Decision Tree – GleeYM https://www.gleeym.com GleeYM offers complete risk management , PMO, Mind Mapping, Training and Digital Transformation Servces! Tue, 14 Jan 2025 04:07:11 +0000 en-CA hourly 1 https://wordpress.org/?v=6.9.4 https://www.gleeym.com/wp-content/uploads/2022/01/cropped-gleelogo-1-1-32x32.png Decision Tree – GleeYM https://www.gleeym.com 32 32 166212591 Digital Product Development and Decision Making Under Uncertainty https://www.gleeym.com/2020/10/20/digitalproduct/ https://www.gleeym.com/2020/10/20/digitalproduct/#respond Wed, 21 Oct 2020 03:05:26 +0000 https://www.gleeym.com/?p=1961

Digital Product Development and Decision Making Under Uncertainty

Digitization is dominating in this era of 2nd machine age. A webinar. Worth watching it. Isn't?

The failure rate is high in case of product Development. Technology, competitors, and market dynamics are the key factors, but one can’t ignore cost and schedule.
Cost and schedule model development is the starting point to plan the development of a digital product.
Whether it’s agile, scrum, or waterfall, planning is utmost to avoid financial loss.

@RISK helps to model uncertainty to cost and schedule. @RISK has the capability to analyze the schedule without using any scheduling software and apply uncertainty.

Whether it’s agile or scrum or waterfall methodology, developing schedule and cost estimate are essential.  

 

<div id=”wi105″><div class=”sq_wiki_content” style=”position: relative;clear: both;background: white;font-size: 14px;color: #333;max-width: 500px;margin: 5px auto 5px auto;padding: 13px;border: 1px solid #DDD;-webkit-box-shadow: 0 1px 0 #DDD;-moz-box-shadow: 0 1px 0 #ddd;box-shadow: 0 1px 0 #DDD;”><a id=”w_0″ class=”sq_wiki” href=”//en.wikipedia.org/wiki/Decision_theory” title=”Decision theory” style=”display: block;font-weight: 700;color: #0074B7;line-height: 1.4em;margin-bottom: 5px;text-decoration: none;”>Decision theory</a><div class=”sq_wiki_text” style=”font-style:italic;color: #333;”>re-evaluation of what useful <span class=”searchmatch”>decision</span>-<span class=”searchmatch”>making</span> requires. The area of choice <span class=”searchmatch”>under</span> <span class=”searchmatch”>uncertainty</span> represents the heart of <span class=”searchmatch”>decision</span> theory. Known from the 17th</div></div></div><div><br style=”clear:both” /></div>

<a href=”https://www.gleeym.com/2021/02/11/5waysrisk/” title=”5 Ways Risk Management Software Can Help Your Business. Are You Still Not Considering Risk Software?” style=”font-style:italic;color: #333;”>https://www.gleeym.com/2021/02/11/5waysrisk/</a>

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Capital Project Petrochemical Process Plant, IRR, and NPV Analysis – Webinar

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]]> https://www.gleeym.com/2020/10/20/digitalproduct/feed/ 0 1961 Decisive Decision Making for Digital Risk Management (DRM) – A Headway to Digital Transformation https://www.gleeym.com/2020/03/25/decisionmaking/ https://www.gleeym.com/2020/03/25/decisionmaking/#respond Thu, 26 Mar 2020 02:02:44 +0000 https://www.gleeym.com/?p=1475

Digital Transformation

Aligning resources to remain competitive and meeting strategic goals are cumbersome in this era of “Digital Transformation”. Digital transformation is reshaping the corporate world. Pursuing the digitizing requires a sense of urgency, change management agents, and the ability to choose the optimal decision based on the data. Decision making requires Data as input and presenting data in the form of alternatives/options to select the best one i.e. Risk-Based Decision Making.

In the concept phase, the project initial phase, presenting alternatives and choosing the best one is the key to success. Collecting data, reshaping the data, defining correlation and keeping eye on key risk drivers certainly helps to come to a conclusion and get buy-in from the key stakeholders. Presenting numbers/analytics to board members or partners are always a “Game Changer” to win.

Decision Tree Analysis helps to portray various decisions with data. Decision Tree infused with Date helps to determine optimal path and display combination of inputs for the corresponding output. It helps to better understand the uncertainties associated with the data and perform further analysis as per requirement for decision making.   

Case Study: Digital Risk Management

SVP Technical decided to align projects with strategic goals and section of projects as per scorecard or set criteria. He chose SharePoint for an Enterprise solution. SharePoint is a masterpiece for collaboration and share information among the various Stakeholder. SharePoint is a content repository. His idea was based on an Enterprise solution. He presented this idea to the board members for final approval.

During the formal discussion, one prominent and dominant board member and the main investor asked him questions:

1- What if Enterprise deployment fails?

2- What if consider deployment first for Megaprojects only?

This could be the “Show Stopper” to get the final approval and budget allocation. Meeting adjourned with remarks/decision to present the idea again with best possible alternatives and answers these two questions to make the most of it.

After several meetings and brainstorming sessions, developed “Decision Tree” with associated cost and monetary benefits. A data table is given below:

Visit following links for more details:

QUANTITATIVE RISK ANALYSIS

RISK MANAGEMENT SYSTEM

Decision Tree for Decision Making

Decision Tree Data Table by Gleeym
Table 1 - Decision Tree Data Table

Cost of Complete Deployment for Enterprise Project Management was $ 700K. A 65 % probability to save (benefit) $2 M in case of successful deployment and meeting objectives. 35% probability of losses. The losses that would be $200k in case of delays, resistance from the stakeholder for change and interruptions because of low productivity.

More risk-averse approach to deploying and implement the software for megaproject only i.e. Partial Deployment. Cost of Partial Deployment was $350K. Probability of success was 70% with cost-saving of $ 1 M while the probability of failure was 30% with a loss of $ 100k.

Decision Tree with Chance Nodes
Pic 1 - Decision Tree with Chance Nodes

2 -way sensitivity 3-D graph shows a combination of two (2) inputs and the corresponding output. Choose two inputs to display corresponding output values i.e. Expected Values.

Two Way Sensitivity Graph - www.gleeym.com
Graph 1 - Two Way Sensitivity Graph

Strategy Region Graph is used to portray the best decision for the combination of the two inputs. Diamond Shaped icon is used to display the corresponding combination. It provides headway to choose option precisely based on data.

Graph 2 - Strategy Region Graph

Big Picture:

Big Picture portrays Decision Tree in a dynamic way. Eye-catching graphics help to visually illustrate the various components of the complex Decision Tree. It helps to present an idea to the management in an interactive way. Below given Big Pictures are showing the complete Decision Tree in fragments. It provides a framework to analyze various options critically for digital risk management.

Big Picture- Decision Tree 1 by www.gleeym.com
Big Picture- Decision Tree 2 by www.gleeym.com
Big Picture- Decision Tree 3 by www.gleeym.com
Big Picture- Decision Tree 4 by www.gleeym.com
Big Picture- Decision Tree 5 by www.gleeym.com

Conclusion - Decisive Decision Making

After presenting data in a structured and graphical way, the Board approved the project with one condition. The condition was to implement the project in phases:

1- In the first phase deploy software partially for mega-projects only.

2- In the second phase deploy the complete project and align overall project scope.

Decision Making requires Critical Thinking and aligning data to Project scope statement. It helps to collect, reshape and align data to address actual problem and make the right decision in digital risk management!

Thank you

@RISK

]]> https://www.gleeym.com/2020/03/25/decisionmaking/feed/ 0 1475 Decision Tree Analysis for Making Decision Under Uncertainty https://www.gleeym.com/2020/03/06/decisiontree/ https://www.gleeym.com/2020/03/06/decisiontree/#respond Fri, 06 Mar 2020 18:03:26 +0000 https://www.gleeym.com/?p=1291

Why Decision Tree Analysis?

Product Development:

Before discussing anything on product development let us discuss what is the product? Precisely, a product is ‘Any Thing or Things produced’. More precisely, as per engineering and manufacturing perspective, products are purchased as a raw material and sold as finished goods. In a real world, Product can be tangible or intangible.

Typically, either conceiving a design using CAD/CAM tools or reverse engineering is practiced as the best methods for a product development. Although these either conventional processes save time and cost to a launch product, but because of poor product feasibility and financial analysis product fails earlier before meeting the foresee financial gains. So what needs to be followed to develop the product? Are designers really following the correct path to develop the product? Do designers ever think to follow Product Life Cycle tools & techniques to achieve optimum solutions? What are the significance of following Product Life Cycle phases in terms of cost, efficiency, quality, reliability, durability, productivity and much more? It requires a lengthy topic to cover all answers to these questions. In brief Product life Cycle and its significance are described in the next section.

Product Life Cycle:

Traditionally, it is deemed to visualize the product life cycle with engineering perspective only as shown in figure-1:

Free trade, rapid product evolution and increase demand for a cheap product have changed the way to product development concept and visualization. In a broader spectrum, product Life Cycle is a process from product initialization to product sales & marketing.  Even today it covers CRM (Customer Relationship Management).

Product Life Cycle covers:

  • Introduction
  • Growth
  • Maturity (Where Product gets maximum penetration in the market)
  • Decline

Decline phase is also referred to as the evolution phase where the shift of technology or advancement in existing product may occur. For example, VCR was very popular during 90’s and now it has been replaced by the DVD player.

 

Pic 1 - Product Life Cycle
Pic 2 - Product Life Cycle

Decision Tree:

Decision Tree Analysis is widely used for product selection, alternative process selection and cost-benefit outcomes, etc. Decision Tree is one of the best quantitative decision-making methodologies. It helps to calculate the Expected Monetary Value (EMV).

                                                                           EMV = P X I

(Where P refers to probability and I refers to Impact)

Decision Tree is required to construct from left to right with all possible alternatives. Then put all the possible outcomes of each alternative. Decision and chance node are a constituent part of it. It is important to know that the sum of the probability of each alternative at a chance node should not be greater than 100% of 1 (0-1). The figure given below illustrates a basic concept of a Decision Tree.

 

Pic 3 - Basics of Decision Tree

Decision Tree Analysis for Decision Making – Case Scenario:

In context to an earlier discussion, the right decision is important to achieve product objectives in a shorter time with minimum efforts. So I am going to discuss a real case scenario.

Company XYZ* decided to setup a manufacturing and assembling plant. They had available capital of more than $ 110 million. One of the directors of the company was a bit shaky to invest this amount because of the economic crunch, inflation, rapid trend changes, variety of similar products and decreasing buying capacity of the customers.

Therefore, company Top management decided to have an alternative solution and pick the best one on the basis of Quantitative Risk Analysis. It was decided to go with Decision Tree analysis along with other Quantitative Risk Analysis methodologies.

After initial assessment and research following data was obtained.

Data Sheet:**

  • Production:

Units/Day                    =                      3,000

Units/Month                =                      78,000

Units/Year                   =                      936,000

Units for 5 Year           =                      4,680,000

  • Cost***

Unit Price                    =                      $ 40

Unit Price excluding GST

(Ex Factory)                 =                      $ 25.70

Unit Cost                     =                      $ 13.70

Unit Profit                   =                      $ 12

  •  Internal Rate of Return (IRR) and Net Present Value (NPV)

As narrated earlier, factors that were taken-in-account for making right decision:

  • Economic Conditions
  • Capital In hand
  • Cash Flow
  • Loan Required
  • Interest Rate
  • Market Condition
  • Competitors etc.
Table-1: Setup Cost

Development of Decision Tree:

After thorough analysis, there were five proposed options to choose the optimum one. Options were:

  • Complete Product Development along with Sales and Marketing
  • Product Design
  • Prototyping
  • Production
  • Sales and Marketing

Solution one was a complete product development from product initialization to sales and marketing. Other options were product design, prototyping, production and Sales and Marketing respectively. The goal was to get maximum profit by spending less capital while catering other internal and external Constraints as described in the last paragraph of section-4. Collapsed Decision Tree is illustrated in figure 4.

Incomplete product development, a risk of financial loss was greater because of substantial capital as well as production costs.

Development of Decision Tree

                                                  Figure-4: Development of Decision Tree

Complete Decision tree

                                                   Figure-5: Complete Illustrated Decision Tree

 

Outcome of Decision Tree:

After analyzing Decision Tree got the following results. Table -2 illustrates Arrival Probability (This is the probability that this decision encountered, assuming you followed all the optimal choices.) and Benefit of Correct choice (This is the difference of expected values between the optimal choice and the worst choice for this decision.).

Outcome of Decision Tree:

After analyzing Decision Tree got the following results. Table -2 illustrates Arrival Probability (This is the probability that this decision encountered, assuming you followed all the optimal choices.) and Benefit of Correct choice (This is the difference of expected values between the optimal choice and the worst choice for this decision.).

Optimal Choice

Arrival ProbabilityBenefit of Correct Choice
Sales & Marketing100.0000%

     $ 31,173,826.80

                                                                   Table-2: Decision Table

 

Figure-6: Optimal Tree

‘Probability Chart’ and ‘Optimal Tree’ clearly supports the decision. The probability chart indicates the probability of the possible outcome of any decision. Figure-7 indicates that outcome for complete development and Sales and marketing in major. Graphically, it is evident, a possibility of financial loss with low and normal (medium) efforts to market and sell the Juicer Blender while aggressive generation revenue of more than $ 30 million in six years with marketing and sales only.

Figure-7: Probabilities for Decision Tree
Figure-8: Cumulative Probabilities for Decision Tree

Conclusion:

Expected Monetary Value (EMV) helped to select optimal part of Decision Tree. By sticking to Sales and Marketing only, the risk to financial loss minimized with a great opportunity to concentrate on one area only.

*Name of the company is not displayed as per request.

** Only shown relevant data.

Thank You

@RISK

Quantitative Risk Analysis

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GleeYM can help you with a valuable service to pick and incorporate risk management software for decision making and meeting strategic goals!
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