8 Wastes in Lean – DOWNTIME

MUDA – https://en.wikipedia.org/wiki/Muda_(Japanese_term)
TPS – Toyota Production System

Lean manufacturing
https://en.wikipedia.org/wiki/Lean_manufacturing

The original seven mudas are:[7][need quotation to verify]

Transport (moving products that are not actually required to perform the processing)
Inventory (all components, work in process, and finished product not being processed)
Motion (people or equipment moving or walking more than is required to perform the processing)
Waiting (waiting for the next production step, interruptions of production during shift change)
Overproduction (production ahead of demand)
Over Processing (resulting from poor tool or product design creating activity)
Defects (the effort involved in inspecting for and fixing defects)

Reference: https://www.huimfg.com/8-wastes-lean-manufacturing/8-wastes-of-lean-manufacturing-image/

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Net Promoter Score

Calculating your NPS score is as simple as tallying up your responses and subtracting the percentage of detractors from the percentage of promoters. The score is a whole number that ranges from -100 to 100, and indicates customer happiness with your brand experience.

Reference: https://delighted.com/nps-calculator


NPS survey structure. The Net Promoter Score survey consists of a two-part questionnaire. The first part asks your customers to rate – the rating question – your business, product or service on a scale of 0 to 10. The second question is a follow-up, open-ended question as to why the specific score was given.

Reference: https://www.retently.com/blog/nps-survey-templates/

https://en.wikipedia.org/wiki/Net_Promoter

AI Technical Article

Artificial Intelligence Knowledge/Experience

• Theory of Algorithms related to –

o Prediction, Optimization, Time-series, Classification, etc.

o Parametric and Non-parametric learning algorithms

o Pattern recognition and discovery

o Various types of Neural Networks [ANN, RNN, CNN, etc.]

o Structured, un-structured, hybrid data

• Knowledge of problems/approaches/solutions/challenges in – vision, language processing, IoT, speech and inferencing

• Problem-solving domains –

o Character Recognition, Classification, Typical NLP algorithms, etc.

o Optimization in logistics, supply-chain, etc.

o Major domains within Genpact – BFSI, CPG, Life Sciences, etc.

Reference: Dice.com

AI – Model Binary Files

We can store trained models in a binary file for later use.
1. pickle
2. cpickle
3. joblib

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joblib works especially well with NumPy arrays which are used by sklearn so depending on the classifier type you use you might have performance and size benefits using joblib.

Otherwise pickle does work correctly so saving a trained classifier and loading it again will produce the same results no matter which of the serialization libraries you use. See also the docs of sklearn on this topic.

Please note that joblib is included in sklearn.

Copied from: https://stackoverflow.com/questions/36794981/difference-between-saving-a-classifier-with-pickle-and-joblib-dump

—–
https://stackoverflow.com/questions/12615525/what-are-the-different-use-cases-of-joblib-versus-pickle
—–

Test the following for your model
1. Final binary file size
2. Total foot memory footprint
3. Time to load

—-

Customer Success

Goal: Make customer success. So that they will continue to use our product/services

Define what is a success for each customer.
Success means
Solving a business problem
Saving money
Sticking with deliverable timelines
Higher Return of Investment

What is required?
Know about customer interests, long term goals, budget, Key people who make decisions or influence decisions
Communication chart
Organization chart
Timelines of deliverables, renewals
Email, invitation, escalation templates

Other:
Negotiating on agreements terms, timelines
Justifying budgets
Showing return of investments
Helping them to grow and showcase the advantage of our product through metrics
User experience surveys
Spend Quality time with key stakeholders
Mitigating risks like missing timelines, deliverables, production issues, security compromise,
Handling org changes in customer side
Sharing org changes from within teams
Assuring smooth operations on any given day
Work with sales to do upselling, cross-selling
Know the local rules on gifting, parties, and limits to follow in interactions

Work with
Legal team
Security team
Infra team
Dev team
QA Team
Finance team

Titles
Chief Customer Officer
CS Manager
CS Director

Terms:
ARPA – Average Revenue Per Account (ARPA)
MRR – Monthly Recurring Revenue (MRR)
ARR – Annual Recurring Revenue
LTV – Customer Lifetime Value (LTV)
CAC – Customer Acquisition Cost
DCF – Discounted Cash Flow
CRC – Customer Retention Cost
CHC – Customer Health Score

Customer Retention Cost =
Cost of { Customer Success Team
+
Renewals and/or Account Management Team
+
Customer Engagement and Adoption Programs
+
Professional Services and Training
+
Customer Marketing }

Reference:
https://customersuccessbox.com/blog/saas-customer-success-metrics/
https://customersuccessbox.com/blog/10-customer-success-kpis/

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