Problem statement: Manufacturing and production plants operate machines and equipment that deteriorate with usage and time thus requiring maintenance actions to restore them back to their original operational conditions. Approach: This study investigates the current standard of maintenance planning at a steel manufacturing facility in South Africa. The study begins with a thorough literature study to find good characteristics of planning which should be present at any facility that is optimally engaged in performing excellent maintenance planning effectively and efficiently. A number of key observations from the literature are made indicating that planning is a key component of maintenance and affects excellence in maintenance significantly. The literature recommends that planning should be set and based on key principles to allow for standardisation and efficiency. An empirical study in the form of a survey is then completed to benchmark the current maintenance-planning environment of the steel facility against the good characteristics of maintenance planning found in literature. Results: The results from the empirical study show that the current maintenance environment at the steel manufacturer is not optimal. The following issues are uncovered from the study: no schedulers employed to relieve the planners’ work overload, no guiding principles for planning and standardisation of work planning, no efficient shutdown planning and also no existent measures for tracking some of the key performance areas. Conclusion: Key recommendations to be considered for implementation for improving the status of the maintenance environment at the works include: introducing the scheduler position, standardising planning methodologies according to specific principles and rigorously focusing on overall maintenance improvement using standardised methodologies. / MBA (Business Administration), North-West University, Potchefstroom Campus, 2015

Purpose: This study aims to find how the integration between marketing and operations can improve demand management in order to have efficient inventory level and avoid excess inventory. Method used: In order to have optimize inventory level and managing demand, integrating business process between marketing, supply planning and inventory management team was considered in this study. Qualitative data from nine interviews among three direct sales cosmetics companies was gathered. Findings: Empirical findings address excess inventory is caused by several issue in the company as poor management of demand forecasting, wide product portfolio, long lead time, the lack of sharing information between the company and its suppliers and ineffective strategy to avoid excess inventory within the company. Regarding to improve forecast accuracy and manage demand, findings indicate the role of marketing in obtaining knowledge about customer insight is not good enough. Practical Implication: It is critical that works and plans from each function be integrated to optimize inventory. In order to support optimize inventory strategy all pertinent departments must continue reviewing meeting with the aim to reach a consensus about the products planning for the both side of demand and operation align with overall strategic goals of the company. Contribution: Empirical data demonstrate the best way for effective implication of demand management occurs when marketing can provide demand information in time, as well as supply chain management can react flexibly to demand changes in time. Moreover, the lack of integration between marketing and supply chain management is a major barrier in optimizing inventory.

Exurbanization is occurring where large tracts of land are being sold to developers. Typically these are ranches that are then divided into 40-acre parcels and sold by developers, avoiding subdivision regulations requiring paved streets, utilities and other amenities. The result is an unplanned subdivision with no infrastructure, and tax revenues that cannot offset the cost of providing it. Interviews with professional planners suggested there may be independent variables capable of predicting the amount of human disturbance in an exurban area: parcel size, full cash value, tenure, distance to paved roads, site-built housing, mobile homes, and presence of biological or riparian areas. A total of 7,465 acres (3,022 ha) of parcel disturbance were digitized in exurban Arivaca, acreage values were converted to a binary dependent variable and used in logistic regression analysis to test independent variables' predictive value. Four were statistically significant: parcel size, full cash value, mobile homes and site-built housing. Landscape fragmentation was also tested using the presence of the variable scoring highest in probability - site-built housing. Zones of influence with a negative ecological influence surrounded the homes - up to 5,055 acres (2,046 ha) were impacted. Interviews with an exemplary sample of residents regarding their land use ethic found all had very strong opinions on how their properties should be treated as well as undesirable land uses such as overgrazing, over-use of groundwater for short-term economic gain and use of off-road vehicles. An explanation of the small sample size of both planners and residents is warranted. Planners were limited to those working in Pima County government who had professional experience with the study area of Arivaca and were familiar with its particular situation. The number of Arivaca residents interviewed was intended to discern an exemplary group's opinions based on how large a parcel they owned, the various sizes being a typical cross-section of acreage in the study area. What these findings illustrate is (1) the difficulty of predicting human-induced disturbance, (2) land fragmentation is more than the actual areas of physical disturbance and (3) some residents are aware of impacts related to their activities, mitigating damage wherever possible.

A journey planning tool accessible from a mobile device provides travel information on the move. This work studies various aspects of mobile journey planners, including the history and limitations of mobile devices to deal with. First we analyze the connection with phone networks and the Internet for possible choices to place the tool, while recommending the most suitable ones. Then we study the developer environment consisting of various operating systems to show how compatible an application will be while selecting a programming language. Another part covers possible parameters for the journey search, from setting of basic location and time to optimizing other journey attributes. Following is an evaluation of journey planners already available. Then we investigate the source and form of input traffic data along with their availability. Finally we illustrate the ideas how the proposed parameters can be implemented on an example search algorithm. The purpose is to provide a comprehensive overview of the subject while researching possible improvements.

A journey planning tool accessible from a mobile device provides travel information on the move. This work studies various aspects of journey planners in order to develop a solution for this task. We analyze the connection with phone networks and the Internet for possible choices to place the tool. Then we study the development environment consisting of operating systems programming languages. Another part covers possible parameters for the journey search. Then we investigate the source and form of input traffic data along with possible algorithms for this problem. Based on this analysis we design an application, making decisions from the analyzed areas. The design is applied for the following implementation and the resulting program we describe and measure. The purpose of this work is to develop a working application for mobile devices capable of public transit journey search, and to show what development of such application entails.

The purpose of this thesis is to enlighten Boetten Bygg ABconcerning Lean Construction and its methods, which can benefittheir work towards reduction of non-valuable activities during theproduction phase. The examination took place at two projects thatBoetten is working with in Sigtuna and Sollentuna. Both projectswere in the same phase. The method used were to visit the projectsand get an overview of how they’re working today. This was done byattending weekly meetings between the leading parties from Boettenas well as the leading persons from each of the contractors, wealso handed out a survey where the participants evaluate theproblems that can occur. The literature study lifts the basic principles of LeanConstruction, where we have chosen to focus on three differentLean methods that may help Boetten to decrease the wastingactivities in their future projects. We adressed the procurementof a project to lift the pros and cons that this contributes. Byusing this theory, we have discussed the subject round thequestion formulation: •Where in the production process is the biggest potential fordecreasing wasting activities? •Which Lean methods would generate the most valuable improvementadjusted to their organization? •How can Boetten go through with a change in the productionprocess? The gathered result shows the three dominating problem areas werewaiting, the arrangement on the construction site and thecommunication between involved parties. Based on the result wediscussed Boettens way of working to narrate, with grounds fromthe literature study, how an improvement may be carried out. Thestudy shows that an implementation of Lean Construction has thepossibility to minimize the wastage. The methods which wereconsidered to make the biggest change concerning wastingactivities were 5S, Just-In-Time and Last Planner. In order forthis to be possible, a strategy decision is required at amanagement level

The purpose of this research was to gain the perspectives of venue managers and meeting planners on their roles in the meetings industry, their relationship with each other and attributes needed to host a successful meeting. An online survey was conducted involving these two stakeholders to define their roles, assess the strength of their relationship and identify attributes based on their experience with working together and hosting meetings. Results showed that venue managers and meeting planners are essential to the hosting of successful meetings. It also indicated that venue managers are willing to build and maintain stronger relationships with meeting planners and brought to light some attributes such as communication skills, responsiveness and self-competence, which when improved upon, could help achieve this. The study also presents a theoretical model of how stakeholder co-creation activities can drive more successful meetings.

<p>Siemens Industrial Turbomachinery AB, nedan utskrivet som Siemens, är en svensk turbintillverkare placerad i Finspång som tillhör den tyska koncernen Siemens AG. Turbintillverkningen i Finspång började 1911 under namnet STAL men har sedan dess bytt ägarstruktur flertalet gånger. STAL-Laval, ABB STAL och Alstom Power är ett urval av de namn verksamheten har haft innan den blev en del av Siemens.</p><p>Ett problem som Siemens står inför vid produktionsplanering av gasturbiner är de mycket långa ledtider inom den egna produktionen samt från underleverantörerna. Den totala ledtiden för en gasturbin är cirka 2,5 år samtidigt som de flesta kunder beställer sin gasturbin cirka ett år innan leverans. Produktionsplaneringen förlitar sig därför mycket på prognoser vilket skapar en osäkerhet. Detta gör det svårt att sätta en huvudplan som matchar den sanna efterfrågan från kunderna. Problemet förenklas inte av att en turbin är en mycket komplex produkt med djupa och breda produktstrukturer. Utan en säker huvudplan blir det svårt för Siemens att planera för tillverkning och inköp på ett effektivt sätt. Vid examensarbetets utförande fanns ett tydligt behov av att förbättra planeringsprocessen på Siemens för att på sikt få en stabilare och bättre planering.</p><p>Advanced Planner and Optimizer (APO) är ett så kallat avancerat planeringssystem från den tyska affärssystemsleverantören SAP. SAP levererar dessutom Siemens affärssystem R/3. APO kan användas för att planera och styra en hel supply chain med hjälp av sofistikerade matematiska metoder. APO är kopplat till R/3 så data kan överföras dem emellan men arbetet som utförs i APO sker i en fristående miljö så att scenariosimulering kan genomföras.</p><p>Examensarbetets syfte är att analysera hur APO kan stödja produktionsplaneringen på Siemens. För att göra detta har tre frågor ställts och besvarats. Dessa frågor är:</p><ul><li>Vilka behov finns i dagsläget på Siemens i Finspång?</li><li>Vilka funktioner finns i planeringssystemet APO och hur fungerar de?</li><li>Går det att matcha Siemens nuvarande behov med funktionerna i APO? </li></ul><p>Under arbetets första skede behandlades de två första frågeställningarna parallellt. Först när dessa besvarats analyserades matchningen dem emellan som efterfrågas i fråga tre.</p><p>De behov som har observerats är:</p><ul><li>Ersätta och förbättra dagens simuleringsverktyg Testsystem 4 som kommer att försvinna oktober 2010</li><li>Möjliggöra kapacitetsutjämning och simulering vid taktisk planering för att skapa bättre beslutsunderlag till huvudplanen</li><li>Kvotering vid köpa/tillverka-beslut måste effektiviseras för att ge ett bättre underlag till den egna verksamheten och till externa leverantörer</li><li>Minska beroendet av nyckelpersoner samt komma ifrån betydelsen av tumregler vid planeringsarbete</li><li>Förenkla arbetet med prognossammanställning och utveckla arbetet kring prognosuppföljning.</li></ul><p>De funktioner i APO som studerats under examensarbetes gång är Demand Planning (DP), Supply Network Planning (SNP) och Production Planning and Detailed Scheduling (PP/DS). DP kan sammanställa en efterfrågeplan och har verktyg för prognostisering och prognosuppföljning. SNP är ett verktyg för taktiskt planering som klarar av att beläggningsutjämna produktionen genom de tre lösningsmetoderna Optimizer, Heuristics och Capable-To-Match. PP/DS använder matematiska metoder för att underlätta den operativa planeringen i ett företag.</p><p>Författarnas första rekommendation till Siemens är att använda modulen SNP till att förbättra den taktiska planeringen. Detta kan ske genom att huvudplanerare kan kontrollera beläggningsutjämning tidigt samt simulera olika planer för att välja den som anses bäst. På så sätt kommer Siemens planerare få ett bra beslutsunderlag att basera sina beslut på. Genom att använda SNP på detta sätt kommer även behovet av att ersätta TS4 uppfyllas. SNP kan även användas till att hantera behovet av bättre kvotering genom att bygga en separat modell dedikerad till denna uppgift vilket kommer leda till effektivare arbetsrutiner i den operativa planeringen.</p><p>Den andra rekommendationen är att DP används som ett verktyg för att ge stöd vid efterfrågeplanering och prognosuppföljning. Om SNP samt DP nyttjas till de uppgifter beskrivna ovan kommer möjlighet ges till att avlasta nyckelpersoner och även undvika beroendet av tumregler.</p><p>Eftersom det inte finns något överhängande behov av att förbättra detaljplaneringen rekommenderas inte att implementera PP/DS innan de under examensarbetet identifierade behoven har åtgärdats.</p><p>Oavsett hur APO ska används på Siemens är det mycket viktigt att implementeringsarbetet sker grundligt och att mycket fokus läggs på förberedelser. Ett avancerat planeringsverktyg som APO måste implementeras smart om det ska kunna användas fördelaktigt. Beroende på hur APO används kommer det eventuellt att medföra en del förändringar i hur dagens planeringsarbete ser ut vilket är viktigt för Siemens att inse och rätta sig efter. Därför är det av yttersta vikt att implementeringsarbetet utförs av konsulter med mycket kunskap och erfarenhet av APO tillsammans med personal på Siemens med god kunskap om verksamheten och dagens arbete.</p> / <p>Siemens Industrial Turbomachinery AB, called only Siemens below, is a Swedish gas turbine manufacturer located in Finspång and is a part of the German group Siemens AG. The production of gas turbines in Finspång was first started in 1911 a company named STAL. Since then the site has changed name numerous times and was at times part of groups such as ABB and Alstom Power before it finally became a part of Siemens AG.</p><p>Siemens has got a problem in their production planning process due to very long lead times, both within the own production and from their suppliers. The production lead time for a gas turbine is approximately 2.5 years although Siemens’s customers usually order a gas turbine one year before delivery. The production planners therefore have to rely on forecasts which create uncertainties. This makes it hard to make a master plan that match the actual demand. The complexity of a gas turbine and its deep and wide product structures makes it even harder. Without a good master plan it is very hard to plan effectively further down in the planning hierarchy for the procurement and manufacturing planners. There is clearly a need to improve the production planning process to be able to in time create a more stable and better plan.</p><p>Advanced Planner and Optimizer (APO) is a so called advanced planning system from SAP. SAP also delivers Siemens’s ERP-system R/3. APO can be used to plan and control an entire supply chain by using sophisticated mathematical methods. APO is connected to R/3 making data transfer in between easy and also allowing scenario testing outside the actual ERP-system.</p><p>The purpose of this master thesis is to analyze how APO can support the production planning process at Siemens. To do that, these three questions have been asked:</p><ul><li>What are the present needs at Siemens in Finspång?</li><li>What functions are available in APO and how do they work?</li><li>Is it possible to match the needs at Siemens with the functions in APO? </li></ul><p>Firstly the questions number one and two were answered simultaneously. Not until those questions were answered the answer to the third one was explored by analyzing the match between the two first.</p><p>The present needs found at Siemens were:</p><ul><li>To replace and improve the current simulation tool TS4 which will be gone in October 2010</li><li>To make capacity leveling and simulation possible at a tactical planning level that can enable better decision making for the master plan </li><li>Quotation at the make/buy decision has to become more effective to make planning easier at their own workshops as well as at their external suppliers </li><li>To decrease dependence of key human resources and also to stop relying on rules of thumb during the production planning </li><li>To simplify the tasks forecast aggregation and to develop the work regarding follow-up of forecasts </li></ul><p>The APO functions that have been investigated are Demand Planning (DP), Supply Network Planning (SNP) and Production Planning and Detailed Scheduling (PP/DS). DP can aggregate a demand plan and has got tools for statistical forecasting and forecast follow-up. SNP is a tool for tactical planning that also can handle capacity leveling through the three different solving methods Optimizer, Heuristics and Capable-To-Match. PP/DS use mathematical methods to handle detailed planning at a short planning horizon.</p><p>The authors’ first recommendation is to use the SNP module to improve their tactical planning. This could be done by letting the master planner control capacity leveling early on and also run different scenarios and then choosing the best one. This will provide Siemens’s production planners with reliable information to base decisions on. Also by using SNP in this way the need of a TS4 replacement will be met. SNP can also be used to manage the need of better quotation by building a separate model that is dedicated especially for this task which will increase the work efficiency in operational planning.</p><p>The second recommendation is that DP is being used as a supportive tool for demand planning and forecast follow-up. If both SNP and DP are used for the tasks mentioned above it will also be possible to decrease dependence of key human resources and avoid planning by rules of thumb.</p><p>Since there is no obvious need of improved detailed planning at the moment, PP/DS is not recommended to be implemented before the other recommended modules.</p><p>No matter how APO is going to be used at Siemens it is of utter importance that the implementation is thought through and well planned with a lot of focus on the preparations. An advanced planning system such as APO has to be implemented smartly if it is to work properly. Depending on how APO is to be used there might be some changes in the present way of working with planning which is important for Siemens to realize before executing the implementation. It is also very important that the implementation is being done by a team of skilled consultants with expert knowledge and plenty of experience of APO implementations together with people with a broad knowledge of Siemens’s business.</p>

Siemens Industrial Turbomachinery AB, nedan utskrivet som Siemens, är en svensk turbintillverkare placerad i Finspång som tillhör den tyska koncernen Siemens AG. Turbintillverkningen i Finspång började 1911 under namnet STAL men har sedan dess bytt ägarstruktur flertalet gånger. STAL-Laval, ABB STAL och Alstom Power är ett urval av de namn verksamheten har haft innan den blev en del av Siemens. Ett problem som Siemens står inför vid produktionsplanering av gasturbiner är de mycket långa ledtider inom den egna produktionen samt från underleverantörerna. Den totala ledtiden för en gasturbin är cirka 2,5 år samtidigt som de flesta kunder beställer sin gasturbin cirka ett år innan leverans. Produktionsplaneringen förlitar sig därför mycket på prognoser vilket skapar en osäkerhet. Detta gör det svårt att sätta en huvudplan som matchar den sanna efterfrågan från kunderna. Problemet förenklas inte av att en turbin är en mycket komplex produkt med djupa och breda produktstrukturer. Utan en säker huvudplan blir det svårt för Siemens att planera för tillverkning och inköp på ett effektivt sätt. Vid examensarbetets utförande fanns ett tydligt behov av att förbättra planeringsprocessen på Siemens för att på sikt få en stabilare och bättre planering. Advanced Planner and Optimizer (APO) är ett så kallat avancerat planeringssystem från den tyska affärssystemsleverantören SAP. SAP levererar dessutom Siemens affärssystem R/3. APO kan användas för att planera och styra en hel supply chain med hjälp av sofistikerade matematiska metoder. APO är kopplat till R/3 så data kan överföras dem emellan men arbetet som utförs i APO sker i en fristående miljö så att scenariosimulering kan genomföras. Examensarbetets syfte är att analysera hur APO kan stödja produktionsplaneringen på Siemens. För att göra detta har tre frågor ställts och besvarats. Dessa frågor är: Vilka behov finns i dagsläget på Siemens i Finspång? Vilka funktioner finns i planeringssystemet APO och hur fungerar de? Går det att matcha Siemens nuvarande behov med funktionerna i APO? Under arbetets första skede behandlades de två första frågeställningarna parallellt. Först när dessa besvarats analyserades matchningen dem emellan som efterfrågas i fråga tre. De behov som har observerats är: Ersätta och förbättra dagens simuleringsverktyg Testsystem 4 som kommer att försvinna oktober 2010 Möjliggöra kapacitetsutjämning och simulering vid taktisk planering för att skapa bättre beslutsunderlag till huvudplanen Kvotering vid köpa/tillverka-beslut måste effektiviseras för att ge ett bättre underlag till den egna verksamheten och till externa leverantörer Minska beroendet av nyckelpersoner samt komma ifrån betydelsen av tumregler vid planeringsarbete Förenkla arbetet med prognossammanställning och utveckla arbetet kring prognosuppföljning. De funktioner i APO som studerats under examensarbetes gång är Demand Planning (DP), Supply Network Planning (SNP) och Production Planning and Detailed Scheduling (PP/DS). DP kan sammanställa en efterfrågeplan och har verktyg för prognostisering och prognosuppföljning. SNP är ett verktyg för taktiskt planering som klarar av att beläggningsutjämna produktionen genom de tre lösningsmetoderna Optimizer, Heuristics och Capable-To-Match. PP/DS använder matematiska metoder för att underlätta den operativa planeringen i ett företag. Författarnas första rekommendation till Siemens är att använda modulen SNP till att förbättra den taktiska planeringen. Detta kan ske genom att huvudplanerare kan kontrollera beläggningsutjämning tidigt samt simulera olika planer för att välja den som anses bäst. På så sätt kommer Siemens planerare få ett bra beslutsunderlag att basera sina beslut på. Genom att använda SNP på detta sätt kommer även behovet av att ersätta TS4 uppfyllas. SNP kan även användas till att hantera behovet av bättre kvotering genom att bygga en separat modell dedikerad till denna uppgift vilket kommer leda till effektivare arbetsrutiner i den operativa planeringen. Den andra rekommendationen är att DP används som ett verktyg för att ge stöd vid efterfrågeplanering och prognosuppföljning. Om SNP samt DP nyttjas till de uppgifter beskrivna ovan kommer möjlighet ges till att avlasta nyckelpersoner och även undvika beroendet av tumregler. Eftersom det inte finns något överhängande behov av att förbättra detaljplaneringen rekommenderas inte att implementera PP/DS innan de under examensarbetet identifierade behoven har åtgärdats. Oavsett hur APO ska används på Siemens är det mycket viktigt att implementeringsarbetet sker grundligt och att mycket fokus läggs på förberedelser. Ett avancerat planeringsverktyg som APO måste implementeras smart om det ska kunna användas fördelaktigt. Beroende på hur APO används kommer det eventuellt att medföra en del förändringar i hur dagens planeringsarbete ser ut vilket är viktigt för Siemens att inse och rätta sig efter. Därför är det av yttersta vikt att implementeringsarbetet utförs av konsulter med mycket kunskap och erfarenhet av APO tillsammans med personal på Siemens med god kunskap om verksamheten och dagens arbete. / Siemens Industrial Turbomachinery AB, called only Siemens below, is a Swedish gas turbine manufacturer located in Finspång and is a part of the German group Siemens AG. The production of gas turbines in Finspång was first started in 1911 a company named STAL. Since then the site has changed name numerous times and was at times part of groups such as ABB and Alstom Power before it finally became a part of Siemens AG. Siemens has got a problem in their production planning process due to very long lead times, both within the own production and from their suppliers. The production lead time for a gas turbine is approximately 2.5 years although Siemens’s customers usually order a gas turbine one year before delivery. The production planners therefore have to rely on forecasts which create uncertainties. This makes it hard to make a master plan that match the actual demand. The complexity of a gas turbine and its deep and wide product structures makes it even harder. Without a good master plan it is very hard to plan effectively further down in the planning hierarchy for the procurement and manufacturing planners. There is clearly a need to improve the production planning process to be able to in time create a more stable and better plan. Advanced Planner and Optimizer (APO) is a so called advanced planning system from SAP. SAP also delivers Siemens’s ERP-system R/3. APO can be used to plan and control an entire supply chain by using sophisticated mathematical methods. APO is connected to R/3 making data transfer in between easy and also allowing scenario testing outside the actual ERP-system. The purpose of this master thesis is to analyze how APO can support the production planning process at Siemens. To do that, these three questions have been asked: What are the present needs at Siemens in Finspång? What functions are available in APO and how do they work? Is it possible to match the needs at Siemens with the functions in APO? Firstly the questions number one and two were answered simultaneously. Not until those questions were answered the answer to the third one was explored by analyzing the match between the two first. The present needs found at Siemens were: To replace and improve the current simulation tool TS4 which will be gone in October 2010 To make capacity leveling and simulation possible at a tactical planning level that can enable better decision making for the master plan Quotation at the make/buy decision has to become more effective to make planning easier at their own workshops as well as at their external suppliers To decrease dependence of key human resources and also to stop relying on rules of thumb during the production planning To simplify the tasks forecast aggregation and to develop the work regarding follow-up of forecasts The APO functions that have been investigated are Demand Planning (DP), Supply Network Planning (SNP) and Production Planning and Detailed Scheduling (PP/DS). DP can aggregate a demand plan and has got tools for statistical forecasting and forecast follow-up. SNP is a tool for tactical planning that also can handle capacity leveling through the three different solving methods Optimizer, Heuristics and Capable-To-Match. PP/DS use mathematical methods to handle detailed planning at a short planning horizon. The authors’ first recommendation is to use the SNP module to improve their tactical planning. This could be done by letting the master planner control capacity leveling early on and also run different scenarios and then choosing the best one. This will provide Siemens’s production planners with reliable information to base decisions on. Also by using SNP in this way the need of a TS4 replacement will be met. SNP can also be used to manage the need of better quotation by building a separate model that is dedicated especially for this task which will increase the work efficiency in operational planning. The second recommendation is that DP is being used as a supportive tool for demand planning and forecast follow-up. If both SNP and DP are used for the tasks mentioned above it will also be possible to decrease dependence of key human resources and avoid planning by rules of thumb. Since there is no obvious need of improved detailed planning at the moment, PP/DS is not recommended to be implemented before the other recommended modules. No matter how APO is going to be used at Siemens it is of utter importance that the implementation is thought through and well planned with a lot of focus on the preparations. An advanced planning system such as APO has to be implemented smartly if it is to work properly. Depending on how APO is to be used there might be some changes in the present way of working with planning which is important for Siemens to realize before executing the implementation. It is also very important that the implementation is being done by a team of skilled consultants with expert knowledge and plenty of experience of APO implementations together with people with a broad knowledge of Siemens’s business.

The objective of the master’s thesis work is to investigate how an autonomous underwater vehicle (AUV) should act in an underwater tunnel environment. The thesis proposes sensors, control strategies, mission statement, among others, required for tunnel assignments. A behavior-based control (BBC) strategy has been developed to control the AUV. The BBC is used in the middle level of the vehicle control, i.e. the reactive control system which describes how the AUV navigates through a tunnel, while other events are considered. The control strategy has also been separated into two parts, and these are: controlling the AUV’s heading and controlling the AUV to a desired distance from the tunnel wall. To be able to evaluate the performance of the system, a graphical user interface (GUI) has been developed. The GUI enables the operator to change control settings during simulations. Two proposed control strategies are presented with simulated results. / Syftet med examensarbetet är att undersöka hur en autonom undervattensfarkost (AUV) bör agera i en undervattenstunnel miljö. Avhandlingen föreslår sensorer, reglerstrategier, uppdragsbeskrivning med mera som krävs för tunneluppdrag. En beteendebaserad (behavior-based) reglerstrategi har utvecklats för att styra AUV:n. Reglerstrategin används i mellersta nivån i farkostens reglering, det vill säga den reaktiva regleringen som beskriver hur farkosten ska styra genom en tunnel samtidigt som andra händelser beaktas. Reglerstrategin har även delats upp i två delar: reglering av AUV:ns kurs och reglering av AUV:n till ett önskat avstånd från tunnelns vägg. För att kunna verifiera funktionaliteten av systemet så har även ett grafiskt användargränssnitt utvecklats. Gränssnittet möjliggör att man kan ändra reglerparametrar under en simulering. Två föreslagna reglerstrategier presenteras med tillhörande resultat.