Nowadays warehousing became one of the most important and critical part in supply chain systems due to the fact that it consumes a considerable part of logistic cost. Designing phase of warehousing system is the most important part in warehousing since most of the strategic and tactical decisions should be taken in this phase. Majority of academic papers are primarily analysis oriented and does not give a systematic method and techniques as a basis for warehouse redesign. So there is a need to develop a structured procedure that can be applied for different type of warehouses. Therefore the purpose of this thesis is to develop a process for redesigning production warehouses, and analyzing major problems during redesign steps. The thesis is designed as a case study, and a mix of quantitative and qualitative methods were used for data collection and data analysis. The methodology focuses around the warehousing process and redesign steps as described in the literature. Results of the thesis develop a seven steps procedure for redesigning of the production warehouse, also different problems and challenges are faced during redesign steps. It was tried to choose the best redesigning method which fit with the characteristics of the warehouse, in order to cover the space reduction of the warehouse with the consideration of existing facilities and reducing of cost. In addition, the performance of the current warehouse system was evaluated based on current design of the warehouse in order to avoid repeating of same mistake in redesign process. Storage assignment policy as one of the redesign steps was discussed and a framework for storage system of the components were suggested. The findings of the thesis to some extent can be applicable to other production warehouses. Further research is suggested for more specific results and new developed redesign methods for all types of warehouses.

Traditional relational database model does not have enough capability to cope with a great deal of data in finite time. To address these requirements, data warehouses and online analytical processing (OLAP) have emerged. Data warehouses improve the productivity of corporate decision makers through consolidation, conversion, transformation, and integration of operational data, and supports online analytical processing (OLAP). The data warehouse design is a complex and knowledge intensive process. It needs to consider not only the structure of the underlying operational databases (source-driven), but also the information requirements of decision makers (user-driven). Past research focused predominately on supporting the source-driven data warehouse design process, but paid less attention to supporting the user-driven data warehouse design process. Thus, the goal of this research is to propose a user-driven data warehouse design support system based on the knowledge discovery approach. Specifically, a Data Warehouse Design Support System was proposed and the generalization hierarchy and generalized star schemas were used as the data warehouse design knowledge. The technique for learning these design knowledge and reasoning upon them were developed. An empirical evaluation study was conducted to validate the effectiveness on the proposed techniques in supporting data warehouse design process. The result of empirical evaluation showed that this technique was useful to support data warehouse design especially on reducing the missing design and enhancing the potentially useful design.

Made available in DSpace on 2014-06-12T15:55:27Z (GMT). No. of bitstreams: 2 arquivo2237_1.pdf: 2502933 bytes, checksum: d37d2a43704ed90dc4402e107f41af53 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Estudos abrangentes relacionados a data warehouse temporais e espaciais têm sido conduzidos. Data warehouse temporais permitem lidar com dados variáveis no tempo tanto em tabelas de fatos quanto em tabelas de dimensões. Uma ampla variedade de aplicações precisa capturar não só características espaciais, mas também temporais das entidades modeladas. Entretanto, estudos que unam essas duas áreas de pesquisa não têm sido suficientemente considerados. É neste contexto que o presente trabalho de dissertação está definido. Ele propõe um modelo conceitual para data warehouses espaço temporais. Este modelo permite aos usuários definir níveis, hierarquias e dimensões tanto com características espaciais como temporais. Como consequência disso, é possível representar atributos espaciais variáveis no tempo. Além disso, este trabalho define um conjunto de operadores espaço temporais que poderia ser útil na consulta de data warehouses espaço temporais. Diferentemente de propostas existentes, nossos operadores integram não só operadores multidimensionais e espaciais, mas também espaciais e temporais (i.e., espaço temporais) em uma única sintaxe. Um esquema taxonômico, o qual classifica os operadores propostos, também é definido. A importância da taxonomia proposta é que ajuda no desenvolvimento de tecnologia OLAP espaço temporal. Com o objetivo de automatizar a modelagem de esquemas espaço temporais, uma ferramenta CASE foi desenvolvida. Além de permitir a definição de esquemas conformes com o modelo conceitual proposto, esta ferramenta também permite a geração automática do esquema lógico correspondente usando uma abordagem objeto relacional. As ideias propostas são validadas com um estudo de caso na área meteorológica. O estudo apresenta uma aplicação prática do modelo conceitual espaço temporal e dos operadores espaço temporais apresentados neste trabalho

Data warehousing is a relatively new field in the realm of information technology, and current research centers primarily around data warehousing in business environments. As new as the field is in these environments, only recently have educational institutions begun to embark on data warehousing projects, and little research has been done regarding the special considerations and characteristics of academic data, and the complexity of analyzing such data. Educational institutions measure success very differently from business-oriented organizations, and the analyses that are meaningful in such environments pose very unique and intricate problems in data warehousing. This research describes the process of developing a data warehouse for a community college, focusing on issues specific to academic data.

Allocation of products in a warehouse is done by various storage policies. These are broadly classified into three main categories: dedicated storage, randomized storage, and class-based storage. In dedicated storage policy a product is assigned a designated slot while in random storage policy incoming product is randomly assigned a storage location close to the input/output point. Finally, the class-based storage is a mixed policy where products are randomly assigned within their fixed class. Dedicated storage policy is most commonly used in practice. While designing large warehouse layout, the product information in terms of throughput and storage level is either uncertain or is not available to the warehouse designer. Hence it is not possible to locate products on the basis of the throughput to storage ratio method used in the above mentioned storage location policies. To take care of this uncertainty in product data we propose a fuzzy C-means clustering (FCM) approach. This research is mainly directed to improve the efficiency (distance or time traveled) by designing a fuzzy logic based warehouse with large number of products. The proposed approach looks for similarity in the product data to form clusters. The obtained clusters can be directly utilized to develop the warehouse layout. Further, it is investigated if the FCM approach can take into account other factors such as product size, similarity and/or characteristics to generate layouts which are not only efficient in terms of reducing distance traveled to store/retrieve products but are effective in terms of retrieval time, space utilization and/or better material control.

Decision support systems are interactive, computer-based information systems that provide data and analysis tools in order to better assist managers on different levels of organization in the process of decision making. Data warehouses (DWs) have been developed and deployed as an integral part of decision support systems. A data warehouse is a database that allows to store high volume of historical data required for analytical purposes. This data is extracted from operational databases, transformed into a coherent whole, and loaded into a DW during the extraction-transformation-loading (ETL) process. DW data can be dynamically manipulated using on-line analytical processing (OLAP) systems. DW and OLAP systems rely on a multidimensional model that includes measures, dimensions, and hierarchies. Measures are usually numeric additive values that are used for quantitative evaluation of different aspects about organization. Dimensions provide different analysis perspectives while hierarchies allow to analyze measures on different levels of detail. Nevertheless, currently, designers as well as users find difficult to specify multidimensional elements required for analysis. One reason for that is the lack of conceptual models for DW and OLAP system design, which would allow to express data requirements on an abstract level without considering implementation details. Another problem is that many kinds of complex hierarchies arising in real-world situations are not addressed by current DW and OLAP systems. In order to help designers to build conceptual models for decision-support systems and to help users in better understanding the data to be analyzed, in this thesis we propose the MultiDimER model - a conceptual model used for representing multidimensional data for DW and OLAP applications. Our model is mainly based on the existing ER constructs, for example, entity types, attributes, relationship types with their usual semantics, allowing to represent the common concepts of dimensions, hierarchies, and measures. It also includes a conceptual classification of different kinds of hierarchies existing in real-world situations and proposes graphical notations for them. On the other hand, currently users of DW and OLAP systems demand also the inclusion of spatial data, visualization of which allows to reveal patterns that are difficult to discover otherwise. The advantage of using spatial data in the analysis process is widely recognized since it allows to reveal patterns that are difficult to discover otherwise. However, although DWs typically include a spatial or a location dimension, this dimension is usually represented in an alphanumeric format. Furthermore, there is still a lack of a systematic study that analyze the inclusion as well as the management of hierarchies and measures that are represented using spatial data. With the aim of satisfying the growing requirements of decision-making users, we extend the MultiDimER model by allowing to include spatial data in the different elements composing the multidimensional model. The novelty of our contribution lays in the fact that a multidimensional model is seldom used for representing spatial data. To succeed with our proposal, we applied the research achievements in the field of spatial databases to the specific features of a multidimensional model. The spatial extension of a multidimensional model raises several issues, to which we refer in this thesis, such as the influence of different topological relationships between spatial objects forming a hierarchy on the procedures required for measure aggregations, aggregations of spatial measures, the inclusion of spatial measures without the presence of spatial dimensions, among others. Moreover, one of the important characteristics of multidimensional models is the presence of a time dimension for keeping track of changes in measures. However, this dimension cannot be used to model changes in other dimensions. Therefore, usual multidimensional models are not symmetric in the way of representing changes for measures and dimensions. Further, there is still a lack of analysis indicating which concepts already developed for providing temporal support in conventional databases can be applied and be useful for different elements composing a multidimensional model. In order to handle in a similar manner temporal changes to all elements of a multidimensional model, we introduce a temporal extension for the MultiDimER model. This extension is based on the research in the area of temporal databases, which have been successfully used for modeling time-varying information for several decades. We propose the inclusion of different temporal types, such as valid and transaction time, which are obtained from source systems, in addition to the DW loading time generated in DWs. We use this temporal support for a conceptual representation of time-varying dimensions, hierarchies, and measures. We also refer to specific constraints that should be imposed on time-varying hierarchies and to the problem of handling multiple time granularities between source systems and DWs. Furthermore, the design of DWs is not an easy task. It requires to consider all phases from the requirements specification to the final implementation including the ETL process. It should also take into account that the inclusion of different data items in a DW depends on both, users' needs and data availability in source systems. However, currently, designers must rely on their experience due to the lack of a methodological framework that considers above-mentioned aspects. In order to assist developers during the DW design process, we propose a methodology for the design of conventional, spatial, and temporal DWs. We refer to different phases, such as requirements specification, conceptual, logical, and physical modeling. We include three different methods for requirements specification depending on whether users, operational data sources, or both are the driving force in the process of requirement gathering. We show how each method leads to the creation of a conceptual multidimensional model. We also present logical and physical design phases that refer to DW structures and the ETL process. To ensure the correctness of the proposed conceptual models, i.e., with conventional data, with the spatial data, and with time-varying data, we formally define them providing their syntax and semantics. With the aim of assessing the usability of our conceptual model including representation of different kinds of hierarchies as well as spatial and temporal support, we present real-world examples. Pursuing the goal that the proposed conceptual solutions can be implemented, we include their logical representations using relational and object-relational databases.

<p>Armatec tillhandahåller system, produkter och funktionella lösningar inom värme, kyla och process. Företaget har lager i Sverige, Norge och Danmark. Under senare tid har företaget sett att lagret i Norge utgör en stor kostnad. Armatec har därför börjat fundera på om de skulle tjäna på att centralisera logistikstrukturen dvs. stänga lagret i Norge och utöka centrallagret i Göteborg. Syftet meddetta examensarbete är att undersöka huruvida lagret behövs i Norge eller ej. Den metod som arbetet använder sig av är Dupont - modellen eller avkastningsmodellen. Genom att använda denna modell beräknas lagerkostnaderna, transportkostnaderna, lagerstorlek, kapacitetsutnyttjande osv. Dessutom gör man en analys av leveransprecision, leveranssäkerhet, leveranstid och leverensflexibilitet. Arbetet har gått genom dessa punkter och finner att lagerräntabiliteten ökar med 13% om företaget Armatec stänger sitt lager i Norge. Detta under villkor att leveransservicenivån förbättras. Transportkostnaderna ökar något men inte markant i det här sammanhanget. Arbetet berör också andra typer av metoder som behandlar lagerlokaliserings fråga nämligen tyndpunktsmetoden, matematiska modeller samt Analytic Hierarchy Process (AHP) Uppsatsen använder sig av en differentierad lagerhållningsränta för att beräkna lagerkostnaderna. Man har dock utgått ifrån en multivariabel ABC analys för att klassificera produkterna. En kvalitativ metod nämligen SWOT- analysen dvs. Styrka ,Svaghet, möjligheter och Hot avslutar detta arbete. Slutstaten med detta examensarbete är att Armatec skulle tjäna på en centralisering av lagerstrukturerna. Författaren vill påminna läsaren att Företaget vill behålla sitt lager i Köpenhamn. Det är därför Köpenhamn inte igår i arbetet. Företaget har inget lager i Finland. Arbetet visar också att den finska marknaden inte kommer att påverkas i samband med centralisering</p>

Armatec tillhandahåller system, produkter och funktionella lösningar inom värme, kyla och process. Företaget har lager i Sverige, Norge och Danmark. Under senare tid har företaget sett att lagret i Norge utgör en stor kostnad. Armatec har därför börjat fundera på om de skulle tjäna på att centralisera logistikstrukturen dvs. stänga lagret i Norge och utöka centrallagret i Göteborg. Syftet meddetta examensarbete är att undersöka huruvida lagret behövs i Norge eller ej. Den metod som arbetet använder sig av är Dupont - modellen eller avkastningsmodellen. Genom att använda denna modell beräknas lagerkostnaderna, transportkostnaderna, lagerstorlek, kapacitetsutnyttjande osv. Dessutom gör man en analys av leveransprecision, leveranssäkerhet, leveranstid och leverensflexibilitet. Arbetet har gått genom dessa punkter och finner att lagerräntabiliteten ökar med 13% om företaget Armatec stänger sitt lager i Norge. Detta under villkor att leveransservicenivån förbättras. Transportkostnaderna ökar något men inte markant i det här sammanhanget. Arbetet berör också andra typer av metoder som behandlar lagerlokaliserings fråga nämligen tyndpunktsmetoden, matematiska modeller samt Analytic Hierarchy Process (AHP) Uppsatsen använder sig av en differentierad lagerhållningsränta för att beräkna lagerkostnaderna. Man har dock utgått ifrån en multivariabel ABC analys för att klassificera produkterna. En kvalitativ metod nämligen SWOT- analysen dvs. Styrka ,Svaghet, möjligheter och Hot avslutar detta arbete. Slutstaten med detta examensarbete är att Armatec skulle tjäna på en centralisering av lagerstrukturerna. Författaren vill påminna läsaren att Företaget vill behålla sitt lager i Köpenhamn. Det är därför Köpenhamn inte igår i arbetet. Företaget har inget lager i Finland. Arbetet visar också att den finska marknaden inte kommer att påverkas i samband med centralisering

En quête de performance, les Third-party Logistics (3PL) sont amenés à réorganiser régulièrement leurs activités d’entrepôt. Peu d’études portent sur les systèmes de mesure de la performance (SMP) dédiés au 3PL. Les auteurs étudient les activités indépendamment les unes des autres alors qu’il existe des relations de cause à effet entre ces dernières. La conception d’un SMP pour la réorganisation des activités conduit à nous interroger sur ses conséquences pour le système de contrôle de gestion, au travers du cadre théorique de Simons.Une Recherche-Intervention est réalisée au sein du 3PL : FM Logistic. Notre contribution porte sur la conception d’un SMP sous forme de graphe de problèmes mettant en relation les connaissances sur la réorganisation des activités des experts et de la littérature scientifique. Ce SMP est construit à partir d’un langage inspiré de la théorie TRIZ. La conception du SMP permet l’analyse de l’évolution d’un contrôle de gestion diagnostique vers l’interactivité. / Third-party logistics (3PL) seek performance by reorganizing regularly their warehouse operations. Few researchers study performance measurement systems (PMS) dedicated to 3PL. Researchers in warehousing design are used to study all the different operations one by one while these operations are linked together. As far as we know the literature does not propose any SMP for warehouse operations reorganization. SMP design leads to analyze the consequences for management control system, studied through Simons’ levers of control.An Intervention-research is conducted in a french 3PL : FM Logistic. Our contribution is the design of a performance measurement system in the form of problem graph which linking both knowledge advocated by a French 3PL and quoted in the literature. This SMP has been designed using a semantic and a syntax inspired by TRIZ problem graph. SMP design enables to analyze interactivity development. Results show the development of diagnostics systems towards interactivity.

Historically, the paper market has consisted of few customers with high demands. However, the market has changed and nowadays it consists of many customer with lower demands, which causes problems in the warehouse process of Braviken Papermill. The main problem is that the utilization rates in the storage bays are low due to the relatively large bays compared to the order quantities. The purpose of this project is to develop a new set of bays that fits the current market and increases the utilization rates. The goal of the new set is to decrease the amount of manual operations and instead use the full potential of the allocation system.    The new set of bays consist of four bay types, dimensioned for 2, 4, 8 and 16 reel stacks respectively. Several experiments are done, and the new set of bays performs better than the current one, in terms of the utilization rates for storage events in the bays. The average utilization rate is significantly higher for the new set compared to the current. A decrease in effective storage space can be compensated by an increased degree of utilization. Also, it is beneficial with more and smaller bays in a market situation like the one of Holmen Paper.