An integrated framework for prioritizing the outsourcing performance outcomes

Prajapati, Himanshu; Kant, Ravi; Tripathi, Sandeep Mani

doi:10.1108/jgoss-06-2019-0047

Cited by 18 publications

(23 citation statements)

References 65 publications

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“…Similarly, Zavadskas et al 27 carried out a three‐round Delphi process for selecting facilities management strategy. Besides, considering that the classic Delphi technique has high operation cost, lower degree of convergence, and sometimes are risky to delete key ideas by mistake, some researchers 28–31 utilized fuzzy‐Delphi technique, in which the experts were required to assess the factors by triangular fuzzy numbers. The essential three steps of the fuzzy‐Delphi are shown as follows:① Experts evaluate the significance of the indicators by linguistic scale and then convert the linguistic information to triangular fuzzy numbers.② Defuzzify the fuzzy weights of the indicators by

ω_{j} = \frac{a_{j} + b_{j} + c_{j}}{3},

where

ω_{j}

denotes the defuzzified weight for the

j th

indicator,

a_{j}

b_{j}

, and

c_{j}

denote the elements in the corresponding triangular fuzzy set.③ Deriving the threshold

q

by averaging all the weights and selecting the indicators by the rules: if

ω_{j} < q

, then we remove the

j th

indicator; if

ω_{j} \geq q

, then we remain the

j th

indicator. Software, Hardware, Environment, and Liveware (SHEL) model …”

Section: The Development Of the Aras Methods On Theorymentioning

confidence: 99%

“…Similarly, Zavadskas et al 27 carried out a three-round Delphi process for selecting facilities management strategy. Besides, considering that the classic Delphi technique has high operation cost, lower degree of convergence, and sometimes are risky to delete key ideas by mistake, some researchers [28][29][30][31] utilized fuzzy-Delphi technique, in which the experts were required to assess the factors by triangular fuzzy numbers. The essential three steps of the fuzzy-Delphi are shown as follows: ① Experts evaluate the significance of the indicators by linguistic scale and then convert the linguistic information to triangular fuzzy numbers.…”

Section: ) Delphi Techniquementioning

confidence: 99%

“…Triangular fuzzy set The operation rules in Reference [108] Trapezoidal fuzzy set The operation rule in Reference [100] Gray set The operation rules in Reference [109] Intuitionistic fuzzy set The operation rules in Reference [90] Interval-valued triangular fuzzy set The operation rules in Reference [110] Interval-valued Intuitionistic fuzzy set The operation rules in Reference [111] Interval-valued Pythagorean fuzzy set The operation rules in Reference [112] Hesitant fuzzy linguistic term set The operation rules in Reference [113] Interval type-2 hesitant fuzzy set The operation rules in Reference [114] Rough set The operation rules in Reference [115] Probability multivalued neutrosophic set The operation rules in Reference [116] Z-number The operation rules in Reference [117] Picture fuzzy set The operation rules in Reference [118] information, some researchers converted the linguistic variables to triangular fuzzy number 23,27,29,31,34,41,43,46,48,50,51,60,70,72,76,80,82,84 some converted linguistic variables to gray numbers, 42,58,104 some converted the linguistic variables to rough numbers. 106 In addition, combining with the situation that experts are usually hesitant when giving their evaluations, Liao et al 8 extended the ARAS method to hesitant fuzzy linguistic environment and proposed a BWM-ARAS framework for finance supplier selection in digital supply chain, in which they used the operation rules for hesitan...…”

Section: Information Forms Operation Rulesmentioning

confidence: 99%

“…For the studies on supply chain management, most of them are related to supplier selection, such as selection of potential suppliers, 104 green supplier evaluation and selection, 46 selection of supplier with digital supply chain, 7,8,38 THK linear motion guide supplier selection, 72 resilient supplier selection, 31 and construction supplier selection. 54 Besides, Rostamzadeh et al 100 established a model to evaluate the supply chain management performance of small-mediumsized companies.…”

Section: The Studies About Supply Chain Managementmentioning

confidence: 99%

“…54 Besides, Rostamzadeh et al 100 established a model to evaluate the supply chain management performance of small-mediumsized companies. Considering the importance of outsourcing for business, Prajapati et al 31 proposed a method to identify and assess the crucial factors in outsourcing, which could provide suggestions for business management.…”

Section: The Studies About Supply Chain Managementmentioning

confidence: 99%

See 4 more Smart Citations

An overview of ARAS method: Theory development, application extension, and future challenge

Liu

Zhang

2021

Int J Intell Syst

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Multi‐attribute decision‐making (MADM) is one of the most important parts in decision‐making theory, and related research is becoming more and more popular over the past few years. Investigating that the information could be qualitative and quantitative, and the different measurement units cause difficulties in some MADM problems, the additive ratio assessment system (ARAS) method was proposed. The method tries to solve MADM problems through a simple way efficiently, and at the same time eliminates the influence of different measurement units. Till now, the method has received extensive attention and has been extended to different information environments and application fields. To know about the development of the method and improve the method efficiently, this paper reviews the studies on the ARAS method from the perspectives of basic information (including the bibliometrics analyses and the outline of ARAS method), the development on theory (including the development on MADM mechanism, different information environments, and combination with different methods), the development on the application and the future challenge. From the overview, the basic situations and the development of the ARAS method are presented clearly, and the analyses of the challenges can also provide useful and sufficient instructions for the future application and improvement of the method.

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ω_{j} = \frac{a_{j} + b_{j} + c_{j}}{3},

where

ω_{j}

denotes the defuzzified weight for the

j th

indicator,

a_{j}

b_{j}

, and

c_{j}

denote the elements in the corresponding triangular fuzzy set.③ Deriving the threshold

q

by averaging all the weights and selecting the indicators by the rules: if

ω_{j} < q

, then we remove the

j th

indicator; if

ω_{j} \geq q

, then we remain the

j th

indicator. Software, Hardware, Environment, and Liveware (SHEL) model …”

Section: The Development Of the Aras Methods On Theorymentioning

confidence: 99%

Section: ) Delphi Techniquementioning

confidence: 99%

Section: Information Forms Operation Rulesmentioning

confidence: 99%

Section: The Studies About Supply Chain Managementmentioning

confidence: 99%

Section: The Studies About Supply Chain Managementmentioning

confidence: 99%

See 3 more Smart Citations

An overview of ARAS method: Theory development, application extension, and future challenge

Liu

Zhang

2021

Int J Intell Syst

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show abstract

A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty

Gharibi

Abdollahzadeh

2021

JEIM

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PurposeTo maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.Design/methodology/approachThe design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.FindingsThe results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.Originality/value(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

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A comparative study of market potential for logistics outsourcing in Estonia and Finland

Solakivi

Kiisler

Hilmola

2021

JGOSS

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Purpose This research analyzes the development of logistics outsourcing market in two countries, Estonia and Finland, with different paths as members of the single European market. The purpose of this paper is to examine whether the two markets have become more similar or whether their logistics costs and logistics markets have developed differently over time. Design/methodology/approach The development of the logistics market is addressed through two survey-based variables. Logistics costs are used to measure the size of the logistics market, whereas logistics outsourcing is analyzed to measure the development phase as well as the market potential for logistics service provision. Findings Estonian logistics outsourcing market was found to be underdeveloped and small compared to the Finnish market. At the same time, the logistics costs of Finnish companies are high and rising, whereas the costs of Estonian firms are declining. Research limitations/implications The results imply that the level of outsourcing might explain the visibility of logistics costs, which should be taken into account when making estimates on logistics costs both at the firm as well as on country level. Social implications Logistics sector is an important source of national competitiveness and employment. This research identifies subareas for the two countries on how to develop competitiveness through the logistics market. Originality/value This research provides a unique method to estimate the size of logistics outsourcing market in these two countries. It also represents as one of the rare works to provide multiyear comparison between countries in logistics costs.

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An integrated framework for prioritizing the outsourcing performance outcomes

Cited by 18 publications

References 65 publications

An overview of ARAS method: Theory development, application extension, and future challenge

An overview of ARAS method: Theory development, application extension, and future challenge

A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty

A comparative study of market potential for logistics outsourcing in Estonia and Finland

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