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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. By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. IntroductionThe analytic hierarchy process (AHP) is widely used in multi-criteria decision-making tool for tackling multi-attribute decision-making problems in real situations. It represents a powerful technique for solving complicated and unstructured problems that may have interactions and correlations among different objectives and goals.
دانلود expert choice. نرم افزار اکسپرت چویس که مناسب ترین نرم افزار برای تحلیل سلسله مراتبی (AHP) است را دانلود و با کمک فیلم راهنما، نصب نمایید. از آن جایی که این نرم افزار روش تحلیل سلسله مراتبی، کاملا شناخته شده است، خروجی.
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The AHP helps the decision makers to organise the critical aspects of a problem into a hierarchical structure similar to a family tree. It is based on experts’ judgements through pairwise comparisons. Experts are interviewed and pairwise comparison judgements are applied to pairs of homogenous criteria, eventually generating the overall priorities for ranking the alternatives.AHP gained substantial attention as a possible solution to the decision-making problems in different organisational areas, for example, the selection of maintenance policy or factors of employee suggestion schemes which will be more deeply illustrated in the following section. However, this method can also be utilised in many other fields.
For example, in the study , the fuzzy AHP was employed to prioritise and select a suitable organisational structure. The AHP method has been widely used in the decision-making problems that involve multiple criteria in multiple levels as well. The method helps to decompose their decision problem into hierarchy of factors, each of which can be analysed independently and once the hierarchy is built, the decision makers systematically evaluate its various elements by comparing them one to another, two at a time, with respect to their impact on an element above them in the hierarchy. AHP method is used to measure the importance of these factors.Moreover, this technique allows for the search of relative importance placed on product attributes and attribute levels of the analysed complex goods.
To make the pairwise comparisons, we need a scale of numbers that indicate how many times a more important or dominant element over another element was, with respect to the criterion or property, with respect to which they are, compared.In this chapter two case studies are presented: first, empirical case study will be utilised to evaluate and select the most appropriate maintenance approach; the second one, based on expert opinion will present a possibility to formalise the importance levels for the importance of factors of sustainability of employee suggestion schemes. Review of maintenance approachesDifferent maintenance approaches (i.e. Strategies and concepts, methodology or philosophy) have been developed in the last few decades.The development of maintenance approaches are discussed by many authors –. Failure-based maintenance (FBM) prescribes activation of maintenance in the event of failure. No action is taken to detect or to prevent failure. Maintenance is carried out only after a breakdown. This approach is only appropriate in a case where customer demand exceeds supply and profit margins are large.
However, increasing global competition, small profit margins, safety awareness and strict environmental regulations are changing the environment that most companies are facing today. In this regard, more emphasis is being placed on developing maintenance concepts. However, it is always possible that a failure is allowed to occur. This depends on the existence of secondary damage, redundancy and the ease to repair. Waeyenbergh and Pintelon suggest that one should determine the economic feasibility in order to evaluate the technical feasibility of FBM for a critical component or a non-critical component.Preventive maintenance (PM) is comprised of maintenance activities that are undertaken after a specified period of time or amount of equipment used. Therefore, traditional preventive maintenance models are using policies such as age replacement and block replacement.
One of the disadvantages of the PM is that PM is only suitable when the standard deviation of the failure population is small. This means that if the distributions have a small standard deviation, they are usually a candidate for PM and in such cases PM is economical. Another shortcoming of PM is the lack of decision support systems and insufficient historical data ,.Condition-based maintenance (CBM) is a maintenance strategy that monitors the actual condition of the asset to decide what maintenance needs to be done. CBM is defined as the preventive maintenance based on performance and/or parameter monitoring and the subsequent actions. Using a condition monitoring (CM) system, the machine condition is assessed by the current and historical measurements of one or more of relevant CM parameters.
Vibration-based maintenance (VBM) is the most frequently used technique under the CBM approach. By an efficient use of VBM policy, which means utilisation of the information provided by vibration monitoring system for planning and performing maintenance actions, the machine can be run until just before failure as defined by the monitored parameter reaching a predetermined unacceptable value. Al-Najjar indicated that the implementation of VBM policy provides possibilities for obtaining early indications of alterations of machine-state.Al-Najjar proposed a strategy called total quality maintenance (TQMain), which sustains not only machinery but also the essential elements constituting a manufacturing process, such as production/operation, environmental conditions, quality, personnel, and methods. TQMain was defined by Al-Najjar as a means for monitoring and controlling deviations in a process, working conditions, product quality and production cost, and for detecting damage causes and their developing mechanisms and potential failures in order to interfere (when it is possible) to ‘stop’ or reduce machine deterioration rate before the production process and product characteristics are intolerably affected and to perform the required action to restore the machine/process or a particular part of it to as good as new. Further, Al-Najjar and Alsyouf also presented what characterises TQMain and distinguish it from other maintenance concepts (e.g.
Reliability-centred maintenance (RCM), total productive maintenance (TPM)). They highlighted that TQMain supports the use of a common database, continuous improvement, implementation of CBM such as VBM, and it is based on intensive use of real-time data acquisition and analysis to detect reasons behind deviations in product quality and machine condition.Reliability-centred maintenance was first introduced by the aircraft industry of the United States in 1978. There have also been several improvements to the traditional RCM methodology (e.g. Moubray’s book is a key reference. RCM can, among other things, improve system availability and reliability, reduce the amount of preventive maintenance, unplanned corrective maintenance and increase safety. These are all important aspects for organisation in order to sustain in a competitive environment.
RCM aims to increase the asset’s lifetime and create a more efficient and effective maintenance. But, Al-Najjar pointed out that RCM cannot completely exploit the use of condition monitoring (CM) techniques, and the progress of damage cannot be monitored until just before a failure. Further, Pintelon and Parodi described that available statistical data used in RCM are insufficient or inaccurate, and that there is a lack of insight in the equipment degradation process (failure mechanisms) and the physical environment (e.g. Corrosive or dusty environment) is overlooked.
There have been already attempts to combine RCM and AHP in the maintenance domain—for example, development of a hybrid model for trunk road network maintenance prioritisation. The proposed hybrid model was used to establish failure diagnostic and multi-criteria decision making, respectively.Total productive maintenance (TPM) is a methodology originating from Japan to support its lean manufacturing system, since dependable and effective equipment are essential prerequisite for implementing lean manufacturing initiatives in any organisation. To do so, the overall equipment effectiveness (OEE), which is defined as the product of availability, speed and quality performance, is used to assess the reached level.
Nakajima , a major contributor of TPM, has defined TPM as an innovative approach to maintenance that optimises equipment effectiveness, eliminates breakdowns and promotes autonomous maintenance by operators. The definition of TPM includes five major elements :.1. Overall equipment effectiveness maximisation;.2. A thorough system of preventive maintenance for the equipment’s whole life span;.3. Implementation by various departments (engineering, production, maintenance, etc.);.4. Total employee involvement from top management to the workers on the floor; and.5. Motivation management through small group activities and teamwork.Though the concept of TPM is simple and obvious, there are some reported limitations.
First, TPM does not provide clear rules to decide which basic maintenance policy will be used, and second, calculation of the OEE is not really a complete analysis. Cost and profits are not taken into account, and therefore it is not a comprehensive measure. Moreover, TPM also requires changing the organisational culture, what is not easily to accomplish. In this regard, Tsang and Chan indicated that those organisations that will not change their culture will not be successful in implementing TPM.
A study regarding application of AHP in the implementation of TPM decision making in manufacturing organisations was performed by Ahuja and Singh. Review of employee suggestion system factorsThe existing research aptly identifies the enablers to the employee suggestion schemes.
note that researchers trying to ascertain which factors affect employees to submit suggestions focus on three main streams of research. The first considers work environment, the second focuses on the features of the scheme, weighs the influence of feedback about suggestions against management support of the system as well as rewards for successful suggestions and the third deals with the characteristics of the individuals. Carrier reports that the majority of researchers consider organisational creativity to be fostered through the personal characteristics and motivation of creative individuals while the other group of researchers turned their attention to context and organizational factors. Axtell et al. argue that different sets of variables influence these two stages of employee suggestion system process, i.e. The creative and the implementation phases.
So, it is evident that the drivers that trigger the suggestion scheme comprise individual, organisational and contextual variables. Moreover, the innovation process is a complex phenomenon and many variables have roles to play in determining its process. Amabile et al. also contend that the organisational context can impede or support the generation of ideas. Everyone has ideas all the time, not all are creative, nor do they all lead to innovation. Therefore, creativity needs to be nurtured to turn into valuable suggestions.
Application of AHP in two organisational settingsFirst, in an empirical case study for selecting the most appropriate maintenance policy AHP was utilised aiming to evaluate and select the most appropriate maintenance approach. The case study was conducted at a Slovenian paper mill company.Secondly, an expert opinion study was conducted in the context of UAE organisations using suggestion systems to formalise the importance levels for sustainability of employee suggestion system factors. The illustration of AHP for these two case instances is discussed in the next sections. Case study 1: an AHP-based framework for maintenance policy selectionThe method proposed for selecting the most appropriate maintenance approach is based on a hierarchical model composed of a set of criterion and sub-criterion. AHP method is demonstrated by the following case study from the paper industry. One of the Slovenian paper mill companies is the subject of the case study in this research. It could be argued that maintenance is highly crucial for this company, since production process in this company is running 24/7.
Equipment life, equipment availability and equipment condition is very important in order to ensure smooth running of a paper machine, and provide on-time delivery at low prices. As such, the objective of this case study is to identify the most appropriate maintenance policy concerning the above mentioned objectives. Figure 1.Steps for conducting an AHP study.In order to select the most appropriate maintenance policy for the paper machine, this research paper uses the AHP methodology.
Based on the guidelines proposed by Saaty , an AHP framework was developed for facilitating the study. In this regard, Saaty proposed four steps to make a decision in an organised way:.1.
Define the problem and determine the kind of knowledge sought,.2. Structure the decision hierarchy from the top with the goal of the decision, then the objectives from a broad perspective, through the intermediate levels (criteria on which subsequent elements depend) to the lowest level (which usually is a set of the alternatives),.3. Construct a set of pairwise comparison matrices.
Each element in an upper level is used to compare the elements in the level immediately below with respect to it,.4. Use the priorities obtained from the comparisons to weigh the priorities in the level immediately below. Do this for every element. Then for each element in the level below add its weighed values and obtain its overall or global priority and.5.
Continue this process of weighing and adding until the final priorities of the alternatives in the bottom most level are obtained.Using these guidelines, an AHP framework was established for facilitating the study. Figure 2.A hierarchy model for maintenance policy evaluation/selection.Step 4: Construct a hierarchy framework for analysisAfter the goal of this study had been established, relevant criteria and sub-criteria of maintenance performance measurement were identified via steps 1 and 2. These criteria and sub-criteria were then structured into a hierarchy descending from the overall objective or goal to the various stages and related sub-criteria in successive levels. The top level of the hierarchy represents the defined objective, while the second level of the hierarchy consists of three main maintenance criteria, followed by various sub-criteria (see ). Finally, the fourth level of the hierarchy characterises the alternative maintenance approaches/policies.Step 5: Collection of empirical information and dataThis step is concerned with the collection of empirical information and data through the combined judgements of the individual evaluators from industry and academia.
For this purpose, a group of three evaluators were chosen for evaluating the selected criteria and sub-criteria. Two evaluators were chosen from academia having experience in the field of maintenance, and one from industry also experienced in the field of maintenance.
They had sufficient knowledge, expertise and understanding of the maintenance approached used in this study.Step 6: Perform pairwise comparisons for each level of criteria and sub-criteriaOnce the evaluators were identified and relevant empirical information and data were collected, the next step was to determine the relative importance among the criteria and sub-criteria. Before conducting the pairwise comparison, all team members were given the instruction on how to complete the comparison.
Invited evaluators were asked to carefully compare criteria of each hierarchy level by assigning relative scales in a pairwise fashion with respect to the goal of this study. Evaluators' judgements were then combined using the geometric mean approach at each hierarchy level to attain the corresponding consensus.
A relational scale of real numbers from 1 to 9 was used in the ranking process. The purpose of this scale is to determine how many times a more important or dominant element is prioritised over another element with respect to the criterion or property with respect to which they are compared. Step 7: Perform the consistency testIn this step consistency test was performed.
A measure of inconsistency is useful in identifying possible errors in expressing judgements as well as actual inconsistencies in the judgements themselves. AHP provides a method called the consistency ratio (CR) which is used to gauge whether a criterion can be used for decision making. In the AHP the pairwise comparisons in a judgment matrix are considered to be consistent if the CR is less than 10%. On the contrary if CR is bigger than 10%, possible cause should be examined. However, the standard consistency test has been critiqued by a number of authors –. For these reasons, we adopted a quality control approach for the consistency test, proposed by Karapetrovic and Rosenbloom. Authors recommended that quality control of consistency can be performed using the simple Shewhart Xbar-R chart or exponentially weighted moving average (EWMA) chart.
In this study, EWMA chart was used. This chart is suitable due to its possibility to identify small shifts in the consistency index (CI). CI can be calculated using the following equation: CI = λ max – n/ n – 1, where ‘ n’ is the number of criteria or sub-criteria of each level and λ max is the biggest eigenvector in the matrix.
In place of dividing each CI by the ‘random index’, we used an approach to plot the average values for CI (taking into consideration all decision makers) into EWMA chart. For this purpose, a free software environment for statistical computing and graphics R was applied using the QCC (an R package for quality control charting and statistical process control) package. We used a default value of smoothing parameter ( λ), which was set at 0.2 in the aforementioned R package. Shows that all EWMA values were within the defined control limits.
This means that decision makers were consistent. Step 8: Calculate the global weights of each criteria and sub-criteriaThe next step comprises a calculation of local and global weights. While local weights refer to the preceding hierarchical level, the global weights take into account the highest hierarchical level. The local and global weights as well as the corresponding ranks are presented in.Step 9: Synthesising the resultsIn order to obtain final results, all alternatives were multiplied by the global weight of the single decision criteria. The results are presented in.In, the global priorities are calculated for each of the alternatives.
The highest value (0.498) corresponds to the TQMain, followed by TPM (0.207) and RCM (0.162). As expected the lowest value refers to the FBM. Step 10: Sensitivity analysisIn this step, a sensitivity analysis is held to show the effect of altering different parameters of the model on the choice of the maintenance policy selection. First, the current values of the model are presented. Demonstrates the current importance of each alternative considering all criteria used in this model. As one can see from, the highest value corresponds to TQMain (49.8%). Additionally, also shows the values of the weights of all three main criteria from level 2 ( C1—equipment-/process-related measures, C2—financial measures and C3—health, safety and environment measures).Furthermore, a series of sensitivity analysis were performed to investigate the impact of changing the priority of the criteria on the alternatives’ ranking.
Dynamic sensitivity of expert choice was accomplished to analyse the change in outcome caused by a change in each of the main criterion. The aim of sensitivity analysis is to explore how these changes affect the priorities of the selected alternatives. In the following three scenarios are presented. We investigated the impact of changing the priority of three main criteria on overall results. First, the criterion ‘equipment-/process-related measures’ was increased for approximately 25% (from 53 to 66.2). The results are presented in.
This figure consists of two parts. The results presented on the left side of are criteria and their corresponding weights, while the right side of the figure illustrates the ranking of the alternative as expressed by importance (in percentage). The results of the sensitivity analysis showed that change (an increase of 25%) in the first criterion has no significant influence on the final ranking of the alternatives.
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As such, the overall rank of the final outcome remained unchanged. Figure 5.Scenario 1.Finally, the last criterion ‘health, safety and environment measures’ was also increased by 25% (from 19.9 to 25.1), and the model was tested for the change of the final ranking. The results show that the criterion ‘health, safety and environment measures’ has no major impact on the final outcome as well, and therefore TQMain remains the best alternative.Overall, the results of the sensitivity analysis revealed that the ranks of the alternatives remained stable in all cases. Additionally, a sensitivity analysis was carried out in which main criteria were decreased by 10%. The results displayed that the model is stable also when weights are decreased.
This indicates that the proposed model is stable and robust and thus appropriate for decision-making process. Case study 2: prioritisation of factors for sustainability of employee suggestion schemesEmployee suggestion system (ESS) is a tool widely used by the corporations to elicit employees’ creative ideas. It should elicit suggestions from employees, classify them and dispatch them to the ‘experts’ for evaluation. After this, the suggestion might be adopted, in which case the suggestion may well be rewarded. `Experts’ are dedicated committees who evaluate the suggestions and propose them for its implementations. Intensity of importanceDefinitionExplanation1Equal importanceTwo factors equally contribute to the objective3Somewhat more importantExperience and judgment slightly favour one over other5Much more importantExperience and judgment strongly favour one over other7Very much more importantExperience and judgment very strongly favour one over the other. Its importance is demonstrated in practice9Absolutely more importantThe evidence favouring in ever the other is of the highest possible validity2, 4, 6, 8Intermediate-valuesWhen compromise is needed.
Employee suggestion systems create win-win situation for employers and employees alike. However, despite the many benefits of the suggestion systems, sustaining them is still a challenge for organisations. Organisations need to assess their suggestion schemes to determine their sustainability and to examine if the right conditions exist for the suggestion schemes to flourish. After all, suggestion systems can contribute to build organisations innovative capability.The variables identified in the literature review were first subjected to a factor analysis.
This enabled the emergence of five factors, namely—Leadership and Work Environment, System Effectiveness, System Capability, Organisational Encouragement and System Barriers. These five factors were considered in an AHP analysis to determine the importance levels. Expert opinion study was conducted to formalise the importance levels for the importance of factors of sustainability of employee suggestion schemes.
Once the importance was identified, the initial framework was created to place the indicators in the order of their importance. An AHP expert opinion questionnaire following Saaty’s rating scale as reference for the expert to decide the importance of the indicators in the numerical range 1–9 or their reciprocals, i.e. 1/2–1/9 was used.The steps for conducting the AHP analysis were briefed in the following steps:The data were collected from three suggestion system implementers. These implementers were contacted through an email requesting their participation in this research study. The implementers had varied experience using suggestion systems, for example, 10–15 years and were active members of IdeasArabia Group. IdeasArabia Group conducts an annual conference on suggestion systems and awards the organisation and individuals for best suggestions. The data were collected in the form of semi-structured interview and after explaining the objective of the study and the application of the AHP method, from two participations.
The third participant was then shown the data collected from two participants for the final judgment as to which two users opinion about the importance level of factor indicators was more appropriate for adjusting the importance level of factor indicators for pairwise comparisons or that both opinions were incorrect as per the knowledge of the third practitioner. The third user expressed satisfaction over factor importance level established by the first user and suggested that the same should be adopted in this study.The steps for conducting the AHP analysis have been briefed in the following steps:Reciprocal matrix: the first step is to construct a set of pairwise comparison matrices. Each element in an upper level is used to compare the elements in the level immediately below with respect to it. Pairwise comparisons were carried out for all factors to be considered, usually not more than seven and the matrix is completed.Eigenvector: the next step is the calculation of a list of the relative weights, importance or value of the factors, which are relevant to the problem in question and this list is called an eigenvector.
Eigenvector is calculated by multiplying together the entries in each row of the matrix and then taking the nth root of that product gives a very good approximation to the correct answer.Consistency index: the consistency index for a matrix is calculated from ( λ max − n)/ n − 1. Consistency ratio: the final step is to calculate the consistency ratio for this set of judgments using the CI for the corresponding value from large sample of matrices of purely random judgments using, derived from Saaty’s book, in which the upper row is the order of the random matrix and the lower is the corresponding index of consistency for random judgments. CR should be less than 0.1 and if the CR is much in excess of 0.1 the judgments are untrustworthy because they are too close for comfort and to randomness and the exercise is valueless or must be repeated. The overall factor importanceAfter the study and analysis of the reciprocal matrix and AHP calculations were done, it can be interpreted that:.
Leadership and Work Environment is slightly more important than both—System Effectiveness (2) and System Capability (2), somewhat more important than Organisational Environment (3) and much more important than System Barriers. System Capability is equally important to System Effectiveness (1) and slightly more important to both Organisational Encouragement (2) and System Barriers (2). However, it is slightly less important than Leadership and Work Environment (1/2). System Effectiveness is equally important to both System Capability (1) and Organisational Environment (1) and slightly more important to System Barriers (2). However, it is slightly less important to Leadership and Work Environment (1/2).
Organisational Encouragement is equally important to System Barriers (1) and System Effectiveness (1). However, it is less important than System Capability (1/2) and somewhat less important to Leadership and Work Environment (1/3). System Barriers is equally important to Organisational Encouragement (1).
However, it is slightly less important to both System Capability (1/2) and system Effectiveness (1/2) and much less important to Leadership and Work Environment (1/5).depicts the importance order for five sustainability factors based on the above interpretations. The Leadership and Work Environment is the more important factor when compared to the other four. The indicator System Capability stands at the next importance on rank 2, System Effectiveness at importance rank 3 and Organisational Encouragement at rank 4 and System Barriers at rank 5. DiscussionThis chapter discusses application of the AHP method in two different organisational settings based on two case studies.First, AHP is applicable as an evaluation technique that eases the decision maker's task of choosing the most efficient maintenance policy. Diverse management practices can be implemented by manufacturing organisations in order to improve organisational performance by continuous improvement trough implementation of process changes ,.
Maintenance can be seen vital for sustainable performance of a production plant. Sharma et al. concluded that development, adoption and practice of new maintenance strategies had become crucial. Selecting a suitable maintenance policy is definitely one of the essential decision-making tasks in improving the cost-effectiveness of the production systems ,. Recent studies indicate that appropriate maintenance can extend the life of an asset and prevent costly breakdowns that may result in lost production. The maintenance function plays a critical role in a company’s ability to compete on the basis of cost, quality and delivery performance ,.
It appears that aim of the maintenance function is to contribute towards a company’s profit, clearly bringing the need for maintenance operations to be in harmony with corporate business objectives. Further, the growing importance of maintenance regarding improving company’s profitability and competitiveness , strengthens the need for selecting a proper maintenance policy. Therefore, using the proposed AHP framework, the criteria for maintenance policy selection can be clearly recognised and the issue can be structured systematically. More importantly, it can effectively support the decision makers in the process of selecting the most appropriate maintenance policy.Three main criteria for the maintenance policy selection were used in this study and are as follows: equipment- and process-related measures, financial measures as well as health and safety and environment measures.
Furthermore, the following sub-criteria are considered to be the most important: OEE, maintenance savings, number of accidents and productivity and availability. The latter can be explained in the context of a production process which in the paper mill is running 24/7. Therefore, used criteria play an important role, especially from the perspective of accomplishing the production goals. Based on the selected criteria as well as on the decision makers’ evaluations, the TQMain was selected as the most appropriate maintenance approach.
Among others, the TQMain is focused on maintaining and improving continuously the technical and economic effectiveness of the process elements , which were indeed important criteria in our study.To ensure that final solution is stable and robust, we additionally applied sensitivity analysis. With Expert Choice software, AHP enables sensitivity analysis of results which is very important in practical decision making.Secondly, the AHP method was applied to identify the important of each of the factors that would impact the sustainability of the suggestion scheme.
This analysis resulted in placing importance ranks among the five factors. These factors are arranged in the order of their importance as below:.1. Leadership and Organisational Environment.2.
System Capability.3. Organisational Encouragement.4.
System Effectiveness.5. System BarriersThe most important factor is placed at the centre and the least important factor is placed at the bottom of the list. Leadership and Work Environment is the first most important factor, System Capability is the next important factor followed by System Effectives that is placed at the next level. The Organisational Encouragement is placed at layer four and the last important factor is the System Barriers.The success of the suggestion scheme is related to the management support, practices, commitment and their leadership ,. Truly, senior management ought to demonstrate their faith in the scheme, promote and support it and encourage all managers to view it as a positive force for continuous improvement. Typically, the management support is seen as crucial to the implementation of ideas.
For example, while a person can be creative and generate new ideas on her/his own, the implementation of ideas typically depends upon the approval, support and resources of others, which essentially calls for different forms of management support. If employees make a lot of suggestions, then the opportunity for them to be translated into implementations is greater when there are higher levels of support. Without senior level management support the workers will not be motivated to turn in suggestions.The management support is also very essential for the facilitation of communication mechanism within the organisation.
Management, therefore, has a responsibility to satisfy this need for participation and create a culture which is supportive of employee involvement in the decisions that affect his or her work. Thus, the leadership-employee relation is of top most importance that can help the creativity practice to grow in the organisation.Secondly, the knowledge possessed by individual employees can only lead to a firm, competitive advantage if employees have the incentive and opportunity to share and utilize their individual knowledge in ways that benefit the organisation. Systems that capture the ideas and the capability of such system to evaluate them and provide necessary feedback and reward the employees for the suggestion are other core elements that are necessary once the top management and leadership support is obtained.Thirdly, organisations need to nurture the system by establishing mechanisms such as team works to improve the employee participation and must ensure that the right expertise and supervision is provided to guide the employees to make their suggestions.The benefits of the suggestions must be visible. Therefore, it is important that the suggestions result in desired outcomes so these benefits are accrued, and such systems can be sustained. ConclusionsManagers should identify the potential benefits of maintenance policy in terms of productivity, quality and profitability.
The latter is essential in order to achieve cost-effective decision making. The proposed framework for the maintenance policy selection appears to enable the structured and systematic way of selecting the most appropriate maintenance policy. By upgrading the traditional AHP method with a EWMA chart for consistency test, our proposed framework for maintenance policy selection represents a valuable tool for decision makers in the field of maintenance.The second case study showed that AHP method can be successfully applied to arbitrate the importance levels or ranks for the indicators in determining the sustainability of employee suggestion system. By using the proposed importance levels, organisations can assess the suggestion schemes for its sustainability and to recognise if the right conditions exist for the suggestion schemes to flourish.However, we acknowledge the limitations of using the traditional AHP method.
This method is often criticised because of its inability to adequately handle the uncertainty and imprecision associated with the mapping of the decision makers’ perception to a crisp number. Nonetheless, Karapetrovic and Rosenbloom suggested that quality control approach can be used with any of the variations of AHP. Future studies could therefore consider different versions of AHP for maintenance policy selection through the lens of a quality management approach.
Industry | Computer software |
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Founded | 1983; 37 years ago |
Founder | Ernest Forman |
Ernest Forman (CEO) | |
Website | www.expertchoice.com |
Expert Choice is decision-making software that is based on multi-criteria decision making.
Expert Choice implements the Analytic Hierarchy Process (AHP)[1] and has been used in fields such as manufacturing,[2] environmental management,[3][4] shipbuilding[5] and agriculture.[6]
Created by Thomas Saaty and Ernest Forman in 1983,[7] the software is supplied by Expert Choice Inc.[8]
References[edit]
- ^Saaty, T. L. (1980). 'The Analytic Hierarchy Process'. McGraw-Hill: New York.Cite journal requires
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(help) - ^Chan, F. T. S.; Chan, H. K. (2010). 'An AHP model for selection of suppliers in the fast changing fashion market'. The International Journal of Advanced Manufacturing Technology. 51 (9–12): 1195. doi:10.1007/s00170-010-2683-6.
- ^Najafi, A.; Afrazeh, A. (2011), 'Analysis of the Environmental Projects Risk Management Success Using Analytical Network Process Approach', International Journal of Environmental Research, 5 (2), pp. 277–284
- ^Yatsalo, B. I.; Kiker, G. A.; Kim, J.; Bridges, T. S.; Seager, T. P.; Gardner, K.; Satterstrom, F. K.; Linkov, I. (2007). 'Application of Multicriteria Decision Analysis Tools to Two Contaminated Sediment Case Studies'. Integrated Environmental Assessment and Management. 3 (2): 223–233. CiteSeerX10.1.1.511.1234. doi:10.1897/IEAM_2006-036.1. PMID17477290.
- ^Saracoglu, B.O. (2013). 'Selecting industrial investment locations in master plans of countries'. European Journal of Industrial Engineering. 7 (4): 416–441. doi:10.1504/EJIE.2013.055016.
- ^Pažek, K.; Rozman, Č.; Bavec, F.; Borec, A.; Bavec, M. (2010). 'A Multi-Criteria Decision Analysis Framework Tool for the Selection of Farm Business Models on Organic Mountain Farms'. Journal of Sustainable Agriculture. 34 (7): 778. doi:10.1080/10440046.2010.507531.
- ^French, S.; Xu, D. L. (2005). 'Comparison study of multi-attribute decision analytic software'. Journal of Multi-Criteria Decision Analysis. 13 (2–3): 65. doi:10.1002/mcda.372.
- ^McGinley, P. (2012), 'Decision analysis software survey', OR/MS Today, 39
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