Despite its contemporary relevance, we still have limited empirical knowledge about the forces underlying complex internationalization processes as when multinational corporations (MNCs) seek to enter new growing markets. Based on a real-time process study comprising ninety interviews and two hundred hours of observation made between 2009 and 2012, Atlas Copco’s entry into the Chinese mid-market was investigated. The intra-organizational analysis showed that three inter-related processes were underlying Atlas’ market entry and the results suggest that multiple interrelated motors may drive many contemporary internationalization processes. The processes identified are a sequential strategy process, an evolutionary process which shows that routines changed, and a political process. A somewhat surprising finding is that the main driver of internationalization according to received theory, the firm’s accumulated experiences, not only can drive internationalization, but may also hamper MNC managers’ possibilities to enter many of today’s new and growing markets. The findings add to our knowledge of the internationalization process in an increasingly complex international business setting, and especially highlight the need to distinguish between the sequential strategy process – more in line with received theory – and the other processes, in order to get a more full-fledged picture of what internationalization in large MNCs is all about.

Process planning is a task comprising a broad range of activities to design and develop an appropriate manufacturing process for producing a part. Interpretation of the part design, selection of manufacturing processes, definition of operations, operation sequences, machining datums, geometrical dimensions and tolerances are some common activities associated with the task. Process planning is also “the link between product design and manufacturing” with the supplementary commission to support design of competitive products. Process planning is of a complex and dynamic nature, often managed by a skilled person with few, or no, explicit methods to solve the task. The work is heuristic and the result is depending on personal experiences and decisions. Since decades, there have been plenty of attempts to develop systems for computer-aided process planning (CAPP). CAPP is still awaiting its breakthrough and one reason is the gap between the functionality of the CAPP systems and the industrial process planning practice. This thesis has an all-embracing aim of finding methods that cover essential activities for process planning, including abilities to predict the outcome of a proposed manufacturing process. This is realised by gathering supporting methods suitable to manage both qualitative and quantitative characterisation and analyses of a manufacturing process. The production research community has requested systematisation and deeper understanding of industrial process planning. This thesis contributes with a flow chart describing the process planning process (PPP), in consequence of the methodological studies. The flow chart includes process planning activities and information flows between these activities. The research has been performed in an industrial environment for high volume manufacturing of gear parts. Though gear manufacturing has many distinctive features, the methods and results presented in this thesis are generally applicable to precision manufacturing of many kinds of mechanical parts. / <p>QC 20140522</p>

Thesis (MScIng)--University of Stellenbosch, 2004. / ENGLISH ABSTRACT: As result of the increasingly competitive performance in today’s industrial environment, it has become necessary for production facilities to increase their efficiency. An essential step towards increasing the efficiency of these production facilities is through tighter processes control. Process control is a monitoring and modelling problem, and improvements in these areas will also lead to better process control. Given the difficulties of obtaining theoretical process models, it has become important to identify models from process data. The irregular behaviour of many chemical processes, which do not seem to be inherently stochastic, can be explained by analysing time series data from these systems in terms of their nonlinear dynamics. Since the discovery of time delay embedding for state space analysis of time series, a lot of time has been devoted to the development of techniques to extract information through analysis of the geometrical structure of the attractor underlying the time series. Nearly all of these techniques assume that the dynamical process under question is stationary, i.e. the dynamics of the process did not change during the observation period. The ability to detect dynamic changes in processes, from process data, is crucial to the reliability of these state space techniques. Detecting dynamic changes in processes is also important when using advanced control systems. Process characteristics are always changing, so that model parameters have to be recalibrated, models have to be updated and control settings have to be maintained. More reliable detection of changes in processes will improve the performance and adaptability of process models used in these control systems. This will lead to better automation and enormous cost savings. This work investigates and assesses techniques for detecting dynamical changes in processes, from process data. These measures include the use of multilayer perceptron (MLP) neural networks, nonlinear cross predictions and the correlation dimension statistic.The change detection techniques are evaluated by applying them to three case studies that exhibit (possible) nonstationary behaviour. From the research, it is evident that the performance of process models suffers when there are nonstationarities in the data. This can serve as an indication of changes in the process parameters. The nonlinear cross prediction algorithm gives a better indication of possible nonstationarities in the process data; except for instances where the data series is very short. Exploiting the correlation dimension statistic proved to be the most accurate method of detecting dynamic changes. Apart from positively identifying nonstationary in each of the case studies, it was also able to detect the parameter changes sooner than any other method tested. The way in which this technique is applied, also makes it ideal for online detection of dynamic changes in chemical processes. / AFRIKAANSE OPSOMMING: Dit is belangrik om produksie aanlegte so effektief moontlik te bedryf. Indien nie, staar hulle die moontlikheid van finansiële ondergang in die gesig – veral as gevolg van toenemende mededinging die industrie. Die effektiwiteit van produksie aanlegte kan verhoog word deur verbeterde prosesbeheer. Prosesbeheer is ‘n moniterings en modellerings probleem, en vooruitgang in hierdie areas sal noodwendig ook lei tot beter prosesbeheer. Omdat dit moeilik is om teoretiese proses modelle af te lei, word dit al hoe belangriker om modelle vanuit proses data te identifiseer. Die ongewone optrede van baie chemiese prosesse, wat nie inherent stogasties blyk te wees nie, kan meestal verklaar word deur tydreeks data vanaf hierdie prosesse te analiseer in terme van hul nie-liniêre dinamika. Sedert die ontdekking van tydreeksontvouing vir toestandveranderlike stelsels, is baie tyd daaraan spandeer om tegnieke te ontwikkel wat inligting uit tydreekse kan onttrek deur die onderliggende geometriese struktuur van die attraktor te bestudeer. Byna al hierdie tegnieke aanvaar dat die dinamiese proses stationêr is, m.a.w dat die dinamika van die proses nie verander het tydens die observasie periode nie. Die vermoë om hierdie dinamiese proses veranderinge te kan identifiseer, is daarom baie belangrik. Ook in gevorderde beheerstelsels is vroegtydige identifisering van dinamiese veranderinge in prosesse belangrik. Proses karakteristieke is altyd besig om te verander, sodat model parameters herkalibreer moet word, modelle opgedateer moet word en beheer setpunte onderhou moet word. Meer betroubare tegnieke om veranderinge in prosesse te identifiseer sal die aanpasbaarheid van proses modelle in hierdie beheerstelsels verbeter. Dit sal lei tot beter outomatisering en sodoende lei tot enorme kostebesparings. Hierdie werk ondersoek tegnieke om dinamiese veranderinge in prosesse te identifiseer, deur die analise van proses data. Die tegnieke wat gebruik word sluit die volgende in:multilaag-perseptron neurale netwerke, nie-liniêre kruisvoorspelling statistieke en die korrelasie dimensie statistiek. Die tegnieke is op drie gevallestudies toegepas om te sien of hulle die dinamiese veranderinge in die data kan identifiseer. Vanuit die navorsing is dit duidelik dat proses modelle nadelig beinvloed word deur niestationêre data. Dit kan dien as ‘n indikasie van veranderinge in die proses parameters. Die nie-liniêre kruisvoorspellings algoritme gee ‘n beter indikasie van dinamiese veranderinge in die proses data, behalwe waar die tydreeks baie kort is. Toepassings van die korrelasie dimensie statistiek gee die beste resultate. Hierdie tegniek kon dinamiese veranderinge vinniger as enige ander tegniek identifiseer, en die manier waarop dit gebruik word maak dit ideaal vir die identifisering van dinamiese veranderinge in chemiese prosesse.

Business process management is fast becoming one of the most important approaches for designing contemporary organizations and information systems. A critical component of business process management is business process modelling. It is widely accepted that modelling of business processes from scratch is a complex, time-consuming and error-prone task. However the efforts made to model these processes are seldom reused beyond their original purpose. Reuse of business process models has the potential to overcome the challenges of modelling business processes from scratch. Process model repositories, properly populated, are certainly a step toward supporting reuse of process models. This thesis starts with the observation that the existing process model repositories for supporting process model reuse suffer from several shortcomings that affect their usability in practice. Firstly, most of the existing repositories are proprietary, therefore they can only be enhanced or extended with new models by the owners of the repositories. Secondly, it is difficult to locate and retrieve relevant process models from a large collection. Thirdly, process models are not goal related, thereby making it difficult to gain an understanding of the business goals that are realized by a certain model. Finally, process model repositories lack a clear mechanism to identify and define the relationship between business processes and as a result it is difficult to identify related processes. Following a design science research paradigm, this thesis proposes an open and language-independent process model repository with an efficient retrieval system to support process model reuse. The proposed repository is grounded on four original and interrelated contributions: (1) a set of requirements that a process model repository should possess to increase the probability of process model reuse; (2) a context-based process semantic annotation model for semantically annotating process models to facilitate effective retrieval of process models; (3) a business process relationship meta-model for identifying and defining the relationship of process models in the repository; and (4) architecture of a process model repository for process model reuse. The models and architecture produced in this thesis were evaluated to test their utility, quality and efficacy. The semantic annotation model was evaluated through two empirical studies using controlled experiments. The conclusion drawn from the two studies is that the annotation model improves searching, navigation and understanding of process models. The process relationship meta-model was evaluated using an informed argument to determine the extent to which it meets the established requirements. The results of the analysis revealed that the meta-model meets the established requirements. Also the analysis of the architecture against the requirements indicates that the architecture meets the established requirements. / Processhantering, också kallat ärendehantering, har blivit en av de viktigaste ansatserna för att utforma dagens organisationer och informationssystem. En central komponent i processhantering är processmodellering. Det är allmänt känt att modellering av processer kan vara en komplex, tidskrävande och felbenägen uppgift. Och de insatser som görs för att modellera processer kan sällan användas bortom processernas ursprungliga syfte. Återanvändning av processmodeller skulle kunna övervinna många av de utmaningar som finns med att modellera processer. En katalog över processmodeller är ett steg mot att stödja återanvändning av processmodeller. Denna avhandling börjar med observationen att befintliga processmodellkataloger för att stödja återanvändning av processmodeller lider av flera brister som påverkar deras användbarhet i praktiken. För det första är de flesta processmodellkatalogerna proprietära, och därför kan endast katalogägarna förbättra eller utöka dem med nya modeller. För det andra är det svårt att finna och hämta relevanta processmodeller från en stor katalog. För det tredje är processmodeller inte målrelaterade, vilket gör det svårt att få en förståelse för de affärsmål som realiseras av en viss modell. Slutligen så saknar processmodellkataloger ofta en tydlig mekanism för att identifiera och definiera förhållandet mellan processer, och därför är det svårt att identifiera relaterade processer. Utifrån ett designvetenskapligt forskningsparadigm så föreslår denna avhandling en öppen och språkoberoende processmodellkatalog med ett effektivt söksystem för att stödja återanvändning av processmodeller. Den föreslagna katalogen bygger på fyra originella och inbördes relaterade bidrag: (1) en uppsättning krav som en processmodellkatalog bejöver uppfylla för att öka möjligheterna till återanvändning av processmodeller; (2) en kontextbaserad semantisk processannoteringsmodell för semantisk annotering av processmodeller för att underlätta effektivt återvinnande av processmodeller; (3) en metamodell för processrelationer för att identifiera och definiera förhållandet mellan processmodeller i katalogen; och (4) en arkitektur av en processmodellkatalog för återanvändning av processmodeller. De modeller och den arkitektur som tagits fram i denna avhandling har utvärderats för att testa deras användbarhet, kvalitet och effektivitet. Den semantiska annotationsmodellen utvärderades genom två empiriska studier med kontrollerade experiment. Slutsatsen av de två studierna är att modellen förbättrar sökning, navigering och förståelse för processmodeller. Metamodellen för processrelationer utvärderades med hjälp av ett informerat argument för att avgöra i vilken utsträckning den uppfyllde de ställda kraven. Resultaten av analysen visade att metamodellen uppfyllde dessa krav. Även analysen av arkitekturen indikerade att denna uppfyllde de fastställda kraven.

The purpose of my thesis is to create one-person show lasting approximately twenty minutes. The following paper contains the step-by-step process of developing this piece of work, audience reactions to the final performance, and thoughts on continuing the process in hope of sharing the play with other communities. / text

Today most demolition projects undertaken are complex and involve many parties who must comply with a myriad of regulations as well as other constraints (e.g. finance). The management of demolition projects requires individual team members to have a wide range of skills and knowledge in addition to practical experience. Demolition processes currently operated are frequently performed in an unstructured, intuitive manner with considerable reliance on experience, skill, knowledge or subjective judgement of the demolition engineer or other individual responsible for a demolition project. Research on demolition ‘process management' is a new area that has rarely been addressed. However, within the building design and construction management sectors, there are a number of intelligent solutions that have been developed to assist in the management of business processes and business process re-engineering. Process management systems and process mapping through the use of Information Communication and Technology (ICT) technology have been applied to help the construction industry significantly improve performance. In an attempt to provide intelligent support to the management of dynamic demolition processes, this research reports upon that was developed using ICT applications a management system prototype entitled “Demolish-IT”. The prototype of Demolish-IT utilises four tiers of information which includes: (1) the standard demolition process map; (2) user-defined demolition process map; (3) demolition task manager; and (4) database of demolition process requirements. The prototype provides on ICT platform that unifies demolition processes and requirements to achieve improved process management. The research involved a combination of face-to-face interviews with demolition experts; real projects document reviews and site observation to produce the generic demolition process map. Other products of the work included: database solutions to store demolition process requirements; and process modelling to manage demolition task compliance flow within relevant industry standards. Evaluation of the research work proved its validity, acceptance and applicability within industry. Future work will aim to transpose the outputs into commercially viable software.

Business process models are an important means to design, analyze, implement, and control business processes. As with every type of conceptual model, a business process model has to meet certain syntactic, semantic, and pragmatic quality requirements to be of value. For many years, such quality aspects were investigated by centering on the properties of the model artifact itself. Only recently, the process of model creation is considered as a factor that influences the resulting model's quality. Our work contributes to this stream of research and presents an explorative analysis of the process of process modeling (PPM). We report on two large-scale modeling sessions involving 115 students. In these sessions, the act of model creation, i.e., the PPM, was automatically recorded. We conducted a cluster analysis on this data and identified three distinct styles of modeling. Further, we investigated how both task- and modeler-specific factors influence particular aspects of those modeling styles. Based thereupon, we propose a model that captures our insights. It lays the foundations for future research that may unveil how high-quality process models can be established through better modeling support and modeling instruction. (authors' abstract)

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider.

Continuous process improvement is a critical element in maintaining competitiveness of the process industries. An important category of process improvement is process debottlenecking which is associated with plants that have sold-out products while making a profit. In such cases, market conditions and the prospects for enhancing revenues and profits drive the process to increase production. To overcome the limitation of conventional sequential unit-by-unit debottlenecking approach, this work introduces a new approach. This new approach is simultaneous in nature and is based on posing the debottlenecking task as a process integration task which links all the design and operating degrees of freedom and exploits synergies among the units and streams to attain maximum debottlenecking. Additionally, this new approach considers heat integration of the process while simultaneously performing the debottlenecking. Because of the general nonconvexity of the process model, a rigorous interval-based bounding technique is used to determine the target for maximum extent of debottlenecking aside from the problem nonconvexity. Inclusion isotonicity using interval arithmetic is used to determine a global bound for the maximum extent of process debottlenecking. Focus is given to no/low cost debottlenecking such as modest changes in design and operating degrees of freedom. Two case studies are solved to illustrate the applicability of the new approach and its superior results compared to the conventional sequential approach. Intensification, to debottleneck a process and to improve process safety is also addressed in this work. A new definition and classification of intensification is introduced. This classification distinguishes between two types of intensification: single unit and whole process. Process integration and optimization techniques are used to develop a systematic procedure for process intensification. Focus is given to the interaction among the process units while enhancing the intensification of the process. A case study is solved to illustrate the usefulness of the developed approach.

Much research in the area of process integration has focused on steady-state processes. However, there are many process units that are inherently unsteady-state or perform best when operated in an unsteady-state manner. Unsteady-state units are vital to chemical processes but are unable to be included in current process optimization methods. Previous methods to optimize processes containing unsteady-state units place restrictions or constraints on their use. This optimization still does not give the best system design because the solution found will only be the best out of the available options which likely excludes the true optimal design. To remedy this, a methodology was created to incorporate unsteady-state process units into process optimization analysis. This methodology is as general as possible. Unlike many existing unsteadystate optimization methods, it determines all three main components of process design: the network configuration, sizes of units, and operation schedule. This generality ensures that the truly optimal process design will be found. Three problems were solved to illustrate the solution methodology. First, a general mass exchange network was optimized. The optimization formulation resulted in a mixed-integer nonlinear program, and linearization techniques were used to find the global solution. A property interception network was also optimized, the first work done using property integration for systems with unsteady-state behavior. Finally, an industrial semi-batch water purification system was optimized. This problem showed how process integration could be used to optimize a hybrid system and gain insights into the process under many different operating conditions.