Free Essay

Centralized Warehousing

In:

Submitted By dlordisgood
Words 610
Pages 3
The organization set-up of the stores depends upon the requirements, and have to be tailor-made to meet the specific needs of an organization.
There are two broad classifications of Stores :

Functional Stores and Physical Stores based on physical considerations.

Physical considerations: There can be various types of stores based on the quantity of stocks held or distance from the point of usage, like central stores sub-stores, transit stores, site store etc.

Central store:
There can be a central store serving three or four factories or several shops in a large factory or it can be a central warehouse containing finished goods. The word ‘central’ only denotes that it severs various units each of which may have separate sub-stores or departmental stores. Central stores also exists in multi-plant situations.

Usually for better control, Organization keeps a Central Stores which is usually responsible for all activities mentioned above for entire organization and then sends them on ,as and when basis, to other stores which are usually attached to the production capacities located at various places.

For example a hospital may have a central stores with separate ones for each category of ward i.e. stores for linen, surgical instruments, drugs, and general requirements.

The stores in an airlines company may have the following sections – receipts, quarantine (pre-inspection), commercial stores, general paint and oil stores, stationary stores, raw materials stores, aircraft spares subdivided into engine spares and accessories, general, radio, instrument and maintenance.

Advantages of a Centralized Store :

1. Centralized Store can offer a wider range of goods is provided for all users than is possible in smaller stores.

2. Inventory can be minimum as material is ordered based on requirement of all other attached parties and material can be shunted to and from one store to other one attached to the Central Stores. This is especially so in the case of tools fixtures, equipment and spares.

3. Better control is possible.

4. Economies in storage is possible. Goods in bulk will occupy less space.

5. Bigger storehouses enables better and more modern handling methods (mechanical or automatic).

6. Delivery at a single point decreases cost of delivery.

7. Receipt and inspection of goods can be more efficiently organised.

8. Opportunities of standardization are improved.

9. Stock turnover is increased and the probability of deterioration during storage is correspondingly decreased.

10. Less personnel will be required for managing. Unnecessary duplication of records takes place in decentralized Stores. For example, one may have ten different Kardex cards for one martial stocked in ten places. Similarly, accounting work is multiplied.

Disadvantage of a Centralized Store:

1. Extra handling is involved and staff will be required for transportation from stores to the various production units.

2. If the system is not well organized there can be severe shortages at work places causing unnecessary interruptions in production. Inefficiency can also result in Production keeping some buffer inside the unit, which can lead to cluttering of space, and to pilferage because of the absence of security.

3 More internal documentation may become necessary

4. If a fire takes place there is a greater risk, because entire stocks can be lost bringing production to a total halt.

5. It is apparent that there can be myriad’s of types of materials which are stores, depending upon the type and complexity of the industry which the Store serves. There can be small items like nuts and bolts or heavy items like steel plates, there can be gases in cylinders (like LPG or oxygen), powders, liquids, some of them dangerous like sulphuric acid, or inflammable, like petrol, and so on. The variety is almost infinite.

Similar Documents

Premium Essay

The Importance of Data Warehousing

...The Importance of Data Warehousing Brenda L Bach The Digital Firm and Business Communications/BU 204-8A November 15, 2014 Ron Rosalik Kenneth and Jane Laudon state that a data warehouse is a database that stores current and historical data that can be of potential interest to decision makers throughout the corporation (Laudon, 2011. p.225). They go on to explain that the data can originate from many core operational transaction systems and could include data from Web site transactions (Laudon, 2011 p.225). Data warehouse extract current along with historical data from all operational systems within an organization. The data warehouse makes the data it collects and stores available to anyone and can be accessed and viewed as needed but cannot be altered in any manner. These data warehouses also provide a large range of ad hoc as well as analytical tools and graphical reports that represent the data. Companies often build enterprise data warehouses and either uses a central data warehouse or a smaller decentralized warehouse called a data mart to preserve the data they collect through its many sources. A data mart is a subset of a data warehouse that summarizes on a highly focused portion of the organization’s data and is placed within a separate data based for a very specific population of users. For example, a car dealer that deals with car sales as well as service may use a data mart to develop marketing and sales data that are specifically focused on the data of the...

Words: 774 - Pages: 4

Premium Essay

Microsoft and Vmware Comparison

...NT1210: INTRO TO NETWORKING WEEK 5 ESSAY Microsoft and VMware Comparison Tyler Reed In today’s world of cloud storage, cloud computing, and virtualization; I think it is important to understand what virtualization is before comparing the different options. Virtualization first started in the 1960’s as a way to logically divide the system resources provided by mainframe computers between different applications. It is very important to remember that computer strength and hardware performance wasn’t as strong or efficient when this method was introduced. While today’s smartphones are inherently stronger than the best computer during that time, the base problem still exists. Mainly because of the amount of resources needed to run multiple applications, allow server access to potentially thousands of computers and devices accessing the same computer or hardware. Since the concept’s inception the meaning has broadened into hardware virtualization, mobile virtualization, and desktop virtualization. Virtualization has helped organizations reduce cost and increase efficiency in the data center. New virtualization products allow for different benefits and challenges and must be carefully considered for businesses to realize the long term benefits, such as increasing user productivity and decreasing IT costs. Microsoft has the largest range of desktop virtualization and management products that can be integrated into a flexible solution for many different scenarios. Microsoft...

Words: 502 - Pages: 3

Premium Essay

System Analyst

...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...

Words: 1220 - Pages: 5

Premium Essay

Healthcare Data Warehousing

...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...

Words: 1887 - Pages: 8

Premium Essay

Star Schem & Snowflake Schema

...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...

Words: 557 - Pages: 3

Premium Essay

Business Data Warehouse

...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...

Words: 685 - Pages: 3

Free Essay

Teradata Introduction

...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...

Words: 1512 - Pages: 7

Premium Essay

Tools for Business

...Mid Sweden University The Department of Information Technology and Media (ITM) Author: Katarina Lundqvist, kalu9700@student.miun.se Study program: Master of Science in Engineering – Computer Engineering, 270 higher education credits Examiner: Tingting Zhang, tingting.zhang@miun.se Tutor: Aron Larsson, aron.larsson@miun.se Tutor: Mats Olsson, mats.olsson@sogeti.se Scope: 20102 words 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...

Words: 25350 - Pages: 102

Free Essay

Case Study: the Big Data Challenges

...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 problem from the first 1,000 units can...

Words: 577 - Pages: 3

Premium Essay

Kiako

...价值持续贡献,从而全面提升企业盈利能力。 4. Give an example of an experience that you have had with a company that uses a CRM system? IBM全球企业咨询服务部 IBM商业价值研究院 招商银行借咨询之力, 以CRM系统推进转型 尽管银行对批发业务的CRM(客户关系管理)需求旺盛,但是银行 界却鲜有成功案例。招商银行借助IBM的帮助,采用咨询与开发 同步的创新模式,成功地构建了批发业务CRM系统,并为其批发 业务管理的提升以及招商银行二次转型奠定了坚实的基础。不仅 如此,招商银行的创新举措再次创中国银行业的风气之先。 5. Provide an example of an organization with a CRM system that seems to increase comfort or satisfaction. Provide an example of an organization using a CRM system that may cause customer concerns over the loss of privacy. 10. What is the difference between a data warehouse, a data mart and a database? Database 1. Used for Online Transactional Processing (OLTP) but can be used for other purposes such as Data Warehousing. This records the data from the user for history. 2. The tables and joins are...

Words: 545 - Pages: 3

Premium Essay

Data Warehousing

...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 processing because the modeling technique will slow down and complicate transaction processing. Also, there are server technologies that that may speed up query and reporting processing but may slow down transaction processing (e.g., bit-mapped indexing) and server technologies that may speed up transaction processing but slow down query and report processing (e.g., technology for...

Words: 1160 - Pages: 5

Premium Essay

Miss

...international growth, Pick 'n Pay is South Africa's leading grocery retailer - one of the largest and most consistently successful retailers of food, clothing, and general merchandise. Since 1967, Pick 'n Pay has been dedicated to superior customer service, convenience, shopping efficiency, breadth of inventory, and employee growth. BUSI N ESS CHALLENGE Significant growth in the number of new Pick 'n Pay stores over the past few years resulted in increased data volumes and increased concurrent user query and reporting demands. Pick 'n Pay's 32 processor T eradata warehouse was not able to provide acceptable performance at an acceptable cost. SOLUTION Pick 'n Pay selected a management information reporting system (MIS) based on Sybase IQ data warehousing architecture to assure faster and more cost-effective delivery performance for corporate reporting. WHY SYBASE? Pick 'n Pay has a history with Sybase that goes back more than 10 years. With Sybase's business intelligence experience, IQ's list of numerous successful benchmark results and consistent history of customer success stories that showcase proven ROI, Sybase...

Words: 520 - Pages: 3

Free Essay

Ecommarce

...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...

Words: 593 - Pages: 3

Free Essay

Best Practices in Data Modeling

...Best Practices in Data Modeling Dan English Objectives • • • • • Understand how QlikView is Different from SQL Understand How QlikView works with(out) a Data Warehouse Not Throw Baby out with the Bathwater Adopt Applicable Data Modeling Best Practices Know Where to Go for More Information QlikView is not SQL (SQL Schemas) SQL take a large schema and queries a subset of tables. Each query creates a temporary “Schema” of only a few tables. Query result sets are independent of each other. Query 1 Query 2 QlikView is not SQL (QV Schemas) QlikView builds a smaller and more reporting friendly schema from the transactional database. This schema is persistent and reacts as a whole to user “queries”. A selection affects the entire schema. QlikView is not SQL (Aggregation and Granularity) Store Table Store A B SqrFootage 1000 800 Sales Table Store A A A B B Prod 1 2 3 1 2 Price $1.25 $0.75 $2.50 $1.25 $0.75 Date 1/1/2006 1/2/2006 1/3/2006 1/4/2006 1/5/2006 Select * From Store, Sales Where Store.Store = Sales.Store will return: SqrFootage 1000 1000 1000 800 800 Store A A A B B Prod 1 2 3 1 2 Price $1.25 $0.75 $2.50 $1.25 $0.75 Date 1/1/2006 1/1/2006 1/1/2006 1/1/2006 1/1/2006 Sum(SqrFootage) will return: 4600 If you want the accurate Sum of SqrFootage in SQL you can not join on the Sales table in the same Query! QlikView is not SQL (Benefits) • QlikView allows you to see the results of a selection across the...

Words: 1267 - Pages: 6

Premium Essay

Rarara

...Dealing with Missing Information in a Data Warehouse Today businesses are investing many resources 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...

Words: 988 - Pages: 4