The franchise decision support system is a computer-based decision-making system that provides an interface through which interactions with human beings are achieved. It utilizes data models in decision-making and provides methods of achieving decisions by offering support for human beings who work on them.
By focusing on decision-making as an effective process, the system facilitates decision-making through structured or non-structures approaches.
Current trends in the business world indicate that franchise business formats exist in two forms, business format, and product or trade franchise. Franchising makes a lot of business sense in the world economy today. Franchising places individuals at levels where they acquire the ability to compete with large business organizations (Dennis, George, Jessup, Nunamaker & Vogel, 1988).
A key element in the franchising business world is their order of development or growth. That is the case with the current business franchise model.
It is worth noting that, the franchise model was developed through the following stages. These included the infancy stage, the adolescence stage, and the maturity stage. Each stage is marked by distinguishing characteristics.
At the infancy stage, the business model was conceptualized and the operational requirements were identified in the process before the idea was tested and an effective framework was created by group members that could support all aspects of its growth. That idea was projected to grow and if successfully deployed, be duplicated into a larger area of application.
It was projected to grow to the extent that it could attract customers seeking services who may want to access a national network, build brand identity, and target marketing by offering national marketing services. The brand identity is focused on providing a valuable brand for the franchise system. The system was created to reduce market concentration as is the characteristic that defines a franchise marketing system.
The relationship could be enhanced, as a strategic objective with customers whose age bracket could be anywhere between 21 and 35 years. Perhaps customers in the age bracket could be attracted through economic empowerment and projected with franchisees.
It is projected that once the system reaches its maturity stage, new opportunities will be identified in the process. These opportunities will target customers in the age group of 21 to 35 years. A strong business brand will be developed in the process, new opportunities will be adapted into the process, and team building will characterize the franchisees. A strong market share will be built in the process, systems activities will be effectively documented in the process, and the whole business community will stay competitive in the marketplace. Ways to succeed (Druzdzel & Flynn, 2002).
Strategic marketing approaches will be used in marketing the brand there created; ensuring strong financial systems are incorporated into the system and specific analysis of the strength of the brand.
Long Distance Outlets
The system addresses issues related to frontier markets and their strategic growth, bearing in mind the stiff competition for profits in the franchise market. The system can create a franchise chain away from its established base. In addition to that, frontier markets play a critical role in determining the efficiency of the market.
The market density, expansion, market outlets, tailoring services, and products to target specific age groups, and environmental challenges were some of the key issues considered in the design and development of the system.
Profitability
Profitability is a key component in the success of the franchising system. Profits generated with the target age group could be daunting depending on their incomes and willingness to access and use services offered by the business organization. Other issues that could be drawn into influencing the profitability of the company could largely depend on the infrastructure network, value addition activities, and other related components (DeSanctis & Gallupe, 1987).
However, the system comes with unique benefits. Among them is that it is uniquely designed and tailored to target a given income group and age group. In addition to that, the system can be hosted on a variety of hardware and software platforms. These unique capabilities could enhance decision-making at different levels of franchising to drive the business into greater profitability raising the company’s revenue further.
Theoretically, an analysis of consumer behavior across a number of countries is illustrated below.
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Consumer prevalence, market conditions, business processes, and demand for specific products such as rental houses are some of the factors considered in the design of the franchise system (DeSanctis & Gallupe, 1985).
The architecture of the Decision Support System
This system is characterized by two major components. These include the or ware or human-based decisions and computer-based decisions. Computer hardware and software are part of the whole system that becomes complete when the human factor is integrated into the system. Orgware are human based decisions that are based on intuition.
The system is typically defined by a database management system used to manage logical data structures. The DBMS provides large data storage capabilities from which data mining could be done by the relevant department or personnel to influence decision making. Decisions made could address the actual class of problems on the ground. It provides transparency to users of the system where they are separated from the architectural aspects of the system. The DBMS provides capabilities for users to data types stored in the system and methods of accessing the data stored in data structures within the system (Carter, Murray, Walker & Walker, 1992).
Another architectural component of the system is an analogous data model from the DBMS, the model base management system (MBMS). The Decision Support System (DSS) separates different models used in the design and development of the DSS from other applications, thus creating interdependence between them. These applications become autonomous from each other through the MBMS. In addition to that, the MBMS provides assistance to the users of the system in modeling the data they want to use to generate reports from system. This is so since users’ data is in most cases unstructured.
The next component is the dialogue generation and management system. This system or component provides the insight with which users interact with the system. The DSS is equipped with user interfaces that are easy to interact with and use. They provide capabilities for the user to navigate through system’s interface and manipulate the system to perform certain or specified tasks with ease and flexibility. The component therefore, provides capabilities for novice users to freely and easily navigate with the system and access any information needed at the moment for decision making.
The interfaces aid managers and other decision makers in model building particularly when a need for advice on particular decisions is required. They aid in gaining insight into decision making. The system provides enhanced services during decision making for users.
These three components play a critical role in the architecture of the system at large. It is important to note that the user’s entry into the system is through the DGMS. Once the user has logged into the DGMS, then the DGMS communicates with the DBMS and the MBMS components that form an integral part of the whole decision support system. All implementations and the architectural framework are supported in the system while each component of the architecture is made transparent to the user. Therefore, the user does not know how each decision is implemented except for the solutions that are generated at the interface. The following diagram provides a conceptual view of the decision support system.
Functions and decision making process and components
Every phase of decision making is supported in the process. Thus a striking contrast exists between management information systems and a franchise decision support system. Every process is facilitated in decision making despite the fact that the system does not focus on efficiency but focuses on enhanced decision making approaches. Each of the three stages of decision making that characterized human beings are incorporated in the process.
The human decision making stages are defined by intelligence as a key element in the process, design that provides a framework for the system, and choice which is an option that the human being can opt to take to suit one’s personal goals. Each problem is defined at the intelligence stage. These decision are made based on data and information retrieved from the system using a variety of techniques such as data mining, transactions that have been processed through the system, financial statements, individuals who have logged into the system, and a host of other data and information in the process.
The degree to which information generated from the system could influence decision making could directly have a bearing on the system’s abilities in facilitating decision making. However, it is worth noting that current decision making systems can be sued to evaluate decisions made and projected influence these decision could have on the final task or activity.
Thus, future projections and consequences of decisions made through information and data obtained from this system could be used to plan future financial tasks by creating an accounting model targeting the organization’s goals and objectives. That could include all data and information about the demographic trends of customers and franchisees aged between 21 and 35 years.
Components of the System
A user dashboard is one of the components of the system that is customized to meet all the data and information needs of the users. These users are the target group designed for the franchisee system. These, as identified above are people aged 21 to 35 years. Other users are the system controllers, business managers and any other individual that may be authorized to access the system. A reporting tool is one of the key components of the system.
Reporting and report generations are vital in evaluating a business organization’s financial position and performance. In addition to that, reports enable managers make projections on the future position of a business by constantly revising the strategic objectives aimed and achieving an organization’s long term goals and vision. At this point, alignments are made to move the organization closer to its objectives (Bonczek, Holsapple, & Whinston, 1981).
Data Model Management
The decision support system was designed and enveloped to address the what-if decision making tailored at driving the business franchise towards achieving business goals. These goals are achieved through an aggregate of technologies that include functional applications and disciplines that contribute to decision making into the specific decision making system.
Functional applications include the marketing and financial decision making applications. These applications include an excel spreadsheet that illustrates the financial performance of the business and statements which can be used to project the future financial position of the business. On the other hand, the decision support system constitutes decision makers such as managers and other entitled individuals, data and system interfaces through which decision makers access data and read reports, and data and the data model. Data is stored in the database from which data mining is done. Reports generated can be used to facilitate decision making and answer the question, what-if.
On the other hand, the user interface is a vital component that enables users to easily manipulate data in the data base. The interface allows users to update information stores, delete data from files, and provides ways to input and output data and reprints generated from the system (Alter, 1980). An example of decision making on the what-if criteria is illustrated below.
In the decision making process, objectives are set after which planning takes place. Then the best compromise decisions are taken as discussed elsewhere, after which the management makes decisions based on gathered data facilitated by the decision support system. The decisions are evaluated moving on to the control stage where after he decisions are implemented in the process. Structured decision making incorporates the of the franchise system where data is modeled and entered into the system for enhanced decision making (Alavi & Joachimsthaler, 1992). Then computer programs are run in the process to determine the most appropriate outcome.
On the other hand, decision making cannot be complete without incorporating the element of semi-structured decision making. In this case, reports generated from the database, previous business activities and financial statements, and some degree or extent of adaptation drives the semi-structured decision making process. The decision making process becomes complete by incorporating an unstructured approach to decision making. In this case, decision making based on analogy, human intuition, and general approximations. Thus, decision making is facilitated in the process as discussed above.
Real-time information can be retrieved to provide actual information about the performance of specific areas of the market and best sport as graphically illustrated above. The system could be used to capture all information on the demographic distribution of the target group, their specific needs, and their income levels. Information on the income levels could be used to identify appropriate needs that may always be tailored to meet their needs and expectations to help the franchisee capture the market and gain a strong position. That could translate t higher profits and organizational efficiency.Graphical presentations in decision making for best site
References
Alavi, M. & Joachimsthaler, E.A. 1992. Revisiting DSS implementation research: a meta-analysis of the literature and suggestions for researchers. MIS Quarterly 16 (1): 95–116. (A rigorous and quantitative review of the Empirical DSS implementation literature.).
Alter, S.L., 1980. Decision Support Systems: Current Practice and Continuing Challenges, Reading, MA: Addison-Wesley. (This book provides case studies and detailed information about usage and implementation patterns of DSS.).
Bonczek, R.H., Holsapple, C.W. & Whinston, A.B. 1981. Foundations of Decision Support Systems, New York: Academic Press. (A classic DSS textbook which suggests an architecture of a language system, a knowledge system and a problem processing system.).
Carter, G.M., Murray, M.P., Walker, R.G. & Walker, W.E. 1992. Building Organizational Decision Support Systems, Boston, MA: Academic Press. (This book provides step-by-step guidance to those who are interested in building organizational DSS.).
Dennis, A.R., George, J.F., Jessup, L.M., Nunamaker, J.F., Jr., & Vogel, D.R. 1988. Information technology to support electronic meetings’, MIS Quarterly 12(4): 591-624. (Electronic meeting systems are proposed to support group meetings, which may be distributed geographically and temporally.).
DeSanctis, G. & Gallupe, B. 1985. Group decision support systems: a new frontier’, Data Base 16 (2): 3–10. (An introduction to hardware, software, people and procedures of group DSS systems.).
DeSanctis, G. & Gallupe, B. 1987. A foundation for the study of group decision support systems’, Management Science 33 (5): 589–609. (This influential group DSS article provides foundational concepts for further group DSS research guidance and future directions.).
Druzdzel, Marek J. & Flynn, Roger R. 2002. Decision Support Systems. Web.