Editorial
Article Type: Editorial From: Journal of Modelling in Management, Volume 5, Issue 3
Welcome to the last issue of Volume of JM2! My goodness, how time really passes on […] It is like a Swift Modeling Approach!! We have again an interesting and diverse combination of research topics connected with modeling issues!
In the first paper readers can see Ettore Settanni and Jan Emblemsvåg applying a non-deterministic conceptual life cycle costing model to manufacturing processes. The aim of this paper is to introduce uncertainty analysis within an environmentally extended input-output technological model of life cycle costing.
Input-output analysis (IOA) provides and computational structure which is interesting for many applications within value chain analysis and business processes analysis. A technological model, which is built bottom-upwards from the operations, warrants that production planning and corporate environmental accounting are closely related to cost accounting. Monte Carlo methods have been employed to assess how the uncertainty may affect the expected outcomes of the model.
Making reference to a vertically integrated, multiproduct manufacturing process, the author has shown how production and cost planning can be effectively and transparently integrated, also taking the product usage stage into account. The uncertainty of parameters has been explicitly addressed to reflect business reality, thus reducing risk while aiding management towards taking informed actions. The model is subject to all the assumptions characterizing IOA.
Developing a formal structure which is common to environmental, or other physically driven, assessments and cost accounting helps identifying and understanding those drivers that are relevant for both, especially the effects different design solutions may have on both material flows and the associated life cycle costs. This approach integrates corporate physical and monetary measures, the computational mechanisms have been made transparent.
In the second paper, Roberto Sarmiento tackles some issues with the modeling of manufacturing performance, the trade-offs and look at the cumulative capabilities paradox. This is a conceptual paper designed to highlight issues with current methodologies and rationales used to model the achievement of high business performance in operations management studies, particularly those dealing with the trade-offs and cumulative capabilities concepts. The paper also attempts to provide solutions to these problems.
The author makes use of a “thought experiment” in order to illustrate the issues that need to be addressed. Widely accepted approaches and rationales used to model the achievement of high levels of performance are limited. This is particularly true when more recent theoretical developments in the field are considered. Thus, more comprehensive methodologies, rationales and terminologies that resolve the identified difficulties are necessary.
The investigation makes two important and novel contributions to this area of research:
- 1.
it brings attention to problems that have been largely ignored; and
- 2.
it provides solutions to these issues.
Lead logistics providers (LLP) tend to integrate the best of Third Party Logistics (3PL) service providers increasingly and allow for synchronized and optimized operations. The LLP uses the services of the 3PL and hence the issue of choosing them reliably takes greater significance. Rajbir Singh Bhatti,Pradeep Kumar and Dinesh Kumar use an analytic hierarchy process (AHP) approach to model the choice parameters. Systematic logistic evaluation assists to prioritize the alternatives from the decision makers. The AHP based model is applied to a case study using data from three leading LSPs operating in the province of Uttrakhand (in India).
Thomas Rohleder, David Cook and Paul Rogers introduce a dynamic model of the systemic cause for patient treatment delays in emergency departments. The system model was developed to understand why average emergency departments (ED) length of stay (LOS) was rapidly increasing while the number of ED visits was relatively constant. The authors’ focus was to identify systemic causes for poor patient flow so that the model could then be used to evaluate improvement options using a more complete view of the causal structure for the ED delays. In the case study, a disciplined system dynamics approach was used that included development of a dynamic hypothesis, causal loop and stock flow diagramming.
Results support the dynamic hypothesis that an aging population and shortages of resources to treat chronically ill patients (among other dynamics) were causing longer average LOS. Older and sicker patients were consuming more ED resources and causing less acute patients to leave without being seen or to avoid visiting the ED in the first place. In essence, the ED was acting as a safety valve for the wider healthcare system as many parts of this wider system became overloaded.
What are the implications for practice? Due to the systemic causes for the patient treatment delay in the ED, simple local solutions are unlikely to be effective. The authors’ system model can be used as a basis to understand the underlying dynamics of the systemic causes for poor patient flow and identify robust and long-term solutions. The research presents a process for developing a dynamic model to engage the various participants in a healthcare system in understanding the causes for delays and poor patient flow. The modeling approach can be used as a means for health care managers/administrators to identify improvement options that address the systemic problems.
To select proper projects for bidding is a problem of multiple attribute group decision making. Yong-tao Tan, Li-yin Shen, Craig Langston and Yan Liu propose a fuzzy technique for order preference by similarity to ideal solution(TOPSIS) for assisting contractors to select proper projects for bidding by considering multiple attributes and integrating decision group’s opinions. The linguistic variables chosen include project condition, firm, competition,and risk. Linguistic terms are defined by triangular fuzzy numbers of ratings of alternatives and weights of criteria. A numerical example is used to demonstrate the application of the fuzzy TOPSIS approach in project selection.
JM2 is also introducing a novel feature in each of its issues. We are introducing a new tutorial section (an idea given by Josef Mazanec! Thank you) which is to be edited by Graeme D. Hutcheson. This new tutorial section will be placed at the end of each issue of JM2 and is designed to:
provide a forum for papers that deal with the more practical side of modeling;
give information about analysis techniques and the practicalities of running analyses;
provide practical examples and demonstrations where readers will be able to access the data and necessary software to recreate the analyses;
showcase software that can be used to analyse data;
demonstrate the use of techniques that can be applied to the analysis of social science data;
provide instruction in the application of specific techniques; and
provide easy-to-follow examples analyses, amongst other general and specific aims.
We do hope that you continue to enjoy the scholarly content of JM2and that you will find useful, the new important feature of JM2 –the tutorial section! See you in Vol. 6!
Luiz Moutinho, Kun Huang Huarng
