...Warehousing in logistics system Name Institution The role of warehousing in logistics system A warehouse is the building for storing goods while warehousing refers to the place or building where goods are stored. Warehousing plays the role of consolidation in the logistics system. Consolidation is the process of reducing the costs of transportation by consolidating the movement (Ross, 2004). Most of the companies supply their client with the same goods through a warehouse. From the warehouse; commodities are sent in a bulk shipment to the consumer. Therefore, rather than transporting the products in small shipment, it is economical having a consolidation warehouse. Safety stocking is another role of warehousing. In meeting different contingencies like stock outs or delays in the transportation, safety stocks should be maintained (Voortman, 2004). Safety stocking ensures that the inbound site of production does not arise, and the needs and requirements of the consumers are fulfilled in time as planned. Docking also enables the issue of full shipment from different suppliers like manufacturer and the process of distributing directly to the consumer without storage in the warehouse (Ross, 2004). After receiving the shipment, the products are located to the respective clients and moved to the vehicle for onward delivery to relevant consumers. Product mixing role enables the company...
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...Warehouse Management Systems (WMS) The evolution of warehouse management systems (WMS) is very similar to that of many other software solutions. Initially a system to control movement and storage of materials within a warehouse, the role of WMS is expanding to including light manufacturing, transportation management, order management, and complete accounting systems. To use the grandfather of operations-related software, MRP, as a comparison, material requirements planning (MRP) started as a system for planning raw material requirements in a manufacturing environment. Soon MRP evolved into manufacturing resource planning (MRPII), which took the basic MRP system and added scheduling and capacity planning logic. Eventually MRPII evolved into enterprise resource planning (ERP), incorporating all the MRPII functionality with full financials and customer and vendor management functionality. Now, whether WMS evolving into a warehouse-focused ERP system is a good thing or not is up to debate. What is clear is that the expansion of the overlap in functionality between Warehouse Management Systems, Enterprise Resource Planning, Distribution Requirements Planning, Transportation Management Systems, Supply Chain Planning, Advanced Planning and Scheduling, and Manufacturing Execution Systems will only increase the level of confusion among companies looking for software solutions for their operations. Even though WMS continues to gain added functionality, the initial core functionality...
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...Coffing Data Warehousing Education Outline 02/17/05 TERADATA EDUCATION OUTLINE Coffing Data Warehousing has provided quality Teradata education, products and services for over a decade. We offer customized solutions to maximize your warehouse. Toll Free: 1-877-TERADAT Business Phone: 1-937-855-4838 Email: mailto:CDWSales@CoffingDW.com Website: http://www.CoffingDW.com In addition to the course material listed in this outline, we also offer Teradata classes in Teradata Basics, Implementation, SQL, Database Administration, Design and Utilities. Please contact us so we can customize a course to fit your specific needs. © 2006 Coffing Data Warehousing – All rights reserved. Confidential. 1 Coffing Data Warehousing Education Outline 02/17/05 PURPOSE Coffing Data Warehousing has been providing quality Teradata education for over a decade. We offer customized courses to maximize the effectiveness of each class. The purpose of this proposal is to build a lasting relationship with your company. To this end, we have combined our comprehensive Teradata education services in a unique package that we feel best suits the diverse needs of your company while offering our high quality product at competitive pricing. Coffing Data Warehousing is excited to offer you, our preferred partner, an innovative new way to look at training at the CoffingDW Teradata University (CDW-TU). This approach provides the ability to maximize learning potential. Our goal is to make your employees...
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...Data Warehouse Design: Dimensional Modeling II Data Technology Chularat Tanprasert, Ph.D. Recap Dimensional modeling Popular, useful, and pragmatic approach Based on Kimball Fact table Dimension tables Design process in steps Database Schema Design Star Schema (With Attributes) Example Designs A useful way to learn about data warehouse design principles is by using examples – reuse. Kimball – Data warehouse lifecycle toolkit Adamson & Venerable – Data warehouse design solutions Let’s take a look at inventory, shipments, and financial services. Inventory An inventory system serves as a “middleman” between the manufacturer and the retailer – value adding process. There are threee types of inventory model Inventory snapshot Delivery status Transaction Inventory Snapshot Model For specific time periods, inventory levels are measured and recorded. Delivery Status Model Create one record for each complete shipment of a product to a warehouse. Transaction Model Record every transaction that affects the inventory. Shipments The shipments process is where the product leaves a company and is delivered to a customer. Typically, accompanying each shipment is a shipment invoice. Each line item on the shipment invoice corresponds to an SKU. Shipments Shipments Shipments Financial Services Typically a large bank. Services...
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...Data Warehouses The basic reasons organizations implement data warehouses are: To perform server/disk bound tasks associated with querying and reporting on servers/disks not used by transaction processing systems most firms want to set up transaction processing systems so there is a high probability that transactions will be completed in what is judged to be an acceptable amount of time. Reports and queries, which can require a much greater range of limited server/disk resources than transaction processing, run on the servers/disks used by transaction processing systems can lower the probability that transactions complete in an acceptable amount of time. Or, running queries and reports, with their variable resource requirements, on the servers/disks used by transaction processing systems can make it quite complex to manage servers/disks so there is a high enough probability that acceptable response time can be achieved. Firms therefore may find that the least expensive and/or most organizationally expeditious way to obtain high probability of acceptable transaction processing response time is to implement a data warehousing architecture that uses separate servers/disks for some querying and reporting. To use data models and/or server technologies that speed up querying and reporting and that are not appropriate for transaction processing There are ways of modeling data that usually speed up querying and reporting (e.g., a star schema) and may not be appropriate for transaction...
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...Week 3 - The Big Data Challenges May 2014 Case Study: The Big Data Challenges Glendoria Early Strayer University CIS 500 – Information Systems for Decision Making Dr. Vince Osisek May 12, 2014 Case Study: The Big Data Challenges Week 3 - The Big Data Challenges Page 2 What if your car could talk? Well Volvo CIO's had to wonder the same thing as well. Volvo, a Swedish multinational manufacturing company, that not only produce cars, but light and heavy weight trucks as well as buses. Volvo Car Corporation has a history of using innovation to produce premium automobiles. The manufacture chose to rely on information technology to gain the innovative edge to help the company grow by integrating the cloud infrastructure into their networks. The Volvo Data Warehousing Program, started in 2006 (Data in the Driver's Seat), it draws insight from this multi-terabyte resource to create clear business advantage by integrating information from four primary sources: a system for managing vehicle and hardware specifications, one for managing on-board software specifications, the system that collects vehicle diagnostic data from service centers worldwide, and the warranty administration system. (Tobey, 2010) Joining all the various pieces of data together, Volvo can be warned about potential mechanical issues that may show up in the early part of a car's lifecycle. Volvo can spot patterns from the data that may indicate a potential flaw in a particular part well before a...
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...Objective To become Data warehousing Techno-Functional Consultant Current Role: Team member Synopsis of Experience: ➢ Over 2+ years of IT experience in Application Development, Testing of Data Warehousing Projects. ➢ Worked extensively on ETL tool (Informatica 9.1). ➢ Working knowledge on BI Tool (BOXI) ➢ Good exposure to data analysis, data extraction and data loading. ➢ Extensively worked on Oracle 10G. ➢ Strong understanding of Dimensional Modeling, Star and Snowflake Schemas. ➢ Having good inter-personal, analytical and communication skills. Work Experience: ➢ Have been working since Dec 2011 to Till Date as Developer in Vertiv solutions (INDIA) pvt ltd. Education: B.Tech from Jawaharlal Nehru Technological University Technical Skills: Operating System : MS-DOS, Window 7, UNIX Database : Oracle 9i, MS-SQL Server ETL Tool : Informatica Power Center 9.1 OLAP Tool : Business Objects Scheduler : Autosys Other Utilities : TOAD 8.0 Project Information: Project#1 Vertiv solutions (INDIA) pvt ltd. Client: Hewlett Packard, Houston USA Project: E-WARRANTY MANAGEMENT SYSTEM Duration: Dec 2011 – Mar 2013 Environment: Windows XP, UNIX, Informatica 7.1.2, Business Objects 6.5, Oracle9i, TOAD8.0, MS-SQL Server2000, MS-DTS About the Client: Hewlett Packard...
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...inclusive of appendices Date: 2010-03-17 M.Sc. Thesis in Computer Engineering AV, 30 higher education credits Tools for Business Intelligence A comparison between Cognos 8 BI, Microsoft BI and SAP BW/NetWeaver Katarina Lundqvist Abstract 2010-03-17 Tools for Business Intelligence A comparison between Cognos 8 BI, Microsoft BI and SAP BW/NetWeaver Katarina Lundqvist Abstract The aim of the thesis was to conduct a general study of Business Intelligence and BI systems followed by a comparison of Cognos 8 BI, Microsoft BI and SAP BW/NetWeaver. The goal was to distinguish similarities and differences between the tools regarding technique, cost, usability and educational need and to provide a mapping for different customer situations. The method consisted of a theoretical study followed by a practical part including development, testing and interviews. The comparison showed that SAP and Microsoft both use the client/server model while Cognos is an integrated web-based system built on SOA. SQL Server can only be installed on Windows while BW and Cognos also support UNIX, Linux and IBM. SSRS report formats are HTML, PDF, CSV, XML, TIFF, Word and Excel. In BW, query results can be viewed as HTML, CSV and Excel. Cognos report formats are HTML, PDF, CSV, XML and Excel. The educational need for SQL Server and Cognos is low and may often be solved internally or through e-learning. In contrast, BW uses its own terminology and the enhanced star schema,...
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...in building data warehouses and data marts to obtain timely and actionable information that will give them better business insight. This will enable them to achieve, among other things, sustainable competitive advantage, increased revenues and a better bottom line. In the early '90s, data warehousing applications were either strategic or tactical in nature. Trending and detecting patterns was the typical focus of many solutions. Now, companies are implementing data warehouses or operational data stores which meet both strategic and operational needs. The business need for these solutions usually comes from the desire to make near real-time actions in a constantly changing environment while receiving information from both internal as well as external source systems. Dealing with missing or unknown data is critical in these types of environments. Unknowns skew metrics and results to produce incorrect decisions. Knowledge of the unknown allows at least for further examination of any conclusions drawn from incomplete data. Furthermore, in a well-designed business intelligence environment, these unknowns are often resolved later as data that is more complete is entered into the operational systems. Irrespective of the nature of the applications, missing information has always been a problem for data warehouses. As business intelligence environments become more mature, real time and mission critical, the increased number of operational applications accentuates this problem. It is important...
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...Hello One and All, In my last Blog we discussed about Dimensional Modelling and some of its components. Today we will go through different Schema that can be used during Dimensional Modelling to create a Data Warehouse. Before we start with today's topic , For my viewers those who are new to this field i would like to revisit some of the key points of my previous blogs: 1) Business Intelligence is mainly divided into three parts as per my understanding a) Data Warehouse design and Implementation (ETL process) b) Data Analysis (Using OLAP cubes) c) Reporting and Dashboard Creation For further details revisit my First blog 2) Important Components involved in Dimensional Modelling or Data Warehouse Designing a) Fact Tables (Additive Facts, Semi-Additive Facts, Non- Additive Facts) b) Dimension Table c) Grain For further details revisit my Second blog After a thorough revision of previous concepts lets start our today's discussion about different Schema involved in Dimensional Modelling or Data Warehouse Designing. First of all i would like to explain the meaning of the topic i.e Snow Covered Wagon Hitched to a Star = SnowFlake Schema and Star Schema are two types of Schema that are used while designing a Data Warehouse, Hence they can be explained as follows: Star Schema: A Star Schema is one of the simplest and easiest schema to understand. A...
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...1. Define, and illustrate using a diagram, the following: Primary Data Warehouse and Data Mart. In this connection, explain the difference between ROLAP and MOLAP. A Primary Data Warehouse is a central repository of a database of a complete organization. It holds multiple subject areas and very detailed information. A Data Mart is a subset or an aggregation of the data stored to a primary data warehouse. It often holds only one subject area – for example, a specific department, finance or sales. It may hold more summaried data, and is typically smaller than a warehouse because of its employment on a different grain. Figure 1.1 illustrates the difference between data mart and a primary data warehouse. Since the data mart typically holds one subject area, it is much smaller than a primary data warehouse. These data marts can be viewed as small, local data warehouses replicating the part of primary data warehouse which is required by a specific domain or department. Data Warehouse Data Mart Data Warehouse Data Mart Figure 1.1 A data warehouse does not necessarily use a dimensional model, since it is partly normalized RDBMS, but data marts are multidimensional cubes. This connection gives arise to two concepts, ROLAP and MOLAP. ROLAP is an implementation based on a relational database, in our case which is a primary data warehouse, and MOLAP is an implementation based on a multidimensional database which are data marts. ROLAP tools use the relational database to access...
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...reason why I chose this company is because I have done internships in both COMPANY X and Sun Life Malaysia and through that I am equipped with firsthand knowledge about the company. COMPANY X is based in Kuala Lumpur, Malaysia and their primary business lies around Southeast Asia. It is considered a huge company with 1,080 branches across the region and over 40000 employees. The first problem that I see when I was working there is that there were too many paper works. They have been using data from periodic paper report, data from other regional office through papers and many other reports that were manually submitted through papers. Some of the employee’s full time job is to extract details from these papers and upload them to their system in the HQ. For an example, I was doing my internship in the procurement department, basically the purchases are all made through Oracle software and communication between us and the vendors are all done through that. The accounting departments have their own software where they work on Accounts Payable, Invoices and things like that. The way they integrate to data to match the amounts, units purchased between the purchasing department and the accounting department is all manual. Obviously this is inefficient because there is always time lag and human error which will cause mismatches in accounts and delay payments. The main reason why this is happening is because different departments use different software hence reports are generated in...
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...• Provide a consistent information of various cross functional activity. Historical Data. Access, Analyze and Report Information. Augment the Business Processes Why is BI so Important Information Maturity Model Return on Information BI Solution for Everyone BI Framework Business Layer Business goals are met and business value is realized Administration & Operation Layer Business Intelligence and Data Warehousing programs are sustainable Implementation Layer Useful, reliable, and relevant data is used to deliver meaningful, actionable information BI Framework Business Requirements Data Sources Data Sources Data Acquisition, Cleansing,& Integration Data Acquisition, Cleansing, & Integration Data Stores Data Stores Information Services Information Delivery Information Delivery Business Analytics Business Analytics Business Applications Business Applications Business Value Business Value Development Data Resource Administration Data Resource Administration Data Warehousing BI & DW Operations Program Management BI Architecture ERP/BI Evolution Data Warehouse Standard Reports ROI Custom Reports Effort ERP Rollout Data Marts Views Excel Key Sites BI Focus Smaller Sites Time Customer Satisfaction BI Foundation Key Concepts: • Single source of the...
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...knowledge of Teradata as a Developer with proficiencies in Teradata SQL Assistant utilities like Fastload, Multiload, BTEQ, FastExport, and Tpump. * Experience in Teradata Data Warehouse architecture. * Proficient in application development and support, for batch processes hosted on an IBM mainframe, with a core focus in COBOL, DB2, JCL and VSAM. * Hands on experience in performance tuning, resolving on-going maintenance issues and monitoring of production runs. * Data modeling using Erwin, Star Schema, Snowflake, FACT and dimension tables. * Hands on experience in UNIX shell scripting. * Excellent communication and interpersonal skills. TECHNICAL SKILLS Frameworks | Informatica Data Director (IDD), Informatica System Integration Framework (SIF), Teradata version 12, Mainframe z/OS | Application Modules | UNIX, Informatica Application Designer, Workflow Manager, Workflow Monitor, Repository Manager, Informatica B2B...
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...Healthcare Data Warehousing Doug Kelley Health Informatics I Professor Lu December 7, 2012 Abstract ` Dimensional modeling lays the groundwork for data warehouses. Dimensional modeling is a similar process to traditional Entity/Relationship modeling in regards to tables (entities) having joins (relationships) with other tables via primary keys. Dimensional modeling has been used as a standard in industry for decision support systems in other areas such as transportation, production, sales and marketing. (Parmanto, 1) Because healthcare has many complex events, it has lagged behind other industries in terms of data warehousing. This paper will discuss several techniques that can help overcome these complexities. Introduction A data warehouse has been defined as a database optimized for long-term storage, retrieval, and analysis of records aggregated across patient populations, often serving the longer-term business and clinical analysis needs of an organization (Shortliffe, 932). For a data warehouse to perform these roles, it must be architected or modeled appropriately. There are a couple of different approaches to modeling data warehouses. Dimensional modeling is becoming standard approach. Background Review Designing a data warehouse for healthcare presents many unique challenges for designing a database. These include such complexities as multiple diagnoses, multiple payers, multiple physicians; primary and secondary, and late arriving data, such...
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