Discuss MADM Model and Theory

Scenario: You are the VP of Franchise services for the Happy Buns Restaurant. You have been assigned the task of evaluation the best location for the next HB that a prospective franchisee has suggested in the Columbus, Ohio, area. You are using the standard template that provides for which criteria (attributes) you should evaluate. But the specific weights for these are open to adjustment depending on the specific area. These are the six criteria that you will use to evaluate this decision.

·        Close to drive through traffic – traffic counts (avg. thousands/day)

·        Property cost/investment and taxes = NPV of investment ($$)

·        Size of building (square feet in thousands)

·        Size of parking (max number of customers parking)

·        Insurance costs (thousands $ per year)

·        Ease of access from streets (subjective evaluation from observation)

There are five possible locations. You have collected the data from various sources including your VP Finance, Real estate agents, etc. This document summarizes the raw data for each of the five locations: Abberton, Bellview, Casstown, Denton, and Eddington, all suburbs of Columbus. See Data Below.

Assignment

Review the information and data regarding the different alternatives for restaurant location. Develop a MADM table with the raw data. Convert the raw data to utilities (scaled on 0 to 1). Determine the relative weights of each criteria. Evaluate the Decision Table for the best alternative. Do a sensitivity analysis.

Write a report to your boss, Executive VP. Explain your analysis and your recommendation. Provide a rationale for your decision including the logic you used to determine your weights.

Data

Download this Word doc with the data: Happy Buns Raw Data.docx

Summary of Raw Data

for location of Happy Buns

in the Columbus, Ohio, area.

 

 

Criteria     Location	Traffic count (avg. thousands/day)	NPV of investment ($000,000)	Bldg. size (sq ft. 000)	Lot size (Max customer parking)	Insurance ($000 / yr)	Access (subjective)
Abberton	17	1.3	3.0	44	5.2	Good
Bellview	10	2.1	3.8	54	5.6	Excellent
Casstown	11	1.5	2.6	65	5.0	Fair
Denton	20	3.0	3.6	52	6.4	Poor
Eddington	15	2.8	4.2	50	6.3	Good

written report and Excel file

SLP Assignment Expectations

Analysis

·        Accurate, complete analysis (in Excel and Word) using the MADM model and theory.

Written Report

·        Length requirements = 2–3 pages minimum (not including Cover and Reference pages)

·        Provide a brief introduction/ background of the problem.

·        Complete and accurate Excel analysis.

·        Written analysis that supports Excel analysis, and provides thorough discussion of assumptions, rationale, and logic used.

 

·        Complete, meaningful, and accurate recommendation(s).

 

MADM Model and Theory

Multi-Attribute Decision Making (MADM)

This decision method assumes certainty. In other words, there are no probabilities of future states to determine. And the data and costs are assumed to be known and accurate. The most common type of decision is a preference decision. The decision maker wants to determine which of several options is the best to achieve some set of goals or fulfill a set of criteria or attributes. Common examples are: deciding which car to buy, which house to buy, which apartment to rent, where to go on vacation, which machine to buy for production, which supplier to use, and many more.

The decision process consists of the Decision Maker (DM) identifying the need for some object (or person) or concept that he/she currently does not have. Or it could be to replace some object that has outlived its usefulness, such as replacing a copying machine.

The decision consists of determining a set of criteria that the object must have or meet with some level of satisfaction. For example, when buying a car, the DM might consider its price, color, fuel efficiency, safety rating, warranty, comfort/ride, among other factors. This process is important because it provides and defines the performance and outputs that the user will expect.

The step for this decision is to search for and find the choices (alternatives or options) to be considered. There may be one criteria that is used as a filter, such as price. In the car buying example, the DM may have a price range that fits into his/her budget. They may also have a preference of Make, such as Chevrolet or Ford. But this second preference may actually be a bias and could limit the choices and exclude some viable choices. The search for alternatives usually generates choices in a serial manner. Specific alternatives are identified one at a time. Although it is possible to find several choices at nearly the same time, for example, being shown several different makes and models of cars at one dealership during a single trip.

The DM now has identified the choice options as well as the criteria to be fulfilled. Each alternative will fulfill each criterion at some level of value. The DM must collect this data and put it into a table for easy analysis. Here is an example of a decision table for purchasing a car.