Ashish Kumar Sharma

“The central theme is that good statistics involves principled argument that conveys an interesting and credible point.” said Sir Robert Abelson, while describing the intent of writing the book “Statistics as Principled Argument”.  Unfortunately, the statistical courses do not focus extensively or directionally influence user around the argumentative nature of claims they make using various statistical theories. This in turn leads to misleading, misinterpreted and misguided theories largely impacting the possible narrative.
Data in its various form is characterised with several properties and can be presented differently that widely changes the persuasive forces around acceptability, impact and usage. This is guided by Robert as MAGIC.

 
Intent of defining MAGIC was to allow the Statistical Analysis be used to make compelling or specific outcome and claims.
·         Magnitude – provide specific details around how BIG or SMALL the effect of analysis is. Larger the effect – more compelling it is
·         Articulation – Analysis outcome should be precise and restricted interpretation.
·         Generality – General application has faster acceptance and are more compelling.
·         Interestingness – Outcome should ideally result in change in the belief system in reference to the topic. More Interesting and Surprising effect – faster is the acceptance and larger compelling
·         Credibility – Refers to believability. There should be a method displayed and should result in a logical theory base.  Any conclusion which is contradictory to already existing belief will have slow acceptance and less compelling.
 
Since DMAIC Methodology is a data driven quality strategy used to improve processes, MAGIC Criteria has an influencing effect.
1)      Define – Project Charter VOC, VOP, VOE, VOB and Need for Project has compelling results on the Problem definition and its precise articulation, SMART GOAL and Expected Outcome be clearly defined along with project team, project plan. Historical Data around Metric significantly called out HOW BIG and SINCE WHEN the Problem is. Defining the scope is characterised by Generality principal to know coverage and restrictions around applicability. Communication plan and ARMI Chart articulates responsibility framework and ensures credibility, allowing Stakeholders to approve and make investment and at the same time team to put efforts in making it successful.
 
2)      Measure and Analyze – Process Map, Baselining, Fishbone or VMS approach, 5 WHY analysis tool and Process / Data Door approach makes the analysis and status quo meaningful, inclusive – since the entire team is included and believing in the change becomes easy. Articulation, Interestingness and Credibility are the narrative that best fits the Stage. Potential X’s along with occurrence data (Pareto Analysis) helps in providing meaningful insights - magnitude to the major contributors.
 
3)      Improve and Control – Optimizing Best Solution, FMEA, PHUG, Decision making matrix are some examples that explicitly calls out enhanced believability and interesting way to prove To be Status. Mistake Proofing, Standardization and Automation and phasing out each counter measure to documented SOP relates to compelled Articulation, tracking the improvement and showcasing the growth to Stakeholders is an excellent way to win trust and acceptance of concerned teams - Credibility.
 
While MAGIC Criteria has proved out to have its relevance and applicability across various streams including psychology, ecology, sociology, but due to its elementary composition, MAGIC and DMAIC goes a long way to Optime and Sustain Improvement. DMAIC follows the exact philosophy to highlight the Magnitude of the issue, point out variance and reasons - Articulation, method to identify solution and categorically improve and sustain performance.
 
“WHAT, BY WHEN, BY WHOM, HOW MUCH –is all inclusive and very well resonates with MAGIC”
 

Analytic Hierarchy Process (AHP) is an organized decision-making method that enables analysis around a problem, needed for making a choice between available alternatives by determining the criteria basis which selection or prioritization will be done.  It is a process of quantifying criteria and alternatives and relating each element to the desired outcome.
Pugh Matrix is most popular decision making six sigma tool that uses scores awarded to criteria and scoring them for each alternative. It is a qualitative technique which allows stakeholders to make a choice between alternatives basis scores.
While both are used for the same purpose but preference and usage are largely driven by the stakeholder approach, problem in hand and proof of concept needed. Let’s look at some of the differences.
AHP
PUGH Matrix
1.
Pair wised Matching – compare two criteria at a time and amongst alternatives
1.
Each alternative is independently awarded a score and compared with DATUM and against the weightage decided for each criteria
2.
Quantitative method used for evaluation
2.
Qualitative method by awarding scores  
3.
Complex Statistical Method
3.
Simple Method based of Ranking
4.
Enables a direct comparison between alternatives and via defined criteria
4.
Each Alternative is not compared with each other
5.
Consistency Index (<10%) aids validation of the comparison outcome. Improving a decision is possible
5.
No such validation and standard are possible
6.
Based on Continuous Data – Ratio
6.
Based on Discrete Data – Ordinal Data Type
7.
Is not LEAN SIX SIGMA QUALITY Tool
7.
Is integral part of the LEAN SIX SIGMA QUALITY Playbook
8.
Very difficult and time-consuming process especially with more criteria  
8.
Preferred tool to handle several criteria’s
9.
Based on stimulus – response, a mathematical numeric relationship is established
9.
Based on Logical thinking, experience and willingness of stakeholders.
10.
Individual and Group decisions can be combined. Everyone has a strong reason to believe in the outcome
10.
Stays Subjective to a great extent, enables understanding of each alternative compared to existing one
 
The complexity involved and the ability to run AHP, differentiates the choice to be made in comparison with Pugh. AHP is more time consuming and requires complex calculations to reach towards conclusion, ability to handle data and using the method is the key. Hence, AHP is less preferred compared to Pugh and mostly due to simplicity factor.
Example scoring movies to judge the most preferred for annual reward compared to a preference of IT Software involving investments. In first case Experience and Knowledge of stakeholders in reference to the problem in hand, governs the success and accuracy of Pugh Matrix outcome. Hence Pugh Matrix can handle sensitive analysis better and where quantitative data is available in abundant. Where as the client would want to have more statistical proof concept for deciding which Software to install and WHY, AHP will be the preference.  
 

S Curve – Project Management
 
Background and Need
Project Management is a strategic initiative of any organization and has focus and attention of entire leadership and stakeholder. It is not just about the expected impact from Transformation but also about the risk associated with Return of investment while prioritizing ideas that influences stakeholders to invest in the project. Hence effective governance and communication of Project Milestones, Investment, Timelines - planned vs actual analysis takes the center stage.
Stakeholders are focused on investment made, planned and related outcome whereas Project Management Organization should have their focus on execution, analysis and proactive communication.
When it comes to Project Analysis and Communication, while there are many visual tools and techniques to visually monitor and communicate Project Progress, S Curve stands out and serves to be a perfect tool to showcase comparison of important data sets of a project with Timelines.
In simple words, S Curve allows the Project team to monitor project progress at a high level. It is a graphical representation of how the project is trending compared to plan for identified lever and in respect to Timelines.

 
WHY an “S” Curve?

Typically, all Projects have a slow start where core teams are formed and stakeholders get on boarded, more of planning stage and less of doing. Investment are needed more as execution begins (point of inflection) and slows down as the project reaches its end stage. Here investment might be Man hours, quantity inventory, Machine deployed etc. show cased cumulatively on Y axis and Timelines is always on X axis. The graphical image is very similar to the Alphabet S and hence it is called as S Curve.
 
Types of S Curve?

S Curve is may be used to show case:
1)      Target Stage (Milestones)
2)      Cost invested
3)      Growth, Revenue
4)      Man Hours invested
5)      Baseline vs Actual Comparison
All of the above levers are displayed on Y Axis and Timelines is always shown as X Axis.
 
Uses of S Curve
1)      Performance Evaluation and Stakeholder Communication:  
A lot of performance discussion are around the progress made against the commitment. Often there is an element of best in class and past performances that works like a Benchmark. S Curve has the capability to display Actual, Benchmark, Target and Future forecast in the same graph and displayed against the timelines. It sets the tone around how good or how far the project performance is.
 
2)      Forecasting:
The Management team while evaluating progress, would also want to have a close look on the next stage investments and obviously be comfortable with the planning around it. S Curve can showcase the trend around the forecast. Example, S curve can showcase how many man hours are deployed as on date and how much is the expectation for coming month / quarter. Human Resource team would relate to and own the plan well to ensure in time availability of labor.
 
3)      Project Management Organization
The PMO team can effectively use S Curve to strategize next steps along with various possibilities. A combination of levers in S Curve graph is a fantastic medium to get a direct representation on the gap in performance, management can identify various possibilities to influence the outcome. It is also called as Banana Graph.
 
4)      Baselining and Growth  
S Curve in Project Management supports estimating time, cost, material, data to be mapped with expected outcome and often against the best in class. It promotes discussion around what is needed to influence the change rather than reactions and explanations around the delays.
 
5)      Determining Slippage
There are often differences observed between the schedule start, baseline and plans for end of the task. This difference is monitored effectively via S Curve and hence reduces the chance of surprise outcomes.
 
 
Limitation and Conclusion


S Curve aids more like a descriptive tool unless it is complemented with a team that can comprehend decisions and tap the variances. S Curve is dependent on the Project team to go deeper and understand the root causes to deviation between actual and plan. But that’s the basic characteristic of most graphical tools. The S Curve is not a great serve to investments in new areas example new software technology replacing old configuration.
Project Management requires a lot of matured strategic base in the organization, from winning stakeholders trust to deliver and meet the expectations. Hence it is inevitable for team to track multiple parameters and scale them up to past performance as well as planned future. S Curve is very helpful in displaying the progress and invites comparison. It is a great tool to track cost in comparison with timelines and most of the projects fail or get discarded due to failure in comparing the actual with plan and in time re addressing the execution plan. It is a great way to convince stakeholders and get sign off on future budgets.