Harriet Black Nembhard

A REAL OPTIONS DESIGN FOR PRODUCT OUTSOURCING

ABSTRACT Product outsourcing is recognized as a way to gain flexibility for competitive advantage. We formulate the outsourcing problem using real options. We develop a financial model to assess the option value of product outsourcing. Specifically, we consider a three state-variable problem and use Monte Carlo simulation to estimate the value of the option. This valuation gives decision makers a way to choose the appropriate outsourcing strategy based on an integrated view of the market dynamics. A case example from the apparel manufacturing industry is used to demonstrate the application of real options to value outsourcing flexibility. We show that the inability of classical net present value methods to address dynamics in the market condition leads to an undervaluing of the outsourcing strategy. Numerical results and sensitivity analysis show how the real options approach can be used to give a better view of the long-term value of outsourcing.

Real options in engineering design, operations, and management

Introduction, H. Black Nembhard and M. Aktan Real Options in Practice, J. Mun, Ph.D. Origins of Real Options in Engineering, H. Black Nembhard and M. Aktan Real Options in Manufacturing Operations, H. Black Nembhard, M. Aktan, and L. Shi Real Options Valuation for Quality Improvement, H. Black Nembhard and M. Aktan Real Options in Outsourcing, M. Aktan, H. Black Nembhard, and L. Shi Barriers to Real Options Adoption and Use in Architecture, Engineering, and Construction Project Management Practice, D.N. Ford and M.J. Garvin Identifying Real Options to Improve the Design of Engineering Systems, R. de Neufville, O. de Weck, J. Lin, and S. Scholtes Real Options in Underground Mining Systems Planning and Design, V. Kazakidis and Z. Mayer Real Options in Engineering Systems Design, K. Kalligeros Real Options Model for Workforce Cross-Training, D.A. Nembhard, H. Black Nembhard, and R. Qin Real Options Design for Sustainable Product Quality Management, J. Ann Stuart Williams, M. Aktan, and H. Black Nembhard Real Options in Nanotechnology R&D, A. Ayanso and H. Herath Real Options-Based Analysis in Pharmaceutical Partnerships for Research and Development, M. Kim Hands-On Applications: Real Option Super Lattice Solver Software, J. Mun Index

REAL OPTION MODELS FOR MANAGING MANUFACTURING SYSTEM CHANGES IN THE NEW ECONOMY

ABSTRACT The manufacturing environment is becoming increasingly dynamic with upsurges in electronic-commerce, supply chain management, forecasting, and procurement and resource planning. It also includes trends toward more process data acquisition and analysis, shorter production runs, and more stringent quality requirements. These drivers lead to an opportunity for companies to collect and use information to identify changes that will affect their manufacturing systems. In conjunction with an industry partner who produces home fashion products, we developed a case-study that highlights four major manufacturing transitions: new product introduction; moving a product from research and development (R&D) to commercialization: new plant location; and starting or restarting production of existing products. These types of changes cross many levels of the operation - including the product level, plant level, and organizational level - and typically present significant operational challenges. We use this case-study to motivate the theoretical and applied research needed to support a real option framework for system changes in manufacturing. The key elements of our framework are to quantify manufacturing changes, develop a real option model for these activities, value the options to identify the best scenarios, and integrate these elements so that we can monitor and manage the overall process. The advantage of this approach is that it allows us to directly incorporate a market driven perspective, tying the manufacturing operations with the organizational economic goals.

A REAL OPTIONS DESIGN FOR QUALITY CONTROL CHARTS

Abstract We develop a financial model for a manufacturing process where quality can be affected by an assignable cause. We value the real options associated with applying a statistical process control chart using the Black-Scholes equation, binomial and pentanomial lattices, and Monte Carlo simulation methods. This valuation gives decision makers a way to choose the appropriate quality control strategy based on an integrated view of the market dynamics with the manufacturing operational aspects. An industry case is used to demonstrate the application of real options to value control chart decisions. Web based programs are given to value the alternatives in the case study, making the valuation task accessible to other users.

Integrating Experimental Design and Statistical Control for Quality Improvement

As a result of a successful case study, we advance a methodology for combining design of experiments (DOE) and cumulative score (Cuscore) charts to control and monitor an industrial process subject to a feedback control scheme. We use DOE to screen out the most important variable from a larger set of variables. We use the critical variable found in the DOE phase as a compensating factor in a feedback controller. We derive the proper Cuscore statistic that will identify spike signals in a dynamic system that is subject to feedback control. We then apply this Cuscore statistic to the output error from the control scheme. We illustrate the effectiveness of this process improvement methodology using an industrial case study where we identify and eliminate a recurring output quality problem in a pleating and gluing manufacturing operation.

A Demerits Control Chart for Autocorrelated Data

Implementing traditional statistical process control may increase the frequency of false alarms in situations where autocorrelation exists. Often, the assumption of uncorrelated process data is violated, resulting in inadequate monitoring and corrective..

A real options design for product outsourcing

We develop a financial model to assess the option value of outsourcing. We value the real options associated with outsourcing an item using Monte Carlo simulation. This valuation gives decision makers a way to choose the appropriate outsourcing strategy based on an integrated view of market dynamics. A simulation example is used to demonstrate the application of real options to value outsourcing. The simulation program code was written in JavaScript so that the valuation task would be accessible to other users because of its web enabled feature.

The use of Bayesian forecasting to make process adjustments during transitions

In many manufacturing operations, a system may exhibit dynamic behavior before reaching a steady-state level. This is most frequently associated with a transition in production like a product style change or a grade change. During the transition phase, the output does not respond instantaneously to a change in input. However, there is typically some information about the past transition phase performance available. We develop an adjustment policy for transition periods based on using a Bayesian forecast to incorporate the prior information. We present computational results showing average process improvements under various system and noise disturbance conditions. ” 2001 Elsevier Science B.V. All rights reserved.

A real options design for quality control charts

We develop a financial model for a manufacturing process where quality can be affected by an assignable cause. We evaluate the options associated with applying a statistical process control chart using pentanomial lattice and Monte Carlo simulation methods. By connecting the aspects of market dynamics with the manufacturing operational aspects, we now have a way to help decision makers address the bottom-line profitability associated with the quality control decision.

Integrating discrete-event simulation with statistical process control charts for transitions in a manufacturing environment

We present a model that integrates real-time process control charting with simulation modeling to illustrate the effects and benefits of SPC charts for quality improvement efforts. The integrated model is particularly significant in addressing transition issues arising from changes in the input material. A case study based on a medical manufacturing industry process is used to illustrate the approach.