Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning

Post-seismic inventory and logistics planning under incomplete and fuzzy information is an important yet understudied area in supply chain risk management. The goal of this paper is to propose a system dynamics model to analyze the behaviors of disrupted disaster relief supply chain by simulating the uncertainties associated with predicting post-seismic road network and delayed information. The simulation results indicate: (1) information delay has different influences over the relief head-quarter (the upstream) and the disaster-affected areas (the downstream); and (2) the change of road conditions and shipment schedules have impact on the on-time transportation rate in supply chain management. Furthermore, this paper defined and tested supplies' replenishment solutions combined with three inventory planning strategies and four forecasting methods under different lead time uncertainties. The results show that: (1) a strategy that considers information from both the post-seismic management center and the affected areas can provide a better logistic plan than an one takes information from one side; (2) the smooth-the-trend forecasting method is suitable for inventory and logistic planning when the post-seismic situations are volatile, while the quick-response forecasting method has good performance in stable environments. In addition, this paper proposes decision tree to help decision makers choose the appropriate stocking strategies.     

Economic value added of supply chain demand planning: A system dynamics simulation

The development of a successful demand plan is typically a joint effort between different functional units such as Logistics, Marketing, Sales and executive management at one hand and between different business units on the other. Starting a project to structurally improve the demand planning often requires convincing all parties involved in such an effort. The key is to quantify the bottom-line impact of an increased demand planning reliability in the supply chain. This paper proposes a system dynamics simulation modeling framework that allows different managers to examine how improvements in their demand reliability will impact the overall corporate bottom-line. For example, supply chain managers can investigate how proposed changes in the supply chain demand forecasting structure, different suppliers, different logistics routes, or alternative inventory methods, may increase the overall profitability. The simulation model has been tested, validated with a real-life case of LG. Philips Displays Europe.     

SCOlog: A logic-based approach to analysing supply chain operation dynamics

In a complex business world, characterised by globalisation and rapid rhythms of change, understanding supply chain (SC) operation dynamics is crucial. This paper describes a logic-based approach to analysing SC operation dynamics, named SCOlog. SC operation is modelled in a declarative fashion and it is simulated following rule-based execution semantics. This approach facilitates the automated explanation of simulated SC operational behaviours and performance. The automated explanation support provided by SCOlog is found to improve the understanding of the domain for non-SCM experts. Furthermore, SCOlog allows for maintainability and reusability.     

Combining technology roadmap and system dynamics simulation to support scenario-planning: A case of car-sharing service

Due to the volatile market environment, the use of scenario approach comes to the forefront in business strategy. As a means of scenario planning, several approaches have been proposed and conducted. However, previous research, mainly having resorted to the expert judgment for planning and evaluation, still remains conceptual and lacks a systematic link to the planning process. In response, this paper provides an integrative approach to the technology roadmap and system dynamics to support scenario planning. The proposed approach consists of three parts: scenario building, technology roadmapping, and system dynamics simulation. The first step is to construct the scenarios which are used as inputs for the scenario planning. Second, technology roadmap is developed, incorporating the scenarios built in the first step. The technology roadmap works as a strategic framework to realize the hypothetical scenarios, linking the external and hypothetical business and internal strategies. Finally, the strategic model for technology roadmap is transferred to the operational viewpoint using system dynamics. When the simulation ends, the result of each scenario is reflected to the technology roadmapping, making the multi-path technology roadmapping. As an illustrative example, three scenarios of car-sharing business are developed and analyzed.     

A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t-test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s⁻¹), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m² m⁻²), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use.     

Path-based approach to integrated planning and control for robotic systems: A path-based approach provides an analytical integration of robot planning and control. As a result, the task level control can be achieved, and the system is capable of coping with unexpected or uncertain events

Our newly developed event-based planning and control theory is applied to robotic systems. It Introduces a suitable action or motion reference variable other than time, but directly related to the desired and measurable systems output, called event. Here the event is the length of the path tracked by a robot. It enables the construction of an integrated planning and control system where planning becomes a real-time closed-loop process. The path-based integration planning and control scheme is exemplified by a single-arm tracking problem. Time and energy optimal motion plans combined with nonlinear feedback control are derived in closed form. To the best of our knowledge, this closed-form solution was not obtained before. The equivalence of path-based and time-based representations of nonlinear feedback control is shown, and an overall system stability criterion has also been obtained. The application of event-based integrated planning and control provides the robotic systems the capability to cope with unexpected and uncertain events in real time, without the need for replanning. The theoretical results are illustrated and verified by experiments.