Radiation effects on silicon Quarterly progress report, 1 Jul. - 30 Sep. 1966

Electrical conductivity and electron spin resonance experiments in study of radiation effects on silico

Radiation effects on silicon Sixth quarterly progress report, Apr. 1 - Jun. 30, 1966

Radiation effects on high purity n-type silicon solar cell

Radiation effects on silicon Summary report 1 Nov. 1965 - 20 Apr. 1967

Radiation-induced displacement effects measured in n and p type, low and high resistivity silico

Radiation effects on silicon solar cells Fourth monthly progress report, Apr. 1-30, 1962

Radiation effects on silicon solar cell

Decoupling thinking in service operations: a case in healthcare delivery system design

The notion of decoupling thinking has been well established in the manufacturing operations and supply chain management literature. This paper explores how this decoupling thinking can be applied in service operations and in particular in health care. It first reviews the relevant literature on decoupling fundamentals, the front- and back-office distinction, and new emerging decoupling thinking in service operations. Subsequently, a flow-based framework including content and process is developed for decoupling thinking in service operations. The framework provides an integrated perspective on customer contact, flow driver and flow differentiation (level of customisation). The framework hence, through flow differentiation, introduces the concept of standardisation versus customisation in a service context. This is followed by a health care case example to illustrate how the framework can be applied. The managerial implications are primarily in terms of a modularised approach to system design and management. The framework offers potential for benchmarking with other service systems as well as with manufacturing systems based on the shared foundation in decoupling thinking. Finally, suggestions are provided for further research opportunities derived from this research

Radiation effects on silicon solar cells Final report, Dec. 1, 1961 - Dec. 31, 1962

Displacement defects in silicon solar cells by high energy electron irradiation using electron spin resonance, galvanometric, excess carrier lifetime, and infrared absorption measurement

IOBPCS based models and decoupling thinking

The inventory and order based production control system (IOBPCS) is mainly a model of a forecast driven production system where the production decision is based on the forecast in combination with the deviation between target inventory and actual inventory. The model has been extended in various directions by including e.g. WIP feedback but also by interpreting the inventory as an order book and hence representing a customer order driven system. In practice a system usually consists of one forecast driven subsystem in tandem with a customer order driven subsystem and the interface between the two subsystems is represented by information flows and a stock point associated with the customer order decoupling point (CODP). The CODP may be positioned late in the flow, as in make to stock systems, or early, as in make to order systems, but in any case the model should be able to capture the properties of both subsystems in combination. A challenge in separating forecast driven from customer order driven is that neither the inventory nor the order book should be allowed to take on negative values, and hence non-linearities are introduced making the model more difficult to solve analytically unless the model is first linearized. In summary the model presented here is based on two derivatives of IOBPCS that are in tandem, and interfaces between them related to where the demand information flow is decoupled and the position of the CODP

A technique to develop simplified and linearised models of complex dynamic supply chain systems

There is a need to identify and categorise different types of nonlinearities that commonly appear in supply chain dynamics models, as well as establishing suitable methods for linearising and analysing each type of nonlinearity. In this paper simplification methods to reduce model complexity and to assist in gaining system dynamics insights are suggested. Hence, an outcome is the development of more accurate simplified linear representations of complex nonlinear supply chain models.  We use the highly cited Forrester production-distribution model as a benchmark supply chain system to study nonlinear control structures and apply appropriate analytical control theory methods. We then compare performances of the linearised model with numerical solutions of the original nonlinear model and with other previous research on the same model.  Findings suggest that more accurate linear approximations can be found. These simplified and linearised models enhance the understanding of the system dynamics and transient responses, especially for inventory and shipment responses.  A systematic method is provided for the rigorous analysis and design of nonlinear supply chain dynamics models, especially when overly simplistic linear relationship assumptions are not possible or appropriate. This is a precursor to robust control system optimisation

The Rippling Effect of Non-linearities

Non-linearities can lead to unexpected dynamic behaviours in supply chain systems that could then either trigger disruptions or make the response and recovery process more difficult. In this chapter, we take a control-theoretic perspective to discuss the impact of non-linearities on the ripple effect. This chapter is particularly relevant for researchers wanting to learn more about the different types of non-linearities that can be found in supply chain systems, the existing analytical methods to deal with each type of non-linearity and future scope for research based on the current knowledge in this field