Management Approach for Resource-Productive Operations

273 Pages·2019·4.38 MB·English

by  Markus Hammer

Industrial Management
Markus Hammer
Management Approach
for Resource-Productive
Operations
Design of a Time-Based
and Analytics-Supported
Methodology Grounded in Six Sigma
Industrial Management
Series editor
H. Biedermann, Leoben, Austria
C. Ramsauer, Graz, Austria
W. Sihn, Wien, Austria
Die Schriftenreihe behandelt Themen des gesamten Lebenszyklus von Pro­
dukten aus und in der produzierenden Industrie. Damit umfasst das Industrial
Management die Produktinnovation, die Produktionsüberleitung, von der eigent­
lichen Produktion und Auslieferung der industriell produzierten Erzeugnisse bis
hin zum Recycling. Die Beherrschung der neuen Themen wie Digitalisierung,
Agilität, Interdisziplinarität, demographischer Wandel und Offenheit beein­
flussen den Erfolg im Produktentstehungsprozess. In der Produktentwicklung
stehen modernste technische Methoden zur Verfügung gepaart mit Rapid­Proto­
typing­Maschinen, um beispielsweise in FabLabs und anderen öffentlichen oder
privaten Makerspaces Prototypen zu bauen. Das Anlaufmanagement beschäftigt
sich damit, wie man rasch Produkte mit maximaler Kapazitätsauslastung produ­
zieren kann. Die industrielle Produktion benötigt meist hochverfügbare Maschi­
nen mit hohem Investitionsbedarf. Das strategisch­operative Asset Management
spielt dabei eine entscheidende Rolle. Aus ökonomisch­ökologischer Sicht hat
die Ressourceneffizienz (insbesondere Material und Energie) hohe Bedeutung.
Die in der Industrie beschäftigten Mitarbeiter bedürfen wegen des hohen Inno­
vationsgrades weiterer Befähigung und benötigen ergonomisch gestaltete Arbeits­
plätze. Die Kostenposition wird nicht zuletzt von der Wahl des Industriestandorts
und der richtigen Fabriksplanung beeinflusst. Die Digitalisierung betrifft die ge­
samte Wertschöpfungskette und ist die Voraussetzung für neue Geschäftsmodelle
und ein agiles Unternehmen. Das Industrial Management entwickelt zu all diesen
Themen Theorien, Konzepte, Modelle, Methoden und praxisrelevante Instru­
mente.
Reihe herausgegeben von:
Prof. Dr. Hubert Biedermann Prof. Dr. Wilfried Sihn
Montanuniversität Leoben Technische Universität Wien
Österreich Österreich
Prof. Dr. Christian Ramsauer
Technische Universität Graz
Österreich
More information about this series at http://www.springer.com/series/16058
Markus Hammer
Management Approach
for Resource-Productive
Operations
Design of a Time-Based
and Analytics-Supported
Methodology Grounded in Six Sigma
With a foreword by
Univ.-Prof. Dipl.-Ing. Dr. techn. Christian Ramsauer
and Prof. Dipl.-Wirt.-Inf. Dr. Christian Terwiesch
Markus Hammer
Graz, Austria
Dissertation Graz University of Technology, Austria, 2017
ISSN 2523­3866 ISSN 2523­3874 (electronic)
Industrial Management
ISBN 978­3­658­22938­2 ISBN 978­3­658­22939­9 (eBook)
https://doi.org/10.1007/978­3­658­22939­9
Library of Congress Control Number: 2018949896
Springer Gabler
© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
or information storage and retrieval, electronic adaptation, computer software, or by similar or
dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publication does not imply, even in the absence of a specific statement, that such names are exempt
from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors, and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authors or the editors give a warranty, express or implied, with respect to the material contained
herein or for any errors or omissions that may have been made. The publisher remains neutral with
regard to jurisdictional claims in published maps and institutional affiliations.
Printed on acid­free paper
This Springer Gabler imprint is published by the registered company Springer Fachmedien Wiesbaden
GmbH part of Springer Nature
The registered company address is: Abraham­Lincoln­Str. 46, 65189 Wiesbaden, Germany
Foreword
Academics often spend too much time developing complex mathematical models
while failing to recognize that business leaders struggle with very different deci-
sions. One of these under-researched decisions that we see many business leaders
facing, relates to their search for management approaches that help them connect
their operational decision making with their corporate financial performance.
The research work of Mr. Hammer presents a unique implementation meth-
odology of a management approach that aims to optimize profits using a financial,
time-based target operating parameter, e.g., profit per hour. By connecting the de-
cision making with real time data, this work provides decision makers with a sim-
ple yard stick they can use to run their operations.
The research of Mr. Hammer is clearly conceptual and application focused
and we view it as an important contribution on how to implement performance
management systems. The work also touches on related topics that, in our view,
have received insufficient attention from the performance management literature,
including Big Data, Data Analytics, as well as Industry 4.0.
The abundance of data that modern plant managers in theory have access to
is in sharp contrast to the sophistication of their current planning methodologies.
This deficit is especially becoming a challenge in times of increasing volatility and
complexity.
Throughout the document, Mr. Hammer uses relevant sources of literature
and builds upon the current body of international publications in the field. Through
his careful referencing and citations, Mr. Hammer demonstrates a high degree of
academic integrity. He only claims credit for what he contributes himself, while
being generous pointing to the contributions of those who went before him.
The work submitted by Mr. Hammer provides a significant contribution to
the field of industrial operations management.
Univ.-Prof. Dipl.-Ing. Dr.techn. Christian Ramsauer
Institute of Innovation and Industrial Management
Prof. Dipl.-Wirt.-Inf. Dr. Christian Terwiesch
The Wharton School of the University of Pennsylvania
Acknowledgements
I would like to thank my supervisor Prof. Dr. Christian Ramsauer for being a role
model and for his support and trust. I thank Prof. Dr. Christian Terwiesch for being
my second assessor and early thought partner on pursuing a PhD. Furthermore, I
would like to acknowledge Prof. Dr. Stefan Vorbach, for his advice and for being
the head of the examination board.
Being part of the team at the Institute for Innovation and Industrial Manage-
ment has been a fantastic experience. Thank you Alex, Christian, Christoph,
Daniela, Elma, Hans, Hugo, Jasmin, Kerstin, Mario, Martin, Matthias, Nils,
Patrick, Philipp, Sascha, Silke, Stefan, Thomas, et al. I would also like to thank
my work colleagues, in particular, Joris, Ken, Olivier, and Robert and the three
industrial companies who agreed to contribute with the practical cases.
I owe deep gratitude to my family, my parents Roswitha and Helmut, my
loving wife Raquel, and my two sons Lucas and David, to whom I dedicate this
work.
Dipl.-Ing. Dr. techn. Markus Hammer
Abstract
The objective of this doctoral thesis is to investigate a time-based and analytics-
supported operations management approach and develop a structured implemen-
tation methodology.
In the context of digitization, the Industry 4.0 and the Industrial Internet of
Things the amount of available technology and data is continuously rising. At the
same time increasing volatility, uncertainty and complexity demand making
operations decisions in ever shorter intervals trending towards real-time.
This research explores five perspectives, the needs of industry, in particular
manufacturing in process industries; the impact of digitization, with focus on Big
Data and analytics; the management of operations through time-based perfor-
mance metrics; how operations improvement methods and advanced process
control help achieve resource-productive operations; and learning from practice
based on two empirical case studies.
Next an implementation methodology for a time-based and analytics-
supported operations management approach is conceived, explained and tested.
The methodology is structured around five phases known from Six Sigma: Define,
Measure, Analyze, Improve, Control and contains 17 specific steps which are
explained and subsequently validated in an industrial case study.
This thesis discusses the criteria when this approach is meaningful, for
example, situations when trade-off decisions between conflicting targets are
required, time is the constraint, close to real time decision making is required,
cumulative profit maximization is the desired long term goal, and where invested
capital and/or resource intensity is high.
Pre-conditions for implementations are stated, for example, infrastructure
such as sensors, meters, or data storage; data to compute the metric; access to
advanced algorithms required to solve for profit per hour as a target function; an
implementation process; and the required skills.
It can be concluded that a time-based and analytics-supported operations
management approach for maximizing profits is meaningful if the pre-conditions
are met. The final case study proves that the developed implementation
methodology works in practice.
Contents
List of Figures ................................................................................................... XV
List of Tables ................................................................................................... XIX
List of Equations .............................................................................................. XXI
List of Abbreviations .................................................................................... XXIII
1 Introduction ..................................................................................................... 1
1.1 Initial Situation and Motivation .............................................................. 1
1.2 Objective of the Research ....................................................................... 2
1.3 Research Questions ................................................................................. 4
1.4 Research Design and Structure ............................................................... 4
1.5 Thesis Outline ......................................................................................... 8
2 Industry Perspective: Challenges in Manufacturing in Process Industries .... 11
2.1 Megatrends in Industry ......................................................................... 11
2.2 Manufacturing Industries ...................................................................... 18
2.3 Process Industries ................................................................................. 22
2.4 Summary: Manufacturing in Process Industries ................................... 25
3 Digitization Perspective: Impact of Digital Technologies in Manu-
facturing ........................................................................................................ 27
3.1 Digitization ........................................................................................... 27
3.2 Industry 4.0 ........................................................................................... 32
3.3 Industrial Internet of Things ................................................................. 40
3.4 Big Data ................................................................................................ 48
3.5 Advanced Analytics .............................................................................. 56
3.6 Summary: Impact of Digitization on Manufacturing ............................ 67
4 Management Perspective: Performance Opportunities with Decision-
Support Systems ............................................................................................ 69
4.1 Decision Making ................................................................................... 69
4.2 Performance Measures .......................................................................... 75
4.2.1 Return on Invested Capital ........................................................ 79
4.2.2 Time-Based Performance Measures .......................................... 82
4.2.3 Current State, Challenges and Opportunities ............................. 86
XII Contents
4.3 Performance Measurement and Management Systems......................... 87
4.4 Decision Support Systems .................................................................... 91
4.5 Summary: Performance Opportunities with Decision-Support
Systems ................................................................................................. 94
5 Operations Perspective: Achieving Resource-Productive Operations .......... 97
5.1 Resource-Productive Operations .......................................................... 97
5.2 Lean .................................................................................................... 108
5.3 Six Sigma ............................................................................................ 112
5.4 Theory of Constraints ......................................................................... 117
5.5 Agility ................................................................................................. 119
5.6 Advanced Process Control .................................................................. 122
5.7 Summary: Achieving Resource-Productive Operations ..................... 136
6 Practice Perspective: Learning from Case Research in the Cement
and Ammonia Industry ................................................................................ 139
6.1 Cement ................................................................................................ 139
6.1.1 Context .................................................................................... 140
6.1.2 Application .............................................................................. 140
6.1.3 Results ..................................................................................... 143
6.2 Ammonia ............................................................................................ 144
6.2.1 Context .................................................................................... 144
6.2.2 Application .............................................................................. 145
6.2.3 Results ..................................................................................... 145
6.3 Summary: Challenges and Opportunities in Practice.......................... 146
7 Interim Conclusion: Scope of Work ............................................................ 147
7.1 Research Gap ...................................................................................... 147
7.2 Delimitation of Research Focus and Summary of Requirements ....... 148
8 Methodology Conception: Framing a Time-Based and Analytics
Supported Operations Management Approach ........................................... 151
8.1 Concept ............................................................................................... 151
8.1.1 Technology .............................................................................. 152
8.1.2 Economics ............................................................................... 154
8.2 Classification ...................................................................................... 155
8.2.1 Application Oriented Theory ................................................... 155
8.2.2 System Theory Classification .................................................. 156
8.2.3 Model Theory Classification ................................................... 159
8.2.4 Summary of Relevance............................................................ 160
8.3 Applicability ....................................................................................... 160