Analytic Hierarchy Process Weight Research Articles

Empirical Investigation of Lean Six Sigma Enablers and Barriers in Indian MSMEs by Using Multi-Criteria Decision Making Approach

Lean Six Sigma (LSS) is an inclusive approach based on the concept of waste and variation reduction to achieve quality and sustainability. The successful adoption of any approach depends upon the awareness and readiness measures of a particular one. This paper contributes toward Micro-Small and Medium Enterprises (MSMEs) to adopt a comprehensive LSS approach in their existing system through the identification, analysis, and prioritization of LSS enablers and barriers. In the present study, 25 enablers and 21 barriers of LSS adoption are identified through literature review and are validated by using questionnaire-based survey. The screened enablers and barriers are further formulated into logical groups using factor analysis. These formulated groups are prioritized through Analytical Hierarchy Process (AHP) and compared the obtained results with the fuzzy decision-making trial and evaluation laboratory (Fuzzy-DEMATEL). The result reveals that among the main group of enablers, “management-based factor” is the top-ranked followed by “training- and education-based factor” and “technology-based factor” with AHP weights 0.4008, 0.2742, and 0.1465 respectively. On the other hand, among the main groups of barriers, “training and education based factor” is at first rank trailed by “management-based factor” with AHP weights 0.4780 and 0.2915 respectively. The outcomes of current research will facilitate the MSMEs to make a favorable environment toward the LSS approach for increasing productivity and profitability dynamics. An engineering manager can use this paper to enhance social sustainability through the incorporation of LSS approach in the communities where they work. Moreover, this study provides a guiding reference for practitioners and academicians to undertake similar nature of problems into other industrial sectors.

Selecting Freight Transportation Modes in Last-Mile Urban Distribution in Pamplona (Spain): An Option for Drone Delivery in Smart Cities

Urban distribution in medium-sized cities faces a major challenge, mainly when deliveries are difficult in the city center due to: an increase of e-commerce, weak public transportation system, and the promotion of urban sustainability plans. As a result, private cars, public transportation, and freight transportation compete for the same space. This paper analyses the current state for freight logistics in the city center of Pamplona (Spain) and proposes alternative transportation routes and transportation modes in the last-mile city center distribution according to different criteria evaluated by residents. An analytic hierarchy process (AHP) was developed. A number of alternatives have been assessed considering routes and transportation modes: the shortest route criterion and avoiding some city center area policies are combined with traditional van-based, bike, and aerial (drone) distribution protocols for delivering parcels and bar/restaurant supplies. These alternatives have been evaluated within a multicriteria framework in which economic, environmental, and social objectives are considered at the same time. The point in this multicriteria framework is that the criteria/alternative AHP weights and priorities have been set according to a survey deployed in the city of Pamplona (Navarre, Spain). The survey and AHP results show the preference for the use of drone or bike distribution in city center in order to reduce social and environmental issues.

Resetting the Evaluation Indicators for School Garden Education Service

Background and objective The vitalization of urban agriculture has increased various forms of experience-based education using school gardens, which raised the importance of school gardens in terms of value as well as the need to develop an implementation system for education-based agricultural experience service using school gardens. Thus, we reset the evaluation indicators from the previous study to establish objective evaluation indicators that enable quantitative comparison of school garden education services. Methods Analytic hierarchy process (AHP) and direct question (DQ) surveys were conducted on 20 experts from October 12 to 19, 2020 after establishing the purpose and subjects of evaluation, and then the weights were calculated using the Expert Choice 2010 program. Results First, we analyzed the problems of the previous indicators by categorizing the performance indicators and comparing and verifying them with six requirements of valuation. Then, we added ‘welfare values’ and established sub-indicators accordingly. The importance of value indicator in AHP was in the order of education values (0.544), health values (0.182), welfare values (0.164), environmental values (0.062), and economic values (0.049). The importance of environmental and economic values was relatively low, less than 0.1. The importance of sub-indicators was highest in cultivating character (0.144), followed by enhancing ecological sensitivity (0.141) > promoting mental health (0.134) > cultivating agricultural literacy (0.120) > improving social skills (0.104). And mitigating climate change in environmental values was lowest (0.009). Increase in income was the lowest (0.036). This can be regarded as the expression of change to increase the educational effect based on collective life and the connotative meaning of ‘school’. In the case of DQ, the AHP weight and order were the same, but the environmental and economic values were relatively low, and the result was different from AHP weight. For sub-indicators, the importance in DQ was highest in promoting mental health (0.136), followed by promoting physical health (0.085), ]cultivating character (0.082), social integration (0.072), and enhancing ecological sensitivity (0.071). After reviewing related experts, we came up with 5 evaluation indicators and 16 sub-indicators for school garden education service, which are objective evaluation indicators that enable quantitative comparison. Conclusion In the future, we will validate the socioeconomic values of school garden education services and contribute to revitalizing school gardens by establishing policy alternatives for effective operation and management of school gardens.

Combined soft measurement on key indicator parameters of new competitive advantages for China's export

The estimation of the difference between the new competitive advantages of China's export and the world’s trading powers have been the key measurement problems in China-related studies. In this work, a comprehensive evaluation index system for new export competitive advantages is developed, a soft-sensing model for China’s new export competitive advantages based on the fuzzy entropy weight analytic hierarchy process is established, and the soft-sensing values of key indexes are derived. The obtained evaluation values of the main measurement index are used as the input variable of the fuzzy least squares support vector machine, and a soft-sensing model of the key index parameters of the new export competitive advantages of China based on the combined soft-sensing model of the fuzzy least squares support vector machine is established. The soft-sensing results of the new export competitive advantage index of China show that the soft measurement model developed herein is of high precision compared with other models, and the technical and brand competitiveness indicators of export products have more significant contributions to the new competitive advantages of China's export, while the service competitiveness indicator of export products has the least contribution to new competitive advantages of China's export.

Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)

The development of landslide inventory is the first step towards landslide hazard management and planning. The proper inventory will also help to develop landslide susceptibility maps which will result to minimize economic and human losses. This work investigates landslide hazard mapping in the rugged mountain terrain vis-a-vis highly economically significant route between China and Pakistan i.e. Karakuram Highway (KKH). KKH is passing through the Karakorum mountainous region where landslides occur frequently and pose a serious threat to local travelers and tourists as well as to trading caravans. In this work, landslide inventory was developed (302 landslides) along KKH by visual interpretation of Sentinel and google images. Field survey was also carried to validate landslide datasets. The landslide dataset was divided into modelling/training (70%) and testing/validation (30%) datasets to develop and validate landslide susceptibility maps (LSM) using three models Frequency Ratio (FR), weight of evidence, and Analytic Hierarchy Process (AHP). To develop LSMs, landslide controlling factors that include Slope, Aspect, Landcover, Geology, Proximity to Fault, Distance to Road and Stream, and Precipitation are correlated and considered using GIS techniques. LSMs generated using testing datasets are validated by Area Under Curve (AUC) criterion. The results show that weight of evidence, FR and AHP have success rate curves of 61%, 84% and 72%, respectively. In addition, most highly accurate three models are validated for their prediction power using testing landslide datasets. The results for prediction capacity for weight of evidence, FR and AHP are 72%, 64%, and 58%, respectively. At the end, landslide susceptibility index (LSI) maps were classified into susceptibility zones. We also compared variation of weights in each class in AHP and FR model. The overall trend of increase or decrease in weights remains same except in few classes like precipitation class. The validation and prediction results show that FR model is the most reliable and accurate model which can be used for landslide management and planning. Our results will be helpful to minimize landslide hazard losses along KKH, ultimately assisting in successful implementation of CPEC idea between China and Pakistan.

Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)

The development of landslide inventory is the first step towards landslide hazard management and planning. The proper inventory will also help to develop landslide susceptibility maps which will result to minimize economic and human losses. This work investigates landslide hazard mapping in the rugged mountain terrain vis-a-vis highly economically significant route between China and Pakistan i.e. Karakuram Highway (KKH). KKH is passing through the Karakorum mountainous region where landslides occur frequently and pose a serious threat to local travelers and tourists as well as to trading caravans. In this work, landslide inventory was developed (302 landslides) along KKH by visual interpretation of Sentinel and google images. Field survey was also carried to validate landslide datasets. The landslide dataset was divided into modelling/training (70%) and testing/validation (30%) datasets to develop and validate landslide susceptibility maps (LSM) using three models Frequency Ratio (FR), weight of evidence, and Analytic Hierarchy Process (AHP). To develop LSMs, landslide controlling factors that include Slope, Aspect, Landcover, Geology, Proximity to Fault, Distance to Road and Stream, and Precipitation are correlated and considered using GIS techniques. LSMs generated using testing datasets are validated by Area Under Curve (AUC) criterion. The results show that weight of evidence, FR and AHP have success rate curves of 61%, 84% and 72%, respectively. In addition, most highly accurate three models are validated for their prediction power using testing landslide datasets. The results for prediction capacity for weight of evidence, FR and AHP are 72%, 64%, and 58%, respectively. At the end, landslide susceptibility index (LSI) maps were classified into susceptibility zones. We also compared variation of weights in each class in AHP and FR model. The overall trend of increase or decrease in weights remains same except in few classes like precipitation class. The validation and prediction results show that FR model is the most reliable and accurate model which can be used for landslide management and planning. Our results will be helpful to minimize landslide hazard losses along KKH, ultimately assisting in successful implementation of CPEC idea between China and Pakistan.

An integrated multicriteria decision‐making approach for distribution system expansion planning

The project selection problem in an electric distribution utility, which is part of the network expansion planning task, requires a formal process of prioritizing some projects since the budget is limited. A complete solution to this problem, involving optimization and a decision-making model, is still a few explored in the literature. We proposed an integrated multicriteria decision-making methodology, including optimization and solutions ranking. The optimization module finds a set of Pareto-optimal portfolios, using the multi-objective genetic algorithm nondominated sorting genetic algorithm-II to select and schedule the reinforcement projects in a multistage planning horizon. The proposed decision support module ranks the project portfolios based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Simple Multi-Attribute Rating Technique (SMART), considering the decision-maker profile embedded into the model by the Rank-Order Centroid (ROC) and Analytic Hierarchy Process (AHP) criteria weights. This paper tackles this methodology's theoretical foundations, taking into account some essential attributes, such as power quality and operational performance indexes, the number of consumers, and projects' financial impacts. We presented realistic case studies to show how portfolios are composed, not only when all nine attributes of projects are included in the optimization module, but also when the criteria weights are changed in the decision support module. This paper's main contribution lies in proposing a unique model to find optimal expansion plans and rank these project portfolios, considering the decision-maker profile adherent to its goals. About the decision-making methods applied in this problem, we infer that the TOPSIS performance overcomes SMART in all analyzed problems and the ROC and AHP weights indicate the same alternative in most of the analyzed cases.

Symmetric projection group approach for promoting homogeneity in the analytic hierarchy process

The complexity of the socio-economic environment requires the involvement of a group of decision makers in the decision-making process. They can contribute the diversity of knowledge and experience that allow all the important aspects of the decision-making problem to be taken into account. In the aggregation process, the weights of the decision makers play an important role. In this paper we have focused on the group analytic hierarchy process (AHP). We emphasized the homogeneity of the opinions of the decision makers as the basis for determining the importance weights of the decision makers. We reviewed the projection method and its upgrades for determining decision makers weights and demonstrated with examples that they are not suitable for application in AHP. We proposed a new symmetric projection method that eliminates the imperfections of the previous projection methods. We proposed a Symmetric Projection Group Approach (SPGA) to derive group weights in AHP. To validate the SPGA, we selected four evaluation measures. The evaluation of the examples shows that the SPGA achieves good evaluation results compared to the other methods for deriving decision makers weights and is suitable for real-world applications.

Safety risk assessment and early warning of chemical contamination in vegetable oil

Edible vegetable oil is a necessity in people's daily diet, and its safety has attracted the attention of the government and consumers. In recent years, the safety incidents of edible vegetable oil are reported, and the safety problems of edible vegetable oil caused by chemical hazards such as benzopyrene, heavy metal and aflatoxin B1 are more serious. In this research, a risk assessment model through dietary exposure assessment and margin of exposure (MOE) were built to assess the health risks of benzopyrene, aflatoxin B1, and heavy metals in edible vegetable oils. And then an early warning model of food oil safety risk was established by using analytic hierarchy process (AHP) and back propagation (BP) neural network. According to the national standards and sampling data, the risk early warning model determined eight evaluation indexes of vegetable oil quality and safety, and calculated the risk value of chemical pollution in vegetable oil by Entropy Weight-Analytic Hierarchy Process(EW-AHP). The data of eight evaluation indexes were taken as the input of the model, and the comprehensive risk value was taken as the output of the model. The model learning process was carried out, and four algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Radial Basis Function Neural Network (RBF) and Neural Network (BP), were selected for construction and comparison of model. The daily sampling data of chemical hazard factors in edible vegetable oil were quantified into specific chemical hazard risk levels. Thus the goal of predicting chemical hazard levels was achieved in edible vegetable oil. This study provides targeted reference suggestions for the safety supervision of edible vegetable oil, so as to improve the efficiency of supervision and ensure the consumption safety of edible vegetable oil.

The Research and Development of Safety Assessment Software System for Portal Cranes

The safety assessment index system is formulated for portal cranes according to the operation features of this type of crane. The comprehensive assessment matrix and subsequent assessment results are finalized for all levels of index according to the variable weight analytic hierarchy process (AHP). The safety assessment software system is developed for portal cranes on the basis of above outcomes. Relying on C# programming language, the software has enabled the functions of data acquisition and result processing. In addition, it shows a remarkable utility value of the safety assessment of portal cranes and is capable of the safety assessment for the portal cranes in service.

Research on Ordering and Transportation of Raw Materials in Manufacturing Enterprises Based on TOPSIS Model

In real life, the selection of raw material suppliers is very important for production enterprises. This paper mainly studies the optimal ordering and operation scheme to ensure the production of enterprises. In order to quantify the supply characteristics, emphasize the guarantee of enterprise products and make decisions on suppliers, this paper selects five indicators, such as supply risk, supply intensity, supply fluctuation, supply operation capacity and order cost. Then, this paper uses the analytic hierarchy process and entropy weight method to subjectively weight and objectively weight the five indicators, respectively, and couples the subjective and objective weights according to the Lagrange multiplier method to solve the optimal comprehensive weight. Finally, the total weight is brought into the TOPSIS model. The TOPSIS Model under the combined weight of analytic hierarchy process and entropy weight method is established to solve the final result.

Comprehensive safety evaluation of metallurgical crane based on AHP and entropy weight method

The determination of safety evaluation weight of most engineering equipment mainly depends on the subjective experience of experts, which ignores the objective requirements of indicator status evaluation, is unable to satisfy the needs of the evaluation system with the advances in modern equipment health monitoring technology. Hence, the authors establish a safety evaluation model to form a comprehensive weight using experience factors, based on the objective weighting of the entropy weight method (EWM) and the subjective weighting of the analytic hierarchy process (AHP), to integrate the objective analysis data and subjective prior knowledge. Then, a safety evaluation system with state value as the main consideration was built on the combination of knowledge and fuzzy comprehensive evaluation. Finally, the availability of the proposed evaluation method was verified by the example.

Reliability Index Evaluation System of Distribution Network Based on Maximum Deviation and Application

With the in-depth reform of the power industry, the reliability index evaluation system of the distribution network has become an important direction for the research of my country’s distribution network. Firstly, the load points are classified based on the power supply information of the distribution network, and the power supply reliability indicators of the system and consumers are established. Secondly, a comprehensive evaluation system of distribution network indicators is established based on the entropy weight-analytic hierarchy process with maximum deviation. Finally, the comprehensive evaluation index results are used to establish a distribution network reliability incremental cost calculation model, and the distribution network reliability electricity price that takes into account the differentiated reliability requirements is obtained. The analysis of calculation examples shows that the proposed evaluation system can obtain comprehensive reliability evaluation indicators with subjective and objective evaluation. And the results can be applied to reliability electricity price calculation to obtain reliability electricity prices that take into account both reliability and economy.

Determining Optimal Location for Mangrove Planting Using Remote Sensing and Climate Model Projection in Southeast Asia

The decreasing area of mangroves is an ongoing problem since, between 1980 and 2005, one-third of the world’s mangroves were lost. Rehabilitation and restoration strategies are required to address this situation. However, mangroves do not always respond well to these strategies and have high mortality due to several growth limiting parameters. This study developed a land suitability map for new mangrove plantations in different Southeast Asian countries for both current and future climates at a 250-m resolution. Hydrodynamic, geomorphological, climatic, and socio-economic parameters and three representative concentration pathway (RCP) scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2070 with two global climate model datasets (the Centre National de Recherches Météorologiques Climate model version 5 [CNRM-CM5.1] and the Model for Interdisciplinary Research on Climate [MIROC5]) were used to predict suitable areas for mangrove planting. An analytical hierarchy process (AHP) was used to determine the level of importance for each parameter. To test the accuracy of the results, the mangrove land suitability analysis were further compared using different weights in every parameter. The sensitivity test using the Wilcoxon test was also carried out to test which variables had changed with the first weight and the AHP weight. The land suitability products from this study were compared with those from previous studies. The differences in land suitability for each country in Southeast Asia in 2050 and 2070 to analyze the differences in each RCP scenario and their effects on the mangrove land suitability were also assessed. Currently, there is 398,000 ha of potentially suitable land for mangrove planting in Southeast Asia, and this study shows that it will increase between now and 2070. Indonesia account for 67.34% of the total land area in the “very suitable” and “suitable” class categories. The RCP 8.5 scenario in 2070, with both the MIROC5 and CNRM-CM5.1 models, resulted in the largest area of a “very suitable” class category for mangrove planting. This study provides information for the migration of mangrove forests to the land, alleviating many drawbacks, especially for ecosystems.

The Relevance Analysis of Regulation Clarity Degree and Human Error Contribution to Ship Collision Accidents

Yang, B.; Zhao, Z., and Zheng, G., 2020. The relevance analysis of regulation clarity degree and human error contribution to ship collision accidents. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 32–36. Coconut Creek (Florida), ISSN 0749-0208.Analyzed in this paper is the correlation between the risk degree of the human risk factor in collision accidents at sea and the clarity degree of regulations related to the risk factor. The risk degree of each factor is analyzed based on the fuzzy rule-based Fine-Kinney method. The defuzzification contribution value of each risk factor to collision accidents is obtained. The confidence value in a priori algorithms of each human risk factor in accidents is obtained by mining the data of 481 accidents in the Liaoning sea area from 2005 to 2014. Nine experts score the clarity degree of the management regulations which are relevant to each risk factor. And the Analytic Hierarchy Process (AHP) weight value of each factor's clarity is calculated by the AHP. The AHP weight values cannot express the clarity degree, but they are approximately equal to one in a million of evaluation scores in the Fine-Kinney method whose score can express the clarity degree. Then the AHP weight values, instead of Fine-Kinney scores of the clarity degrees, are compared with the fuzzy rule-based Fine-Kinney defuzzification contribution values and the a priori algorithm confidence values of the human risk factors in collisions, respectively. The AHP weight values are a negative linear correlation with the defuzzification values and confidence values. This demonstrates that the more clarified the relevant regulations are, the lower the risk of the human factor in ship collision avoidance operation will be.

Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities

The emergence of Industry 4.0, also referred to as the fourth industrial revolution, has entirely transformed how the industry or business functions and evolves. It can be attributed to its broadening focus on automation, decentralization, system integration, cyber-physical systems, etc. Its implementation promises numerous benefits in terms of higher productivity, greater volatility, better control and streamlining of processes, accelerated enterprise growth, sustainable development, etc. Despite the worldwide recognition and realization of Industry 4.0, its holistic adoption is constrained by the requirements of specific skills among the workforce. The personnel are expected to acquire adaptive thinking, cognitive and computational skills, predominantly in the area of information technology, data analytics, etc. Thus, the universities that laid the foundation for future talents or trends in society have to adapt and modernize the existing programs, facilities, and infrastructure. This reshaping of higher education in consonance with the vision of Industry 4.0 possesses its opportunities and challenges. There are, of course, a multitude of factors involved and they need a reasonable assessment to strategically plan this metamorphosis. Therefore, this work aims to explore and analyze the different factors that influence the progression and enactment of Industry 4.0 in universities for sustainable education. For this purpose, a systematic approach based on a questionnaire as well as a SWOT (strengths (S), weaknesses (W), opportunities (O), and threats (T)) integrated with the analytic hierarchy process (AHP) is adopted. The questionnaires are administered to university employees and students (or stakeholders) to assess their viewpoint, as well as to estimate the priority values for individual factors to be included in SWOT. The AHP is implemented to quantify the different factors in terms of weights using a pairwise comparison matrix. Finally, the SWOT matrix is established depending on the questionnaire assessment and the AHP weights to figure out stakeholders’ perspectives, in addition to the needed strategic scheme. The SWOT implementation of this research proposes an aggressive approach for universities, where they must make full use of their strengths to take advantage of the emerging opportunities in Industry 4.0. The results also indicate that there are fundamental requirements for universities in Industry 4.0, including effective financial planning, skilled staff, increased industrial partnerships, advanced infrastructure, revised curricula, and insightful workshops. This investigation undoubtedly underlines the importance of practical expertise and the implementation of digital technologies at the university level to empower novices with the requisite skills and a competitive advantage for Industry 4.0.

Research on Quality Evaluation of Customized Plank Furniture Based on the Entropy Weight-Analytical Hierarchy Process

Custom-made furniture is a product of the marketplace. At present, the concept of whole-house custom-made furniture has not been clearly defined. In the market, custom-made furniture for the whole house is mainly designated panel furniture. As the custom furniture market continues to expand, consumer complaints about custom furniture are increasing. However, there is no effective supervision model for the new decoration business of whole-house custom-made furniture. The market for customized furniture is constantly expanding. Under the current circumstances, the regulatory approach is difficult to implement effectively for a time. Faced with this transitional stage, this article proposes to evaluate the quality risk of custom-made furniture by means of evaluation. Combined with industry experts’ discussion on consumer complaints and concerns, this article builds a customized furniture product quality evaluation system. According to the constructed quality evaluation system of customized furniture products, with the help of the basic theory of entropy weight method and analytic hierarchy process, determine the weight of each index. Finally, through empirical research, it is proved that this method has a strong operability for the quality evaluation of customized furniture.

Comparison of PROMETHEE –TOPSIS method based on SAW and AHP weighting for school e-learning readiness evaluation

As the demand for using e-learning rising for all subjects at schools in digital era, the successful implementation of e-learning is important to be well prepared. The successful is influenced by many factors, including the provision of technology infrastructure, Human Resources, organizational culture and leadership factors. For this reason it is necessary to evaluate the readiness of schools in implementing e-learning, which is known as e-learning readiness (ELR). Evaluation of school ELR is carried out to analyse the strengths, weaknesses and dominant factors in implementing e-learning in schools, so that it can be a reference in policy making by related parties. ELR evaluation is carried out using the PROMETHEE and TOPSIS methods based on the weighting of Simple Additive Weighting (SAW) and Analytical Hierarchy Process (AHP). The data used is the readiness for e-learning implementation on the specified criteria. There are eight criteria to measure e-learning readiness (ELR) of schools, namely Psychological readiness, Sociological readiness, Environmental readiness, Human resource readiness, Financial readiness, Technological skill (aptitude) readiness, Equipment readiness, Content readiness. The evaluation model using the PROMETHEE and TOPSIS methods will provide the results of a school’s e-learning readiness ranking, based on the weighting of criteria determined using SAW and AHP. In addition, these results are expected to reveal the weaknesses that need improvement, as well as the strengths that support the implementation of e-learning.

Designing of an agility control system: a case of an Indian manufacturing organization

PurposeAgility is the ability of an organization to adjust its supply chain tactics and operations to respond quickly against altering business environments such as fluctuating demand pattern, supply chain disruption and global competition. An agile organization must possess a promising capability of swiftly responding to dynamic conditions while being cost-effective without compromising the efficiency. Such high-performance adaptability necessitates the role of supply chain managers to maximize the agility of the supply chain through the efficient use of input resources. Therefore, the purpose of this study to reveal a new decision support tool that would allow the key decision-makers to maximize the agility of the supply chain while deploying the input resources more effectively.Design/methodology/approachIn present study, an integrated approach of popular analytic hierarchy process (AHP) and goal programming (GP) has been adopted as a potential solution methodology. AHP has been implemented to allocate the local and global weights to decision variables, whereas GP incorporates the AHP weights into the desired model.FindingsIt was found that the proposed decision support tool restricts the value of the decision variables for maximizing the agility and optimizing the usage of input resources. The results obtained from the model validate the objective of achieving targeted agility level within the available resource limitations.Research limitations/implicationsThe decision support tool developed in the proposed study offers a systematic and effectively simple approach to supply chain managers with a goal of identifying the degree of focus under each decision variable in the respective manufacturing organizations.Originality/valueA novel decision support tool has been developed known as an agility control system), which helps the decision-maker to achieve the required agility in the supply chain by controlling the decision variables.

Analysis and Strategy of Economic Development Policy for SMEs in Indonesia

This study analyzes the strategy of determining economic development planning on SME improvement in Indonesia. A qualitative approach was conducted through interview questionnaires with twenty respondents, including sixteen Regional Apparatus Work Unit, Fisheries and Marine Office lecturers, subdistrict heads, and development observers. The method used is Analytical Hierarchy Process (AHP). AHP prioritizes criteria and sub-criteria to increase economic growth, reduce poverty, and increase community participation, while indicating the sub-criteria to optimize resource potential, strengthen micro-economy, develop tourism potential, utilize fishery potential and cultivation, and improve institutional performance. The AHP weighting results based on competitiveness and regional development of Bantaeng Regency is the priority in policy-making for a solution. The research shows that the government strategy, based on the perception of the stakeholders, is: optimizing resources strategy with priority to maximize the resource-carrying capacity that includes agriculture and farm, tourism development potential strategy, self-reliance of community groups and coaching and management. Strategies to strengthen micro economy include: industrial control, SMEs, community cooperatives and infrastructure improvements, strategies to improve institutional performance work capacity and work ethic, and strategies to utilize fisheries and cultivation potential technological development, provision of processed industries of fishery products, and improvement of the quality of the environment.