Project Topics
Get Complete Project Material File(s) Now! »
Innovation and competitiveness in SMI
According to Teknikföretagen, SMI included 3600 member companies with nearly 300,000 employees account for nearly half of Swedish goods exported. Swedish companies also have a large local production in growing markets such as China, India and Brazil, which supplements the exports from Sweden [60].
For Sweden’s part, sales and production on growing markets have made industrial activity in Sweden more knowledge-intensive. This is shown by the fact that Swedish engineering companies have clearly more white-collar than blue-collar employees. At the same time, company activities are increasingly integrated across national boundaries and global corporate cultures are being cultivated [60].
World Economic Forum 2010 competitiveness index ranks Sweden 2nd most competitive, behind Switzerland [61]. In addition Sweden is considered to be one of the most advanced countries in terms of innovation. According to the book, The Flight of the Creative Class, by the U.S. urban studies, Professor Richard Florida of University of Toronto, Sweden is ranked as having the best creativity in Europe for business and is predicted to become a talent magnet for the world’s most purposeful workers. The book compiled an index to measure the kind of creativity it claims is most useful to business — talent, technology and tolerance [62]. Definitely high creativity and innovation has been one of the main reasons which help SMI to maintain this level of competitiveness. Simulation in its turn is one of the tools which contribute a lot to facilitation of innovation process in SMI.
Simulation: a tool for Innovation
With SMI companies betting their futures on product and process development, the cost of being wrong and the value of being right have never been greater. External market conditions are squeezing the margin for error to the point where they cannot afford (and maybe not even survive) even one instance of being wrong. At the end of the day, executives at every level need to have confidence that the product and process they envisioned is the ones that makes it to market. In addition, today’s declining price points and margins have forced manufacturers to drive costs out of every aspect of their supply chain, from earliest production stage to the final distribution steps. At the same time, increasing competition from traditional and emerging channels has placed new emphasis on rapid innovation and continuous differentiation.
To bridge the gap between brilliant ideas and successful business initiatives, leading SMI companies implement engineering simulation. Simulation is a method which contributes to innovation process by facilitation of virtual experimentation. Joe Tidd and Scott Isaksen in their book “Meeting the Innovation Challenge” name the Visualization as a tool for innovation management and define it as “a method that consists of creating a mental image of a desired outcome, and repeatedly playing that image in the mind [63]”.
Simulation can dramatically reduce a product’s time to market, mitigate the risk of change management and even help to protect brand images. Not investing in engineering simulation could put organizations at a competitive disadvantage.
Simulation Modeling
Simulation modeling, which mimics the real-world experiment, can be seen as virtual experimentation, allowing one to answer questions about the behavior of a system. As such, the particular technique used does not matter. Whereas the goal of modeling is to meaningfully depict a system presenting information in an understandable, re-usable way, the aim of the simulation is to be fast and accurate. A simulation model is a tool for achieving a goal (design, analysis, control, optimization . . .) [52]. Therefore a fundamental prerequisite is some assurance that conclusions drawn from modeling and simulation (tools) can be accepted and applied with confidence. The establishment of this confidence is associated with two distinct activities; namely, verification and validation [51].
Discrete event logistics system
Modeling LSCM issues of manufacturing industries is classified as DELS issues. Networks of resources, through which goods and people flow, are called Discrete Event Logistics System (DELS). The term logistics refers to the fact that there are physical flows. Each node of the network corresponds to some resource or set of resources by which the materials are either converted in some way, i.e., refined, shaped, assembled, disassembled, etc., moved, i.e., transported within one facility or between facilities, or simply held for some period of time as work-in-process (WIP) or stored in a warehouse. Material handling and transportation are key components of DELS. DELS can be found in such domains as transportation, distribution, and manufacturing. DELS are discrete because they move material in discrete quantities, and because their behavior can be characterized effectively in terms of events happening at discrete points of time, i.e., the start or end of some conversion, transport, or storage process. While logistics systems are typically discrete, the term ‘‘discrete’’ refers to the fact that DELS are not related to production logistics in process industries, i.e., for example, in manufacturing processes inside a refinery. A DELS may range from simple to complex. They may take the form of a single warehouse, a portion of a factory, a complete factory, or a global supply network [2].
There are several examples of DELS in related literatures. The wafer fab which produces integrated circuits (also called chips) is one of them. A semiconductor chip is a highly miniaturized, integrated electronic circuit consisting of thousands of components. Semiconductor manufacturing starts with thin discs, called wafers, made of silicon or gallium arsenide. A large number of usually identical chips can be produced on each wafer by fabricating the electronic circuits layer by layer in a wafer fabrication facility [2]. Wafer fab consists of hundreds of machines ([44]) and a sophisticated automated material handling system (AMHS) ([45]). Lots are the moving entities within wafer fab. The routes of the lots contain several thousands of processing steps. Consequently, the cycle time of the lots is between four and six weeks. Wafer fabrication is widely considered to be amongst the most difficult of all manufacturing environments ([46]).
Causal Loop Diagramming
Causal loop diagramming (CLD) is a fundamental tool used in system dynamics. It is the causal loop diagram’s unique ability to identify and visually display sophisticated processes and root causes. Every system behaves in a particular manner due to the influences on it. Some of these influences can be modified, some cannot, and some can diminished. Causal loop diagrams bring out the systematic feedback in processes by showing how variable X affects variable Y and, in turn, how variable Y affects variable Z through a chain of causes and effects. By looking at all the interactions of the variables, the behavior of the entire system is recognized. With a CLD, a practitioner no longer needs to focus only on one interaction between two variables, but can focus on the whole system, along with its many variables and its many causes and effects [57].
Continuous Simulation
Continuous modeling is the mathematical practice of applying a model to continuous data (data which has a potentially infinite number, and divisibility, of attributes). These models have often mathematical equations which fit the problem assumptions. In simulation of continuous models, time advances in equal steps and model equations and values are recalculated at each time step. Many LSCM parameters with continuous data like inventory, demand, cost, etc. could be modeled using continuous simulation modeling.
Depending on the problem, different software and toolboxes have been developed. Simple problem could be modeled even with Microsoft excel. Middle class issues could be modeled with simulation toolbox of Matlab. However there is plenty of simulation software that can solve complex continuous models. These packages usually have predefined blocks for arithmetic operations and statistical distributions.
OR Techniques
Operations Research (OR) is a discipline that deals with the application of advanced analytical methods to help make better decisions. Employing quantitative techniques such as Linear Programming, Game Theory, Integer Programming, Nonlinear Programming, etc. , OR arrives at optimal or near-optimal solutions to complex decision-making problems. In supply chain management context, OR is often concerned with determining the maximum (of profit, performance, or yield) or minimum (of loss, risk, or cost) of some enterprises. Barely some of LSCM strategic issues like supplier selection are modeled by the use of other techniques like Game Theory. Like continuous simulation there is a plenty of software packages with predefined functions that are used to model OR problems. Some of them like Excel and Matlab are applicable in continuous simulation also. Nevertheless Lingo, Lindo and SAS are among the OR special packages.
Discrete Event Simulation
For a class of simulation modeling called discrete-event, system models are depicted at an abstraction level where the time base is continuous, but during a delimited time-span, only a finite number of relevant events take place. These events can cause the state of the system to change. In between events, the state of the system is fixed. This is unlike continuous models in which the state of the system may alter continuously over time [49].
SD and DES: Comparison and use
Discrete event simulation (DES) and system dynamics (SD) are two modeling approaches widely used as decision support tools in logistics and supply chain management (LSCM). Simulation models, in both DES and SD, are usually built to understand how systems behave over time and to compare their performance under different circumstances [54]. Some technical differences exist between the two modeling approaches related to their underlying principles. For example, DES models systems as a network of queues and activities where state changes occur at discrete points of time, whereas SD models represent a system as a set of stocks and flows where the state changes occur continuously over time [55]. In DES entities (objects, people) are represented individually. Specific attributes are assigned to each entity, which determine what happens to them during the simulation. On the other hand, in SD individual entities are not specifically modeled, but instead they are represented as a continuous quantity in a stock. DES models are generally stochastic in nature, where randomness is represented by the use of statistical distributions. SD models are generally deterministic and variables usually represent average values. In DES state changes occur at irregular discrete time steps, while in SD state changes are continuous, approximated by small discrete steps of equal length. [1].
DES and SD have both been used to model a wide range of LSCM issues. “Evidently, DES and SD are capable of modeling the complexity and uncertainty inherent in LSCM environment. They are powerful techniques that can be integrated in LSCM to undertake “what if” analysis with a wide range of scenarios [1]”. According to A. A. Tako et al. [1] DES is used more frequently than SD for supply chain modeling and the use of DES in LSCM context is growing at a faster rate.
There is a general belief that SD modeling is more suitable for modeling at a strategic level and DES at an operational/tactical level. However, based on the literature review done by A. A. Tako et al. on the journal papers identified in the period (1996–2006); “in terms of application of DES and SD to support decisions at a strategic or operational/tactical level, there is little evidence of any difference within LSCM context. It may be that SD, when it is used, is marginally used proportionately more often for strategic issues. Overall, DES and SD are used more frequently to model operational/tactical issues in LSCM context [1]”.
What appears is a limited separation in the use of DES and SD for LSCM. Findings from a recent empirical study on users’ perceptions of DES and SD shows that “two approaches were not perceived as significantly different; implying that from the user’s point of view the type of simulation approach makes little, if any, difference as long as it is suitable for addressing the problem situation at hand [56]”.
Table of contents :
1. Introduction
1.1 Aim of study
1.2 Problem Statement
1.3 Research delimitation
2. Theoretical Background
2.1 Swedish Manufacturing Industries (SMI)
2.2 Innovation and competitiveness in SMI
2.3 Simulation: a tool for Innovation
2.4 Simulation Modeling
2.5 Discrete event logistics system
2.6 System theory point of view on discrete event logistics systems
2.7 Modeling for Discrete event logistics system
2.8 System dynamics modeling
2.8.1 Causal Loop Diagramming
2.8.2 Continuous Simulation
2.8.3 OR Techniques
2.9 Discrete Event Simulation
2.10 SD and DES: Comparison and use
3. Research approach
3.1 Identification of journal articles and simulation approach adopted
3.2 Creation of a schema for classifying papers by LSCM issue
3.3 Distinguishing between strategic and operational/tactical LSCM issues
3.4 Classifying papers by the LSCM issues addressed
4. Result
4.1 The frequency of use of DES and SD in LSCM context of SMI
4.2 Most frequently modeled LSCM issue
4.3 The focus of modeling efforts on strategic and operational/tactical levels
4.4 Some companies involved
5. Analysis and Discussion
6. Summary and Conclusions
References
