Scientific decision making in business or daily life – Why use science for decision making?
Today, I will be going to show you how to take the best decision in a scientific matter. No matter whether you are running a business or deciding between a specific item of need in your personal life, this method will yield the best possible solution rationally. The technique I will be showing you is called the scoring model.
The Importance of Scientific Decision Making
Scientific decision-making is the process of using scientific methods and evidence-based research to make decisions. This type of decision-making is used in both business and daily life.
In business, scientific decision-making is used to make decisions about product development, marketing strategies, and customer service.
In daily life, scientific decision-making is used to make decisions about health, lifestyle, and financial planning.
In business, scientific decision-making involves gathering data, analyzing it, and using it to inform decisions. This data can come from market research, customer surveys, and other sources. Once the data is collected, it is analyzed to identify trends and patterns. This analysis helps business leaders make informed decisions about how to best serve their customers.
The Most Simple Method: SCORING MODEL
Decision making is a critical aspect of both business and daily life, and making informed decisions often requires the use of various techniques and tools to help weigh the potential outcomes and risks. One such tool that can be particularly useful in decision making is the Scoring Model.
In simple words, the scoring model is nothing, but the identification of decision-making criteria and rating of your preference based on these criteria.
A Scoring Model is a tool that uses quantitative data to assign a score to different options, which can then be used to make a decision. This model is based on the idea that each option can be evaluated based on a set of predefined criteria, and that the option with the highest score is the best choice.
One of the key benefits of using a Scoring Model is that it allows for objective, data-driven decision making. By using quantitative data to evaluate options, the model can help to eliminate subjective biases and emotions from the decision making process. This is especially important in business, where decisions can have a significant impact on a company’s bottom line.
Scoring Models can also be useful in daily life, particularly when it comes to making important personal decisions. For example, when buying a house, you can use a scoring model to evaluate the different options based on criteria like location, size, and price. By assigning a score to each option based on these criteria, you can make an informed decision about which house to buy.
In order to create a scoring model, you’ll first need to identify the criteria that are most important for making the decision. These criteria can vary depending on the specific situation, but they should be directly relevant to the decision you’re trying to make. Once you’ve identified the criteria, you can assign a weighting to each one based on how important it is. Then, using the data available, you can evaluate the different options and assign a score to each one.
Procedure of Scoring Models
The steps involved in scoring model are:
Examples of Scoring Model Usage
Scoring Model Usage In Personal Decision Making
Clark and Julie Anderson are interested in purchasing a new boat and have limited their choice to one of three boats manufactured by Sea Ray Incorporation: the 220 Bowrider, the 230 Overnighter, and the 240 Sundancer. The Bowrider weighs 3100 pounds, has no overnight capability, and has a price of $28,500. The overnighter weighs 4300 pounds, has a reasonable overnight capability, and has a price of $37,500. The Sundancer weighs 4500 pounds, has the excellent overnight capability (kitchen, bath, and bed), and has a price of $48,200. Anderson provided the scoring model information separately as shown below:
| Criteria | Weight | 220 Bowrider | 230 Overnighter | 240 Sundancer |
|---|---|---|---|---|
| (Customer Preference – Scale: 1-5) | (Customer Satisfaction – Scale: 1-9) | (Customer Satisfaction – Scale: 1-9) | (Customer Satisfaction – Scale: 1-9) | |
| Cost | 5 | 8 | 5 | 3 |
| Overnight Capability | 3 | 2 | 6 | 9 |
| Kitchen/Bath facility | 2 | 1 | 4 | 7 |
| Appearance | 5 | 7 | 7 | 6 |
| Engine/Speed | 5 | 6 | 8 | 4 |
| Towing/Handling | 4 | 8 | 5 | 2 |
| Maintenance | 4 | 7 | 5 | 3 |
| Resale Value | 3 | 7 | 5 | 6 |
| Criteria | Weight | 220 Bowrider | 230 Overnighter | 240 Sundancer |
|---|---|---|---|---|
| (Customer Preference – Scale: 1-5) | (Customer Satisfaction – Scale: 1-9) | (Customer Satisfaction – Scale: 1-9) | (Customer Satisfaction – Scale: 1-9) | |
| Cost | 3 | 7 | 6 | 5 |
| Overnight Capability | 5 | 1 | 6 | 8 |
| Kitchen/Bath facility | 5 | 1 | 3 | 7 |
| Appearance | 4 | 5 | 7 | 7 |
| Engine/Speed | 2 | 4 | 5 | 3 |
| Towing/Handling | 2 | 8 | 6 | 2 |
| Maintenance | 1 | 6 | 5 | 4 |
| Resale Value | 2 | 5 | 6 | 6 |
Clark Anderson’s Scoring Results
| Criteria | 220 Bowrider | 230 Overnighter | 240 Sundancer |
|---|---|---|---|
| Cost | 40 | 25 | 15 |
| Overnight Capability | 6 | 18 | 27 |
| Kitchen/Bath facility | 2 | 8 | 14 |
| Appearance | 35 | 35 | 30 |
| Engine/Speed | 30 | 40 | 20 |
| Towing/Handling | 32 | 20 | 8 |
| Maintenance | 28 | 20 | 12 |
| Resale Value | 21 | 15 | 18 |
| SUM | 194 | 181 | 144 |
Julie Anderson’s Scoring Results
| Criteria | 220 Bowrider | 230 Overnighter | 240 Sundancer |
|---|---|---|---|
| Cost | 21 | 18 | 15 |
| Overnight Capability | 5 | 30 | 40 |
| Kitchen/Bath facility | 5 | 15 | 35 |
| Appearance | 20 | 28 | 28 |
| Engine/Speed | 8 | 10 | 6 |
| Towing/Handling | 16 | 12 | 4 |
| Maintenance | 6 | 5 | 4 |
| Resale Value | 10 | 12 | 12 |
| SUM | 91 | 130 | 144 |
Scoring Model Usage In Business Decision Making
Ford Motor Company needed benchmark data in order to set performance targets for future and current model automobiles. A detailed proposal was developed and sent to five suppliers. Three suppliers were considered acceptable for the project.
Because the three suppliers had different capabilities in terms of teardown analysis and testing, Ford developed three project alternatives:
- Alternative 1: Supplier C does the entire project a]one.
- Alternative 2: Supplier A does the testing portion of the project and works with Supplier B to complete the remaining parts of the project.
- Alternative 3: Supplier A does the testing portion of the project and works with Supplier C to complete the remaining parts of the project.
For routine projects, selecting the lowest-cost alternative might be appropriate. However, because this project involved many nonroutine tasks, Ford incorporated four criteria into the decision process.
The four criteria selected by Ford were as follows:
- Skill level (effective project leader and a skilled team)
- Cost containment (ability to stay within the approved budget)
- Timing containment (ability to meet program timing requirements)
- Hardware display (location and functionality of teardown center and user-friendliness)
Using team consensus, a weight of 25% was assigned to each of these criteria; note that these weights indicate that members of the Ford project team considered each criterion to be equally important in the decision process.
Each. of the four criteria was further subdivided into sub-criteria. For example, the skill-level criterion had four sub-criteria: project manager leadership; team structure organization; team players’ communication; and past Ford experience. In total, 17 sub-criteria were considered. A team-consensus weighting process was used to develop percentage weights for the sub-criteria. The weights assigned to the skill level sub-criteria were 40% for project manager leadership; 20% for team structure organization; 20% for team players’ communication and 20 % for past Ford experience.
Team members visited all the suppliers and individually rated them for each sub-criterion using a 1-10 scale (1-worst, 10-best). Then, in a team meeting, , consensus ratings were developed. For Alternative 1, the consensus ratings developed for the skill-level sub-criteria were 8 for project manager leadership, 8 for team structure organization, 7 tor team players’ communication, and 8 for past Ford experience. Because the weights assigned to the skill-level sub-criteria were 40%, 20%, 20%, and 20%, the rating for Alternative 1 corresponding to the skill-level criterion was
Rating = 0.4(8) + 0.2(8) + 0.2(7) + 0.2(8) = 7 .8
In a similar fashion, ratings for Alternative 1 corresponding to each of the other criteria were developed. The result obtained was a rating of 6.8 for cost containment, 6.65 for timing containment, and
8 for hardware display. Using the initial weights of 25% assigned to each criterion, the final rating for Alternative 1 = 0.25(7 .8) + 0.25(6.8) + 0.25(6.65) + 0 .25 (8) = 7 .3. In a similar fashion, a final rating of 7 .4 was developed for Alternative 2, and a final rating of 7 .5 was developed for Alternative 3.
Thus, Alternative 3 was the recommended decision. Subsequent sensitivity analysis on the weights assigned to the criteria showed that Alternative 3 still received an equal or higher rating than Alternative 1 or Alternative 2. These results increased the team’s confidence that Alternative 3 is the best choice.
Limitations of Scoring Model
It’s important to note that Scoring Model, although powerful in decision making, they have the potential to be overfitting, where the model is taking into account irrelevant data that doesn’t correspond to the real world scenario. The data you use should be relevant, reliable and unbiased, else the scoring model might not be an accurate representation of the decision.
Conclusion
In conclusion, a Scoring Model is a valuable tool for decision making in both business and daily life. By using quantitative data to assign scores to different options, it can help to make objective, data-driven decisions that can have a significant impact on the bottom line. But to make sure your decision is accurate, it is important to make sure that the data used is relevant and unbiased.
