Thesis (D.B.A.)--Victoria University (Melbourne, Vic.), 2007. / Includes bibliographical references.

Supply chain management (SCM) is an area that offers organizations significant opportunities for both cost reductions and revenue enhancement. In their article, "Supply Chain Management: Implementation Issues and Research Opportunities," Lambert, Cooper and Pagh defined SCM as the "integration of key business processes from end user through original suppliers that provides products, services, and information that add value for customers and other stakeholders." Adopting and implementing appropriate technology has emerged as a source of competitive advantage for supply chain member firms through the integration of business processes with suppliers and customers. It is important to understand the factors influencing an organization's decision to acquire such technology. In the context of this study, connective technologies are defined as wireless communication devices and their accompanying infrastructure and software which may enhance coordination among supply chain partners. Building on previous literature in the areas of supply chain management, marketing strategy, and organizational innovation, a model was developed to test the relationships between organizational, interorganizational, and technology-related factors and the adoption of advanced connective technology, using radio frequency identification (RFID) as the test case, in the supply chain. A Web-based survey of supply chain professionals was conducted resulting in 224 usable responses. The overall model was statistically significant with four of the predictors significantly influencing the adoption of RFID in the supply chain. Size, centralization, new product advantage and time to achieve targeted ROI were significantly related to adoption of connective technology (RFID). Interorganizational related factors were not significant predictors of connective technology adoption. The study contributes to theory by testing scales from marketing and management in a supply chain context in order to better understand behavioral dimensions of supply chain management and logistics. The conceptualization and measurement of market orientation at the interfirm level advances the market orientation literature. Finally, the study contributes to the technology adoption literature by considering organizational-related, interorganizational-related, and technology-related factors that influence adoption of connective technology in the supply chain.

Motivation • Warum sollten wir Daten analysieren? Beyond Excel • Wie können wir Daten analysieren? Fallbeispiele • Welche Erkenntnisse haben wir gewonnen? • Flächenbedarf der Montage • Arbeitskräftebedarf in der Logistik • (Auto-) Korrelation

Often, in both the military and industrial sectors, the unavailability of essential components renders a complex system inoperable. Therefore, the primary objective of this research is to develop a methodology for determination of feasible strategies for the repair/replace decision. In the general equipment replacement problem, a finite planning horizon may be partitioned into stages such that an end item deteriorates toward a final stage where it is no longer economically or operationally feasible to continue to repair the item, or the item experiences fatal failure. This multi-stage deterioration process is very amenable to a dynamic programming solution methodology where the output from one stage becomes the input to the next stage. In the multi-stream replacement problem, the population of end items is grouped into streams depending upon such parameters as item age, the number of operational hours, or the environment in which the item operates. The reliability function is used to describe the survivor probability in this population model. A dynamic repair/replace program is formulated where the state functions are characterized by two parameters - item age and current operational condition. A computerized model is then developed that facilitates evaluation of repair/replacement strategies with respect to total life cycle costs of a logistics system. The solution methodology accommodates both stochastic and/or deterministic demand; different hazard models; a budget constraint; repair capacity constraint; various levels of repair; technological improvement; and organizational implementation issues. The operations impact of a generalized methodology for supporting the repair/replace decision and mode of repair is to provide opportunities for a more efficient use of organizational resources such as capital and repair facilities. / Ph. D. / incomplete_metadata

Huang Hongyan. / "February 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 134-142). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

A data-driven approach is considered a viable means of dealing with thehigh degree of dynamics caused by the constant changes that occur withinproduction logistics systems. However, there is a dearth of knowledgeregarding the consequences of employing a data-driven approach inproduction logistics in real industrial environments. This thesis aims toextend the existing body of knowledge concerning the opportunities andchallenges of a transition to a data-driven state in relation to productionlogistics through investigating real industrial cases.In addition to reviewing the literature, this thesis aims to answer threeresearch questions. First, it seeks to determine how enabling technologiescontribute to value creation in a data-driven production logistics system.Second, it studies three industrial companies, analyses their productionlogistics flows and compares the tradition approach to a data-drivenapproach by means of discrete event simulation. Third, through interviewswith several experts with different competences who work for the casecompanies, it aims to identify the challenges associated with the transitionto a data-driven approach.The results show that following a systematic and balanced approach totechnology implementation is important with regard to value creation. Thepotential benefits include improved operational performance, improvedvisibility through real-time control and the possibility for dynamicscheduling and planning. The challenges associated with the transition canbe divided into two major categories: organisational and technical.Moreover, the identified challenges can be mapped against each step in theproduction logistics data life-cycle.Among the identified challenges, some represent potentially valuableavenues for future research. Investigating the possibilities for addressingthe data ownership challenge among stakeholders is one such avenue.Additionally, future studies could address the fact that the technologiesrelated to data analytics, such as artificial intelligence, big data andblockchain, lack a large-scale implementation history when compared withtechnologies such as radio frequency identification. Given the limitations ofprior studies, another possible research avenue involves analysing the dataanalytics use cases in more detail within real industrial environments. / Datadrivna metoder betraktas som ett sätt att hantera den höga dynamik som orsakas av ständiga förändringar i industriella system för produktionslogistik. Dock finns det idag begränsad kunskap gällande konsekvenser av att tillämpa datadrivna tillvägagångssätt på produktionslogistik i industriell miljö. Denna avhandling syftar till att utöka den befintliga kunskapen om möjligheter och utmaningar vid övergången till datadriven produktionslogistik genom att utreda verkliga industrifall och genomföra litteraturstudier. Tre forskningsfrågor har formulerats för att nå detta syfte. Först, att utreda hur den möjliggörande tekniken bidrar till värdeskapande i ett datadrivet produktionslogistiksystem. För det andra, att utreda potentiella förbättringar i och med en övergång till datadrivet produktionslogistiksystem, där studier har genomförts på tre industriföretag, deras produktionslogistikflöde samt en jämförelse (genom diskret händelsestyrd simulering) mellan nuläge och börläge. För det tredje, att identifiera utmaningarna vid en övergång till datadrivet produktionslogistiksystem, där flera experter med olika kompetenser har intervjuats i företagen. Resultatet visar att ett systematiskt balanserat tillvägagångssätt för teknikimplementering är viktigt för värdeskapande. Potentiella fördelar inkluderar förbättrad driftsprestanda, förbättrad synlighet genom att ha realtidskontroll och underlätta dynamisk schemaläggning och planering. Övergångsutmaningar är indelade i två huvudkategorier; organisatoriska och tekniska. De identifierade utmaningarna kartläggs mot varje steg i produktionslogistikens livscykel. Bland de identifierade utmaningarna har vissa en särskild potential för framtida forskning. Att undersöka möjligheten att ta itu med utmaningen för dataägande bland intressenter är en av möjligheterna för vidare forskning. Dessutom, teknologier relaterade till dataanalys, såsom AI, big data och block chain har mindre storskalig implementeringshistorik jämfört med annan teknik, såsom RFID. Ett möjligt alternativ för vidare forskning är att analysera användningsfall av dataanalys i mer detalj, givet alla begränsningar som finns inom verklig industriell produktionsmiljö. Nyckelord Produktionslogistik, Data-driven, Smart, Transition, Teknologi, Simulering

A concept for the mathematical modeling of multiple repairable equipment and logistic systems (MREAL systems) is developed; These systems consist of multiple populations of repairable equipment, and their associated design, procurement, maintenance, and supply support. MREAL systems present management and design problems which parallel the·management and design of multiple, consumable item inventory systems. However, the MREAL system is more complex since it has a repair component. The MREAL system concept is described in a classification hierarchy which attempts to categorize the components of such systems. A specific mathematical model (MREAL1) is developed for a subset of these components. Included in MREAL1 are representations of the equipment reliability and maintainability design problem, the maintenance capacity problem, the retirement age problem, and the population size problem, for each of the multiple populations. MREAL1 models the steady state stochastic behavior of the equipment repair facilities using an approximation which is based upon the finite source, multiple server queuing system. System performance measures included in MREAL1 are: the expected MREAL total system life cycle cost (including a shortage cost penalty); the steady state expected number of shortages; the probability of catastrophic failure in each equipment population; and two budget based measures of effectiveness. Two optimization methods are described for a test problem developed for MREAL1. The first method computes values of the objective function and the constraints for a specified subset of the solution space. The best feasible solution found is recorded. This method can also examine all possible solutions, or can be used in a manual search. The second optimization method performs an exhaustive enumeration. of the combinatorial programming portion of MREAL1, which represents equipment design. For each enumerated design combination, an attempt is made to find the optimal solution to the remaining nonlinear discrete programming problem. A sequential unconstrained minimization technique is used which is based on an augmented Lagrangian penalty function adapted to the integer nature of MREAL1. The unconstrained minimization is performed by a combination of Rosenbrock's search technique, the steepest descent method, and Fibonacci line searches, adapted to the integer nature of the search. Since the model contains many discrete local minima, the sequential unconstrained minimization is repeated from different starting solutions, based upon a heuristic selection procedure. A gradient projection method provides the termination criteria for each unconstrained minimization. / Ph. D.

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Crude oil is among the most in-demand and desirable commodities across the world today. The multiple uses of crude oil are widespread, ranging from the generation of energy to its use as fuel for the petrochemical and transportation sectors. Because of the high use of and demand for crude oil worldwide, the petroleum industry (also known as the oil and gas industry) is a significantly role player in the world economy. Libya‘s oil and gas industry is deemed the most essential and important sector in this country, as Libya is mainly dependent on proceeds from this sector. However, existing information technologies are not always optimally applied and utilised. This, together with the challenge that information technologies in the supply chain system and logistics processes of some of Libya‘s oil and gas companies are not always sufficient, the productivity of the country‘s oil and gas supply chain is compromised, and this results in revenue losses because of wasteful expenditure. This research therefore aimed to determine what innovative information technologies should be considered by Zueitina Oil Company (as case study) in Libya to enhance its supply chain and logistics processes. Two primary research questions (PRQs) were formulated for this research, namely: (i) What is the current status of Libya‘s oil and gas sector in terms of oil production and export? (ii) What innovative information technologies need to be considered by Zueitina Oil Company in Libya to contribute towards optimising its supply chain and logistics processes? This study followed the qualitative research methodology, as the data collected were qualitative and the research design was inductive. A case study strategy was adopted, with Zueitina Oil Company in Libya as the case under study. The primary sources for the research were the management of the supply chain and logistics departments of Zueitina Oil Company. The qualitative data collection method selected was a survey conducted by means of telephone and focus group interviews. The data were analysed using thematic analysis. In total, five themes and six sub-themes emerged from the findings obtained from the analysis. This research contributes to the scientific body of knowledge by proposing an Information Technology Optimisation Model for the Supply Chain of Zueitina Oil Company in Libya in order to achieve increased revenues, reduced costs, and improved customer services, among others.