Decision Support System

19 Pages   |   4,037 Words
Table of Contents
Aspects of Decision Making. 3
Feasibility of a New Product. 7
Harrah’s High Payoff from Customer Information. 14
References: 19
  
What are the various aspects of decision making? Discuss and give examples of analytic and heuristic approaches of decision making style. Also, identify similarities and differences between individual and group decision making.

Aspects of Decision Making

A decision can be described as a choice or conclusion that one makes from a given set of alternatives or options to accomplish a predefined objective or goal. Decision is basically a choice between options and can be based on several elements that help guide one towards a decision, which lay language may be termed as pros and cons. The cognitive process through which an individual examines and evaluates all possibilities to arrive at a final and optimum choice is known as the process of decision making. This decision making process involves weighing each option against the advantages that are being gained and the goals that are being met. A simple example of the decision making process is when a student has to decide the courses that he or she wants to pick at the start of each semester. The student can select any mix of courses provided that there are no overlaps in the timings and days of the selected courses and that the prerequisites for each course have been completed beforehand. The student’s objective in this scenario would be to select courses that can contribute positively towards his Cumulative Grade Point Average (Conteh, 2009; Haris, 2009).
 

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Decision making can be broadly categorized into programmed or routine decisions and non-programmed or innovative decisions. Programmed decisions deal with common, day to day and repetitive situations that have well defined solutions based on experience, business rules, corporate policies or habit. For example, many companies have standardized procedures for handling product returns in cases where the consumer is not satisfied with the product or service and wants his money back. Non-programmed decisions deal with unique and one of kind situations that are ambiguous in nature and have outcomes that are uncertain. Such decisions require creative solutions that are customized for that particular scenario. Take the example of a consumer goods company entering a new market. The company will have to devise a marketing strategy that is based on the unique insights gathered from that market (Anderson, n.d.; The Institute of Working Futures, n.d.).

The process of decision making consists of several components namely the decision environment, information, alternatives, criteria, goals, values and preferences. These elements can play a critical role in defining the decision making process of a person. The decision environment is the set of all feasible and available knowledge, choices, values and inclinations available at the time that the decision is made. Information is the amount of detail and knowledge available about the decision and alternatives refer to the number of options to choose from and criteria are the requirements that an alternative is expected to meet in order to be selected. Values refer to the desirability of the decision in terms of emotional or monetary standards and preferences pertain to the personal beliefs and philosophy of the decision maker (Decision Making Models of Decision Making, n.d.; Heuristic Rule, n.d.).
There are two approaches to the cognitive style of decision making i.e. heuristic and analytic. The heuristic approach uses rules of thumb or mental shortcuts to make decisions. This approach is used when there is a time constraint or the information required to make the optimal decision is limited. Decision making through the analytic method adopts a planned, mathematical and rational approach to reach decisions. This approach stresses logic and cause and effect relationships rather than relying on intuition, common sense, trial and error and experience. In short, where the heuristic approach uses qualitative information to make decisions the analytic approach uses quantitative models to predict decision outcomes (Dietrich, 2010; Heuristic Rule, n.d.).

An example of the heuristic approach is when during a job interview the interviewer concludes that the candidate is unsuitable for the position based on his poor posture which in the interviewer’s experience denotes a lack of confidence. Another example is when a production plant uses its Management Information System to, automatically order, raw materials from its vendors when the inventory declines to a certain quantity such as 400 units. An example of the analytic approach is when a company enters a new market and bases its marketing strategy on quantitative data collected through surveys, questionnaires, interviews and the census. This ensures that the decision outcome is customized to that specific market and based on facts and figures (Dietrich, 2010; Heuristic Rule, n.d.).

Individual and group decision making are similar in two ways. Firstly, both of them share the same objective which is to find the best solution from a series of available alternatives. Second, both types help members gain deeper insight in terms of product or market knowledge and develop skills such as people, time and stress management (Dietrich, 2010; Heuristic Rule, n.d.).

The two types differ in terms of speed as individuals are faster at making decisions since only one person needs to be consulted whereas groups take input from all members before arriving at a decision. Despite that, groups can provide a round of feedback which may be more constructive than a single person’s feedback on the required decision. Another difference between them is that groups have the potential to generate a wider variety and superior quality of decision options and choices by tapping into the unique strengths, as well as, capabilities of its members (“Group Decision Making”, n.d.). This results in a more effective solution as compared to one reached by the individual. Even with that, groups may actually take more time in reaching the same conclusion that may have been easier and faster to reach without involving the entire group as group discussions take ample time. Another difference is that groups often suffer from a phenomenon called groupthink which occurs when individuals in a group feel pressured to agree with the dominant view in the group even if they disagree with it. Group decisions also promote a sense of ownership and lead to a greater collective understanding of the reasoning behind a decision as it involves the view points of the stakeholders who may be affected by it (“Group Decision Making”, n.d.).
 
The senior management at Hanson-Smith Pty Ltd wishes to have a thorough analysis of every new product that is introduced into the market. The company has employed an Australian consulting firm to advice on the feasibility of a new product.

Feasibility of a New Product

Cost of production: $45.00 per unit
Annual overhead cost: $190,000
Initial investment needed: $1,100,000
Estimated selling price: $82.00 per unit
Market at time of introduction: 440,000 units per year
Market growth: 9% per year
Market share: Most likely 18%
Assumed economically useful lifetime: 4 years, commencing 2012
Discount rate used to analyze new product proposals is 11%
 
 
Net Present Value:
 
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0 -   1,100,000 190,000 -   -    (1,290,000)  (1,290,000)
Year 1 86,328 -   190,000 3,884,760 7,078,896 3,004,136 2,706,429
Year 2 94,098 -   190,000 4,234,388 7,715,997 3,291,608 2,671,543
Year 3 102,566 -   190,000 4,615,483 8,410,436  3,604,953 2,635,911
Year 4 111,797 - 190,000 5,030,877 9,167,376 3,946,499 2,599,681
NPV              9,323,563
 
 
The claim regarding NPV to be USD 2.5 Million is incorrect; the NPV turns out to be much larger when estimated cash flows are evaluated.
 
When conducting a risk analysis for this decision, the graph and data table for the net present values should be as suggested below:
 
 
Market Share 18% and Unit Cost USD 45
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0                                           -                    1,100,000       190,000                                   -                                              -                     (1,290,000)                  (1,290,000)
Year 1                                    86,328                                  -         190,000                    3,884,760                            7,078,896                      3,004,136                    2,706,429
Year 2                                    94,098                                  -         190,000                    4,234,388                            7,715,997                      3,291,608                    2,671,543
Year 3                                   102,566                                  -         190,000                    4,615,483                            8,410,436                      3,604,953                    2,635,911
Year 4                                   111,797                                  -         190,000                    5,030,877                            9,167,376                      3,946,499                    2,599,681
NPV                                9,323,563
Market Share 18% and Unit Cost USD 35
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0 -   1,100,000 190,000 -   -   (1,290,000) (1,290,000)
Year 1                                    86,328                                  -         190,000                    3,021,480                            7,078,896                      3,867,416                    3,484,159
Year 2                                    94,098                                  -         190,000                    3,293,413                            7,715,997                      4,232,583                    3,435,260
Year 3                                   102,566                                  -         190,000                    3,589,820                            8,410,436                      4,630,616                    3,385,866
Year 4                                   111,797                                  -         190,000                    3,912,904                            9,167,376                      5,064,471                    3,336,124
NPV                              12,351,409
Market Share 18% and Unit Cost USD 55
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0                                           -                    1,100,000       190,000                                   -                                              -                     (1,290,000)                  (1,290,000)
Year 1                                    86,328                                  -         190,000                    4,748,040                            7,078,896                      2,140,856                    1,928,699
Year 2                                    94,098                                  -         190,000                    5,175,364                            7,715,997                      2,350,633                    1,907,827
Year 3                                   102,566                                  -         190,000                    5,641,146                            8,410,436                      2,579,290                    1,885,955
Year 4                                   111,797                                  -         190,000                    6,148,849                            9,167,376                      2,828,526                    1,863,238
NPV                                6,295,718
Market Share 15% and Unit Cost USD 45
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0                                           -                    1,100,000       190,000                                   -                                              -                     (1,290,000)                  (1,290,000)
Year 1                                    71,940                                  -         190,000                    3,237,300                            5,899,080                      2,471,780                    2,226,829
Year 2                                    78,415                                  -         190,000                    3,528,657                            6,429,997                      2,711,340                    2,200,585
Year 3                                    85,472                                  -         190,000                    3,846,236                            7,008,697                      2,972,461                    2,173,438
Year 4                                    93,164                                  -         190,000                    4,192,397                            7,639,480                      3,257,082                    2,145,541
NPV                                7,456,392
Market Share 15% and Unit Cost USD 35
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0                                           -                    1,100,000       190,000                                   -                                              -                     (1,290,000)                  (1,290,000)
Year 1                                    71,940                                  -         190,000                    2,517,900                            5,899,080                      3,191,180                    2,874,937
Year 2                                    78,415                                  -         190,000                    2,744,511                            6,429,997                      3,495,486                    2,837,015
Year 3                                    85,472                                  -         190,000                    2,991,517                            7,008,697                      3,827,180                    2,798,401
Year 4                                    93,164                                  -         190,000                    3,260,754                            7,639,480                      4,188,726                    2,759,244
NPV                                9,979,597
Market Share 15% and Unit Cost USD 55
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0                                           -                    1,100,000       190,000                                   -                                              -                     (1,290,000)                  (1,290,000)
Year 1                                    71,940                                  -         190,000                    3,956,700                            5,899,080                      1,752,380                    1,578,721
Year 2                                    78,415                                  -         190,000                    4,312,803                            6,429,997                      1,927,194                    1,564,154
Year 3                                    85,472                                  -         190,000                    4,700,955                            7,008,697                      2,117,742                    1,548,474
Year 4                                    93,164                                  -         190,000                    5,124,041                            7,639,480                      2,325,438                    1,531,838
NPV                                4,933,188
Market Share 21% and Unit Cost USD 35
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0                                           -                    1,100,000       190,000                                   -                                              -                     (1,290,000)                  (1,290,000)
Year 1                                   100,716                                  -         190,000                    3,525,060                            8,258,712                      4,543,652                    4,093,380
Year 2                                   109,780                                  -         190,000                    3,842,315                            9,001,996                      4,969,681                    4,033,504
Year 3                                   119,661                                  -         190,000                    4,188,124                            9,812,176                      5,434,052                    3,973,332
Year 4                                   130,430                                  -         190,000                    4,565,055                          10,695,272                      5,940,217                    3,913,005
NPV                              14,723,221
Market Share 21% and Unit Cost USD 45
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0                                           -                    1,100,000       190,000                                   -                                              -                     (1,290,000)                  (1,290,000)
Year 1                                   100,716                                  -         190,000                    4,532,220                            8,258,712                      3,536,492                    3,186,029
Year 2                                   109,780                                  -         190,000                    4,940,120                            9,001,996                      3,871,876                    3,142,502
Year 3                                   119,661                                  -         190,000                    5,384,731                            9,812,176                      4,237,445                    3,098,383
Year 4                                   130,430                                  -         190,000                    5,869,356                          10,695,272                      4,635,915                    3,053,821
NPV                              11,190,735
Market Share 21% and Unit Cost USD 55
  Number of Units for the Year Initial Investment Overheads Cost of Production Revenues for the Year Profits for the Year Discounted Profits
Year 0                                           -                    1,100,000       190,000                                   -                                              -                     (1,290,000)                  (1,290,000)
Year 1                                   100,716                                  -         190,000                    5,539,380                            8,258,712                      2,529,332                    2,278,677
Year 2                                   109,780                                  -         190,000                    6,037,924                            9,001,996                      2,774,072                    2,251,499
Year 3                                   119,661                                  -         190,000                    6,581,337                            9,812,176                      3,040,838                    2,223,435
Year 4                                   130,430                                  -         190,000                    7,173,658                          10,695,272                      3,331,614                    2,194,637
NPV                                7,658,248
                 
 
 
 
Based on the above, the cumulative frequency of the risk analysis is given below:
 
 
  NPV Chance Expected NPV Probability Cumulative Probability
15%, USD 55 4,933,188 25% 1,233,297 0.06 0.06
18%, USD 55 6,295,718 25% 1,573,929 0.06 0.11
21%, USD 55 7,658,248 25% 1,914,562 0.06 0.17
15%, USD 35 9,979,597 25% 2,494,899 0.11 0.28
18%, USD 35 12,351,409 25% 3,087,852 0.11 0.39
21%, USD 35 14,723,221 25% 3,680,805 0.11 0.50
15%, USD 45 7,456,392 50% 3,728,196 0.17 0.67
18%, USD 45 9,323,563 50% 4,661,782 0.17 0.83
21%, USD 45 11,190,735 50% 5,595,367 0.17 1.00
 
For at least 80% of the frequency, the NPV amount comes up to approximately USD 4.6 Million which is beyond the expectations of the CEO. In this context, the project can be easily invested into. When comparing this result to actually what the CEO expected, that is USD 2.5 Million we can see that the actually NPV based on 80% probability is USD 4.6 Million. Basing the decision on financial theoretical framework, it is a known fact that when the project gives more net present value in its estimation, in comparison to the baseline net present value, then the project has more value that can be received in the future. Therefore, such projects should be taken up by such an organization. Based on the cumulative frequency table and graph above, it is seen that the project to be invested into may actually give up to USD 4.6 Million and thus is a profitable venture for the organization.

Harrah’s High Payoff from Customer Information

Introduction
Data mining is a process through which raw data is input into an Information Technology system in order to understand the data. Understanding the data helps companies predict future trends and make business decisions that are supported by the results of data mining. In the case of Harrah’s, the business strategy focuses on customer relationship hence information produced through data mining focuses on the customer’s behaviors and preferences,  Harrah’s is able to design and share offers customized to gauge customer attention by understanding their preferences. This makes Harrah's a customer's   preferred choice.
 
Data Collected Through Data Mining
The raw data collected is information such as which casino customer prefers, their various patterns with respect to their favorite games, how much money and time they spend on the respective games and which specific offers entice them to return to Harrah’s. Additionally their frequency of visits, duration of stay and eating preferences can also be recorded as part of raw data. The customers have identification keys that correspond to their respective data, and the identification keys are stored in the data warehouse systems. This data is then plugged into the information systems, and an in-depth analysis is available to Harrah’s through data mining. The analysis provides information on market segmentation as common characteristics are determined between the different groups of people visiting the casino. The customers are then segmented into groups so experiments to determine preferences can be run on the different groups. Customers with similar profiles are part of the same group.
Additionally it is also possible to conduct closed loop marketing through data mining once the different groups have been identified and segmented. Closed loop marketing is when Harrah’s sends customers customized offers based on data-driven testing instead of sending arbitrary offers. This allows them to understand their customers and their preferences better and helps generate offers and deals specific to their customer preferences.
 
Data mining at Harrah’s is performed through a system known as WINet (Winner Information Network). The system collects different data from various source points, sorts the data accordingly and utilizes it to identify different customer profiles and created customized offers for those visiting Harrah’s casinos. The data undergoes analysis and is a main part of Harrah’s operations and overall strategy. Through a loyalty program, ‘Total Gold’ the customers’ moves would be recorded in the data warehouse, helping Harrah’s understand their customers better. The source points for the data vary and may be made available through both computerized as well as human functions. For games such as the slot machines, the computers can easily capture data with the help of the loyalty card. The customer simply has to input it into the machine and data is automatically stored and updated. In contrast, data that is made available at the time of checking in to a hotel is input into the system through the reservation clerks themselves. Data such as smoking preferences, permanent home address, is also available for all customers in the data warehouse as this information is shared at the time of check in.
 
WINet consists of a Patron Database (PDB) which is a data storehouse as well as the Marketing Workbench (MWB) which is the data mining system. The data is extracted and checked for validity before it is stored in the PDB. Data from all casinos, hotels and various touch point’s is entered into the PDB, and it is the single storage point no matter which casino the customer has visited. The MWB is Harrah’s data warehouse and receives its information from the PDB. While the PDB supports storage, MWB performs analysis to see which offers were popular, determine customer preferences and use the relevant analysis to generate lists of customers to send the relevant offers to. MWB analyzes market segmentation as well as customer scoring. All in all both the PDB and MWB are part of a cycle where the information comes from the customers, relevant information is stored in the PDB, the data is then sent to the MWB for analysis after which customers are sent relevant offers accordingly.

Additionally WINet Offers is Harrah’s a specific system that helps create offers for the customers. Analysts use identification keys to analyze different segments and profiles. Based on these profiles, they create lists of customers. The identification key is input into the PDB and creates customized offers. The PDB also records whether the customers accept or reject the offer. The systems of specific hotels, restaurants etc featured in the offer are connected to offer as well in order to record whether the offer has been utilized. The offers can apply to all of Harrah's properties, or specific casinos depending on the group that is targeted, and the response of the customers helps Harrah’s determine which offers were successful and which ones failed. The previous results help generate future offers that have higher success rates.
 
Data’s Help to the Management
The information generated by data mining is particularly helpful to management because once experiments are run and preferences are determined the customers can be sent customized incentives that aim to build and sustain the relationship.
 
Decisions are made based, in part, on Harrah’s Customer Relationship Lifecycle Model; which determines a selection of customers and what offers are sent to them. Customers are sent offers depending on their positions on the Lifecycle and the frequency of their visits. For instance, new customers may have specific characteristics that determine their potential value. If the potential indicates that they have a high value, then Harrah’s will make generous offers to them in order to attract their interest. Out of state guests may be offered hotel discounts while customers who spend long hours gambling relative to their profile can be rewarded with incentives such as tickets or discounts. Data mining allows Harrah’s to make decisions that meet the customer’s specific needs and encourage them to visit the casinos more often. Analysis also provides a window into the customer’s history at Harrah’s, and if someone hasn’t visited in a while then offers can be sent targeting them in order to rebuild the relationship. That way Harrah’s does not lose previous customers.
 
Closed loop marketing in specific also uses information from data mining to make decisions, and Harrah’s has implemented it in various ways. One instance is when Harrah’s identified two similar groups of frequent slot machine players from Mississippi and performed an experiment on them. One of the groups was offered a promotional package of a free room, steak dinners and $30 worth of free chips at the Tunica Casino. The other group was offered $60 worth of chips in an attempt to determine the preferences of players with a similar mindset from the same demographic. The offer which offered $60, generated more gambling, indicating that the primary interest of the group of players from Mississippi was to gamble. Based on these findings, Harrah’s was wasting money by offering these customers free rooms and dinner. As a result, Harrah’s focused its offers in this market on free chips, and this caused a significant increase in the casino’s profits.
 
Another experiment was run focusing on a group of players who were located close to the casino. Due to their proximity to the casino, they had specific characteristics such as a speedy response to slot machine buttons. In order attract them to return, Harrah’s sent them free cash and food coupons and offers with a two week expiration date. This led to an increase in their monthly visits hence through closed loop marketing; Harrah’s is able to determine and target their customers accordingly. Customized offers also play a significant role in helping Harrah’s acquire competitive advantage over other businesses.
 
Through data mining, Harrah’s does have a sustainable competitive advantage as it recognized the importance of customer relationship management from the very start and focused on loyalty from existing customers rather than investing on extravagant new casinos. Harrah’s innovative strategy coupled with the significant investments on IT systems has helped the casino secure the largest base of casino customers as well as create loyalty which has helped the business get through market volatility unaffected.
 
Data mining allows Harrah’s to measure revenue growth in each of its casinos as well as the levels of customer loyalty. Customer loyalty is determined by how frequently the customer visits the casino, how profitable the visit has been and what games were most popular.
Based on this information, management can monitor and implement changes in order to maximize customer loyalty. Additionally personal information allows Harrah’s to send mails addressing the customer and sharing customized offers.  According to the case study, the direct mail campaign doubled the profitability of a Harrah’s casino in Tunica.
 
Their customer relationship strategy consists of giving customers great service, rewarding loyalty through rewards and a loyalty program across all of Harrah’s casinos. The strategy involves creative marketing and IT and customers are offered benefits and guest rewards when they play at Harrah’s. An increased focus on customer satisfaction is key to operations hence Bill Harrah ensured the customers experienced comfort, the games were fair, and that everyone had a good time. Employees are trained to take a personal interest in the customers and memorize their names, so they feel at home.
 
As part of Harrah’s strategy, various casinos operate under the umbrella of one brand in an integrated manner in different locations. Harrah’s created a common experience for customers at all its casinos and the brand was promoted through advertising and frequent new offers. Data mining results also help management make decisions with respect to the type of branding identity they would like to implement. Customer profiles help determine what customers look for and what makes Harrah’s most attractive to their loyal customers.
 
Through extensive data mining and analysis, Harrah’s focuses heavily on understanding its customers by familiarizing itself with their personal preferences. The procedures in place allow them to get the relevant information to understand their target customer segment better, respond to their preferences and make business decisions that yield maximum profitability.

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