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A Simulation-based Framework for Improving the Ecological and Economic Transparency in Multi-variant Production

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Multi-variant production has to cope with various challenges caused by external factors such as a customer and competition driven increase in variants, the corresponding growth of internal complexity as well as the rising demand for more resource efficiency. In order to being able to optimize their manufacturing systems continuously and target-oriented, companies need to improve the transparency about ecological and economic inefficiencies. This paper presents a simulation-based framework for improving the ecological and economic transparency in manufacturing systems. Within the presented framework energy consumption and costs are allocated according to their actual cause. This enables a user to identify influencing variables, which cause variety-induced non-value adding energy consumption as well as costs in manufacturing systems. Based on this knowledge, target-oriented lean and green optimization can be applied.
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Procedia CIRP 26 ( 2015 ) 179 184
Available online at
2212-8271 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of Assembly Technology and Factory Management/Technische Universität Berlin.
doi: 10.1016/j.procir.2014.07.101
12th Global Conference on Sustainable Manufacturing
A simulation-based framework for improving the ecological and economic
transparency in multi-variant production
Andreas Kruse
*, Steffen Butzer
, Tom Drews
, Rolf Steinhilper
Fraunhofer IPA Project Group Process Innovation, Universitaetsstrasse 30, 95445 Bayreuth, Germany
Bayreuth University, Chair Manufacturing and Remanufacturing Technology, Universitaetsstrasse 30, 95445 Bayreuth, Germany
* Corresponding author. Tel.: +49-921-557332; fax: +49-921-557305. E-mail address:
Multi-variant production has to cope with various challenges caused by external factors such as a customer and competition driven increase in
variants, the corresponding growth of internal complexity as well as the rising demand for more resource efficiency. In order to being able to
optimize their manufacturing systems continuously and target-oriented, companies need to improve the transparency about ecological and
economic inefficiencies. This paper presents a simulation-based framework for improving the ecological and economic transparency in
manufacturing systems. Within the presented framework energy consumption and costs are allocated according to their actual cause. This
enables a user to identify influencing variables, which cause variety-induced non-value adding energy consumption as well as costs in
manufacturing systems. Based on this knowledge, target-oriented lean and green optimization can be applied.
© 2014 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of Assembly Technology and Factory Management/Technische Universität Berlin.
Keywords: transparency; multi-variant production; simulation-based; lean and green optimization
1. Challenges of multi-variant production
In today’s globalized business environment almost every
industry sector faces challenges associated with complexity.
The complexity can be found in processes and products as
well as in the business organizations themselves and causes
multiple problems at the operational and strategic levels.
Especially manufacturing companies are confronted with
these problems and perceive the corresponding complexity
e.g. through the frequent adaption of the manufacturing
system in order to meet the current and future market
demands. These market demands mainly result from
megatrends such as the diversified customer demands, shorter
product life cycles, shortage of resources and declined
manufacturing depths. [1]
In particular the diversified customer demands and the
shortening of product life cycles lead to an enormous increase
in product variety and enhance the need for managing the
induced complexity of multi-variant manufacturing systems.
This perception becomes even more important since the
proliferation of product variety is a trend in numerous
industries and can be considered as one possible strategy to
enable companies to maintain and increase market shares
through satisfying the variety seeking behavior of customers.
The development of product variety and the subsequently
change of manufacturing systems towards a more multi-
variant production is illustrated in Figure 1. [2]
Fig. 1. Development of the proliferation of product variety and manufacturing
systems towards a multi-variant production [3].
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
Peer-review under responsibility of Assembly Technology and Factory Management/Technische Universität Berlin.
180 Andreas Kruse et al. / Procedia CIRP 26 ( 2015 ) 179 – 184
The steadily increasing product variety externally induces
complexity and is one of the main reasons for the lack of
transparency regarding the ecological and economic
assessment of manufacturing systems with multi-variant
production. In order to identify ecological and economic
inefficiencies caused by variety-induced complexity and to
optimize such systems it is therefore fundamentally important
to establish transparency within the processes. [4]
The existence of transparency is the basis for the
identification of efficiency potentials which subsequently
provide an insight into energy consumption and costs caused
by an enhanced multi-variant production. The exact
calculation of such complexity costs still remains a highly
difficult task since the available approaches have yet to be
entirely successfully implemented in industry practice. [5]
However, multiple studies prove the influence of complexity
on costs and estimate e.g. that 30 to 40 % of the complexity-
caused costs in a manufacturing company can be directly
linked to the manufacturing process itself [6] and that the
costs for product and process complexity in manufacturing
companies can make up to 25 % of the total costs. [7]
The existing approaches for the estimation of the
complexity related costs used in these studies are usually
based on general complexity indicators and confirm the
general perception of a direct connection between variety-
induced complexity, loss of efficiency and increase of
manufacturing costs. The increase in complexity related costs
leads to a decrease of competitiveness and results in a
proliferation of product variety in order to increase market
shares. This cycle is known as the complexity trap and
represents a major challenge for manufacturing companies
nowadays (Figure 2).
Fig. 2. Complexity trap of multi-variant production in today’s
manufacturing systems [8].
To overcome this cycle it is necessary to develop
conceptual approaches which focus on the establishment of
transparency in order to identify and consequently minimize
the loss of ecological and economic efficiency. This becomes
even more important since the existing approaches do not
provide a specific guideline to evaluate the economic and
ecological impact of product variety induced complexity on a
manufacturing system process level. [2]
2. Conceptual basis of the simulation-based framework
The simulation-based framework presented in this paper
describes the entire manufacturing system as a sequence of
state-based modules with particular inputs and outputs. This
approach enables the modeling on an operational level and
allows the continuously consideration of product variety and
its economic and ecological effects throughout each process
step of the manufacturing system. This form of segmentation
of the different process steps of a manufacturing system with
multi-variant production was chosen to improve the overall
transparency and to simulate different aspects of the
considered manufacturing system.
The main components of the simulations-based framework
are shown in Figure 3.
Fig. 3. Main components of the presented simulation-based framework.
The focus of the final evaluation is the identification and
assessment of influencing variables regarding the
improvement of ecological and economic efficiency. This is
achieved through the described modeling technique of the
manufacturing systems within the simulation-based
framework as well as through the combination of multiple set
of scenarios with a systematic design of experiments
concerning the influencing lean and green optimization
3. System understanding and cause-based allocation
Description of the manufacturing system
In regard with the aims of this paper, the improvement of
transparency about ecological and economic inefficiencies in
multi variant production, the manufacturing system as a whole
has to be fragmented into functional elements. These elements
(e.g. manufacturing or transportation processes) can be
generalized as process modules [9], which are separated in
production process modules and logistics process modules
(Figure 4). Each process module is characterized by several
input (e.g. raw material, parts and energy) and output variables
(e.g. products and waste) and aims to transform an input into a
specific output. Each process module can furthermore be
subdivided into two factors of production (human and
equipment) [10] [11] that perform the transformation
process. The factor of production ‘human’ includes e.g.
logistics employees or machine operators whereas means of
conveyance and production machinery are part of
‘equipment’. In a manufacturing system multiple production
and logistics process modules are coupled to a process chain,
or, in case of a more complex system, to parallel process
chains or network structures with multiple input and output
Andreas Kruse et al. / Procedia CIRP 26 ( 2015 ) 179 – 184
connections. [9] This structure represents the actual
production sequence of the manufacturing system.
Fig. 4. Process modules of a manufacturing system.
In the course of the transformation process a process
module is characterized by different operating states. A
production process module can assume the operating states
Ramp-up, Setup, Processing, Waiting, Shutdown, a logistics
process module the operating states Buffering and
Transportation. In the context of batch production of variants a
distinction has to be drawn between activity quantity induced
(aqi) and activity quantity neutral (aqn) states. The batch size
determines the duration in which a process module is in an
activity quantity induced state (e.g. Processing) whereas in the
other case the duration is independent of the batch size.
Table 1 provides an overview of the operating states and the
Table 1. Operating states of process modules and classification.
Process module type
Operating state
3.2. State based energy consumption of process modules
The energy consumption of a process module depends on
the duration and frequency a process module is in the different
operating states, and each operating state is characterized by a
specific energy consumption. [12] [13] Operating states can be
generally separated into value adding states (Processing) and
non-value adding states (all other operating states). [9] The
operating states of a logistics process module are considered
non-value adding, too, as they are classified as ‘muda’ in lean
philosophy (they do not add value to a product) and merely
have a supportive character for the transformation process.
In the context of inefficiencies in manufacturing systems,
other non-value adding operations, e.g. production of process
scrap and setup scrap, have to be considered as well. For the
differentiation whether an operation is value adding or non-
value adding, two aspects have to be taken into account: The
operating state and the output of a process module. By this
means, inefficiencies caused by process scrap, reworking,
failures etc. can be included in the analyses. Only the
production of good parts or products that leave the
manufacturing systems and can be sold to a customer is
considered value adding. [9] Consequentially, processing
operations of a production process module, which result in
scrap, are classified non-value adding as well. The same
applies to reworking and to setup scrap, which increases with
more frequent setup operations caused by higher product
variety and smaller batch sizes.
Figure 5 illustrates the described approach of considering
operating states and the output of a process module on the
example of a production process module. As the energy
consumption of a production process module is mainly
determined by the production machinery (respectively by the
means of conveyance in case of a logistics process module)
the ecological evaluation focuses on the factor of production
Fig. 5. Two perspectives: Operating states and Output of process modules.
State based costs of process modules
Similarly, costs in a manufacturing system can be allocated
according to their actual cause for each process module and
operating state. Therefore, the same state based logic as
described above for energy consumption is applied here as
well. A specific hourly rate is assigned to each operating state,
whereas a distinction is made between the hourly rate for
182 Andreas Kruse et al. / Procedia CIRP 26 ( 2015 ) 179 – 184
employees (factor of production ‘human’) and the machine
hour rate (‘equipment’).
The specific hourly rates can vary for different operating
states, e.g. if setup operations are performed by specialized
employees with a higher hourly rate compared to the machine
operator or if machine failures (operating state Waiting) have
to be rectified by highly qualified (and therefore more
expensive) maintenance staff. The same applies to the
machine hour rates. During the operating state processing the
machine hour rates can be higher e.g. due to additional tool
costs. Through the knowledge of the state-based hourly rates
for ‘human’ and ‘equipment’ the costs that are caused for the
production of a specific production program in a process
module can be calculated by multiplication of the durations a
process module is in the operating states and the specific
hourly rates.
The same logic applies for the Transportation state of a
logistics process module whereas the costs for Buffering are
calculated by the present value of a product at this specific
process module (material costs plus added value) and a rate
for capital being tied up.
As already mentioned before, the output of a process
module has to be considered as well in order to take other
inefficiencies like scrap or reworking into account. As a result
of this, material costs of process scrap and setup scrap
(including the processing costs of upstream process modules)
are classified non-value adding. The only value adding costs
are those that are caused for the processing of a good part,
which can be sold to an internal or external customer. The
costs of the material for good parts are treated as a transit
Improved transparency in multi-variant production by
state-based assessment
By using the above described state-based logic, the
cumulated energy consumption and the cumulated costs for
the production of a specific production program within a
manufacturing system can be simultaneously calculated.
Therefore the material (raw material, parts, products), which
passes through the process modules of the manufacturing
system (according to the production sequence and production
program) serves as a transit item. Every time a transit item
passes through a process module the energy consumption
respectively the costs that were caused are allocated to this
Moreover, in addition to an evaluation of the overall energy
consumption and costs of the manufacturing system, a detailed
assessment of single process modules as well as of single
product variants is possible (Figure 6). Thus it is possible to
evaluate a particular production program in the following
three possible dimensions at the same time:
Manufacturing system
Process module
Product variant
Fig. 6. Evaluation dimensions.
Furthermore, the cumulative amount of energy
consumption and costs can be separated into value adding and
non value adding shares, which eases a target-oriented
analysis for inefficiencies caused by variety.
In order to being able to quantitatively assess the effects of
variety and variety-related influencing variables, the
evaluation logic is applied to different scenarios, which differ
from the production program (e.g. number of product variants,
lot sizing, sequencing). Besides other influencing variables
(e.g. setup times, lead times, scrap rates, machine availability),
which might be potential starting points for green and/or lean
optimization, are varied.
By using the design of experiment (DOE) methodology, the
effects of variety (e.g. customer-driven increase in product
variants) and of the influencing variables (e.g. optimization of
setup times of a specific process module) can be calculated
and influencing variables can be prioritized. Furthermore the
system behavior concerning the variety and the variety-
induced influencing variables of the manufacturing system can
be described by transfer functions by this means.
4. Implementation in a simulation environment
In order to being able to handle the calculation efforts for
various scenarios respectively configurations of the
production program and the manufacturing system, the
described framework was modeled and prototypically
implemented in the discrete event simulation software
Tecnomatix Plant Simulation (Figure 7). The use of
simulation software offers several benefits and analysis
Existing operating states of the simulation environment can
be adapted for the evaluation of energy consumption and
The discrete event approach enables the consideration and
detailed evaluation of different general aspects for the
entire manufacturing system but it is also possible to trace
every single object or a specific group of objects (e.g.
product variant or a production batch).
Influencing factors (e.g. setup times, scrap rates, machine
failures) can easily and automatically be alternated by the
use of the experiment manager module.
Andreas Kruse et al. / Procedia CIRP 26 ( 2015 ) 179 – 184
The modelled process modules (logistic process module
and production process module) as well as the implemented
evaluation logic for energy consumption and costs can easily
be transferred to other manufacturing systems due to a
modular structure. For the modelling of another
manufacturing system the process modules merely have to be
coupled according to the actual production sequence of this
system. Specific process data and information about the
production program, of course, has to be gathered respectively
implemented into the simulation environment for each
manufacturing system to be analyzed, which can be a quite
time-consuming activity [14] depending on the data
In order to minimize the number of simulation runs, the
experiments should be performed in two steps: Firstly a
screening is conducted with a wide variety of influencing
factors that are examined, e.g. by the use of fractional
factorial screening designs. After a reduction of the
influencing factors, secondly, the remaining influencing
factors and their contribution to energy consumption and costs
are examined in more detail. Therefore experimental designs
for nonlinear correlations (e.g. response surface designs) are
applied, in order to get transparency about the influencing
factors and their interdependencies. The experimental designs
can be generated in DOE or statistic software suites like
Minitab. Afterwards the designs can be directly imported to
the experiment manager module of the simulation
5. Conclusion and Outlook
This paper presented a simulation-based framework which
increases the ecological and economic transparency in
manufacturing systems for multi-variant production. The
framework focuses on the quantification of energy
consumption and costs in a manufacturing system. In order to
identify inefficiencies, energy consumption and costs are
allocated according to their actual cause.
The framework was prototypically implemented in the
discrete event simulation software Tecnomatix Plant
Simulation, which is common in science and industry. By the
use of the design of experiments methodology, the
implementation allows the detailed examination of different
scenarios through the systematic alternation of influencing
variables. Furthermore it is possible to trace single objects
(e.g. products) and measure their specific ecological and
economic contribution to single process modules as well as to
the entire manufacturing system. The achieved transparency
about inefficiencies in a manufacturing system as well as the
knowledge about the effects of the alternation of influencing
factors (e.g. setup times or scrap rates of a specific process
module) provides a starting point for target-oriented lean and
green optimization.
As part of further research the presented simulation-based
framework should be applied to different manufacturing
systems of different branches and sizes in order to get a broad
Fig. 7: Prototypical implementation in the simulation environment Tecnomatix Plant Simulation (screenshot).
184 Andreas Kruse et al. / Procedia CIRP 26 ( 2015 ) 179 – 184
insight about the ecological and economic effects of variety
and possible influencing factors for target
-oriented lean and
reen optimization.
Furthermore, the efforts for data collection (e.g. energy
nsumption of specific operating states) could be
reduced by using production machinery with integrated
ensors for measuring the energy consumption and by
creating an interface between ERP-/MDC-Systems and the
mulation environment
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... Although DES is commonly used in the manufacturing and supply chain studies, Brandenburg et al. (2014) report in their literature review that DES is hardly applied in sustainability studies. However, a few authors only benefited from the simulations of scenarios that DES provides to study the best L&G condition in different production systems (Chandrakumar et al., 2016;Diaz-Elsayed et al., 2013;Golzarpoor and Gonzalez, 2013;Greinacher et al., 2015;Kruse et al., 2015;Miller et al., 2010;Paju et al., 2010;Ugarte et al., 2016). ...
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Manufacturing companies have been forced to rethink their management and operations strategies due to the growing demand for greener products and services. To support these initiatives, the Lean and Green (L&G) approach has gained increasing attention from academic and industry. However, according to the literature in the field not all Lean practice has a positive environmental impact. In addition, there is a lack of empirical research on L&G to understand the catalyzers and trade-offs between both paradigms. Therefore, this study attempts to fill this gap by assessing the applicability of Lean Manufacturing (LM) tools and investigate how Lean impacts Green performance in a real-world company case. This research paper assesses the integration of Lean & Green in the job shop of a Brazilian company and is supported by a discrete-event simulation (DES) model. Green analysis regarding water, energy and raw material consumption are performed for every single operation. Environmental and production variables are simultaneously evaluated, and the environmental impacts resulting from the implementation of the Lean practices and tools, such as Kanban, are also discussed. While the outcomes of the research reveal a positive correlation between Lean & Green on performance indicators, they also substantiate a trade-off on water consumption. The empirical study along with the simulation-based analysis demonstrate that generalizations such as “Lean is Green” must be carefully revisited and also emphasize the importance of Lean & Green relationship in the improvement of manufacturing processes.
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... In doing so of a slight reduction of the amount of goods produced is approved. Kruse et al. [14] provide a framework for improving the ecological (and economic) transparency in multi variant production based on a simulative approach but there are still no practical use cases for this approach so far. ...
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Full-text available
Two influences are decisive in today's industrial production: a significant increase of variants and an upwards trend of energy costs. Therefore it is necessary to clarify, if and in which dimensions the energy consumption is influenced by variety and how it can be handled. For this reason a simulation-based model is developed, which allows the examination of the effects of variety on energy consumption (electricity, compressed air, gas). The benefits of this model are that interdependencies within the production can be considered and that it is possible to quantify the potential for improvement. Starting points to increase the energy efficiency can be identified. The model was implemented in a discrete event simulation environment and verified in a case study in the metalworking industry. The results show, that an efficient variant management allows a significant improvement of the energy efficiency without compromising in matters of produced variants.
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The current research studies a flexible die-casting plant in order to increase productivity pondering investment risks in case of placing new components in the production line. Digital models were developed by means of a Plant Simulation software package. Modeling tools are helpful to represent the movements and functions of the production line components and also to identify the bottlenecks in the production line which improves the decision-making process to increase the productive efficiency. Several numerical models were evaluated; findings suggest significant reductions in the production cycle times which span from 1.13 to 65.25% at the best scenario. The most drastic change in the simulations was to add a new robot to the system improving the process flow. Moreover, the results suggested that the productivity increased for more than 300%, mainly due to the synchronization of the flexible plant elements.
... The total energy consumption and the total load profile are merely the consolidation of the energy consumption and the power load profiles of single machines respectively. Thereby, the manufacturing system has to be fragmented into functional elements like processing machines (Kruse et al., 2015a). ...
Any manufacturing activity creates product and energy flows whose management provides an overview of how a manufacturing system interacts with its environment. Recently, simultaneous management of these flows using discrete event simulation is increasingly being discussed in literature. However, the reliability of such projects basically depends on the method used to model energy consumption and quality of input data. Mindful of these challenges, this work reviews some existing energy related discrete-event simulation projects and discuss the issue of energy related input data management. Against this background, it proposes an operation-based concept to model the energy consumption of a manufacturing system and put an emphasis on how the load profiles could be acquired, processed and introduced into the simulation model, depending on its variabilities and the complexity of the associated energy-using equipment. The main proposal of this article is to introduce the possibility to model complex energy consumption behaviours of various pieces of equipment using stochastic distributions. The case of an injection moulding system is presented to validate the proposed method and to evaluate its accuracy. A comparison with actual measurements of energy consumption shows a deviation of less than 3% of the discrete-event simulation model.
... In these cases, one speaks of dynamic complexity. The differentiation in a structural component and dynamic component of complexity is agreed upon within the scientific community and can be found in various disciplines e. g. the analysis of remanufacturing [27], product complexity [28], ecological transparency [29], production logistics [30], effects on organizations [31] and effects on companies [24,32]. Derived from this general understanding and based on the nature of system dynamics, a classification of systems in terms of complexity and potential applications of system dynamics can be evaluated (see Fig. 2 ...
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Lean principles are the central core of many industrial companies for improving their production system. In order to be able to optimize their manufacturing and logistics processes, companies have to choose the most suitable lean principles to solve problems or to reach their target state. To solve this decision problem, it is important to identify objectives based on values of the decision-maker and to determine effects of lean principles and objectives. This paper presents a value-focused thinking driven identification of objectives and a system dynamics approach for understanding the interdependencies and dynamics of lean principles and objectives. This provides a transparency and better understanding for these interactions for the decision-maker in the decision-making process. Based on this knowledge, the most effective lean principles for the design of the production system can be chosen and successfully applied.
Lean-Green is a concept that associates value aggregation and efficiency in operational and environmental terms. This concept arises as a corollary effect of companies’ challenges for rethinking their goals and strategies in order to add more value while contributing to social equity and prevent environmental burdens. The eco-efficiency concept translates the idea of “creating more with less”, in order to reduce ecological impacts and resource intensity. Lean is a strategy that encompasses a wide variety of management practices, in an integrated system, to streamline business processes, minimize waste and improve financial performance. When Lean and Green are associated in the so called Lean-Green link, many of those savings also result in environmental benefits. This paper reports the ongoing effort to provide models for the Lean-Green integration. A comparative analysis of the few models identified is provided, and the most frequently used KPI acknowledged. Findings show the existence of a limited number of Lean-Green models, published from 2012, denoting a narrow breadth of dissemination. The fundamental goal of Lean-Green models was mostly found to be related to improve the systems productivity while reducing the environmental impacts. Integrate Lean-Green initiatives constitute a valuable approach to sustain and endure a greener industrial activity.
A literature review was conducted aiming at investigating the use of Sustainability, Lean, Green and eco-efficiency concepts, as well as meaningful combinations of those, on the field of Production and Operations Management. The study reports on the scientific papers published in all major journals in the field over the period 2001–2015. A set of 83 papers from 40 journals were selected for further analyzes, aiming at uncovering the existing level of awareness and use of the synergic and symbiotic relationship between Lean Manufacturing and Green Production. The findings show that a modest share of papers, about 30 %, explicitly recognize the Lean-Green joint approach. The same study testifies a clear growth pattern, which is patently reinforced in the last two and a half years, on the number of papers that behold a combined approach towards more efficient and cleaner production activities. The research has highlighted that the Lean-Green link does, in fact, exist and is gaining momentum, but requires further reinforcements from the scientific community, as well as from the companies, to deliver excelled and environmentally sound production systems.
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This book presents the results of several years’ research work on how to characterize complexity in engineering design with a specific regard to dependency modeling. The 52 complexity metrics that are presented show different facets of how complexity takes shape in design processes. The metrics are supported by a modeling method and a measurement framework to employ the metrics in a goal-oriented manner. The detailed description of all involved metrics and models makes it possible to apply the analysis approach to common process modeling methodologies. Three case studies from automotive process management illustrate the application to facilitate the transfer to other cases in an industrial context. The comprehensive appendix supplies additional details and checklists for structural analysis to generate a complete overview of current means of structural analysis.
Dieses Buch führt in die Simulation diskreter Prozesse ein. Typische Anwendungsbeispiele sind Fertigungsprozesse, Strassenverkehrssituationen, Menschenströme und Geschäftsprozesse. Der Autor erklärt die ereignisorientierte Simulation sowie deren programmiertechnische Realisierung. Anhand vieler Beispiele wird die Lösung spezieller Detailprobleme mit grundlegendem Charakter gezeigt. Die durch die Optimierung von Geschäftsprozessen entstandenen neuen Herausforderungen bezüglich der Simulation bilden einen Schwerpunkt der Darstellung, ein separater Teil ist den theoretischen Grundlagen gewidmet. Lernziel des Buches ist die Fähigkeit, diskrete Prozesse mit den passenden Mitteln zu modellieren und dazu eine Simulationssoftware zu entwerfen bzw. die Mechanismen bestehender Simulationssoftware so zu begreifen, dass sie gewinnbringend eingesetzt werden kann. Die wichtigen Programmstrukturen sind in Form von Pseudocode dargestellt und dadurch völlig unabhängig von einer Programmiersprache oder Spezialsoftware. Damit ist das Buch für eine breite Leserschaft geeignet.
The concrete tools manufacturing enterprises need to thrive in today's global environment. For a manufacturing enterprise to succeed in this current volatile economic environment, a revolution is needed in restructuring its three main components: product design, manufacturing, and business model. The Global Manufacturing Revolution is the first book to focus on these issues. Based on the author's long-standing course work at the University of Michigan, this unique volume proposes new technologies and new business strategies that can increase an enterprise's speed of responsiveness to volatile markets, as well as enhance the integration of its own engineering and business. Introduced here are innovations to the entire manufacturing culture: An original approach to the analysis of manufacturing paradigms. Suggested methods for developing creativity in product design. A quantitative analysis of manufacturing system configurations. A new manufacturing "reconfigurable" paradigm, in which the speed of responsiveness is the prime business goal. An original approach to using information technology for workforce empowerment. The book also offers analysis and original models of previous manufacturing paradigms' technical and business dimensions-including mass production and mass customization-in order to fully explain the current revolution in global manufacturing enterprises. In addition, 200 original illustrations and pictures help to clarify the topics. Globalization is creating both opportunities and challenges for companies that manufacture durable goods. The tools, theories, and case studies in this volume will be invaluable to engineers pursuing leadership careers in the manufacturing industry, as well as to leaders of global enterprises and business students who are motivated to lead manufacturing enterprises and ensure their growth.
Das Buch bietet eine Einführung in die Organisation der Produktion in Industriebetrieben. Es behandelt Fragen der Produktionsziele, der Wirtschaftlichkeit, der Aufgaben der an der Produktion beteiligten Funktionsbereiche sowie ganzheitliche Konzepte der modernen Produktion und Methoden der Produktionsoptimierung. Es liefert ein Grundverständnis für die Funktionsweise industriell arbeitender Unternehmen und für die Konzepte der Planung, des Betriebs und der Leistungsoptimierung von Produktionsbetrieben. Das Buch ermöglicht einen schnellen Einstieg in die Thematik und ist zu empfehlen für Studierende an Universitäten und Fachhochschulen sowie für Quereinsteiger in die Unternehmenspraxis.
Assessing the impact of external and internal complexity on production plant performance
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