Chandrashekhar Hande

Robert Abelson used the MAGIC criteria to theorize that the goals of the statistical anaylsis should be factual and present compelling accurate and precise claims with wider applicability and credibility. The MAGIC acronyms stands for – Magnitude, Articulation, Generality, Interestingness and Credibility. Below is the detailed explanation of each along with the connectivity with DMAIC project component. Magnitude: It provides evidence on how big is the effect. In Supply chain and Logistics, the faster delivery of goods to the customer had negative impact of CO2 emissions. How much faster delivery? Or how much negative impact? does it vary from items types delivered? How does it compare with delivering through other mediums? This is a classic input to the project charter to provide the impact and magnitude of the problem to be solved which is covered in Define stage but will also come across in Analyse phase once data is reviewed. Articulation: How specific is the problem in hand? Precise statements – 1 Day Delivery increases the CO2 emission by 20% reducing the packet induction by 10%. More than 3 days delivery has no impact against the current benchmark set to 100% fill and induction. If we consider the 3 days benchmark, will 2 day delivery be better than 1 day and by what extent. Should be also conclude that most deliveries should follow 3 days window as default. This will be a part of MSA as well as setting limits on the data to be gathered and will be a part of Define and Measure phase. Generality: Can we generalise this conclusion? Does the Supply chain and emission be generalised for all categories and the regions. What use cases are covered to gain maximum interest in benchmarking across a wide set of products? This a is part of Analyse & Improve phase and can be also used to see where additional data points could be used. Interestingness: How important is the issue? How surprising is the conclusion? Interesting effects have potential through analysis. More compelling and surprising effects are important. The supply chain delivery impact might have the most impact in product mix which may remove the worry that might impact the delivery and no impact on CO2 emissions. In DMAIC, This will be a part of the Analyse phase where surprising revelations help us to understand and discern standard directions to unique directions. Credibility: How much are the results credible from the results point of view? How much constitute to the true results expected? And how much contradicted are our well-established understandings? This is a part of Improve & control phase which provides an opportunity to improve our understanding and build iterative methodologies to dive Deep further to arrive at the best possible results expected. How to use this Methodology: Asking questions at every steps using the MAGIC acronym will help to remain true to the data and the provide a clear case of our understanding with might strengthen our case or will assist us to kill projects at the right time. This methodology is a good tool to look at the criteria as a group and build validate both data and outputs against the expected outcomes anticipated.

Annova Test: Annova is used study for variation analysis and significance in an experiment setting. Annova can be one-way or Two- way depending on number of independent variables (X). Annova is used with your analysis of two mean population is to be done with the Output (Y) is continuous and the inputs (X) are discrete (categorical). Example : Analyse the Mean AHT of resource groups based on location, Experience level, Education level. See below table for details definition, comparison and used cases / examples Analysis Type Definition Key Inputs Outputs Use Cases and Examples One-Way Annova One- way Annova is used to compare two or more population means from one independent categorical factors (many level) Continuous Output (Y), Categorical Independent variable (X), Degree of Freedom (DF) Testing two means for significance of the independent variable F-Distribution Example 1: Manufacturing of Springs using two different machining process – Twisting, Forging, Turning. Example 2: Market Survey of Healthy bars sales using Flavours – Choco / Nuts / Dry Fruits Example3 : School program enrolment % using location preference Two- Way Annova One- way Annova is used to compare two or more population means from two independent categorical factor (many level) Continuous Output (Y), Categorical Independent variable (X’s) F-Distribution & Additional Interaction effect of the factor Example1 : NHS Patient Wait time analysis using Gender (M/F/T), Severity of incidents (Low, Med, High) Example 2: Machine Productivity analysis using shift pattern, Operator Skill level ANCOVA (Analysis of Covariance) Mixture of Annova and Simple regression + Annova (interaction of Dependent variable after adjusted difference associated with Covariates Continuous Output (Y) and covariates, Dependent Variables Measure joint variability between two variables, Variability within the group and with the groups. Example1 : COVID vaccine testing in study Groups ( Age Groups) + Control Dependent Covariate ( Placebo /drug, dose administration) MANVOCA (Multi-variate Analysis of Covariance) Is multivariate Counterpart of ANCOVA Continuous Output (Y) and One Indep Variable + depended variable + covarites Outputs of MANOVA + effects of Covariates Example : Student Scores in 3 subject in GMAT (dependent variables) impact final score outcome in a age control group (covariate) MANOVA Similar to ANOVA with the input being dependent variables Continuous Output (Y), Categorical, cont. dependent variable (X’s) Multivariate F Value, Example: Three groups assigned same learning content with different modes – Online / Hybrid / On-campus to check cost effectiveness (Y) based on delivery modes.

Until the Late 1980, Quality function was mainly focussed on Quality control and keep defects and processes under control to which organisation could manage and seep out quality defects to customer. Dr. Joseph Juran presented the Juran Trilogy as a new means to manage Quality at organisation Level. The Juran Trilogy is considered a universal way of thinking of Quality which is fit for product and services at all levels and functions making it one of the most used quality management practice across the globe. The three key components of Juran Trilogy are: 1. Quality Planning (targeting COPQ) 2. Quality control in operations (targeting Defect control) 3. Quality Improvement (Targeting CI in Quality) The above graph visually demonstrates Quality in terms of product efficiency / defects in Y-axis along the time scale in X -Axis. The COPQ is a part which is prior to the operational induction of new product or service in production and is tied into Quality improvement to develop new products with cyclic approach of learnings. Quality Planning: All of the Quality planning starts from working backward and understanding customer needs and developing products and services to meet the customer demands from the features of the product or service. One of the key elements in Quality planning is VOC( Voice of customer) and COPQ ( Cost of poor quality) to gather inputs both from customer externally and internally. This helps to define and design product/service features and devise relevant process enabling customer needs. Quality Control: once processes are defined, During production the Quality control adheres to specification limits ( acceptable deficiencies ) to keep the process under control through periodic checks, Audits and inspection to track the quality metrics and take corrective and Preventive actions (CAPA) if the process get out of control. This is a reactive process (Fire fighting) of monitoring and correcting while these deficiencies could be tracked back to bad Quality planning processes. Quality Improvement: This part of the trilogy focusses mainly on Quality improvement opportunities for the product, process or service. This is done through use of Statistical Analysis, TQM or other Quality improvement tools to reduce deficiencies further to remain competitive and meet customer requirements. The three processes are interlinked which finally connects to our purpose of Quality Management system. The Juran Trilogy and Deming’s Cycle are some of the pioneering work in establishing the TQM methodology which is widely used in the 1980s and 1990s. Example of of Juran’s cycle elements Implementation at Miniature motor Manufacturing Organisation 1. Quality Control and Quality Improvement for Coils defects: One of the Largest organisations of DC Stepper Motors introduced Juran’s principle for quality control and improvement mechanism when several manufacturing facilities were transferred from EU to Asia in the late 1990 and early 2000. Some of the key initiative under Quality control and improvement were development of Statistical process Control mechanism (Control charts, Pareto, fishbone to name a few) for solving issues with high rejection rates of Coils and Endcaps in motors due to process changes, training, SOP migration and technology transfers. 2. Quality Improvement used for New Product Design: Some of the application of the Stepper motors manufactured were used in Aerospace Industry. key inputs from VOC was used to analyse the failure pattern from customers and defect rate acceptable to this industry was used develop new products for application in aerospace. The organisation also worked backward to perform FMEA analysis to review Failure modes due to either quality, workmanship or Equipment / process which involved wider functions such as supply chain, Change Management, R&D and production. This internal analysis and external customer inputs were used to build new Quality products and improved the defect rates by over 35% due to failures of endcaps and Coils. 3. Quality Planning Inputs: New Vendor Quality strategy and Inhouse Quality planning was performed with revised sets of inputs from the VOC and FMEA analysis to build interdepartmental Quality Policies at the stepper motor Manufacturing site. There were also key strategic decision on inhouse vs Outsource Quality management developed to improve efficiencies and reduce rework due to defects in assembly line.