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Supporting Opinion | Explorer vs. Detective

Best Analytics Approach: Holmes vs. Columbus

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aaphil
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Re: A matter of degree
aaphil   4/22/2012 12:19:38 PM
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That sounds like a successful implementation. Have they implemented identical analytics to other business units since then?

Murali Sastry
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Prospector
Re: A matter of degree
Murali Sastry   4/20/2012 10:17:11 PM
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Dear all,

I agree with everyone that less is more. From my exposure to organizational goals and objectives, strategic plans provide direction to medium term and tactical initiatives which in turn provide direction to organizational goals and metrics.

Critical few goals (e.g., Profitability, Cost, Quality & Safety, and Growth) provide laser-like focus to the organization. Within the organization every employee has the same goals but the matter and degree (% of contribution) would differ.

This in my past experience has helped the organization to keep a standard, simple, and focused pursuit towards organizational alignment.

Murali Sastry

Murali Sastry
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Prospector
Re: Best Analytics Approach: Holmes vs. Columbus
Murali Sastry   4/20/2012 9:53:07 PM
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Piyanka,

I appreciate your complements.

I forgot 1 last step in the problem solving process.

After corrective action is verified, typically there is an activity widely known as "Lessons Learned" where the process owner shares lessons learned within the organization or if it is a global organization with other facilities to prevent others from creating the same defect.

This step would be very beneficial in any endeavor of the organization.

Thanks again for the opportunity.

Murali Sastry

Piyanka@Aryng
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Master Analyst
Re: Best Analytics Approach: Holmes vs. Columbus
Piyanka@Aryng   4/19/2012 11:15:45 PM
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Murali,

 

Very well laid out "structured" problem-solving to manufacturung defects.. Thanks for sharing.

 

Piyanka

Murali Sastry
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Prospector
Best Analytics Approach: Holmes vs. Columbus
Murali Sastry   4/19/2012 10:48:55 PM
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The defective product in a manufacturing process typically has tell-tale signs in addition to product identification. Using scientific approach to problem solving, the following could be collected in a matter of minutes to an hour:

1. Product produced on an assembly line (date code stamp) on product if available. 2. Review existing Failure Mode and Effects Analysis (FMEA) 3. Control plan for the line and product (shows inspection, and test protocol and criteria etc.) 4. Process flow diagram (process sequence with responsibilities, operation etc).

Based on the above, following actions are typically taken:

1. Containment Action (in suspect product lot) so suspect product is quarantined from rest of "good" production parts.

2. Root Cause Analysis-combination of brainstorming (with process experts), review of tell tale signs of product failure, talking to "parts" (identifying differences and causes of Best of the Best (BOB) and Worst of the Worst (WOW) products, Fish Bone Diagram, etc. could be drawn to move towards most likely cause and testing the cause by turning the problem "on" and "off" to finalize the root cause.

3. The above step is critical in leading towards problem resolution (permanent corrective action) that directly addresses the root cause identified above.

Typically the problem resolution happens in days to weeks depending on complexity of the problem, identification of causes, organization culture, infrastructure conducive to problem solving etc.

I like the article on Holmes Vs. Columbus. It is thought provoking and thanks to Piyanka.

Murali Sastry

Shawn Hessinger
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Re: A matter of degree
Shawn Hessinger   4/18/2012 11:12:36 PM
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Piyanka,

A clear example of how sometimes less is more. It also illustrates your point perfectly. In this case, looking for the right solutuion for an unweildy problem was clearly the right approach.

Daniel
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Data Doctor
Re: A matter of degree
Daniel   4/18/2012 7:44:02 AM
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1 saves
"However, practically, I have seen, the better the tool, the higher the expectation/dependence on the "tool" showing the right answer"

Priynka, you are right. There are many tools for the same reason and the output only depends up on the input datas. Tools cannot create any outputs; it can only segregate the results based on inbuilt equations and decision blocks. So the data flow and decision approaches are more important.

Anish
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Re: A matter of degree
Anish   4/17/2012 6:49:32 PM
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Piyanka's view is what I strongly believe in as well. If the tools were too perfect, nobody would hire an analyst. Most of the tools though advanced and user friendly, unfortunately lack "scope for customization". A perfect GUI can just get you what you need but not interpret the results for you. How would a marketing manager understand  'Rsquare' , 'chi-square' and 'validation misclassification rate'.

It is the analysts who need take the " Statistics to Business reommendations" path rather than relying on a tool with the threat for "Garbage in Garbage out" 

BethSchultz
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Re: A matter of degree
BethSchultz   4/17/2012 4:29:38 PM
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Ah ha! Good point Piyanka. I do think some cuation is needed when introducing data visualization, for the very reasons you spell out. Great for those who "get it," but too much of a detraction for those who only think they get it. Thanks!

 

Piyanka@Aryng
User Rank
Master Analyst
Re: A matter of degree
Piyanka@Aryng   4/17/2012 4:20:37 PM
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Theoretically, you are right!

However, practically, I have seen, the better the tool, the higher the expectation/dependence on the "tool" showing the right answer.  But unfortunately, tool doesn't have the answer, the analyst with a proper method can find the answer. So my experience with great visual tools is it often distracts because it can visualize anything and everything and can thus allow a user to be lazy and dump everything into the tool and see what comes up.. a common mistake I see is folks would get super excited by a deviation from trend and would spend a lot of time on it before realizing that the trend affects very very tiny percentage of the population (like a very small country in emerging markets) and thus has literally no impact on the business (for example: on the overall global business) even though visually, one sees a clear deviation from the mean.. So in my experience

1. good tool + good skills  = great business results

2. good tool <> great business results


 

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