Production Planning Based on DEA Profit Efficiency Models

2017 ◽  
Vol 4 (3) ◽  
pp. 1-14
Author(s):  
Feng Liu ◽  
Mengni Zhang
Keyword(s):  
Production Planning ◽  
Operations Management ◽  
Airline Industry ◽  
Data Envelopment ◽  
Market Demand ◽  
Profit Efficiency ◽  
Optimal Output ◽  
Maximum Profit ◽  
Production Possibility Set ◽  
Output Constraint

In this research, the authors propose DEA (data envelopment analysis) profit efficiency models for production planning which is one of important problems in the production and operations management. Different from traditional models, the constraint that the optimal output is supposed to be not less than the original one from the production possibility set is omitted in their developed no output constraint maximum profit (NOCMP) model. Besides, observing that output prices could be varied with the total market demand in the market, the researchers present the no output constraint maximum profit with varied output price (NOCMP-VOP) model. The authors apply these two DEA profit efficiency models to U.S. airline industry for illustration. The developed NOCMP and NOCMP-VOP models in this study contribute to developments of both the DEA profit efficiency model and its applications.

scholarly journals Penerapan Program Linear Fuzzy dalam Optimalisasi Jumlah Produksi dan Keuntungan di K-Bakery

2021 ◽  
Vol 1 (2) ◽  
pp. 36
Author(s):  
Nelita Anggraini Sitanggang ◽  
Mira Mustika
Keyword(s):  
Production Planning ◽  
Raw Materials ◽  
Linear Program ◽  
Raw Material ◽  
Market Demand ◽  
Safety Stock ◽  
Material Resources ◽  
Maximum Profit ◽  
Limited Supply ◽  
Brown Bread

K-Bakery is a bakery located in Bandar Lampung City, Lampung. K-Bakery produces various types of bread, namely brown bread, nuts, greentea, mocha, and tiramisu bread which involve various raw material resources. Unstable market demand creates obstacles for K-Bakery in formulating the number of each type of bread to be produced so as to produce maximum profit. However, in determining the amount of production must pay attention to the limited supply of raw materials. In this case, there is a need for production planning so that all available resources can be used optimally and produce a combination of production that provides maximum profit. One way to solve this problem is to optimize production using a fuzzy linear program with a tolerance of 10% as the capability of K-Bakery. The use of the fuzzy linear program generates greater profits than the usual linear program, the profit obtained is Rp. 11.247.972,1708by producing 320 chocolate breads, 449,75 peanut breads, 365,667 greentea breads, 250 moka breads fruit, 449,925 srikaya bread, and 499,975 tiramisu breads. In addition, the value of l = 0,5 is obtained, or in other words, the maximum addition of each raw material is 50% of the available safety stock.

A generalized fuzzy cost efficiency model

2020 ◽  
Vol 54 (6) ◽  
pp. 1775-1791
Author(s):  
Nazila Aghayi ◽  
Samira Salehpour
Keyword(s):  
Cost Efficiency ◽  
Minimum Cost ◽  
Extensive Literature ◽  
Data Envelopment ◽  
Fuzzy Environment ◽  
Decision Making Unit ◽  
Production Possibility Set ◽  
Fuzzy Cost ◽  
The Cost ◽  
Generalized Cost

The concept of cost efficiency has become tremendously popular in data envelopment analysis (DEA) as it serves to assess a decision-making unit (DMU) in terms of producing minimum-cost outputs. A large variety of precise and imprecise models have been put forward to measure cost efficiency for the DMUs which have a role in constructing the production possibility set; yet, there’s not an extensive literature on the cost efficiency (CE) measurement for sample DMUs (SDMUs). In an effort to remedy the shortcomings of current models, herein is introduced a generalized cost efficiency model that is capable of operating in a fuzzy environment-involving different types of fuzzy numbers-while preserving the Farrell’s decomposition of cost efficiency. Moreover, to the best of our knowledge, the present paper is the first to measure cost efficiency by using vectors. Ultimately, a useful example is provided to confirm the applicability of the proposed methods.

How Price Dispersion Changes When Upgrades Are Introduced: Theory and Empirical Evidence from the Airline Industry

2019 ◽  
Vol 65 (8) ◽  
pp. 3835-3852 ◽  
Author(s):  
Yao Cui ◽  
A. Yeşim Orhun ◽  
Izak Duenyas
Keyword(s):  
Operations Management ◽  
Airline Industry ◽  
Price Dispersion ◽  
Consumer Welfare ◽  
Data Set ◽  
Differentiation Strategy ◽  
Valuation Effect ◽  
Price Distribution ◽  
The One ◽  
The Impact

This paper studies the effect of introducing a new vertical differentiation strategy, paying for an upgrade to a premium product after purchasing the base product, on the price dispersion of the base product arising from existing price discrimination strategies. In particular, we examine how a major U.S. airline’s price dispersion in the coach cabin changes after introducing the option to upgrade to a new type of premium economy seating within the coach cabin. We first provide a theoretical analysis that highlights two competing pressures that the new premium economy seating upgrades created on coach class prices. On the one hand, the airline benefits from lowering its prices because by allowing more customers to purchase in the first place, it increases the probability of selling upgrades (admission effect). On the other hand, for some customers, the value of flying with the airline increases because of the upgrade availability, therefore the airline may find it optimal to increase its prices (valuation effect). In the second part of the paper, we conduct an empirical investigation of the impact of upgrade introduction on coach class prices, based on a proprietary transaction-level data set from a major U.S. airline company. The empirical analysis tests the main predictions of our theoretical model and examines further nuances. The results show that the introduction of the premium economy seating upgrades is associated with an increase in the price dispersion and revenues in the coach class, the admission effect is stronger than the valuation effect on the low end of the price distribution, and the opposite is true on the high end of the price distribution. Finally, we discuss implications of our results for firm revenues and consumer welfare. This paper was accepted by Serguei Netessine, operations management.

scholarly journals A Full Ranking for Decision Making Units Using Ideal and Anti-Ideal Points in DEA

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
A. Barzegarinegad ◽  
G. Jahanshahloo ◽  
M. Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Real Data ◽  
The Other ◽  
Data Envelopment ◽  
Production Possibility ◽  
Decision Making Unit ◽  
Common Set Of Weights ◽  
Production Possibility Set ◽  
Ideal Points ◽  
Decision Making Units

We propose a procedure for ranking decision making units in data envelopment analysis, based on ideal and anti-ideal points in the production possibility set. Moreover, a model has been introduced to compute the performance of a decision making unit for these two points through using common set of weights. One of the best privileges of this method is that we can make ranking for all decision making units by solving only three programs, and also solving these programs is not related to numbers of decision making units. One of the other advantages of this procedure is to rank all the extreme and nonextreme efficient decision making units. In other words, the suggested ranking method tends to seek a set of common weights for all units to make them fully ranked. Finally, it was applied for different sets holding real data, and then it can be compared with other procedures.

scholarly journals Implementation of Mass Customization for competitive advantage in Indian industries: a structural equation model

2021 ◽  
Author(s):  
Piu Jain ◽  
Suresh Garg ◽  
Gayatri Kansal
Keyword(s):  
Competitive Advantage ◽  
Mass Customization ◽  
Operations Management ◽  
Structural Equation ◽  
Analytical Framework ◽  
Equation Model ◽  
Market Demand ◽  
Indian Origin ◽  
Using Data ◽  
Indian Industries

Abstract The enduring fluctuations in market demand, exemplified by exceedingly unpredictable customer requirements have given rise to Mass customization, which is acquiring increasing prominence in production and operations management. Fostering on the foundation laid by erstwhile researcher Hart[1], who developed an analytical framework of four pillars of mass customization for organizations, the objectives of this research are to obtain additional discernments on the nature of linkage between the four pillars and MC, in addition to their impact on competitive advantage. The current work is an attempt to explore the mass customization ability of manufacturing organizations of Indian origin and its impact on organisational performance and to propose a comprehensive assessment and decision-making model for manufacturers to implement mass customization for competitive benefits. Literature support is expanded and validated using data collected through survey conducted among managers of various divisions of organization of Indian origin. The final sample contains 276 usable observations. Data analysis was performed expending structural equation modelling(Amos Graphics).

scholarly journals Modifying the convexity condition in Data Envelopment Analysis (DEA)

2020 ◽  
Vol 33 (02) ◽  
pp. 454-467
Author(s):  
Roghyeh Malekii Vishkaeii ◽  
Behrouz Daneshian ◽  
Farhad Hosseinzadeh Lotfi
Keyword(s):  
Data Envelopment Analysis ◽  
Real Data ◽  
Data Envelopment ◽  
Convexity Condition ◽  
Discrimination Power ◽  
Data Set ◽  
Step Procedure ◽  
Proposed Model ◽  
Dea Models ◽  
Production Possibility Set

Conventional Data Envelopment Analysis (DEA) models are based on a production possibility set (PPS) that satisfies various postulates. Extension or modification of these axioms leads to different DEA models. In this paper, our focus concentrates on the convexity axiom, leaving the other axioms unmodified. Modifying or extending the convexity condition can lead to a different PPS. This adaptation is followed by a two-step procedure to evaluate the efficiency of a unit based on the resulting PPS. The proposed frontier is located between two standard, well-known DEA frontiers. The model presented can differentiate between units more finely than the standard variable return to scale (VRS) model. In order to illustrate the strengths of the proposed model, a real data set describing Iranian banks was employed. The results show that this alternative model outperforms the standard VRS model and increases the discrimination power of (VRS) models.

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