustainability, quality, resource utilization and customer services.
SCOR comprises planning, supplying, making, delivering and returning. Based on a systematic review of the literature, a performance measurement system was proposed, taking into consideration the supply chain management through the basic processes identified in the SCOR model. This model was considered feasible according to [16] in terms of its practical applicability.
The theoretical contribution is related either to the unification of the existing literature based on various authors or to the performance measurement system proposed in supply chain management processes, presenting a framework of relevant indicators associated to the SCOR model. In other words, a better perspective of Supply Chain Management in the service sector through unification is suggested, in which the proposed PMS approaches the state-of-the-art in terms of supply chain performance measurement.
Among service companies, the nomenclature ‘make’ would not be the most appropriate. Thereby, future studies following current line of research would possibly replace the term ‘make’ by ‘attend’ or join it to the ‘source’ process, depending on the type of company.
As the main practical result, this chapter obtained the prioritization of the most relevant processes and metrics for clipping the SC under study. Another important result found was the separation of activities occurring in the focus firm on basic processes suggested by the SCOR model.
Based on the review of the literature and even with advances in research on performance measurement in supply chains, many companies are still immature on integration and sharing of information. Therefore, lack of collaboration between the focused firm and suppliers or between the focused firm and customers impair such relationship.
6.1. Directions for future researches
It may be suggested that, in future studies, the integration of enterprises should be observed. In fact, their relationship may be improved due to integration. The chain will turn out to be more responsive. While working with integration among members to reach a perfect order, risks involving supply chain management should be explored. Consequently, research works on SCM that measure chain risks and find ways or methodologies to lessen them are strongly suggested.
Since the relationship between suppliers should be collaborative, suppliers must be chosen not only for reasons based on costs or time, but also on their willingness to help the focused company so that both would be able to satisfy their customers in a sustainable manner.
The limitation of current research is due to the emphasis on a specific sector (service) and a validation has not been done yet. Other results for PMS may be acquired depending on companies and parts involved.
Therefore, future research work may be replicated in practical examples of supply chains so that differences, similarities and particularities of each application may be noted and, consequently, the proposed PMS may be adapted to different situations.
In addition, a PMS validation and verification should be made considering the condition of the supply chain, the particularities of the sector (service or industry), observing which processes and metrics are most important for each case.
The authors would like to thank the reviewers’ for their suggestions for their contribution toward the improvement of the chapter.
References
1.Chithambaranathan P, Subramanian N, Palaniappan PK. An innovative framework for performance analysis of members of supply chains. Benchmarking: An International Journal. 2015;22(2):309-334
2.Pedersen ERG, Sudzina F. Which firms use measures? International Journal of Operations & Production Management. 2012;32(1):4-27
3.Srinivasan M, Mukherjee D, Gaur AS. Buyer-supplier partnership quality and supply chain performance: Moderating role of risks, and environmental uncertainty. European Management Journal. 2011;29(4):260-271
4.Neely A, Mills J, Platts K, Gregory M, Richards H. Performance measurement system design: Should process based approaches be adopted? International Journal of Production Economics. 1996;46-47:423-431
5.Kuo CH, Dunn KD, Randhawa SU. A case study assessment of performance measurement in distribution centers. Industrial Management & Data Systems. 1999;99(1-2):54-63
6.Rompho N, Boon-itt S. Measuring the success of a performance measurement system in Thai firms. International Journal of Productivity and Performance Management. 2012;61(5):548-562
7.Amir AM. Performance measurement system design in service operations. Management Research Review. 2014;37(8):728-749
8.Neely A, Gregory M, Platts K. Performance measurement system design. International Journal of Operations & Production Management. 2005;25(12):1228-1263
9.Upadhaya B, Munir R, Blount Y. Association between performance measurement systems and organisational effectiveness. International Journal of Operations & Production Management. 2014;34(7):853-875
10.Arlbjorn JS, Freytag PV, de Haas H. Service supply chain management A survey of lean application in the municipal sector. International Journal of Physical Distribution & Logistics Management. 2011;41(3):277-295
11.Cho DW, Lee YH, Ahn SH, Hwang MK. A framework for measuring the performance of service supply chain management. Computers & Industrial Engineering. 2012;62(3):801-818
12.Jääskeläinen A, Laihonen H, Lönnqvist A. Distinctive features of service performance measurement. International Journal of Operations & Production Management. 2014;34(12):1466-1486
13.Wang FZ. The study on performance measurement of green supply chain management. In: Yarlagadda P, Yang SF, Lee KM, editors. Information Technology Applications in Industry Ii, Pts 1-4. Applied Mechanics and Materials. 411-414. Stafa-Zurich: Trans Tech Publications Ltd; 2013. pp. 2742-2745
14.Jothimani D, Sarmah SP. Supply chain performance measurement for third party logistics. Benchmarking: An International Journal. 2014;21(6):944-963
15.Santos TF. Proposal for a Performance Measurement System Linked to Supply Chain Management: An Application in the Services Sector. João Pessoa Federal University of Paraiba; 2014
16.Santos TF, Leite MSA. Performance measurement system in supply chain management: Application in the service sector. International Journal Services and Operations Management. 2016;23(3):298-315
17.El-Baz MA. Fuzzy performance measurement of a supply chain in manufacturing companies. Expert Systems with Applications. 2011;38(6):6681-6688
18.Chaharsooghi SK, Heydari J. LT variance or LT mean reduction in supply chain management: Which one has a higher impact on SC performance? International Journal of Production Economics. 2010;124(2):475-481
19.Huemer L. Unchained from the chain: Supply management from a logistics service provider perspective. Journal of Business Research. 2012;65(2):258-264
20.Chen HL. An empirical examination of project contractors' supply-chain cash flow performance and owners' payment patterns. International Journal of Project Management. 2011;29(5):604-614
21.Carvalho KL, Costa RP, Souza RC. Gestão estratégica dos relacionamentos na cadeia de suprimentos da alface. Production. 2014;24:271-282
22.Mentzer JT, DeWitt W, Keebler JS, Min S, Nix NW, Smith CD, et al. Defining supply chain management. Journal of Business Logistics. 2001;22(2):1-25
23.Ellram LM, Tate WL, Billington C. Understanding and managing the services supply chain. Journal of Supply Chain Management. 2004;40(3):17-32
24.Sengupta K, Heiser DR, Cook LS. Manufacturing and service supply chain performance: A comparative analysis. Journal of Supply Chain Management. 2006;42(4):4-15
25.Barros AC, Barbosa-Povoa AP, Blanco EE. Selection of tailored practices for supply chain management. International Journal of Operations & Production Management. 2013;33(8):1040-1074
26.Gunasekaran A, Patel C, Tirtiroglu E. Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management. 2001;21(1-2):71-87
27.Gunasekaran A, Patel C, McGaughey RE. A framework for supply chain performance measurement. International Journal of Production Economics. 2004;87(3):333-347
29.Flynn BB, Huo B, Zhao X. The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management. 2010;28(1):58-71
30.Silvestro R, Lustrato P. Integrating financial and physical supply chains: The role of banks in enabling supply chain integration. International Journal of Operations & Production Management. 2014;34(3):298-324
31.Gimenez C, van der Vaart T, van Donk DP. Supply chain integration and performance: The moderating effect of supply complexity. International Journal of Operations & Production Management. 2012;32(5-6):583-610
32.Olugu EU, Wong KY, Shaharoun AM. Development of key performance measures for the automobile green supply chain. Resources Conservation and Recycling. 2011;55(6):567-579
33.De Giovanni P, Vinzi VE. Covariance versus component-based estimations of performance in green supply chain management. International Journal of Production Economics. 2012;135(2):907-916
34.Paksoy T, Bektas T, Ozceylan E. Operational and environmental performance measures in a multi-product closed-loop supply chain. Transportation Research Part E-Logistics and Transportation Review. 2011;47(4):532-546
35.Takahashi ARG, Santa-Eulalia LA, Ganga GMD, Araujo JB, Azevedo RC. Projeto de cadeia de suprimentos ágeis e verdes: estudos exploratórios em uma empresa de bens de consumo não duráveis. Production. 2015;25:971-987
36.Cooper MC, Lambert DM, Pagh JD. Supply chain management: More than a new name for logistics. The International Journal of Logistics Management. 1997;8(1):1-14
37.Xiao J, Zhang X-M, Dan B. Study on performance of vendor-oriented e-supply chain. Icoscm 2009—Proceedings of the 3rd International Conference on Operations and Supply Chain Management. Vol. 3. 2009. pp. 504-507
38.Meijboom B, Schmidt-Bakx S, Westert G. Supply chain management practices for improving patient-oriented care. Supply Chain Management: An International Journal. 2011;16(3):166-175
39.Hwang Y-D, Lin Y-C, Lyu J Jr. The performance evaluation of SCOR sourcing process—The case study of Taiwan's TFT-LCD industry. International Journal of Production Economics. 2008;115(2):411-423
40.Wong WP, Wong KY. Supply chain performance measurement system using DEA modeling. Industrial Management & Data Systems. 2007;107(3-4):361-381
41.Rao MP. A performance measurement system using a profit-linked multi-factor measurement model. Industrial Management & Data Systems. 2006;106(3-4):362-379
42.Aramyan LH, Lansink AGJMO, van der Vorst JGAJ, van Kooten O. Performance measurement in Agri-food supply chains: A case study. Supply Chain Management—An International Journal. 2007;12(4):304-315
43.Folan P, Browne J. A review of performance measurement: Towards performance management. Computers in Industry. 2005;56(7):663-680
44.Nudurupati SS, Bititci US, Kumar V, Chan FTS. State of the art literature review on performance measurement. Computers & Industrial Engineering. 2011;60(2):279-290
45.Lin L-C, Li T-S. An integrated framework for supply chain performance measurement using six-sigma metrics. Software Quality Journal. 2010;18(3):387-406
46.Bititci U, Garengo P, Doerfler V, Nudurupati S. Performance measurement: Challenges for tomorrow. International Journal of Management Reviews. 2012;14(3):305-327
47.Lohman C, Fortuin L, Wouters M. Designing a performance measurement system: A case study. European Journal of Operational Research. 2004;156(2):267-286
48.Lehtinen J, Ahola T. Is performance measurement suitable for an extended enterprise? International Journal of Operations & Production Management. 2010;30(2):181-204
49.Cousins PD, Lawson B, Squire B. Performance measurement in strategic buyer-supplier relationships: The mediating role of socialization mechanisms. International Journal of Operations & Production Management. 2008;28(3):238-258
50.Gunasekaran A, Williams HJ, McGaughey RE. Performance measurement and costing system in new enterprise. Technovation. 2005;25(5):523-533
51.Chan FTS. Performance measurement in a supply chain. International Journal of Advanced Manufacturing Technology. 2003;21(7):534-548
52.Otto A, Kotzab H. Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. European Journal of Operational Research. 2003;144(2):306-320
53.Schmitz J, Platts KW. Supplier logistics performance measurement: Indications from a study in the automotive industry. International Journal of Production Economics. 2004;89(2):231-243
54.Bremser WG, Chung QB. A framework for performance measurement in the e-business environment. Electronic Commerce Research and Applications. 2005;4(4):395-412
55.Giannakis M. Performance measurement of supplier relationships. Supply Chain Management—An International Journal. 2007;12(6):400-411
56.Bhagwat R, Sharma MK. Performance measurement of supply chain management using the analytical hierarchy process. Production Planning & Control. 2007;18(8):666-680
57.Gaiardelli P, Saccani N, Songini L. Performance measurement of the after-sales service network—Evidence from the automotive industry. Computers in Industry. 2007;58(7):698-708
58.Akyuz GA, Erkan TE. Supply chain performance measurement: A literature review. International Journal of Production Research. 2010;48(17):5137-5155
59.Naini SGJ, Aliahmadi AR, Jafari-Eskandari M. Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain. Resources Conservation and Recycling. 2011;55(6):593-603
60.Kaplan L, Norton DP. The balanced scorecard: Measures that drive performance. Harvard Business Review. 1992;70(1):71-79
61.Tezza R, Bornia AC, Vey IH. Sistemas de medição de desempenho: uma revisão e classificação da literatura. Gestão & Produção. 2010;17(1):75-93
62.Targino MG, Garcia JCR. Ciência brasileira na base de dados do Institute for Scientific Information (ISI). Ciência da Informação. 2000;29:103-117
63.Parasuraman A, Zeithaml VA, Berry LL. SERVQUAL: Multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing. 1988;64(1):12-40
64.Stewart G. Supply chain performance benchmarking study reveals keys to supply chain excellence. Logistics Information Management. 1995;8(2):38-44
65.Brignall S, Ballatine J. Performance measurement in service businesses revisited. International Journal of Service Industry Management. 1996;7(1):6-31
66.Beamon BM. Measuring supply chain performance. International Journal of Operations & Production Management. 1999;19(3-4):275-292
67.Cravens K, Piercy N, Cravens D. Assessing the performance of strategic alliances: Matching metrics to strategies. European Management Journal. 2000;18(5):529-541
68.Holmberg S. A systems perspective on supply chain measurements. International Journal of Physical Distribution & Logistics Management. 2000;30(10):847-868
69.Lambert DM, Pohlen TL. Supply chain metrics. The International Journal of Logistics Management. 2001;12(1):1-19
70.van Hoek RI. The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain. International Journal of Operations & Production Management. 2001;21(1-2):15-29
71.Hausman WH. Supply chain metrics. In: Harrison TP, Lee HL, Neale JJ, editors. The Practice of Supply Chain Management: Where Theory and Application Converge. New York: Springer Science & Business; 2002
72.Bititci US, Mendibil K, Martinez V, Albores P. Measuring and managing performance in extended enterprises. International Journal of Operations & Production Management. 2005;25(4):333-353
73.Parasunaran A, Zeithaml VA, Malhotra A. E-S-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research. 2005;7(3):213-223
74.Shepherd C, Günter H. Measuring supply chain performance: Current research and future directions. International Journal of Productivity and Performance Management. 2006;55(3/4):242-258
75.Sellitto MA, Mendes LW. Avaliação comparativa do desempenho de três cadeias de suprimentos em manufatura. Production. 2006;16(3):552-568
Written By
Tálita Floriano dos Santos and Maria Silene Alexandre Leite
Submitted: 06 October 2017Reviewed: 08 March 2018Published: 20 June 2018
Open access peer-reviewed chapter
