For many years computer scientists have developed systems which have complete intelligence. However, not all of these programs are actually intelligent although virtually all of them have inputs which correspond to senses, choices of actions according to response rules, as well as the capability of acting either as graphics or as text outputs. Majority of the artificial intelligent systems have the potential of memorizing experiences as well as the ability to learn. This paper therefore examines decision support systems and the artificial support systems in the way they were made use of in Frito Lay.
The paper compares and contrasts the various types of decision support systems and artificial intelligence systems in relation to the ones which were implemented by Frito Lay. Tale of Contents Introduction 4 Decision Support Systems (DSS) 4 Types of Decision Support Systems 6 Artificial Intelligence Systems 8 Types of Artificial Intelligence 9 Conclusion 10 Reference 11 Introduction Many organizations such as Frito Lay would want to have unlimited access to more information which would help them in determining the way they operate and manage their organizations.
They would like access to real time and updated information which they can use to run their organizations more effectively. This information helps them compare organizational inputs, processes as well as outcomes during a particular period of time. Still, organizations like Frito Lay need longitudinal information which permits them to anticipate their future organizational needs. The implication here is that organizations need to undertake data-driven decision making; a feat which can be achieved by the use of decision support systems (National Forum on Education Statistics, 2006, p. 9). Decision Support Systems (DSS)
Organizations have realized that the only guaranteed way to get the right data within the reach of those charged with decision making is to invest in a decision support system as a solution to their needs pertaining to information management. To venture into decision support systems is likely to be more beneficial compared to the cost of such an investment (National Forum on Education Statistics, 2006, p. 9). The implication here is that purchasing a decision support system would imply heavy financial and operational commitments since it would involve hardware and software costs (Gregor & Benbasat, 1999, pp.
497-530). Decision support systems are very complicated with regards to the way they manage data as well as their technology architecture. This complexity is sometimes aggravated by conflicting information from the field of technology. Still some ambiguity may result from description as well as the inability to clearly differentiate concepts which are related in a particular way like data mart and data warehouses. At times, this confusion could arise from the vendors who may not be willing to differentiate the products they are dealing in with other prevailing systems in the market.
This implies that possibly there are various types of decision support systems (National Forum on Education Statistics, 2006, p. 9). One of the systems which were embraced by Michael Jordan, the new CEO who took over Frito-Lay on the brink of collapse was the state whereby control of decision making on issues like promotions and product mix were concentrated at the headquarters. This implies that the systems Frito-Lay instituted were those of tightly controlling strategy execution. The system included among others the position of accounts manager while the sales force was segmented.
Still the company had a system where handheld computers (HHC) were used as an information system tool in supporting managerial decision making. In other words Frito Lay implemented a system of micromarketing (Harvard Business School, 2001). The other system which was implemented by Frito Lay involved the subdivision of domestic operations into several geographic areas known as the Frito-Lay Market Areas (FLMAs). In this system, the company developed profit and loss accounts which provided approximated costs, revenues and contribution margins before taxes for all the areas (Harvard Business School, 2001).
In this regard and in the data generated by these analyses, the company embarked on a process of organization wide quality improvement in which case emphasis was laid on identifying the 10 non-performing FMLAs to be improved. The final system embraced by Frito Lay was the system building information infrastructure. In this case, the company implemented a chain of IT projects under the name Pipeline Project in support of operation process redesign within across the functional units.
In line with this system, the company equally implemented a system aimed at redesigning managerial processes; the structures and decisions which directed the way the company operated (Harvard Business School, 2001). A decision support system is not merely a system capable of manipulating data and supporting decision making. For instance, an enhanced user interface which allows querying and analysis of a unit database cannot be considered a decision support system (Gregor & Benbasat, 1999, pp. 497-530). Still, a spreadsheet application having advanced ‘if/then’ characteristics does also not qualify to be called a decision support system.
At the same time, a database management system (DBMS) which allows a user to select and analyze information within one database for the purposes of reporting and analysis equally does not qualify to be called a decision support system for the simple reason that it does not integrate many databases (National Forum on Education Statistics, 2006, p. 9). Types of Decision Support Systems Data warehouse and data mart are the two terms which have always been confused with decision support systems. However, these terms are different although they could be similar in terms of terminology and concept.
A data warehouse is a central repository where all information or a proportion of the information collected by an enterprise is collected (National Forum on Education Statistics, 2006, p. 12). What is emphasized by data warehousing is the storage of data from various sources though it does not in general concern itself with the intended user the way a decision support would. This implies that data warehousing plays the role of querying and reporting large combinations of data as opposed to querying and analyzing data.
(Gregor & Benbasat, 1999, pp. 497-530). In addition, the core purpose of data warehouse is to permit access to historical or transactional data in their fundamental format such as tables as opposed to distilling data into some format which allows for in-depth analysis. A data mart is more or less similar to a data warehouse in the sense that it acts as a repository for data, although such data could be limited in scope depending on the subject, function or even user group (National Forum on Education Statistics, 2006, p. 12).
One component of a decision support system is data quality. In fact data can is said to be the foundation of any decision support system meaning that high quality data is useful for decision-making as seen in the case of Frito Lay. Besides the data should be valid reliable and timely (Gregor & Benbasat, 1999, pp. 497-530). It is important to note that as is applicable to any data-based system, the quality of information generated by the decision support system is influenced by the quality of data which originally went into the system.
Thus, probably the single most important way of ensuring that high quality managerial data is collected and maintained is to consistently apply standard terms and business guidelines all over the organization (National Forum on Education Statistics, 2006, p. 16). The other type pf decision support system is the hardware, software and the data management procedures. As seen in Frito Lay, a decision support system is not a unitary piece of technology like a database or even a network (National Forum on Education Statistics, 2006, p. 17).
Instead, it is a system that incorporates and integrates disparate data sources with the intention of permitting decision-makers to have access to and store data in a way useful to them. Therefore, the technology associated with a DSS would be influenced by the preexisting technology and data architecture of the organization. Artificial Intelligence Systems As earlier stated, artificial intelligence systems in most cases have the capacity of storing experiences along with the ability of learning. Occasionally, the brain fails to do the action instantly though it makes use of the imagination it has.
The brain identifies a response criterion and establishes what situation arises from the action (Gregor & Benbasat, 1999, pp. 497-530). It then identifies again an action pertaining to this new situation and established the most likely outcome (Robot, 2007). Therefore it can choose not only a response criterion but also a complete plan of action. This calls for the programmer to design various functions of the intelligent system as program functions which are also known as sub programs. A robot as an example of an intelligent system operates in an interesting way.
The robot senses transmit information to the brain where the brain verifies if it has concept for the information it has received (Gregor & Benbasat, 1999, pp. 497-530). Otherwise, it creates a composite concept having the different sense information as partitions. What follows is the brain building up the prevailing situation from these concepts. A different domain of the brain checks in the memory to find out any response criterion for this condition before selecting the appropriate response rule and sending the action to the limbs for action (Robot, 2007).
During the beginning of the ‘life’ of the robot, its memory is void of concepts and responses. However, each time the robot experiences something; it stores a new response criterion having the present situation (Gregor & Benbasat, 1999, pp. 497-530). This implies that when the brain is not active, that is when it is ‘asleep’ it evaluates those response criteria while making generalizations implying that it is now equipped with response criteria which can be applied to a variety of situations.
Questions have been asked pertaining to the possibility of using artificial neural nets to achieve the functioning of the brain (Robot, 2007). However, for a computer, concepts and response criterion achieves more efficiency than neural nets. Just writing a computer program which is an intelligent system and running within a computer with an output on a screen is not sufficient. It is important to build complete systems capable of acting in the natural environment of the human being. Apparently, to be of use such a program would need to have an intelligence similar in a way to that of man.
This would signify the need for limbs and senses in order to enable it have same experiences and develop concepts for action similar in a way to that of man (Robot, 2007). Types of Artificial Intelligence Artificial or machine intelligence is the kind of ‘knowledge’ exhibited by any gadget manufactured by man. In most cases, it is hypothetically used to imply computers meant for general purposes though it could equally refer to scientific investigation. It could also refer to the intelligence of an artificial device (Gregor & Benbasat, 1999, pp.
497-530). What need to be noted however are the various types of artificial intelligence that are available. Strong artificial intelligence is concerned with creating some kind of intelligence capable of reasoning. Reasoning in this case being the ability to evaluate and derive a conclusion a certain premise (EconomicExpert, 2009). The other is the weak artificial intelligence which is concerned with creating computer based intelligence capable of reasoning and solving problems on a limited basis.
Such a device would at times operate as if it was intelligence but it wouldn’t have true intelligence (EconomicExpert, 2009). Testing such a device would require a turing test; an idea of testing the capability of a machine to undertake a conversation like man. Artificial intelligence has several fields; one of which is natural languages. Natural languages are used to draw a distinction between the languages spoken by man and the languages used in computer programming (Gregor & Benbasat, 1999, pp. 497-530). Conclusion
From this analysis it is evident that decision support systems are increasingly becoming important in modern day organizations such as Frito Lay. Depending on the configurations they have, decision support systems are powerful tools which organizations can embrace to address variety of questions about organizational performance, management, operations and policy-making (National Forum on Education Statistics, 2006, p. 14). For instance Frito Lay should embrace an appropriate IT infrastructure changes as a shift from the traditional management reporting procedures the company has long been undertaking.
This implies that the company implemented an IT strategy aimed at delivering more focused and timely information which was informed by an improved understanding of the business processes. Reference EconomicExpert. (2009). Artificial Intelligence Retrieved January 13 2009 from http://www. economicexpert. com/a/Artificial:intelligence. htm Gregor, S. , and Benbasat, I. (1999). Explanations from intelligent systems: Theoretical foundations and implications for practice, Mis Quarterly 23 (4) pp. 497-530. Harvard Business School. (2001).
Frito-Lay, Inc. : a Strategic Transition, 1987-1992 (Abridged). The McGraw-Hill Companies National Forum on Education Statistics. (2006). Forum Guide to Decision. Support Systems: A Resource for Educators. U. S. Department of Education, Washington DC: National Center for Education Statistics, pp. 1-34. Retrieved January 13, 2009 from http://nces. ed. gov/pubs2006/2006807. pdf. Robot, A. H. (2007). Artificial Intelligence Systems. New Horizons Press. Retrieved January 13, 2009 from http://www. intelligent-systems. com. ar/intsyst/artis. htm
University/College: University of Arkansas System
Type of paper: Thesis/Dissertation Chapter
Date: 6 October 2016
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