Towards better decision support during planned maintenance in the Oxygen Steel Factory 2 An analysis of the effects of maintenance on steel ladle routing

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Towards better decision support during planned maintenance

in the Oxygen Steel Factory 2

An analysis of the effects of maintenance on steel ladle routing

By

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Towards better decision support during planned maintenance

in the Oxygen Steel Factory 2

An analysis of the effects of maintenance on steel ladle routing

By G.M.A. Reinders

Restricted distribution only

Date Delft, July 3, 2015

Student number 1503340

Report number 2015.TIL.7949

Master Transport, Infrastructure and Logistics

Specialisation Engineering

Graduation committee

Prof. dr. ir. G. Lodewijks (Chairman, TU Delft, Faculty of 3mE) Ir. M.B. Duinkerken (1st_{supervisor, TU Delft, Faculty of 3mE)}

Dr. J. Rezaei (2nd_{supervisor, TU Delft, Faculty of TPM)}

S. van der Wal (Supervisor, Tata Steel) J. van Dalen (Supervisor, Tata Steel)

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Preface

With this report, my Master Transport, Infrastructure and Logistics at the Delft University of Technology is finalized.

Mastering the area of transport, infrastructure and logistics is a challenge in itself. This challenge is even bigger in case an initially more creative bachelor, like Industrial Design Engineering, is the basis for this master. Nevertheless, it turned out to be a fantastic learning experience and a joy to deepen my knowledge of the above-mentioned field. Self-evidently mastering this area of expertise can only be done with the right teachers and coaches.

First of all, I would like to thank my graduation committee for their input, support and feedback. Also special thanks to Erik Ulijn, who has spent hours to help me with the simulation model and the Tecnomatix tool. Secondly, I would like to thank Tata Steel IJmuiden for the challenging thesis topic and all the support during the past ten months. Special thanks also to my roommates and neighbours at Tata Steel, Rob Plug, Paul Boon and Jaap van den Born for their input and the joyful coffee breaks. Last but not least, I would like to thank family and friends for their cheers and encouragements.

I sincerely hope that you will enjoy the reading of this thesis as much as I enjoyed carrying out the underlying research and the writing of this report.

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Abbreviations

English Dutch

A-D test Anderson-Darling test AHP Analytical Hierarchy Process BF Blast Furnace

BWM Best Worst Method

CGM Slab casters Continu Giet Machines

CH Converter bay Converter Hal

CT Cross transport

CV Converter

DES Discrete Event Simulation

DS Ladle drying plant Droog Standen

DSP Direct Sheet Plant Giet Wals Installatie, GWI

GH Casting bay Giet Hal

GK Casting crane Giet Kraan

K-S test Kolmogorov-Smirnoff test KPI Key Performance Indicator

KS Tilting device Kiep Stoel

KTO Quality management and technology development Kwaliteitsbeheer en Technologie Ontwikkeling

LH Loading bay Laad Hal

LMB Last Minute Burner

MCDA Multi Criteria Decision aid Analysis MCDM Multi Criteria Decision making Method OSF2 Oxy Steel Factory 2

PBI Steel refining facilities (incl. secondary steel making) Pan Behandelings Installatie

PO Ladle furnace Pan Oven

PRB Production management PRoductie Beheer

PTC Project and Technical Consultancy

SS Stirring station Spoel Stand

TBO Technical management and development Technisch Beheer en Ontwikkeling

TH Intermediate bay Tussen Hal

TSTH Tata Steel Thailand

VPBI Vacuum degassing installation Vacuum Pan Behandelings Installatie

WHS (pre) heating station Warm Houd Stand

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Executive summary

Tata Steel IJmuiden (Tata Steel) is the successor of Koninklijke Nederlandsche Hoogovens en Staalfabrieken NV. Tata Steel is now part of Tata Steel Group, Indias largest industrial group producing in excess of 20 million tons of crude steel annually. Tata Steel produces seven million tons of high quality coated steel per annum. In chapter 1the Tata Steel factory layout for the Oxygen Steel Factory 2 (OSF2) and process steps are illustrated. The key issue for this research is the logistical friction between (planned) maintenance and production continuity. This logistical challenge is projected on Tata Steels focal points. The chapter concludes with both the goal of this research - to create a scientifically underpinned decision support tool - and the outline of the report.

In order to design the outline for a decision support tool, aiming to investigate the relationship between maintenance and production continuity, the study of the relevant literature is reported in chapter 2. It is evident that varying scientific approaches have been carried out to study steel production factories specifically, and maintenance versus production continuity in general. Roughly three different approaches are distinguished: (1) Integral production process improvement methods, represented by amongst others lean manufacturing, theory of constraints, quick response manufacturing and six sigma, (2) specific production process analysis and improvements methods varying from model- and simulation based approaches to more descriptive research approaches, (3) project management approaches geared towards operational effectiveness.

Since significant historical data from Tata Steel is available and simulation - in particular discrete-event simulation (DES) – has been applied to related problems, DES modelling was chosen in this study, to research the Tata Steel case at hand. In addition, distribution fitting was examined, but for the course of this study this element is referred to Appendix 2, as an optional starting point for further study. Finally, this chapter briefly touches upon the Best-Worst Method (BWM), also recently suggested for ranking alternatives, either generated by DES or otherwise.

In order to create a representative model of OSF2, a detailed systems analysis was performed. The findings are being described in chapter 4. After an initial description of all relevant steel production installations (being converters, secondary steelmaking installations, casting machines and direct sheet plant, tilting devices, heating stations and last minute burners) all optional steel ladle routes are mapped out. Since in practice not all theoretical options are considered relevant, the chapter narrows the steel ladle routes to those options that are practically applied. Consecutively, transportation equipment characteristics and transportation times are being discussed. Eventually, the chapter underlines the differences between norm process times and real life process times, the variation in the installations, the redundancy of casting crane 21 and the significant difference between theoretical and real life transportation times.

As described, DES modelling was chosen to mirror the real life situation of OSF2, in detail reported in chapter 5. Firstly a conceptual model is created which is a representation of reality. In essence, every element and every aspect as described in the systems analysis is represented in the conceptual model. Thereafter this conceptual model is implemented in Tecnomatix Plant Simulation. After implementation, reality is simulated to verify and validate the DES model. Also in this research, this phase turned out to be an iterative process with repeating verification, validation and model refinement. Since various transport equipment (i.e. casting cranes) have dynamic characteristics, the applied model is clearly a simplification of the real life situation. Nevertheless, on a higher level of aggregation, experimental output indicates sufficient validation, since overall production volumes are correctly represented in the experiments. In other words, the model represents reality on an aggregated level and could be a stepping-stone towards further research, applying distribution-based dynamics as laid out in Appendix 4.

During planned maintenance, safety areas are taken into account. Some installations in OSF2 are inoperative during maintenance, because of these safety areas. Due to these areas and the inoperability of installations, bottlenecks occur in OSF2. Tilting device 20 becomes a bottleneck, as well as the slag discharge in some other installations. The chapter continues with the generation of alternatives to reduce these bottlenecks, where after the alternatives are ranked for both

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de-bottlenecking the new crane lifting and the assembly of the new crane. Finally, based on the Best-Worst Method (BWM), the best suitable alternatives - being a combination of rescheduled steel ladle maintenance, steel ladle rerouting and crane track extension – were fed back into the DES model and after simulation indicated de-bottlenecking potential as expected.

The conclusions of this study are derived in chapter 6. From a factory management point-of-view, clearly the experience-based suggested alternatives, to de-bottleneck OSF2 in case of maintenance, are confirmed. Scientifically, the combination of DES modelling, BWM alternative ranking and the first steps towards distribution fitting and implementing dynamic characteristics, turned out to be in line with the relevant literature and in compliance with the systems analysis, as executed in this research. Technically spoken, Tecnomatix Plant Simulation offers a useful tool to represent OSF2, though it is expected that the potential of this software package is only fully exploited when all transportation equipment is modelled including their dynamic characteristics. This would imply on the one hand the application of a full statistical distribution rather than means and on the other hand the usage of detailed installation characteristics such as crane collisions, empty displacements and the like.

In many situations, research leads to new insides and hence a clearer view on new challenges. During this study, it has become clear that implementing the dynamic characteristics of the transportation equipment provides a deeper insight in the logistical effects of maintenance. Although a start was made with the modelling of these characteristics, as illustrated in Appendix 4, it is recommended to research this area further. When looking at the steel production process, steel temperature is key in reaching the production of highest quality steel. A second recommendation would therefore be to include temperature development as a function of logistics design and operation. Clearly, this aspect of modelling would enhance the practical applicability of a decision support tool.

The DES model created is a suitable way to model OSF2. However, to benefit from the features included, it is necessary to work continuously with the software. The BWM method is a useful method to rank alternatives. The application of the method can be improved by providing the professionals up-front with quantitative data, rather than having the professionals making a best guess. Professionals sometimes include implicit criteria during the alternative ranking. A first approach is to explicitly state to the professionals that, within the scope of a specific research, no implicit criteria are allowed to be included. A second approach is to invite the professionals to suggest additional criteria, up to the point that no implicit criteria are left. In other words, to ensure all implicit criteria are made explicit and are all included in the method. Clearly a tight managed process while working with the professionals is required to exploit the full potential of BWM.

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Appendix 1. Overview of literature on maintenance management ... A1 Appendix 2. Distribution fitting on real life data of Tata Steel IJmuiden ... A2 Appendix 3. Morphological chart to generate alternatives ... A6 Appendix 4. Towards a dynamic model: Crane and cross-transport characteristics included ... A11

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1 Introduction: Tata Steel, OSF2 facing maintenance issues

In this introduction history and key information (section 1.1), the factory layout and main process steps (section 1.2), problems at hand (section 1.3), Tata Steel IJmuiden’s focus (section 1.4) and goals of this research (section 1.5) are described.

1.1 Background: History and key information of Tata Steel IJmuiden

The Koninklijke Nederlandsche Hoogovens en Staalfabrieken NV was found in 1918, first producing iron and later on steel and aluminium. In 1999 the Dutch steel producer merged with the larger British Steel to create Corus Group steel company. During this Corus-period, the aluminium production assets were sold off to focus on the steel production. In 2007, Corus Group was purchased by the India-based Tata Steel and was renamed Tata Steel Europe in 2010 (TataSteelIJmuidenBV, 2013).

Tata Steel Europe is part of Tata Steel Group, India’s largest industrial group that produces more than 20 million tons of crude steel per year (TataSteel, 2014b). In turn, Tata Steel Group is part of the Tata Group, in which not only a material branch (including Tata Steel) is housed, but also an automotive branch (Tata Motors), tea branch (Tata Global Beverages), consultancy services branch (Tata Consultancy services), watches and jewellery branch (Titan Company) and some other branches. In 2009 Tata Group was 11th_{in the world rankings of most famous companies and in 2012} they were 45th_{in the world rankings of most valuable brands (Velde, 2014).}

Tata Steel IJmuiden produces and delivers a yearly amount of around seven million tons of high quality coated steel on a site of 750 hectare. Steel originated from IJmuiden will mainly be processed in the automotive-, packaging and construction industry (TataSteelIJmuidenBV, 2013) (Kos, 2014). Since 2007 China increased their steel production with around 300 million tons of steel, with a further growth expected to 500 million tons. Where it was impossible for China to supply their steel to Europe, they decided to focus on the Asian market, which is the market for European steel as well (Pesschier, 2014). Also Tata Steel Thailand (TSTH) is affected by the cheaper imports from China (TataSteel, 2013, p. 17). As shown in Tata Steel’s Annual Report of 2012-2013 (2013, pp. 4, 10, 17, 55, 65) the overhang of the economic crisis and the significant overcapacity in regions like Europe and China continues to stress global capacity utilisation and the demand supply balance. The main issue for the steel industry will be the manner in which the overcapacity in China will be handled in the next couple of years.

For Tata Steel as a whole and Tata Steel IJmuiden especially, it is important to produce high quality steel and to have a reliable steel production to stay competitive. This means that installations and equipment have to be renewed before failure occurs. During these replacements the production has to continue, which may lead to tension between the production and maintenance departments (Budai, Dekker, & Nicolai, 2008) and some temporary changes in the production process.

Reliable production, delivering high quality products and renewing installations and equipment before failure are important aspects for almost all industries. Research is done to the effects of renewing installations on the daily production process, in which the Oxygen Steel Factory 2 (OSF2) of Tata Steel IJmuiden will be used as a case study.

1.2 Design: Factory layout and process steps

This research will focus on the routing of steel ladles through the steel factory (OSF2). Steel ladles are ladles in which liquid steel is transported through the factory. A map is shown in Figure 1.1 with the bays and installations important for these steel ladle routes. All these installations can be combined in seven main stops for the steel ladles: The steel ladle routes start at the converters (CV) where they are filled with steel. From there they are transported via cross transports to the Casting Bay 1 or 2 (GH1 or 2) and from there overhead travelling cranes are used to transport them to the steel refining facilities (PBI) where secondary steel making takes place. From the PBI the steel ladles

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are moved to the Direct Sheet Plant (DSP) and to the slab casters (CGM). After the CGM and DSP all ladles are transported by casting crane to the area where the slag is removed and thereafter to the tilting devices (KS) to prepare the steel ladles for a new charge of steel. When these empty ladles have to wait for a new charge, they are heated at the heating station (WHS). When the ladles are needed, they are moved to the cross transport, eventually extra heated at the Last minute Burners (LMB) and then filled with steel again. The last two steps, the heating station and the LMB, are not required and only used when the ladles are not immediately used again. An overview of the sequence of the installations is given in Figure 1.2.

Figure 1.1 The Oxygen Steel Factory 2 with the important bays and installations. All installations within the red square will be inoperative during step 2 of the crane renovation as described in subsection 1.3. The yellow square gives the inoperative installations during step 3 of the renovation process.

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1.3 Problem: Major maintenance versus production continuity

Casting crane 22 (GK22), situated in Casting Bay 2 (GH2), will be renovated between June and December 2016 (see Table 1.1) due to the fact that its end-of-lifecycle is reached. After the renovation of GK22, casting crane GK23 will be replaced with GK25. Because of safety issues part of casting bay 2 has to be taken out of operation during the placement of casting crane 25. The renovation of GK22 and replacement of GK23 will be performed in different steps as shown in Figure 1.3.

Figure 1.3 The logistics behind the renovation of GK22 (Step 4-6) and the replacement of GK23 (step 8) in casting bay 2 (GH2). Casting crane GK25 will temporary replace GK22 during renovation and thereafter it will replace GK23. The focus is on step 2 and 3.

Step 1 is the current situation with casting crane GK23 at the South and casting crane GK22 at the North of GH2, see also Figure 1.1. In step 2 casting crane GK25 is placed at the right side of GK22 and because of safety issues just one transformer can be used which leads to an inoperative casting crane GK23 and a one-crane-operation. In step 3 GK25 is assembled and tested, where after it will temporary replace GK22 as shown in step 4. Then step 5 is the new situation for around six months. See Table 1.1 for the exact planning. After renovating GK22 it is placed back in step 6 and assembled and tested in step 7. In step 8 GK23 is taken out of operation and replaced with GK25, which leads to the future situation with GK25 at the South of casting bay 2 and GK22 at the North. The focus of this research will be on step 2 and 3.

During maintenance of the casting cranes, safety areas, as shown in Figure 1.1, are taken into account. This leads to inoperative installations. More detailed information is given in chapter 5, but an example of the circumstances that may occur are given below:

• A one-crane-operation in casting bay 2 • Inoperative tilting devices

• The slag removal area has to be moved to another part of the factory • A blocked cross transport

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A number of different actors are involved in the process of steel ladle routing. There are four main sections, namely PTC, TBO, PRB and KTO. The first one is the Project and Technical Consultancy section (PTC). In this section the project management of the complete renovation and revamp project is placed. The second section is the Technical management and development (TBO), which is the technical local maintenance section. This section realizes the campaign planning and is responsible for the actual renovation of revamp only. Thirdly the Production management section (PRB) is important. The production section is responsible for the continuity of the production. The production is influenced by the blast furnaces (BF) and influences the casting process. They also take parts out-of-operation such that the contractors can work safely. The fourth section is Quality management and technique development (KTO), which is the technology side and supports the production process.

Within these sections some specific actors can be named. The Paco is the steel ladle coordinator and responsible for the planning of the steel ladles, observing and lead up a couple of activities, with as main goal a safe and undisturbed production process (SectiePVK, 2013). The Proco is responsible for the planning of the steel qualities and is therefore another important actor. Together with the crane drivers and production personnel, they form the production side. At the other side there are the operators responsible for the installations, cranes and cross transports. There is always a struggle between production and operation because production wants to produce as much steel as possible and operation wants proper handling of the equipment (Boon, 2015).

Table 1.1 Planning renovating and replacing casting cranes 22 and 23 in casting bay 2 (Olierook, Essers, & Olgers,

2014)

To scope the project a couple of restrictions are set. The first restriction is that this specific research will focus on steel ladles in the Oxygen Steel Factory 2 of Tata Steel IJmuiden. Due to the replacement of casting crane GK22 parts of Casting Bay 2 will be inoperative, including the tilting devices and slag removal.

1.4 Purpose: The focus of Tata Steel IJmuiden

There are four focal points for the steel factory as a whole:

• Produced volume in [million tonnes/year] or [charges/91 days]

• Reliability in delivery, e.g. measured with the utilization rate of installations in [%]

• Steel quality, not taken into account in this research, but in practice for instance evaluated by

impurities (optical analysis) or hardness (Rockwell scale)

• Environmental impact, not taken into account, but in practice for instance air pollution

(CO2, SO2, NOx in ppm), negative effect of water and waste production are minimized The first one, produced volume, is the most important during this research. Tata Steel aims to produce more than seven million tonnes of steel each year (which is equal to ± 5359 charges per 91 days). In case a particular solution demands full utilization of an installation or various installations, these installations will likely become the bottlenecks and hence drive the risk of underperformance on delivery reliability. The third focal point for Tata Steel is steel quality. The steel quality is depending on the installations handling the charges of liquid steel and the installations handling the empty steel ladles. The last two points of focus are relevant for Tata Steel, but remain outside the scope of this research, were focus is on produced volume and reliability in delivery.

Activity Lifting GK25 Assembly GK25 Testing GK25 Operational test GK25 Disassembly GK22 Renovation GK22 Lifting GK22 Assembly GK22 Testing GK22 Operational test GK22 Disassembly GK23 scenario 1 Disassembly GK23 scenario 2 2015 2016 2017 apr feb dec oct aug june

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An important constraint during this research is safety. This is very important for Tata Steel and the safety precautions have to be enforced at any time, which is also reflected in the safety area needed during crane renovation and replacement. This is shown in Figure 1.1.

1.5 Research goal: Create a scientifically underpinned decision support tool

Some background information is given in subsection 1.2 followed by the problem description in subsection 1.3. The focus of Tata Steel IJmuiden is given in 1.4. In this section the goal of the research is described, where after the main research question and sub questions are given.

The goal of this research is to create a decision support tool for the transport and production processes in the Oxygen Steel Factory 2. This tool must (1) identify possible bottlenecks in the transport and production processes when installations and transport modes are inoperative and (2) gives insight in the effectiveness of possible solutions. Based on the goal the following research question is composed:

How to minimize the negative effects of maintenance on productivity?

Answering the research question will lead to the above goal. To make it possible to answer the research question, sub questions are composed. It is interesting to study literature to analyse comparable maintenance problems and to get some insight in methodologies already used, to tackle these problems. A systems analysis could lead to a better impression of the current situation at Tata Steel IJmuiden. When a lot of data is available it may be of importance to define distributions that fit this data. Based on the systems analysis and the targets of Tata Steel IJmuiden (given in 1.4) key performance indicators (KPIs) for this research can be defined. When the KPIs are known, literature is studied and the current situation is analysed. A model can be created to define the negative effects of maintenance on productivity of the Oxygen Steel Factory 2, when a safety area is taken into account. It is expected that many alternatives are possible to minimize the negative effects of maintenance on productivity. Based on the systems analysis, the focus of Tata Steel IJmuiden en the key performance indicators for this research specifically, different alternatives can be generated. Finally, it is interesting to define the best alternative to minimize the negative effects of maintenance. Based on the above, the following sub questions are defined:

1. What are the key topics in research on maintenance in relation to production continuity? 2. What does the current situation at Tata Steel IJmuiden look like?

3. Which statistical distributions fit the process time data best?

4. How to translate the managerial focal points into key performance indicators for the model applied?

5. How to model the Oxygen Steel Factory in order to test productivity?

6. Which problems (e.g. bottleneck) occur in the daily process when safety areas are taken into account during planned maintenance situations?

7. Which alternatives are available to minimize the effects of maintenance on productivity?

8. Which of the available alternatives provides the best solution, given the objective of minimizing the effects of maintenance on productivity?

Sub question 1 is answered in chapter 2, followed by the answer of sub question 2 in chapter 4. Sub questions 3 up to and including question 8 are answered in the chapter about modelling (chapter 5).

1.6 Outline of the report

In this section the outline of the report is given. In this outline the input of different methodologies is used (El-Khalil, 2015; Ingemansson & Bolmsjö, 2004; Ingemansson, Ylipää, & Bolmsjö, 2005). It is expected that discrete-event simulation (DES) will be used during this research. The complete literature study is given in chapter 2. El-Khalil describes a methodology to simulate an assembly line with DES, where Ingemansson and Bolmsjö are describing a methodology to reduce disturbances in manufacturing systems by using DES and to use this methodology in a real-world scenario (Ingemansson & Bolmsjö, 2004). Ingemansson et al. gives a more general methodology to identify bottlenecks in manufacturing systems with the help of a DES model (Ingemansson et al., 2005). The outline created for this report is shown in Figure 1.4.

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Figure 1.4 The outline of the report

This chapter (chapter 1) is the introduction of the report, including the problem, the goal and the research questions. In the next chapter, chapter 2, the literature study is described, followed by the methods used in this research in chapter 3. Simultaneously with the literature study a systems analysis of the current situation is performed and described in chapter 4. Based on the literature and systems analysis a conceptual model is created, which is the start of chapter 5. This chapter continues with the implementation of the conceptual model in the software (section 5.2). Thereafter verification and validation are performed (5.2 and 5.4) and an experimental plan for the planned maintenance situations is given and conducted in section 5.6. The result is that bottlenecks are identified and alternatives, to minimize the effects of maintenance on productivity, are generated and ranked in sections 5.6.3, 5.6.4, 5.7.3 and 5.7.4. In chapter 6 the conclusions, recommendations and the reflection are described.

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2 Literature study

Because modelling and management of manufacturing systems have been object of research since many years and from different backgrounds, broad and deep knowledge is made available through a series of scientific articles. In this chapter a summary of a focused literature study is presented, giving direction to this research project.

Within the available scientific research, regarding production and logistics, various specializations have been developed over the past years. Particularly relevant for this research turned out to be: 2.1 Integral production process improvement methods (an integral systems approach) 2.2 Specific production process analysis and improvement methods (a topical approach),

• Modelling production processes: apply routing, queuing and scheduling to optimize

production and to, amongst others, avoid bottlenecks (2.2.1)

• Describing bottlenecks: identify and categorize bottlenecks (2.2.2)

• Executing maintenance: a topical cross section of research, addressing the practical

friction between maintenance and production (2.2.3)

2.3 Project management (optimizing the organization to avoid and react to bottlenecks)

2.1 Integral production improvement methods: An integral approach

Various general process improvement methods are available, with each a different point of view (Ede, 2012). There are methods devised by logistics managers (Lean Manufacturing (LM), Theory of Constraints (ToC) and Quick Response Manufacturing (QRM)), as well as methods created by quality managers (Six Sigma (6S)) and also methods developed by maintenance managers (Total Productive Management (TPM) and Reliability Centred Maintenance (RCM)). An overview of these methods and their primary focus is given in Table 2.1.

In this research a combination of most of the methods described above could be used, with the Lean Manufacturing and the Six Sigma method as exceptions, since waste reduction nor reduction of variation in product quality are object of research in this study. Bottlenecks and their effects on logistics could be studied applying ToC or RCM, possible queues could be researched using QRM and there might be potential to improve productivity by increasing the availability of installations with the application of TPM. When ToC is used, the integral production process is adapted, by lowering capacity, to the bottlenecks, followed by an increase of the capacity of the bottleneck. This method is not just used in production processes, but can also be used to define bottlenecks in for example hospitals (ErasmusUniversiteitRotterdam, 2015). During this adaptation and capacity enlargement, the overall production will be lower, which affects the delivery time and therefore the customer satisfaction. As stated by Tata Steel IJmuiden it is key that during renovations, replacements and in general occurring bottlenecks, the production stays on target.

During the research elements of the various approaches were adapted in the modelling of OSF2, however an integral application of one of these methods was not strived for. Bottleneck detection and alternative generation (ToC), surfacing the relationship between ladle temperature and product quality (Six Sigma) and redesign maintenance schedules to reduce disturbances (RCM) are just examples illustrating how elements of the above mentioned methods have influenced the reasoning in this study.

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Table 2.1 Various general process improvement methods and their primary effects (Ede, 2012)

Logistics Quality Productivity

Lean ToC QRM Six Sigma TPM RCM

Reduce waste, increase added value Increase throughput, eliminate bottlenecks in logistic chains Waiting time-reduction in the process from purchasing raw materials till delivery of (customer specific) products Reduction of variation in quality of products and processes Increase productivity i.e. by increasing machine availability Reducing the impact of machine-disturbances 1. Identify – per product or product family – customer value 1. Detect bottlenecks that are obstructing the goal of the organisation

1. Detect ‘empty time’ in the system

1. Define: in the first step of the DMAIC-cycle it is stated who the customers are and what they can expect

1. Define which machines are crucial for the product, and create

multidisciplinary teams responsible for these machines

1. Detect the critical parts in the installations and split them in as small as possible maintenance units 2. Map with i.a.

Value Stream Mapping which company processes are adding value and which do not. Eliminate 8 types of waste

2. Exploit the bottleneck: Do not lose capacity due to downtime or untimely supply of semi finished products

2. Research how the process from purchase to delivery can be accelerated and which ‘hidden’ benefits there are

2. Measure: per (critical to quality) quality characteristic it is measured if the (customer) expectations are realized 2. Take care of structured work space, standardize work processes, measure the Overall Equipment Effectiveness (OEE) per machine

2. Define per maintainable unit the desired function of this unit in this specific situation 3. Take care of

production flow. Downtime leads to stock within the process and is therefore waste

3. The bottleneck defines the rhythm. All other processes are adapted to this rhythm 3. Organize the organization in a process-oriented way, by creating multidisciplinary workstations with 3-10 staff members 3. Analyse: Search for root causes for the occurrence of substandard quality and look for possible solutions

3. Analyse which improvements can increase the OEE, by preventing ‘hidden losses’ from happening

3. Research per maintainable unit which disturbances could occur and their effects 4. Make production

demand-driven. Producing something that is not ordered is waste as well 4. Increase the capacity of the bottleneck, for example by investments 4. Adapt the workload of the supply and demand workstations, for example with the POLCA-system

4. Improve: Implement in accordance with the process owner the best solution 4. Implement improvements, like autonomous or preventive maintenance 4. Define an optimal maintenance plan per maintainable unit 5. Strive for perfection, go back to step 1 again. 5. Back to step 1 Note: the bottleneck can be anything that limits throughput, e.g. a machine or distribution methods.

5. Back to step 1 5. Control: Are the results as expected? If yes, guarantee the changes.

Back to step 1 or 2

5. Check the results. If it is fine guarantee that the changes are the new standards. Back to step 1 or 2

5. Measure the disturbances that still occur and their effects.

Back to step 1 or 2

2.2 Specific production process analysis and improvement methods

In addition to the above-mentioned integral approach, also specific analysis and improvement methods were studied.

In this subsection firstly the modelling of production processes is described (applying tools like routing, queuing and scheduling). Secondly a description is given of bottlenecks by identifying and categorizing them. Finally, maintenance execution methods are briefly presented, addressing the practical friction between maintenance and production (continuity).

The different topics covered in this subsection, indeed have some overlap resulting from their possible dependencies. The use of two or more hoists on a single track is given as an example to illustrate this overlap and dependency. More hoists on a single track could lead to crane collisions (Paz & Franzese, 2010). To avoid these collisions, routes have to be changed by adjusting the crane schedules. Changing these crane schedules could cause delays in handling (finished) charges/ladles, which could lead to queues in front of installations. Both the crane collisions and queues in the system may become bottlenecks for the production process. All of the disruptions described above may exacerbate when maintenance of an installation, casting crane or cross transport is needed. Baring in mind the possible overlaps, the various modelling approaches are described in the following sections.

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2.2.1 Modelling production processes

In order to create an overview of applicable modelling tools, in steel factory decision support, routing, queuing and scheduling were researched for their usefulness.

Routing

Upfront, it seemed fair to assume that part of the solution of minimizing the effects of maintenance on production continuity, could come from improving ladle routes in OSF2. Therefore ways to determine or change routes are investigated. There are two ways of looking at route optimization: operational route optimization in a given plant layout and, more strategically, designing the plant layout itself.

Operational route optimization is modelled in different ways (Zuidwijk, 2014). Both the vehicle routing problem (VRP), the transportation problem (TP) and the shortest path problem (SPP) are in some way applicable. An overview of these models is given in Table 2.2. As described in the system analysis of the current situation (chapter 4), routing in the steel factory is complex. Especially because the sequence between groups of installations is fixed, but different installations within a group can be chosen, which makes the routing dynamic and complex.

Table 2.2 Overview of the Vehicle Routing Problem (VRP), Transportation Problem (TP) and Shortest Path problem (SPP). Based on Zuidwijk (2014, pp. 12–27)

Figure Main formula Subject to Meaning Extra

Vehicle Routing Problem i=0 depot ! ∈ !\{0} customer demand qi m trucks available !"# !!!! !∈! !!= 2 !∈!(!) ! ∈ !{0} !!= 2! !∈!(!) !!≥ 2! ! !∈! ! ! ⊆ !{0}!! ≠ ∅ !!∈ 0,1 !!! ∉ !(0) !!∈ 0,1,2 !!! ∈ !(0) ! ! = { !, ! : ! ∈ !, ! ∈ !! !"!! ∉ !!! ∈ !}

ca = connection between two nodes r(S) = minimum number of vehicles

required to serve customers in S if ! ! = { !, ! , ! ≠ !!!"! !, ! , ! ≠ !} not easy to solve

Transportation Problem Source si Destination dj !"# !!"!!" ! !!! ! !!! !!"= !!≥ 0 ! !!! !!"≥ 0 !!"= !!≥ 0 ! !!! with !!= !! ! !!! ! !!! Outflow equals productions Inflow equals demand Total outflow equals total inflow

si = production of source dj = demand of destination cij = total costs

xij = amount of cargo flowing from i

to j

If you want to take inventory into account, then dj can be seen as future

demand Shortest Path Problem G=(N,A) !"# !!"!!" (!,!)∈! !!"− ! !!" ! = −11 0 !"!! = ! !"!! = ! !"ℎ!"#$%! !!"≥ 0 (!, !) ∈ Bellman principle: results in stage n are completely determined by results in n-1 and optimization: we need not reconsider optimization in the previous stage xij = outflow of i xji – inflow to i

What goes in, goes out: nothing stays at the nodes

(dynamic programming ! backtracking ! minimize costs for SPP)

Also analogies and partially (while OSF2 related) relevant studies were researched. For example, Roodbergen (2001) describes layout and routing methods for e.g. lift trucks and pedestrian stackers in warehouses. The routing of lift trucks is in some way comparable with the routing of steel ladles. In his thesis the focus is on methods to decrease the time needed to pick an order. Three methods are described: equipment choice, where to store parts and operating policies. The assignment of jobs to lift trucks is closely related to the Vehicle Routing Problem (Roodbergen, 2001, p. 28). As reported by Keus (2008) different production routes are possible in a steel plant to produce a single product. The routes are often based on quality restrictions and logistic preconditions. He defined optimal

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routing when financial grounds play a role. Not just the routing of steel ladles is relevant, also the routing of the (casting) cranes, used to transport the steel ladles, is of interest. Cranes perform a lot of empty movements to move between jobs. Next to that, more hoists on a single track could lead to collisions (Paz & Franzese, 2010). To avoid these collisions, routes have to be changed by adjusting the crane schedules. An example of (re)routing in practice is road bypassing. Road maintenance, capacity increase and new infrastructure are needed in parts of Groningen (Berg & Bolding, 2012; ProvincieGroningen, 2015). Actions are taken in advance (e.g. road users are informed via signs along the road and a special website is released) where after parts of the road are closed and detours are set. No further actions are taken to reduce congestion and delays.

A strategic, completely, new OSF2 design is beyond the scope of this research. Nevertheless, for completeness sake and since also partially new layouts can be of interest, also this research path is briefly reported. Strategically, optimizing the layout of a warehouse or factory generally results in lower material handling costs. These material handling costs are often interpreted as transport costs, which in turn is often treated as a function of travel distance (Roodbergen, 2001, p. 13). This is underpinned by Vincken (2008) who describes the advantages of layout changes at SMI Groep Dokkum: In the new layout all activities are combined at one location instead of three locations in the old layout. This has lead to less needed movements, which has a positive influence on the efficiency and turnaround time of the production process (Vincken, 2008, p. 21).

Zhang and Rose researched an integrated schedule in which crane scheduling is included in production scheduling. Therefore they used the lay-out of a manufacturing plant as given in Figure 2.1, which is comparable with the lay-out of the oxygen steel plant of Tata Steel IJmuiden (see Figure 1.1). Routing is based on a specified sequence of the installations and cranes are identical and can transport any jobs. It is also stated that the hoist and the trolley have enough time to make the necessary vertical and lateral movements as the cranes move from one location to another. Therefore only (the time needed for) the longitudinal movements are taken into account (Zhang & Rose, 2013).

Figure 2.1 Layout of a manufacturing plant used for an integrated schedule for crane movements and production

(Zhang & Rose, 2013, p. 2635)

Queuing

According to the definition of a production line given by Bierbooms (2012), it can be stated that the OSF2 of Tata Steel IJmuiden is one as well: “A production line is a type of manufacturing system in which products visit a number of workstations in a fixed sequential order.” It is defined that a workstation consists of one or more machines at which products of materials are processed, assembled or inspected. A simple discrete-item production line consists of two machines: the product starts at one machine, which has always work available and moves to the second machine. The second machine can be idle and if there is a finite amount of buffer between the two machines, the first machine can be blocked. This production line can be modelled as a queuing system, in which the first machine represents the arrival process to the queue and the second machine represents the service process. A more complex queuing system may occur at the steel plant.

Zhang & Rose (2013) also describe the manufacturing system as a queuing system. The above taken into account, the essentials of queuing theory were studied to derive applicable ways of problem solving for this study of OSF2.

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Queuing theory describes a queuing model to enable to study different phenomena that may occur in a queue. In a queuing model, customers expect service from a processing unit. They arrive at different moments in time and the processing unit serves the customers within a certain time. When more customers arrive then the processing unit can serve, a queue arises. This is schematically shown in Figure 2.2. The processing unit is shown by the circle, the vertical lines represent the queue, the dotted line around it is the waiting area and the arrows are arrivals and departures (Vogels & Dalen, 2013). The queuing model is characterized by the arrival pattern and behaviour of customers, service times, discipline and capacity and the waiting room (Adan & Resing, 2015).

Figure 2.2 Basic queuing system (Adan & Resing, 2015, p. 23). A queue, represented by vertical lines, is located in front of a processing unit (circle) with a waiting area around it (dotted line). The arrow is the route (arrival at the left, departure at the right)

The processing unit can consist of one or more servers. Each of the (parallel) servers is able to serve one customer at the time. If all servers are occupied, a customer has to wait in a queue. The length of this queue is depending on two different processes, namely the arrival pattern of the customers and the service process. The arrival process describes the amount of customers arriving within a certain time. Often the time between two arriving customers is used, the inter-arrival time. The service process describes the time a customer needs service, the service time. Both processes are generally modelled as statistical distributions. One takes averages of the arrival- and service time after which a distribution is defined that matches reality.

Below Little’s law is given, which describes the relation between the expectances E of the amount of customers L and average turnaround time W in a steady state:

!(!) = !! ∙ !(!) (2.1)

! !_! = ! ∙ !(!_!) (2.2)

L = amount of customers in the system W = average turnaround time

λ = average amount of customers per unit of time

When the subscript q is used, only the L and W of the queue are meant Scheduling

Scheduling, in general, offers a key way of thinking about both OSF2 maintenance planning and OSF2 production management. Moreover, literature offers concrete scheduling applications in steel factories. As described by Tang (2002) and Kumar (2006) the steelmaking process, including steel-making and continuous casting, is usually the bottleneck in the iron and steel production. To improve productivity of the entire production system, it is critical that this process is effectively scheduled. Appelqvist and Lehtonen (2005) performed a study in steel production scheduling and specifically in combining the use of optimisation techniques and simulation. They describe a schedule as a plan with reference to the sequence of and time allocated for each item or operation necessary to complete the item. With Tata Steel IJmuiden, in practice, decision rules for scheduling are rather used than deterministic optimization techniques or simulation. Nevertheless, OSF2 shows many characteristics that could be modelled in a classical job-shop scheduling problem (Appelqvist & Lehtonen, 2005). Once the OSF2 would be modelled as a scheduling problem, various algorithms and solution methods are being applied, varying from genetic algorithms (Yang, Djurdjanovic, & Ni, 2008), a linear programming formulation applying Lagrangian relaxation (Tang et al., 2002), a fast heuristic algorithm based on a VRP formulation (Wei, Zhu, He, & Liu, 2014), as well as simulation modelling

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(Zhang & Rose, 2013). Even so, also modelling approaches were identified specifically for the steelmaking process. Scheduling problems associated with steel making and continuous casting are aimed at determining at what time, on which device and in what sequence the molten charge should be arranged at various productions stages from the steelmaking at the converters to the continuous casting machines. This is also underpinned by Cowling and Rezig (2000).

A specific area of research is devoted towards cranes. Crane scheduling is a part of production scheduling in manufacturing plants. However, in most approaches the development of a crane schedule is done in isolation (Zhang & Rose, 2013). In the article of Zhang and Rose an integrated schedule is approach in which crane scheduling is implemented in production scheduling. The advantage is that this approach prevents the jobs from waiting for cranes, which will increase the productivity.

Generally more than one crane (hoist) runs over the same track. Because cranes share the same runway, they cannot pass each other. Crane interference is a main factor in affecting the crane utilization. As described by Zhang and Rose (2013, p. 2634), it is difficult to compare results from a quay crane scheduling problem (Vianen, 2015) with results found for crane scheduling problems in the manufacturing industry. This is because quay cranes load (or unload) container into (or from) ships, rather than transporting items from one location on the runway to another, as overhead cranes do.

2.2.2 Describing bottlenecks

The effect of maintenance on production continuity in OSF2 is, at least, partially expressed in terms of the occurrence of bottlenecks. Therefore, the causes of bottlenecks and the various types of bottlenecks have been identified, driving the approaches to prevention and handling of bottlenecks. The easy model adaptations and the clear view on improvements drive bottleneck research towards DES.

Disturbances in production lines are a common industrial problem according to Ingemansson and Bolmsjö (2004). Another definition of production disturbances is “the time when a manufacturing system is not working properly” (Ingemansson et al., 2005). According to research done between 1990 and 2000, 50 to 60 per cent of the total production time is used for manufacturing and the rest of the time is wasted in different disturbances.

As described by Kuivanen in the article of Ingemansson (Ingemansson & Bolmsjö, 2004) also another definition of production disturbances is possible: “A production disturbance is an unplanned or undesirable state or function of the system”. A classification can be made of production disturbances: downtime (planned/unplanned), speed and quality losses. This is also illustrated in Figure 2.3.

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One of the two case studies described by Ingemansson (2004) is a company that produces equipment and machines to the window-blind industry. In the end the case study showed that by easy means (e.g. different working routines and preventive maintenance) improvements could be made with cost-effective means. The output is increased with 18 per cent. Both studies conclude that it is important to consider the long waiting-times and that, when a disturbance has occurred, the way back to the normal state is important.

Padhi et al. (2013) describe a combined framework of discrete-event simulation (DES) and design of experiments (DOE) to analyse production line disruptions. DES is used to map the operational dynamics of the serial and parallel processes in production lines and DOE is used to decide the optimum values of the process parameters. The advantage of combining these two methods is that “an optimal solution is found through a rigorous scientific approach in a reasonable computing time”. This is instead of relying on experts’ personal opinions, which can be biased and possibly suboptimal.

Where Padhi focused on the analysis of production line disruptions, Ingemansson (2004) used DES to reduce disturbances in manufacturing systems. Discrete-event simulation is used because it is a powerful tool and alterations are easily made in the model. Therefore improvements can easily be shown.

Overhead travelling cranes are critical in heavy industries, ports and construction. They may become a bottleneck for the complete production. When plants are designed or modifications of processes are evaluated, it is important to analyse the crane behaviour and especially the interferences that may occur among each other (Paz & Franzese, 2010). Factors like breakdowns, process time variability, buffer capacities and interferences with other vehicles have to be considered. Therefor linear calculations are considered not applicable and simulation is the best method to use according to Paz and Franzese (2010).

2.2.3 Executing maintenance

Since the effects of maintenance on the production continuity in OSF2 are being studied, a sound classification and understanding of maintenance is of importance. In the scientific literature various maintenance classifications can be found. Essential, in this respect, is the European standard for maintenance terminology (EN13306:2001). In this section an overview of both descriptions as handling approaches of maintenance are covered.

Maintenance can be defined as “the combination of all technical and associated administrative actions intended to retain an item or system in, or restore it to, a state in which it can perform its required function” (Dekker, 1996, p. 230). According to Al-Najjar (2004) the impact of maintenance can be found in different areas in the company, such as production, quality and production logistics.

There is always tension between the production and the maintenance department, because in many cases production units have to be shut down for maintenance. On the one hand shutting down units lead to production losses, but on the other hand maintenance is needed for the long-term well being of the equipment. Therefore it is important to take production into account while planning maintenance or to define and develop an integrated model for both maintenance and production (Budai et al., 2008).

The integration of a maintenance plan in the production plan is important, because they are directly related to each other. A breakdown in maintenance operation results in disruption of production or to additional costs due to downtime, decrease in productivity and quality and inefficient use of equipment, personnel and facilities (Ashayeri, Teelen, & Selenj, 1996, pp. 3312–3313).

Enofe and Aimienrovbiye (2010, p. 23) used the European standards for maintenance terminology (EN13306:2001) to classify maintenance in two major areas, namely preventive maintenance and corrective maintenance. These areas can be subdivided as shown in Figure 2.4. According to Budai (2008) the distinction between preventive and corrective maintenance is depending on the definition of failure. Where one says that failure is an item in a bad state but still functioning, another one does

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not consider this as failure. An important distinction between the two kinds of maintenance is that corrective maintenance is usually unplanned, but preventive maintenance typically is. Preventive maintenance is “all planned maintenance actions” (e.g. periodic inspection, condition monitoring) while corrective maintenance is “all unplanned actions to restore failure” (Enofe & Aimienrovbiye, 2010). According to some other articles, condition-based maintenance can be seen as a third major area in maintenance next to preventive an corrective maintenance, instead of being a subcategory of preventive maintenance.

Usually future downtime opportunities are taken into account when preventive maintenance is needed to lower (maintenance) costs (Tan & Kramer, 1997). The most important goal of implementing maintenance activities in the production system is to reduce unplanned stoppages to the lowest amount possible. Efficient scheduling is one of the opportunities to reach this goal according to Al-Najjar in the article of Gornebrand (2012, p. 18).

Within this classification three different kinds of maintenance activities can be done (Goti & Garcia, 2010). The first one is “perfect maintenance” after which it is assumed that the state of the equipment maintained is ‘as good as new’. Replacement of equipment is also perfect maintenance (Wang, 2002). Secondly “minimal maintenance” is possible, which assumes that the state of the equipment is ‘as bad as old’. The third maintenance activity is closer to many real situations, namely “imperfect maintenance”. This assumes that maintenance improves the state of the equipment by some degree such that it is ‘as good as before’. Overall lots of different maintenance optimization models, techniques, scheduling methods, performance measurement techniques, information systems and policies are present in literature. An overview is given in the article of Garg and Deshmukh (2006, p. 109). Especially figure 1 with a maintenance management tree is important and given in Appendix 1.

The casting crane replacement at Tata Steel IJmuiden, as described in section 1.3, is an example of scheduled condition-based preventive maintenance (Figure 2.4); the current casting crane needs replacement because it reaches the end of its life cycle and its planned for 2016. System failure affects production rapidly. The replacement of casting crane 23 (GK23) is perfect maintenance, where the renovation of casting crane 22 (GK22) is imperfect maintenance. Comparable cases in which preventive maintenance is used are railway systems (Budai, Huisman, & Dekker, 2006), subsea oil pipelines (Castanier & Rausand, 2006), the production of hub caps in the car maker industry (Goti & Garcia, 2010), petrochemical plants (Laggoune, Chateauneuf, & Aissani, 2009), in the steel production industry combining optimisation and simulation (Appelqvist & Lehtonen, 2005) and the cement producing industry (Gornebrand & Johansson, 2012).

Figure 2.4 Maintenance classification, based on EN13306:2001 (Enofe & Aimienrovbiye, 2010, p. 23)

In the research literature, different reasons drive the research effort. For example its influence on production and its optimal schedule. Barata (2002) uses Monte-Carlo simulation to minimize the expected total system costs over a given mission time to develop an optimal condition based maintenance schedule. Monte Carlo simulation is a way of modelling where the input of the model differs for each run and in which a lot of runs are performed. Many different scenarios are the outcome of this simulation, which leads to a good estimation. In an article of Zhou et al. (2011) is

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stated that the partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when equipment need imperfect maintenance. To solve this an algorithm is used that combines the Monte-Carlo based density projection method and the policy iteration. Tian et al. (2011) developed a simulation model to evaluate the costs of the optimal condition-based maintenance (CBM) decisions and CBM policy for wind power generation systems. Zhou (2009) describes an opportunistic preventive maintenance scheduling algorithm for the multi-unit series system, based on dynamic programming. Imperfect maintenance is integrated into the maintenance actions. Yang (2008) proposes a new method for maintenance scheduling in a manufacturing system using the continuous assessment and prediction of the level of performance degradation of equipment, while also the complex interaction between production and maintenance is included. Discrete-event simulation is used to evaluate the effects of maintenance scheduling and a Genetic Algorithm based optimization procedure is used to search for the most cost-effective maintenance schedule. The algorithm is implemented in the simulation environment. Marseguerra et al. (2002, p. 152) propose a method, which couples Monte-Carlo simulation and genetic algorithms, to model a more realistic degradation process and to optimize different objectives simultaneously. Goti and Garcia (2010) focus on the problem of condition monitoring optimization in a manufacturing environment with the objective of ‘determining the optimal age or deterioration levels when a preventive maintenance action should be performed for multi-equipment systems under cost and profit criteria’. This model is implemented using discrete-event simulation and optimized by using the Multiobjective Evolutionary Algorithm (MOEA). As said by Goti and Garcia the suitability of discrete-event simulation to model and modify complex systems is combined with the aptitude that MOEAs have shown to deal with multi-objective problems.

2.3 Project management

A possible hypothesis states that when all key OSF2 planning and scheduling functions, including maintenance planning, would follow a project management approach various downstream issues could be avoided. If the key elements of “project management thinking” would be applied, there could be a serious gain in production management effectiveness.

Why this hypothesis? A project is an organization of people dedicated to a specific purpose or objective. Projects generally involve large, expensive, unique, or high risk undertakings which have to be completed by a certain date, for a certain amount of money, with some expected level of performance (Prabhakar, 2008). These characteristics hold for maintenance planning in OSF2. A project can be defined (Prabhakar, 2008) as possessing the following characteristics: (1) a defined beginning and end (specified time to completion) (2) a specific, preordained goal or set of goals (performance expectations) (3) a series of complex or interrelated activities (4) a limited budget. When maintenance is viewed as a, to a certain extend isolated, project according to the above definition, the series of project management tools could be applicable by program evaluation and review technique (PERT) and critical path method (CPM). In the literature, for instance in planning the construction of an electrical substation (de Miranda Mota, de Almeida, & Alencar, 2009), both PERT and CPM are applied to balance contradicting goals.

2.4 Interim conclusions

Every case study has its own characteristics. This holds also for the analysis of the bottlenecks resulting from the plant maintenance in OSF2. On the other hand, related and generalized problems have been researched extensively and drive the choices that have to be made regarding modelling approach and experimental setup. For instance, the methods described in section 2.1 influenced the way bottlenecks are identified and analysed. The specific modelling methods reported in 2.2 help understanding the opportunities and limitations of modelling OSF2. In essence, this literature study leads to the conclusion that DES is a suitable way of modelling OSF2, based on the available data and the intuitive way of representing OSF2. Finally, in section 2.3, project management is briefly touched upon, based on the hypothesis that a project management approach of maintenance and de-bottlenecking could avoid complex downstream issues.

Towards better decision support during planned maintenance in the Oxygen Steel Factory 2 An analysis of the effects of maintenance on steel ladle routing

Towards better decision support during planned maintenance

in the Oxygen Steel Factory 2

An analysis of the effects of maintenance on steel ladle routing

By

Towards better decision support during planned maintenance

in the Oxygen Steel Factory 2

Preface

Abbreviations

Executive summary

Table of contents

1 Introduction: Tata Steel, OSF2 facing maintenance issues

1.1 Background: History and key information of Tata Steel IJmuiden

1.2 Design: Factory layout and process steps

1.3 Problem: Major maintenance versus production continuity

1.4 Purpose: The focus of Tata Steel IJmuiden

1.5 Research goal: Create a scientifically underpinned decision support tool

1.6 Outline of the report

2 Literature study

2.1 Integral production improvement methods: An integral approach

2.2 Specific production process analysis and improvement methods

2.3 Project management

2.4 Interim conclusions