Explain Mass Customization, and illustrate it With an Example. 3
What is a Database Management System? Discuss Each of the Five Important Software Components of a Database Management System. 6
Data Manipulation Subsystem.. 7
Application Generation Subsystem.. 8
Data Administration Subsystem.. 8
What is Artificial Intelligence? Explain the Artificial Intelligence Systems used Widely in Business. 8
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).
In 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 providesdimensions and defines styling details and fabric prints(Fralix, 2001).
In 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 colorsin the design stage.
Mass 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).
Apparel 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 cyclicalby 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.
With 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).
Adjustments 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.
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 workbooksand 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).
The DBMS engine is conceivably the most essential component of in the DBM system. The DBMS enginerecognizes logical demands from the other DBM systemsubsystems, translates them into their physical correspondents, and essentiallyaccesses the database as well as data dictionary as these exist in a storage space. Databases and DBM systems provide two great advantages in sorting out the logical against the physical view of data. First, the DBM systemengine deals with the physical tasks. This enables the database user to focus solely on the logical data needs. Secondly, even though there is just one physical view of data, there could be many knowledge workers havingdiverse logical views of the data in a database (Howard, 2004).
The data manipulation subsystems assist in changing and deleting data in a database as well as query it for importantdata. 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 specifyingthe 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 rangeof data manipulation tools that include views structured query language, query-by-example tools, and report generators are found.
The data definition subsystem in theDBM systemfacilitates in creating and maintaining the data dictionaryas well as defining the structure of the files found in a database.In creating a database, one must first utilize the data definition subsystem in creatingthe data dictionary and defining the files structure. In the event that a particular file requires an additional piece of data, the data definition subsystem is used in adding an extra field in the data dictionary (Owen, 2009).
The application generation subsystem in aDBM system contains facilities that help in developing transaction-intensive applications. Thesesorts of applications require that performing a detailed sequence of tasks in processing a transaction. Application generation subsystem contains tools for creation of visually attractive and user-friendly data entry screens, programming languages explicit to a particular DBM system and interfaces to universallyutilized programming languages that are autonomous of any DBM system(Howard, 2004).
The data administration subsystem in a DBM system facilitates in managing the entire database environment. It does this by offering facilities for security management, backup and recovery, query optimization, change management, and concurrency control. The data administration subsystem is often utilized by data administrators or database administrators(Dhar & Stein, 2001).
Artificial intelligence (AI) refers to an analytical process that is computer-based which demonstrates actions andbehavior that human observers regard as intelligent. AI endeavors to imitate human thought process. This includes optimization and reasoning. AI is a wide discipline that undertakes to simulate manynatural, human skills such ascase-based reasoning, automatic programming, neural networks, expert systems, decision-making,innate language processing, speech recognition, and pattern recognition (Haag & Cummings, 2010). AI technologies introduce increasingly complicated data-analysis characteristics to current applications.There are numerous definitions that endeavor to enlighten what Artificial Intelligence (AI) entails. It would be regarded as a science that scrutinizes intelligence and knowledge, probably the intelligent applicability of knowledge. One of the objectives of AI is the exploration of human cognition. AI is in reality an exploration into the generation of intelligence(Owen, 2009).
Business applications employ the specific technologies of AI to analyze potentially massive variability. For instance, AI is utilized in exploring unknown relationships or patterns in sales data and customer purchasing habits. Nevertheless, in the corporate world, Artificial Intelligence is widely utilized for intricate decision-support and problem-solving procedures in real-time applications of business. Business applicability of Artificial Intelligence techniques extends across functions that range from finance management, forecasting as well as production. AI technologies facilitate businesses in reducing latency in decision making, minimizing fraud and enhancing revenue opportunities(Norvig & Russell, 2009). A fresh generation of AI technologies has emerged that bear considerable potentiality in helping enhance the forecasting process. This includes applications such as employee turnover, product demand, cash flow and manpower forecasting, inventory, and distribution requirements. These systems that are based on AI are intended to bridge the gap between managerial and quantitative forecasting approaches(Turban, 2006).
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 forthe 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.
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