Theses supervised by Sahand Daneshvar
21 theses · Eastern Mediterranean University
Ranking All Units in DEA
Data Envelopment Analysis (DEA) is a methodology to compare efficiency of Decision Making Units (DMUs). DEA is an extension of Charnes, Cooper and Rhodes work by introducing CCR model in 1978. Ranking DMUs is one of the main purposes of DEA in management and engineering. DEA evaluates some DMUs with efficiency score one as efficient DMUs and we therefore need to produce a reliable method for fully ranking DMUs. Some methods have been proposed in this concept and newly Khodabakhshi and Aryavash (2012) ranked DMUs relative to their combined maximum and minimum efficiency scores where efficiency is defined as ratio of weighted sum of outputs to weighted sum of inputs. Due to some obtained weights (multipliers) in DEA may be zero, previous methods have low ability in ranking DMUs because of eliminating the effect of corresponding input and outputs on DEA evaluations. We expand their method by assigning lower bounds on multipliers using facet analysis and then we propose an equitable and precise method for ranking all DMUs based on the modified CCR. Keywords: Data envelopment analysis, decision making unit, rank.
Modification of Variable Returns to Scale Stochastic Data Envelopment Analysis (DEA) Models
Data Envelopment Analysis (DEA) was introduced under the name of a deterministic model assuming all the deviations from the estimated production frontier were one sided indicating technical inefficiency. Biased estimations of inefficiency and production are provided by the model when deviations do not originate only from inefficiency but also from measurement errors. In 1988, Banker developed Data Envelopment Analysis as a stochastic model to reflect inefficiency and statistical noise simultaneously. However, from deterministic to stochastic, the problem with weak efficient frontiers and related biased results stayed the same. This dissertation proposes a modification over Banker’s stochastic DEA (SDEA) model by applying a limitation on the coefficients of inputs in the original model in order to change weak efficient hyperplane(s) while keeps general assumptions behind production function unaffected. This can change the production possibility set (PPS) while the frontier has the potential to give a better representation of the true production frontier. Comparing the results from the stochastic model and suggested modified model shows that the achieved model is providing a new benchmark for relative efficiency evaluation and production frontier estimation. Keywords: Data Envelopment Analysis (DEA), Stochastic Data Envelopment Analysis (SDEA), Modified Model, Weak Efficient Frontier.
Failure Modes and Effects Analysis (FMEA) Method Based on Data Envelopment Analysis (DEA) Approach for the Efficiency Measurment of Radio Frequency Identification (RFID)
There are many papers which emphasize on the benefits of radio frequency identification (RFID) technology implementation in the management and the production. This technology is used in many fields such as inventory, logistics, return management, order picking in a warehouse, assembly and testing and health care Etc. The existing literature examined the efficiency of RFID based on one of the following factors: accuracy, reliability, service enhancement, cost, time, work efficiency, flexibility, interactivity, risk management, emergency, privacy, energy and big Data. In this research, we will shed the light on the failure modes of this technology by using FMEA Approach, then we will measure the efficiency of solving each of these failures according to their severity, occurrence, detection, cost and time; using data envelopment analysis (DEA). DEA is a non-parametric method that evaluates the efficiency of the collection of decision making units (DMUs) when all of them consume and produce the same inputs and outputs respectively. A DMU can be efficient when it is able to produce more outputs by consuming fewer inputs. This economical point of view transforms the issue into a linear programming problem. Based on DEA concept, each failure mode of the RFID technology will be considered as a DMU, and the above mentioned criteria as inputs/outputs. Then the efficiency of these DMUs will be evaluated by DEA models.
An Evaluation of Efficiency, Productivity and Sustainability of the Hotel Industry in Tunisia using a Two-stage DEA method
Hotel industry is a very important domain of the tourism industry that is known for its large presence and growth. This service industry does not only consume a huge amount of resources (water, electricity, fuel…) but also intensifies the environmental problems. There have been very little work directed towards sustainability in the hotel industry. An understanding of the situation of the hotel industry and its relationship to sustainability may help improve the performance of this industry while taking into account the economic, social and environmental concerns of sustainability. To investigate the performance of the hotel industry, first, two-stage data envelopment analysis (DEA) method is used to evaluate the efficiency of the hotel industry in the tourist regions of Tunisia for the period 2014-2015. Next, the impact of a number of independent sustainability variables on the efficiency of hotel industry is tested using Tobit regression model. Finally, the Malmquist productivity index is assessed to examine the level of productivity in hotel industry of the tourist regions in Tunisia. The results reveal that the average efficiency of the hotel industry in Tunisia is 66.83% for 2014 and 61.41% for 2015, and the average productivity is 57.18%. The results conclude that sustainability has a positive impact of the efficiency of the hotel industry. The results of this thesis provide understanding on ways to improve the performance of the hotel industry and the sustainable development. Keywords: Bootstrapping, DEA, Efficiency, Hotel industry, Malmquist productivity index, Productivity, Sustainability, Tobit regression model.
Fuzzy FMEA Risk Analysis on Chemical Industry
Nowadays, green engineering strategies have become important for the industries, so global warming and landslides are a sign that nature is disturbed, the sustainability of green engineering and to leave a better environment for the future generation we need to show importance to the consumption of natural resources. There are different methods in production to protect green engineering and environmental resources.These techniques vary depending on the production or service sector and their application areas.Some of these techniques are Environmental Failure Mode and Effect Analysis (EFMEA) and Process Failure Mode and Effect Analysis (PFMEA). These two tools were used to evaluate Risk Priority (RPN) numbers for prioritizing the risk assignment in a chemical factory in the concept of green engineering. The identified components are calculated in the multi-criteria decision making (MCDM). This technique identifies RPN numbers and combines the weighting factor to the fuzzy parameter by the help of AHP. The effectiveness of this method is explained in fuzzy parameters of AHP and indicated with numerical example in a case study. In this study, the experts have evaluated fuzzy AHP. Keywords: FMEA, AHP, Risk Assignment, Risk Priority Number,Fuzzy FMEA,green engineering.
Performance Efficiency Evaluation of European Countries Healthcare Systems during the Pandemic (Covid-19) Using the Data Envelopment Analysis
In this paper, a non-parametric method known as Data Envelopment Analysis was used to analyse the efficiency of the healthcare systems of 48 European countries in managing the covid-19 pandemic. Using the constant returns to scale model of Charnes, Cooper and Rhodes (CCR) on input and output variables like total population, total covid-19 cases, total tests performed, total recoveries etc, the results showed that only 10 out of the 48 countries were optimally efficient in their management of the pandemic with some of the richest countries like France and Belgium being some of the poorest performers. Minimally developed countries like the Czech Republic, Andorra and Moldova were some of the best performers with Moldova being the most referenced optimal country in the benchmark test. It was shown that the inefficient countries like France and Belgium had to reduce the number of cases via distancing and lockdown measures to improve efficiency. The same goes for minimally efficient countries like the United Kingdom. The results finally indicate that developmental indices like gross domestic product (GDP) had an insignificant impact on the efficiency of countries in managing the pandemic as very rich countries were poor performers, although their high populations likely skewed the efficiency ratios in comparison to high performing countries with low populations. Keywords: Data Envelopment Analysis, DEA, CCR, European, Countries, Healthcare, Efficiency, Gross Domestic Product, Total Population, Covid-19, Tests
Performance Evaluation of Iranian Private Banks Using Data Envelopment Analysis (DEA)
Assessing performance of any organizations is necessary in guiding their future strategic decisions. Banks also are no exception. The purpose of this study is to evaluate the performance of 15 Iranian private banks using data envelopment analysis in 5 years period using Data Envelopment Analysis (DEA). The result will help these banks to improve their performances by focusing on inadequate factors and evaluate their efficiency among the competitors. In the process of evaluating the banks, all of the important indicators are verified and inputs and outputs have been selected in order to assess the efficiency of the banks based on previous studies and the views of experts in this field. Then, the efficient and inefficient banks and their operational rank were determined. Also, reasons for inefficiency of banks would have been detected and proposal for improvements would be discussed. Keywords: Private Banks, Banking efficiency, DEA, Iranian Banks, Privatization
Application of Data Envelopment Analysis by the Evaluation of the Quality and Operational Factors
In the last decade, soft drink products among FMCG (Fast Moving Consumer Goods) industry have been facing serious problems due to the change in the consumer preferences. Health concerns towards these products had risen and companies keep making strategies to cope with this change. Since, the condition of the market shares getting narrower, maintaining efficient operations throughout the industry supply chain became an essential matter. The study focuses on the efficiency evaluation of soft drink company’s production lines between 2010 and 2015 located in Köprülüköy Cyprus, since the production phase is one of the most essential part of the whole operation. Data Envelopment Analysis is a widely known technique used for the evaluation of technical efficiencies of decision making units where multiple inputs and multiple outputs were under concern. Here, production lines of the production facility have chosen as DMUs and among the models of DEA, standard CCR and standard BCC models were utilized. Since the study was being performed in FMCG sector, where perishable food products were under concern, quality factors were also taken into consideration besides operational factors in the operation. Especially for the food production process, efficiency of the whole operation is definitely affected by the efficiency of the quality operations. In this study a general procedure for the evaluation of the production line efficiencies for the perishable goods had built that could easily adapted to the whole industry. Findings of the models can help management in decision making process, budget planning purposes, categorize production lines, future plans, and help to build a corporate memory for the efficiency of the lines. Keywords: Data Envelopment Analysis, FMCG, Quality Factors, Operational Factors, Efficiency, CCR Model, BCC Model, Food Production, Soft drink.
Modified Data Envelopment Analysis of Multiple Response Experiments in the Robust Parameter Design Procedures
Selecting the optimum process parameter level setting for multi-quality processes is cumbersome. Robust parameter designs procedure that utilizes different strategies for improving performance/productivity during product and process design so that quality response can be obtained efficiently and optimally. An inevitable problem that is associated with the product and process design is in appropriating process variables that will yield optimal response. The complexity of the problem is peculiar with multiple response experiments (processes) where different factor level combinations yield varying responses. Previous methods are plagued with complex computational search, unrealistic assumptions, ignoring the interrelationship between responses and failure to select optimum process parameter level setting. This thesis proposes the implementation of modified variable return to scale (VRS) data envelopment analysis in the Robust Parameter Design (RPD) procedures to estimate and optimize responses of all non-dominated (significant) factors level combinations in multi-response experiments. This study also enhances the discriminatory tendency of the model by imposing VRS partitioning within the model. The model is conducted in a manner that with an adequate BPNN topology, experiment with incomplete, missing or censored data whenever encountered, could be investigated. Here, standard DEA modes are allowed to self-assess, the upper bound is restricted and the VRS penalization coefficient is adopted to determine the optimum process parameter level setting. The proposed procedures are applied to seven different case studies and the results were compared with existing methods of principal component analysis (PCA), DEA based ranking approach (DEAR), genetic algorithm (GA), grey relational analysis (GRA) and benevolent formulation (BF). The effectiveness of the proposed model measured by the total anticipated improvement yielded the highest total improvement over the existing methods. In overall, many inefficient DMUs that would have been promoted as efficient by the standard DEA models were revealed. The discriminative tendency further gives insight to DMUs that are within the convex set of the factor level settings and those that are not, thereby making the computation search for the optimal easy and simple.
Increasing the Performance Efficiency of Bread Bakery Company Using Data Envelopment Analysis (DEA)
The market competition of bakeries in recent years have undergone rapid and massive growth, because of this fast growth and change, this necessitate the need for comprehending the performance relative efficiency as well as efficiency fluctuation and changes of the bakeries. Nowadays, food industries and bakery companies tends to use their current available production resources in an inefficient manner, as such, managing and sustaining the development of the bakery regarding how well it is performing when it comes to the production process is very important. Thus, ten inputs and two outputs of a bakery company were considered as the DMUs. The efficiency of inputs such as flour, salt, sugar, butter, yeast, water, electricity consumption, etc., and outputs (bread and flour waste), involved in the production process of a well-known bakery (Tahir B-bakery) from 2016 to 2018 was quantitatively analyzed and evaluated by adopting one of the most well-known, simple and suitable non- parametric effective technique (DEA) via using the CCR input oriented model. Based on the model used, the overall efficiency of the company in those past three years of Tahir B-bakery with regards to the use of input resources for achieving output was tested, it was found the company’s performance is relatively efficient with an average efficiency score of 92.6. The most efficient months being June and July in both 2016 and 2017 respectively, although there is a slightly decrease of efficiency as the years gone by, especially in the month of June and July in 2018 plus the fact that the aim of any firm is to reach maximum level of efficiency when it comes to utilizing the inputs and outputs production resources, as well as other resources involved. Lastly, given the fact that there is a tendency the performance efficiency to drop more as the year’s progress, they can follow the recommended ways for improving their total efficiency, sustain it and avoid further decrease of efficiency for their future productions to come. Keywords: Bakery Company; Data Envelope Analysis (DEA); CCR Input Oriented Model
Efficiency Improvement with Target Setting Models in Data Envelopment Analysis: Theory and Applications
Data Envelopment Analysis (DEA) evaluates efficiency of homogeneous units using a frontier as an approximation for production function, to identify the efficient and inefficient units. Target setting offers strategic efficiency improvement for inefficient units, thus providing ex-ante efficiency improvement strategy. To that effect, two approaches for target setting are proposed. First approach uses the most productive scale size (MPSS) hyperplane vector to guide an inefficient unit to the efficiency frontier, consequently incorporating feasible productivity improvement and enhancing efficiency. The second approach has two folds which are based on decision makers‘ desire. One is based on predefined inputs, which uses decision makers‘ input capabilities to propose efficient output targets. The other is based on predefined outputs targets by the decision maker, where desired output are presented, and the required efficient inputs are proposed. Empirical analysis with real life applications are used to validate the proposed models. Keywords: Data Envelopment Analysis, Efficiency improvement, Target setting, Most Productive Scale Size, Predefined inputs, Predefined outputs.
Computing Malmquist Index Using Data Envelopment Analysis as an Improvement Measure for Educational Purposes
With the introduction of “Malmquist indices”, MI, (Caves et al, 1982), it has rapidly grown into a standard approach for evaluating productivity over recent years. Meanwhile, Based on the concept of cost efficiency that was first mentioned by Farrell, (1957), the DEA has become a brawny quantitative and analytical tool for measuring and evaluating performance of public and private sectors. With the growth of civilization and vast increase in higher educational institutes around the world, the performance and efficiency of students became very important as far as their evaluation is concerned. Defining educational technology as all necessary resources needed by an institution for accurate student’s performance, we will compute MI using DEA considering some ABET’s accreditation criteria for student outcomes as an improvement measure for educational purposes. As the DEA measure the efficiencies of the student’s performance using a defined set of inputs and outputs, “Malmquist index” conflate the efficiencies with other factors such as surveys to compute an index (productivity) for a course or program which can be compared to unity. Based on this, an educational Malmquist index is defined called Malmquist Educational Index, MEI to evaluate Student Outcomes, performance and monitor continuous improvement of Educational programs. We used a case study example, with real data provided by the chair of the industrial engineering department to compute MEI for each course. Regarding the value MEI, it could be concluded that MEI indicates regress and need improvement, MEI indicates progress and MEI indicates no change for DMU under evaluation. Keywords: Malmquist Index, DEA, Student Outcomes, and Student performance
Modified Data Envelopment Analysis Model Based on Service Quality Concept for Vendor Selection
Purchasing function is the key part of the logistics management in firms, and the prime responsibility for this function is the selection of appropriate vendors i.e. the most efficiently performing vendors. Many analytical and conceptual models for tackling the vendor selection problem have been established. Several criteria are to be considered in evaluating vendors’ relative efficiencies, hence this problem is being recognized as multiple criteria decision making problem. Researchers developed techniques for tackling this multi criteria efficiency evaluation problems in recent years by applying Data Envelopment Analysis (DEA) being the most effective method for evaluating vendor efficiencies, but all their researches did not address the issue of weakly efficient vendors in the DEA. Therefore, this thesis introduce a modified method for figuring vendors efficiency with the issue of weak efficient vendors being properly addressed so that only truly efficient vendors are selected in the appropriation situation. The modified method uses facet analysis in modifying the standard DEA model employed by several researchers in evaluating the vendors’ efficiencies. The criteria chosen in these models are service quality, rate of rejected items, late deliveries and price. The results and comparisons between the modified and standard DEA model shows that the modified DEA model gives a better and true efficiency scores of vendors, this greatly improve the vendor evaluation and selection methods. Keywords: Modified Data Envelopment Analysis, Vendor Evaluation and Selection, Service Quality
Performance Evaluation of After Sale Service in Automotive Industry by Data Envelopment Analysis
In this decade, automotive industry has become second big industry in Iran after oil while more than 1 million and 350 thousand cars are produced yearly by different companies and have influenced in most of Iranian’s living and has been considered by government due to sanction against Iran, appreciation of prices and dissatisfaction of customers in after sale services. This study will investigate 88 after sale service dealers belong to Kerman Motor Co. in all over Iran as a sample of the entire automotive industry in Iran. All data (number of customers, human resources, education, equipment, providing pieces and process as 6 inputs and stopped cars more than 48 hours in the repair shop, reworked cars, and customer satisfaction as 3 outputs) has been gathered during 2016-2017 observation of all dealers. Data Envelopment Analysis (DEA) has been applied as one of the most common evaluation methods for measuring the efficiency of performance of each dealer. Contrary to Iran Standard Quality Institute (ISQI) which sends inspectors to all dealers from all companies to evaluate them yearly by a solid framework and without considering the differences in facilities and population in various regions of Iran, DEA has been utilized in this study to evaluate the dealer’s efficiencies and rank them by their own capacities and potentials. CCR model as one of the basic models of DEA has been used in this study due to its more rigid and inflexible essence in comparison to other models. All weights assigned to inputs/outputs will be analyzed to find out most significant index which is effects inefficiency, also inefficient dealers are compared to efficient dealers with most similar for recognizing the weakness and an improvement plan will be presented for giving insight for inefficient units to be improved at the end.
Application of Modified Data Envelopment Analysis in Evaluating Operational Efficiency and Environmental Impacts
Life Cycle Assessment (LCA) is a technique used in evaluation or assessing the environmental impact of a production processes from the extraction of raw material from earth, to production, development, and processing, manufacturing and final disposal. In this study, an integration of the (LCA) and Data Envelopment Analysis (DEA) is used for efficiency evaluation of mussel cultivation rafts operation. The inputs and outputs used for the efficiency evaluation is obtained using the LCA method, and the efficiency is evaluation is performed using the DEA technique. The sites are considered as a Decision-Making Units (DMU). Standard and modified models are utilized in the efficiency evaluation to give a better analysis of the rafts under evaluation. The modified models identify the weak efficient and highly inefficient rafts. Further analysis of the efficiency results presents interesting findings as to the factors important for the improvement of the mussel rafts operation.
Modification of the Arash Method using Facet Analysis
Data Envelopment Analysis, one of the most popular disciplines in operations research, it is a technique used to estimate the performance of Decision Making Units (DMUs). Technical efficient DMUs and Efficient DMUs are difficult to differentiate without the availability of additional information in the form of weight restrictions or the use of statistical technique and supper efficiency method. The Arash method (2013) distinguishes between Technical efficiency and Efficiency by introducing a small error in input values even if the values are accurate, the efficiency scores of the efficient DMUs does not change, only that of the technical efficient DMUs, it also establishes that, for a DMU to be efficient, technical efficiency is one of the necessary conditions. In this study we expand the Arash Method by using facet analysis to modify the PPS of the Arash method. The proposed modification places an upper bound only on the free variable of VRS Arash method. This modification on the Arash method gives the true efficiency score and rank for the weak efficient DMUs and DMUs which take their efficiency score when compared to the weak part, because, the use of facet analysis on the frontier of the Arash method deduced some essential details about the constructive hyper planes of the production possibility set (PPS), Particularly the weak part of the frontier. Keywords: Data Envelopment Analysis, Arash Method, Efficiency, Technical Efficiency, Facet Analysis, Modified Arash method, rank.
Performance Efficiency of a Chemical Company Using DEA
The chemical companies that produce unsaturated polyester resins generally suffer from the high cost in production. Most of them focus on quality control in order to avoid the cost of rework and increase performance assurance. Data Envelopment Analysis (DEA) models are used recently as one of the most powerful non-parametric methods to computing the efficiency of the multiple inputs and outputs of decision-making units (DMU’s). However, these models are sensitive about inputs and outputs selection, and a number of them against the number of DMUs. This paper uses Data Envelopment Analysis (DEA) models to find the efficiency performance of a chemical company in a certain period of time in a manner that maintenance, production and management cost are minimized taking into consideration environmental hazard and maximizing profit. Constant Return to Scale (CRS) models are used to verify the most significant factors that affect efficiency values in the company and forecasting the desired cost reduction in a future period of time. The results determined the efficient DMUs and contained the values needed for inefficient DMUs to reach optimum targets. Keywords: Unsaturated Polyester Resin (UPR), Constant return to scale, Input-oriented model, Efficiency evaluation, Data Envelopment Analysis (DEA).
Application of AHP Method for Failure Modes and Effect Analysis (FMEA) in Aerospace Industry for Aircraft Landing System
FMEA has been used in the aerospace industry for many years; this industry has been growing rapidly, so reliability and safety guarantee have been of increasing concern within the aerospace industry. Conventional FMEA technique still imposes many common shortages in order to compute RPN, which is product of the Severity (S), Detection (D) and Occurrence (O). Conventional FMEA considers the important of the elements S, O and D with the same weight which is not effective in practical FMEA study. In our base article, the study was done by 4 experts using the fuzzy developed FMEA that yielded crisp RPN scores for the failure modes of the aircraft landing system used in the research, this imposes a problem when ranking the risks of those modes. In this study, we used AHP that helps decision makers find one that best suits their goal and their understanding of the problem. It provides a comprehensive and rational framework for structuring a decision problem, for representing and quantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions. When using the AHP method, expert‘s opinions and weights will be obtained and will be prioritized in a better way than FMEA weights. A pairwise comparison questionnaire was designed and distributed to 35 aerospace experts working in Jet Aviation, in Basel Switzerland, then critical selection factors were identified, consistency vector, index and rate were determined, ―expert choice 11.0‖ was applied to compute the outcomes. FMEA components were compared in the hierarchy, reaching the RPN and ranking its scores. RPN better values obtained showed the significant risk level attained by some FMs in the aircraft landing system that were used in our base study. Keywords: FMEA; AHP; Failure Mode; Risk Priority Number; Decision Making
Applying Data Envelopment Analysis to Improve Performance of Emergency Departments in Iranian Hospital: A Case Study
One of the most important parts of the health system in each society is hospitals. It should not be forgotten that the emergency room of a hospital is the first point of entry for patients and their companions with the medical system, and one of the problems that often affect the performance of the emergency department is the length of time patients wait in the emergency room. This dissertation has been conducted with the aim of reducing the average waiting time of patients in the emergency department, improving the efficiency of nurses, and increasing the performance of the emergency department. The case study was “Razi Hospital” located in Ahwaz, Iran. It is a general hospital wich at present, with an area of 7,971 square meters, medical services including general surgery, orthopedics 1 and 2, internal medicine, infectious diseases, obstetrics, neonatology, CCU, ICU, emergency department, operating room, physiotherapy, radiology, laboratory, dialysis, echocardiography and specialized clinics visit patients. The data were prepared from the emergency department of the hospital for 12 months in 2018, 2019, and 2020. Data analysis obtained in this study is performed using PIM-DEA software based on the CCR model as a method that is one of the techniques of data envelopment analysis. According to the results, the most important priority is the number of patients with CPR procedures. With this in mind, we can focus on accepting patients with CPR in order to increase the level of emergency function. The next most important factor is the average time the patient leaves the hospital. According to the hospital experts, the time of the patient's discharge from the hospital is a very important indicator, because according to the capacity of the hospital and the number of staff, the lower the average time of the patient's discharge from the emergency room, the better iv the emergency performance. Keywords: Cardiopulmonary Resuscitation as CPR, CCR Model, Data Envelopment Analysis as DEA, Emergency Department
Environmental Efficiency Evaluation Based on Waste Reduction for a Chemical Production Company: An Application of Data Envelopment Analysis
Data envelopment analysis technique proposed by Charnes, Cooper and Rhodes in 1978, was used in this thesis in analyzing the efficiency of thirty two decision making units of the chemical production company, simple relation of output-to-input ratio couldn’t be used because of the several number of decision making units. The thesis focus on how to minimize the environmental hazard caused by industries without affecting the production capacity of the industries, the data was analyzed using CCR model as the methodology used which is one of data envelopment analysis technique. There were four Decision making units (DMU6 = June 2017, DMU7 = July 2017, DMU14 = February 2018 and DMU25= January 2018) found to be optimal which meant the minimal waste disposal found to be in the four decision making units, and such units were found to be the benchmarks for the rest of the DMUs in order to minimize waste disposal, and can also be the benchmarks to consider at DMUs in the future production process in minimizing the amount of waste disposes to the environment which would proportionally minimizes the effect of Climate change, and also minimizes energy consumption at industries. Each DMU used served as a month in a particular year. Keywords: Data Envelopment Analysis, Efficiency Evaluation, Environmental Hazard
Incorporating Quality and Operational Factors in Ranking of Production Lines Using Data Envelopment Analysis
The competition in the Fast Moving Consumer Goods (FMCG) industry is high, especially in the perishable goods sector. Manufacturers need to compete for the market share as the demand is limited. For companies to have competitive advantage, they need to operate efficiently, and ranking their production lines will help identify the efficient and most important lines that contribute to their efficiency. This study aims to evaluate efficiency and ranking of production lines by incorporating both operational and quality factors using Ranking models in Data Envelopment Analysis (DAE). A new Modified ranking model is proposed by comparing standard ranking model and modified DAE models. Standard ranking model and modified version are used on a data which collocated from a beverage producing company in Cyprus. It is shown that the modified model will help to identify the efficient production lines. Also the results of the study will help management in proper resources distribution for efficiency improvement and budget planning. The study shows that can production line is the most efficient production line among the five production lines evaluated under standard and modified DAE models, and Pet-2 and Premix line are ranked among the highest by the modified ranking models. The analysis shows that, to improve the efficiency and rank of production lines, combination of operational and quality factor needs to be improved together. Keywords: data envelopment analysis, production lines, quality factors, operational factors, modified BCC, super efficiency.