Learning Launch-Module 4

Challenge

As a process engineer in my organization, one of the key responsibilities is to provide insights from a  subject matter point of view into new digitalization project. One of these project started earlier this year  is creation of a platform which is connected to all required databases and can be used to do both data  collection and analytics for yield improvement. After various ethnographic studies and even traditionally  carrying out projects we understood the flow required , data required , statistical needs, advance  analytical options and insight gathering that is required to drive a yield improvement project. Currently  we are a stage to develop the first prototype of the platform to people , the challenge is continuous  feedback , user accessibility, interface and the overall acceptance of the platform over traditional.  Methods.

Tool used

I have selected the learning launch too , since we shall be deploying a under progress prototype which  will be iterated based on feedback from the users .

Application

We started out with first survey to people and asked them the below questions:

1. What should be the flow of the platform? This would help us structure the prototype  2. What kind of statistical features do you expect to be reflected when baseline data is selected ?

This would make data selection much more better and give a statistical summary on the go

3. What advance analytics do you expect to automate ?

After receiving the surveys we shall carefully list them out and make a Pareto of them for each question  about which recommendation is most asked for[what if/what wows] , which are feasible to be included  from a development stand point of view[what works] and then developing those into the prototype.

After getting a structure the platform shall be rolled out to a selected number of people [10-12] who are  currently assigned their yield improvement projects at respective sites . They shall be trained in depth  with hands on examples so they familiarise themselves with the platform. Then they shall be asked to  start their project on the platform and write their feedbacks weekly as to. What can be done to make it  better.

After all their feedbacks are taken into account , they shall be asked to finally rate the platform for usage  from a scale of 1 to 10. If the average score rated is to be greater than 8 , then it shall be deployed  across all people and the very people who tested would be asked to volunteer to provide training  sessions which will be recorded and available on our learning library to be accessed anytime. For a usage  acceptance , 3 projects from each site will be required to be done using the platform and they shall be  asked for updates uptil completion. Regular feedbacks and improvement interactions will be a part of  the project.

Insights

We gathered insights about the roll out structure which was as mentioned below

1)Data selection(in a dashboard style)→ categorising the data into good bad average->advance analytics  (linear regression and random forest modelling)->graphical insights->accepting the parameters and  printing a report

2)On the dashboard itself average , min , max , standard deviation and quartiles included

3) Classification model was one of the most used model at our organization thus categorising was  included along with regression model.

A total of 42 feedback points was gathered from users with real time deployment out of which 36 were  implemented, 6 left apart due to technical capabilities but parked aside for future.

An average score of more than 8 was achieved and now the platform is deployed across the whole  organization and has completed more than 20 projects on it .

Approach

I would include a weekly update review with all stakeholders as there were many cross functional issues  faced during execution due to communication gap.

