Expert Systems

The expert systems are just computer software. “They are programs that achieve expert level competence in solving problems in particular task area by use of knowledge base about that particular task area”⁽¹⁾. By other words, the expert system is a type of computer system that can perform a task of emulates the decision making that otherwise can be done by a human expert. However, the expert system will do such a task with more efficient abilities and sufficient capability. This type of systems was designed to deal with very complicated problems using the rules of IF-Then instead of following the computer conventional ways.

Expert systems are known as artificial intelligent (AI) programs. These programs are widely spread in different sectors and fields such medical, business, and agriculture. In agriculture, for example, we can recognize CLIPS (C Language Integrated Production System). It is the most expert system been used in this field. It was developed in 1985 at NASA and known today as fast, efficient and free expert system. This expert system in agriculture has had provided a recognizable many advantages to the agricultural field:

- has the ability to imitate human thought and reasoning

- Help to increase the production of corps.

- has the ability to handle uncertain information.

- help farmers to take single point decision

Neural Networks

Progressively, Neural networks becoming more and more prevalent in business world. Nowadays, many businesses are spending more on researches that focusing on neural network and data mining to find appropriate solutions that make the numerous business data readily. It is became obvious that such technologies and methodologies will help any company ( dealing with a wide range of business data) forecasting, modeling, clustering, and classification its data. As a result, decision making will be able to generate affective, useful and successful business plans. However, such results might need an expert to read them right. Thus what are neural networks?

Neural networks are a functional approximation tools that study the relationship between independent and dependent variables, “much like regression or other more traditional applications”⁽²⁾. The only difference between the neural networks and statistical approaches “is that neural networks make no assumptions about the statistical distribution or properties of the data”⁽²⁾. Additionally, the neural networks are classifies to several types based on its purpose, architecture, and its learning algorithm. There are many of those types such as:

1- Multilayered feed forward neural networks

2- Hopfield neural networks

3- Self-organizing neural networks

Some studies asserted that these are the three main models of artificial neural networks (ANN).

The business applications that use these type of ANNs are very helpful in many ways. They help identifying the customers who are, definitely, expected to buy and use the product provided. Such applications also had ability to predict customers’ behavior. Computer operates usually follow a sequential linear processing techniques and “apply some formulas, decision rules, and algorithms”⁽³⁾ to provide results that decision makers can use. Differently, the ANNs improve their own instructions and generate better results. There for, this type of applications can help to determine when a customer is about to switch to use a competitor’s product or service.

The ANNs have had illustrated a great success in terms of sales forecasting. This fact has been confirmed due to ANNs’ abilities to consider multiple variables at a time.

Trading and financial forecasting also was benefited from ANNs applications. They been used in solving problems related to “derivative securities pricing and hedging, futures price forecasting, exchange rate forecasting and stock performance and selection prediction”⁽²⁾.

Likewise, the insurance industry can benefit from such ANNs applications as well. Since Neural networks applications can multiply different variables at a time, the policy holders can be divided int many segments based on their behaviors for example. The ANNs provided results that help to determine effective premium pricing plans.

The field is increasingly grown, and the business applications that use the technologies of ANNs are expanding as well. It is hard to list or enumerate all of them; but we can recognize the following as most popolar such as: ΠAirline security control.

 Investment management and risk control.

Ž Prediction of thrift failures.

 Prediction of stock price index(3).

Sources: 

1- http://www.slideshare.net/VinayakSharma1/6expert-systems 

2- http://www.cob.calpoly.edu/~eli/pdf/neural.pdf

Assignment #3

Competitive Advantage

In order for any business to sustain, it has to learn how to deal with the tornado of competitiveness. The organization’s management might succeed in defining the foundation of competitive advantage which are the resources, capabilities, and core competencies. But, the ability of managing any competitive related issue requires the organization to acquire sufficient effective knowledge. More importantly, knowing how to use the acquired knowledge can be the most challenging issue.

The ability to manage or define the business resources cannot be enough by itself. In fact, resources (tangible and intangible) by itself cannot yield a competitive advantage. Why? That is because the resources are bundled to create organizational capabilities. In turn, capabilities are the source of a firm’s core competencies, which are the basis of competitive advantages. Consequently, the need to know what kind of knowledge is required and needed and how to manage this knowledge is a quite critical issue. Any organization can hire someone who has acquired enough knowledge to design an effective management system to manage the organization; but the need to know how his or her knowledge can be shared required more or different type of knowledge. Thus, the term of “social ecology” was determined to help any company to maintain an effective knowledge framework.

It is obvious that knowledge is based on the quantity and quality of information collected, processed, and generated. Studies had asserted that knowledge management rely on the social ecology of the company more what it does on the information. This fact was justified by referring the term of social ecology to the social system that clarifies how the employees operate the information or their knowledge. Gupta and Govindarajan in their article “Lessons From Nucor Steel” stated that “the determinants of social ecology are culture, structure, information systems, reward systems, processes, people and leadership”.

We all agree that the information systems playing the dominant role to communicate all those determinants. This obvious because, nowadays, the technology is responsible to process people’s activities, record their performance, matching their performance with the rewarding system, and report the results to the leaders of the organization.

Nucor, succeeded in analyzing its social ecology and became the most innovative and fastest growing steel company in during the years of 90s, 80, and 70s. GE also has been known as the first or second in market value added. As Nucor focused on the human capital and its relationship with the knowledge and how that knowledge can be increased, transformed and shared, GE had maintain the same level of successful. While Nucor concentrated its efforts on superior human capital, GE also established its works equation to be:

We look at what the world needs

*

A belief in a better way

+

A relentless drive to invent and build things that matter

=

A world that works better

Will be continued ...