Distributed information management

Distributed information management

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Distributed Information Management; Explain Mass Customization, and illustrate it With an Example.Mass customization refers to the demand heterogeneities among different clientele not as a risk, but, as an innovative prospect, for profits. In order to capture the value, however, a company has to acquire a specific set of abilities to deal with the challenges of such an industry.  Mass customization is the innovative paradigm that substitutes mass production (Haag & Cummings, 2010). The concept proactively manages the product diversity in the environment of speedily evolving products and markets, numerous niche markets, as well as individually customized commodities sold over the internet or through stores. Mass customization customizes commodities rapidly for individual clients or for specific niche markets at superior speed and efficiency than mass production (Feitzinger & Lee, 1997).

A case in point would be the case of mass customization option in the production of apparel. This example illustrates the points at which the customers participate in the process of production as illustrated in the model below.

In the model above, retailers and consumers participate in the design process by choosing fabrics, garment details, or the size dimensions for the clothing items. In this case, mass customization requires facilitative technologies for information, communication, and production (Fralix, 2001).

Patterns StageIn the patterns stage, custom fit and design take place. Patterns are developed for individual clients, according to specifications. Mass customization in this stage is made done through facilitative technologies like digital printing, computer-aided design (CAD), automated cutting, single ply, and body scanning pictures. The client provides dimensions and defines styling details and fabric prints (Fralix, 2001).

Design StageIn this stage, components are selected by the retailer or client from limited sizes, garment styles, as well as fabric options. For instance, retailers may add their store logo or ask for specific colors in the design stage.

Production PlanningMass customization at the production planning phase as well as at the Point-of-Sale (POS) phase is facilitated by electronic links. These electronic links are found among the apparel producer’s departments and between the clientele and the producer. Major retailers in the industry employ electronic data interchange (EDI) for the purposes of ordering, invoicing, as well as shipping. The Internet and EDI links are used to gather consumer procurement data as well as to establish production plans on the data Drucker, 2002).

Assembly StageApparel clientele participate in mass customization at the assembly stage if they desire to repeat a particular order in small quantities or with other fabrics. The order is cyclical by use of CAD equipment and subsequently sent directly to the assembly line. Supple manufacturing strategies like modular manufacturing enhance the effectiveness of small order productions.

DistributionWith the introduction of bar codes and EDI, clients’ Point-of-sale (POS) data are accessible to the retailer as well as the apparel producer. This data introduces the opportunity to deliver apparel to retail clientele based on the relevant sales as well as, inventory needs (Zhang, 2005).

Post PurchaseAdjustments in post-purchase are built into the products for customers to make any adjustments themselves. Sneakers with the options of different colors of laces, un-hemmed pants, air inflation in sports shoes, and gel placed in ski boots, are examples of creative forms of post-purchase adjustments.

Scalability

Improving database management system

A Database management system (DBMS) refers to the software that facilitates a computer to execute database functions of storage, retrieval, addition, deleting and modification of data. A database is comparable to a workbook or a document because they hold information. While spreadsheet and word processing are the software tools utilized to work with workbooks and documents, database management system software is employed to work with databases (Haag & Cummings, 2010). A DBMS comprises of five vital software components namely; DBMS engine, data manipulation subsystem, data definition subsystem, data administration subsystem, and application generation subsystem (Howard, 2004).

Data Manipulation SubsystemThe data manipulation subsystems assist in changing and deleting data in a database as well as query it for important data. Software tools in the data manipulation subsystem are often the chief interface between the data contained in the database and the user. Therefore, while the DBM system engine handles information demands from the physical perspective, it is the data manipulation tools in a DBM system that facilitates in specifying the logical information demands. The logical information demands are subsequently utilized by the DBM system engine in accessing the information needed from a physical perspective. In most DBM systems a range of data manipulation tools that include views structured query language, query-by-example tools, and report generators are found.

References

References

Dhar, V., & Stein. R. (2001). Seven Methods for Transforming Corporate Data into Business Intelligence. Upper Saddle River, NJ: Prentice Hall.Drucker, D. (2002) Internet Marketing Gets More Analytical, Internetweek, 7(1), 22.

Feitzinger, E. & Lee, H. L. (1997). Mass Customization at Hewlett-Packard: The Power of Postponement. Harvard Business Review. 75(1), 116-121.

Fralix, M. T. (2001): “From Mass Production to Mass Customization”, Journal of Textile and Apparel, Technology and Management. 1(2), 1-7

Haag, S., & Cummings, M. (2010). Management Information Systems for the Information Age, (8th Ed.). New York: McGraw-Hill Irwin.Howard, T. (2004). Query Evaluation: Strategies & Optimizations. Information Processing & Management, 31(6), 831.

Norvig, P., & Russell. S. (2009). Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall.Owen, P. (2002). Artificial Intelligence Techniques Enhance Business Forecasts: Computer-Based Analysis Increases Accuracy. Graziadio Business Review, 5 (2). 6.

Turban, E. (2006). Decision Support Systems & Intelligent Systems. Upper Saddle River, NJ: Prentice Hall. Zhang, G. (2005). Neural Networks in Business Forecasting. Information Science Publishing, 7 (2). 6.