The average life expectancy of a company is sadly only 40-50 years. You would think that a company lifetime could easily surpass our lifetimes because many generations can work at a company and pass down its products, brands, know-how, competencies, customer base, etc. to successive generations. But ultimately companies die because they fail to adapt and change. One area of adaption that is the most difficult to navigate is when to start de-investing in the traditional markets that initially built the company, and to invest in building new markets. Too many companies get themselves caught in a trap of continual investment in their core markets, which are no longer growing and missing out on growth adjacencies that can fuel the companys next generation of growth. This paper will explore the reinforcing feedback loops and systemic delays that cause most companies to invest too much and too long in their traditional market and recommends a new R&D portfolio management process that breaks this cycle. Its critical that companies understand what drives long-term success and how to fund innovation and change in a methodical way.
Current research on river ecosystems in Taiwan is mostly focused on water conservancy, ecology, and afforested viewpoints. There is a lack of integrated strategy on urban river ecosystem management. This study aims to examine river quality based on ecological safety. By means of systems engineering technology, the related ecological safety operation mechanisms have been analyzed. Also, through the new Fuzzy Delphi expert survey, the index system of urban river ecological safety (IES) has been compiled in order to explore the variables. The key indicators affecting urban river ecology safety can be fully defined by sensitivity analysis in order to engage in effectiveness evaluation and to create a dynamic simulation model. The study results indicate that the strategic implementation of improved embankment vegetation structure, a reduction in the degree of river channelization, and the maintenance of a high degree of longitudinal connectivity of rivers can effectively enhance the urban river ecology risk prevention, strengthen the efficient use of resources, and promote the sound development of urban river ecology.
A practitioners perspective on the calibration of complex system dynamics models is described in the context of a specific project in which a large, complex business training simulation model was converted from one language into I-Think® and design flaws in the original implementation were corrected. The model utilized 57 inputs and provided 296 outputs. The fact that calibration interacts with verification and validation is acknowledged, as well as the fact that the calibration strategy required for large models may not scale practically to smaller models. In additional to traditional best practices such as units checking, sensitivity testing, transient testing and graphical comparison, the paper focuses on a) simplifying and isolating interactions via submodels, using shims, slowing down feedback loops, creating cause and effect maps, testing at submodel level, and checking qualitative variables; b) Redesigning along the way; c) carefully documenting throughout the process; d) knowing when to step away; and e) building/acquiring automated tools. Having a calibration strategy for a large model is essential. The time required to apply the recommended methods can be significant, but the benefits clearly outweigh these costs. Nevertheless, even experienced modelers often wait too long before initiating the necessary disciplines.
Athletes face tremendous pressure to perform, and, when conventional means prove insufficient for performance improvement, some turn to performance enhancing drugs (PEDs). The present paper uses system dynamics to examine one example: the use of anabolic androgenic steroids in Major League Baseball (MLB), which operates in the United States and Canada. The authors provide an explanation of a detailed causal loop diagram of the problem, along with a stock and flow model, based on the Bass Diffusion Model, of part of the problem. They provide a few policy recommendations based on model runs.
This paper/poster explores the role and contribution of dynamic models in reverse logistics processes. The purpose of reverse logistics processes within a manufacturing supply chain is to disassemble or reutilize products, or their components, in order to generate value. The first part of the paper describes the general process involved in reverse logistics as found in the supply chain management literature. The second part of the paper presents three dynamic models that represent the main factors driving product returns: The green factor or consumer sensitivity towards environmental issues; the regulatory factor or governmental requirements; and the profit factor or business opportunities. The third part of the paper combines the three factors behind products returns in order to create a general dynamic model of reverse logistics. Emphasis is placed on two major issues: The non-linear relationships involved in reverse logistic processes and business opportunities as the main driver of reverse logistics. The last section of the paper centers on the similarities and differences of dynamic models that aim to simulate forward logistics and reverse logistics. While forward logistics are centered in concepts such as assembly and bullwhip effect, reverse logistics models emphasize disassembly and funnel-like behavior.
When final customer demand exceeds available supply, retailers often hedge against shortages by inflating orders to their suppliers. As several retailers compete for scarce supply, the amplification in orders lead to excess supplier capacity, high inventory variability, low capacity utilization, and financial and reputation losses for suppliers and retailers. While the amplification in orders caused by the competition for scarce resources has been described in the literature almost a century ago (Mitchell 1924), there is little research quantifying the impact of such order amplification by retailers.
The performance analysis of a general productioninventory control system under uncertain demand is presented. In the model, the production order releases are determined based on the information feedback on the forecasted demand, work-in-process discrepancy and inventory discrepancy. Stability conditions are obtained in terms of the control parameters that manage the rate at which the above discrepancies are corrected. The service and cost performances of the system in terms of order fill rate, item fill rate and average system cost are analyzed for various values of the control parameters within the stability region. Additional safety stock is considered to help achieve a desired level of service (desired order fill rate). Results based on numerical simulations are presented and their implications are discussed.