scholarly journals A Real Options Approach to Design for Quality Standards Considering Life Cycle of a Product

2021 ◽  
Author(s):  
Aasyfa Mullick
Keyword(s):  
Life Cycle ◽  
Control Chart ◽  
Control Charts ◽  
Product Life Cycle ◽  
Manufacturing System ◽  
Programming Model ◽  
Quality Standards ◽  
Real Options Approach ◽  
Dynamic Programming Model ◽  
Lattice Approach

Flexibility is recognized as a valuable parameter for a manufacturing system. When valuing flexibility for quality standards of a production facility, there must be a tool to precisely justify the value delivered by the preferred standards. In this project, we have developed an options model that accounts for the demand dynamics during the product life cycle. We consider a manufacturing facility that employs a specific quality control chart. A second control chart with more strict standards is generated and the effectiveness of two control charts is analyzed with options framework considering the life cycle of the product. The options model is evaluated by lattice approach and the dynamic programming model. The results show that a set of appropriate levels of quality standards when adopted by a manufacturing system can be profitable for the firm.

scholarly journals A Real Options Approach to Design for Quality Standards Considering Life Cycle of a Product

2021 ◽  
Author(s):  
Aasyfa Mullick
Keyword(s):  
Life Cycle ◽  
Control Chart ◽  
Control Charts ◽  
Product Life Cycle ◽  
Manufacturing System ◽  
Programming Model ◽  
Quality Standards ◽  
Real Options Approach ◽  
Dynamic Programming Model ◽  
Lattice Approach

Flexibility is recognized as a valuable parameter for a manufacturing system. When valuing flexibility for quality standards of a production facility, there must be a tool to precisely justify the value delivered by the preferred standards. In this project, we have developed an options model that accounts for the demand dynamics during the product life cycle. We consider a manufacturing facility that employs a specific quality control chart. A second control chart with more strict standards is generated and the effectiveness of two control charts is analyzed with options framework considering the life cycle of the product. The options model is evaluated by lattice approach and the dynamic programming model. The results show that a set of appropriate levels of quality standards when adopted by a manufacturing system can be profitable for the firm.

Technological Innovation, Product Life Cycle and Market Power: A Real Options Approach

2015 ◽  
Vol 14 (01) ◽  
pp. 93-113 ◽  
Author(s):  
Johnson T. S. Cheng ◽  
I-Ming Jiang ◽  
Yu-Hong Liu
Keyword(s):  
Life Cycle ◽  
Real Options ◽  
Market Power ◽  
Technological Innovation ◽  
Product Life Cycle ◽  
Investment Decision ◽  
Investment Decisions ◽  
Optimal Investment ◽  
Product Life ◽  
Real Options Approach

This paper employs a real options approach to analyze optimal investment decisions. When investment projects have the characteristics of irreversibility, uncertainty and the option to wait or exit, the traditional net present value (NPV) method would underestimate the value of investment, since it neglects the values of timing and operational flexibility. The distinctive feature of this paper is that the effects of product life cycle (PLC) as well as market power are incorporated into the model. In addition, and different to the approach in Liao et al. [Optimal investment decision and product life cycle: A real options approach, Sun Yat-Sen Management Review 11(3) (2003) 1–36], we introduce the concept of technological innovation into the model. It is shown that the optimal waiting time for the investment is longer than both those in the American call options model of McDonald and Siegel [The value of waiting to invest, Quarterly Journal of Economics 101(4) (1986) 707–727], which does not incorporate dividend yield, and Liao et al. [Optimal investment decision and product life cycle: A real options approach, Sun Yat-Sen Management Review 11(3) (2003) 1–36], but is shorter than that in Dixit and Pindyck's [Investment under Uncertainty (Princeton University Press, Princeton, NJ, 1994)] model, which incorporates dividend yield. Finally, a comparative static is used to analyze the determinants of optimal investment decisions. Our results indicate that the investment-ratio threshold will be higher, and thus the optimal entry time for an investment will be delayed, when (1) the PLC is longer, (2) the uncertainty is greater, (3) the discounting rate is higher, (4) market power is larger, (5) jump size intensity is stronger and (6) the payoff out ratio (R&D/revenue) is larger.

Operation Mode for Personnel Integration in Manufacturing System Based on Product Life Cycle

2010 ◽  
Vol 97-101 ◽  
pp. 3169-3173
Author(s):  
Shuang Liu ◽  
Fei Liu ◽  
Ping Yan
Keyword(s):  
Life Cycle ◽  
Information Integration ◽  
Product Life Cycle ◽  
Manufacturing System ◽  
Process Integration ◽  
Operation Mode ◽  
Product Life ◽  
Target Layer ◽  
Function Integration ◽  
Further Development

The function integration, process integration and information integration of the manufacturing system are all in need of the support of the personnel integration. But the complicated relationship among personnel has caused difficulties in personnel integration. In order to analyze the correlation between personnel in manufacturing system, an analysis method based on the relation degree matrix was proposed, meanwhile the personnel in manufacturing system was classified according to the product life cycle(PLC). The above work has paved the path for the further development of a kind of operation mode for personnel integration in manufacturing system based on PLC (MOPI). The mode is composed of four layers, which are the target layer, the PLC layer, the personnel layer, and the supporting system layer. The above mode was implemented in a local manufacturing enterprise, and positive application effect was achieved.

scholarly journals Probabilistic learning curve and real options approach to the valuation of cross-training with product life cycle

2021 ◽  
Author(s):  
Thanasiri Muttulingam
Keyword(s):  
Life Cycle ◽  
Learning Curve ◽  
Real Options ◽  
Product Life Cycle ◽  
Net Present Value ◽  
Individual Characteristics ◽  
Probabilistic Learning ◽  
Product Life ◽  
Real Options Approach ◽  
Cross Training

Learning and forgetting are two important characteristics in manufacturing environments where workers are cross-trained to increase their flexibility of adapting to different tasks. Cross-training is introduced by industries so that one worker can work on multiple stations. This thesis develops two models: (i) a probabilistic learning curve approach to the production lot size problem to determine the economic manufactured quantity (EMQ); (ii) a real options approach to the valuation of cross-training with product life cycle. Different workers perceive the complexity of a certain task differently and each worker will have his/her learning curve with its individual characteristics. So, it is more realistic to assume that the learning curve characteristics are random variables with given probability density functions. Furthermore, for the second model, the demand of the product follows three-regime product life cycle. Each regime is modeled by a geometric Brownian motion. The net present value (NPV) is calculated using the real options. The results show that there is a significant change in the NPV compared to standard model with simplified assumption

scholarly journals Plant layout design and assessment consideration of cellular manufacturing system via G.A

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Dhurgham Aiham Kadhim Alshakarchi ◽  
Maytham Salman azeez
Keyword(s):  
Genetic Algorithms ◽  
Life Cycle ◽  
Product Life Cycle ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Layout Design ◽  
Cellular Manufacturing System ◽  
Plant Layout ◽  
Product Life ◽  
Fast Change

           Cellular manufacturing  is a wide world and many study and researches discussed are concerning  with  it because of the large demand in market and wheel technology push the scientist to find tools that can stand with this fast change in demand according specific market , so that they invent the best way to deal with optimization purpose. In this research the dealing with cellular  optimization in such a way that can be manageable by AI and using  type of AI that named natural optimization .  The applying of genetic algorithms  was carried out on factory of electrical motor also all data was taken from the factory which is depend on the position and sequence of operations took place in the factory . The theory  in this field were also taking in  consideration and the applications was carried out . the research deals with problem in two way  theoretically  that can take in consideration  the positions of machines inside the plant and its equations that covers with some constraints   and the secondly  the routing of part during product life cycle and execute results and applying it on factory configuration . There are multi solutions (results ) in the research for the problem that can achieve the flexibility , simplicity  and also desired distance

scholarly journals Probabilistic learning curve and real options approach to the valuation of cross-training with product life cycle

2021 ◽  
Author(s):  
Thanasiri Muttulingam
Keyword(s):  
Life Cycle ◽  
Learning Curve ◽  
Real Options ◽  
Product Life Cycle ◽  
Net Present Value ◽  
Individual Characteristics ◽  
Probabilistic Learning ◽  
Product Life ◽  
Real Options Approach ◽  
Cross Training

Learning and forgetting are two important characteristics in manufacturing environments where workers are cross-trained to increase their flexibility of adapting to different tasks. Cross-training is introduced by industries so that one worker can work on multiple stations. This thesis develops two models: (i) a probabilistic learning curve approach to the production lot size problem to determine the economic manufactured quantity (EMQ); (ii) a real options approach to the valuation of cross-training with product life cycle. Different workers perceive the complexity of a certain task differently and each worker will have his/her learning curve with its individual characteristics. So, it is more realistic to assume that the learning curve characteristics are random variables with given probability density functions. Furthermore, for the second model, the demand of the product follows three-regime product life cycle. Each regime is modeled by a geometric Brownian motion. The net present value (NPV) is calculated using the real options. The results show that there is a significant change in the NPV compared to standard model with simplified assumption

scholarly journals An Acquisition On Big Data Model For Quality Tracing Of Iron And Steel Industries

2019 ◽  
Vol 8 (10) ◽  
pp. 1780-1783
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Data Model ◽  
Product Life Cycle ◽  
Manufacturing System ◽  
Production Environment ◽  
General Process ◽  
Iron And Steel ◽  
Data Production ◽  
Steel Industries

Quality determine is essential affair for steel industries. Due to complication and variation of nature input that turn to be changed into many forms. Due to this, it is tough to explicit the report and trace over the whole product life cycle from designing, construction, etc. According to big data approach, study of the essence of steel brand and the factor of their manufacturing system and it is effective viable multi row system which consists of four structure , [1]the basis quality bill of material [BQBOM] ,[2]the general process bill of material[GPBOM],[3]the production and scheduling bill of material[PSBOM] ,[4]the final quality bill of material[FQBOM]. This mode would be useful to builders to frame a kind of scheme in big data production environment

Real Options and Its Suitability in Assessing International Digital Investment

2020 ◽  
pp. 235-258
Author(s):  
Jorge Tarifa-Fernández ◽  
Ana María Sánchez-Pérez ◽  
Salvador Cruz-Rambaud
Keyword(s):  
Artificial Intelligence ◽  
Life Cycle ◽  
Real Options ◽  
Product Life Cycle ◽  
Digital Technologies ◽  
Payment Systems ◽  
Global Issues ◽  
Real Options Approach ◽  
Informed Decisions ◽  
Strategic Perspective

Firms have experienced extreme competition because of changes in technological and global issues. Globalization of manufacturing has arisen through a faster transfer of materials, complex payment systems, and compression of product life cycle. Eventually, firms need the integration of technologies to meet the increasingly sophisticated customers' needs. Among the technologies, artificial intelligence has attracted much of the attention as it has been foreseen to have a major impact on all industries. Real options approach may be applied to make informed decisions concerning digital technologies investments. Therefore, firms could decide to defer the option of investing in artificial intelligence for the sake of finding a more favorable future environment. This chapter provides an adequate tool to reduce uncertainty in deciding whether to implement artificial intelligence in their companies. This tool comprises the strategic perspective of the investment in digital technologies that makes it suitable to be incorporated as a part of the set of strategic tools.

Integrated Quality System Oriented Product Life Cycle in CIMS Environment

2000 ◽  
Author(s):  
Qing Ke Yuan ◽  
Xin Chen
Keyword(s):  
Life Cycle ◽  
Quality Management ◽  
Product Life Cycle ◽  
Manufacturing System ◽  
Quality System ◽  
Computer Integrated Manufacturing ◽  
Management Technique ◽  
Product Life ◽  
Integrated Quality System ◽  
Whole Life Cycle

Abstract Since the quality activities is through out the product whole life-cycle, the quality management should be a integrated quality system, facing the product life-cycle. In CIMS (Computer Integrated Manufacturing System), with the assistance of computer and its relative technologies, IQS (Integrated Quality System) can integrate the quality management technique cells into an organic whole, then the quality management will be more coherent and efficient. In this paper the concept, features, components and functions of IQS are been discussed and the key technologies are pointed out.

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