Learn about Marketing/ Sales, Account, Production / Maintenance, Quality, Industries Issues, Do Stratification – Rejection and much more solutions

2.  How Many Inquiry You have?
 Which Sales Man is performing best?
 In next one month, one year, How much
business is assured?
 Which Campaign is Working best for you?
 How Many inquiry are converting in to order?
 How many order you have?
Marketing/ Sales

3. • How Many quotation is lost?
• How much potential business is there?
• How many business you lost because of price,
Delivery?
• Which product is highly demanded?
• Which product is not demanding?
• In which region you have better market?

4.  What is Your net Profit?
 What is your Gross profit?
 What is your inventory turn over ration
 How many product is with you for more than 30
days, 90 days, 120 days?
 How much payment is payable, Receivable?
 Since how many days bills are not paid?
 Does your costing (planned) meet actual costing?
Account

5. Production / Maintenance
• What is a plant capacity
• What is a product Throughput time?
• What is Plant / product Performance Rate
• What is Plant Quality Rate?
• What is COPQ?
• What is overall maintenance cost Rate?
• How much is a labour productivity ?

6. Quality
• Which defect is highest
• Which defect is costly
• Which process generate highest defect

7. Why need Data
Data Generation
Analysis
Action

8. Industries Issues
No data Poor Data
Non Analytical
Data
Misleading
Data
Required Data
not available
Data
consistency
Poor Decision

9. Step 1 – Manage / Identify Process
• Select the process that is really important to
you
– Based on Business
– Based on good or poor process
– Based on your need

10. Step2–DoProcess
Mapping
Preparation
of Tea
Means ( Used Not consumed)
- Utensils
- Stove
- Tools
- Operator
Quality
- Recipe, Method, SOP,
- Time, Temperature
-
Input –(Used)
Milk, Tea, Sugar,
Gas, Water,
Quality Tea,
(100 Ml)
Wastage, ( Tea
dust) ,Gas
Conversion Qty
Rejection

11. Concern About Data Process
• Is process performance good?
• If not what area need improvement
• If yes, why process is doing good?
• How will you Map the process Performance?
• How can we improve?

12. Here is the answer
• Look for the unwanted Result? – Rejection,
Scrap, (Start Monitoring)
• Why the unwanted result is ? – Control

13. Do Stratification – Rejection
Operator Day Stove
Time Temp

14. Data collection -
Operator Male
Female
Qty
Prepared
Qty
Rejected
Reason Time Day
Mr A M 100 20 Skill 4-5 Monday
Mr B M 100 19 Skill 4-5 Monday
Mr c M 100 22 Temp 4-6 Monday
Mr D M 100 23 Temp 56 Monday
Mr E F 100 8 Temp 9-10 Monday
Mr F M 100 18 Skill 10- 11 Tuesday
Mr G M 100 9 Other 4-6 Tuesday

15. Operator – Quantity

16. Male – Female – Qty / Rejection

17. Male Female -

18. Prepare Parato
Defect
Category # of Defects Cumulative Percentage
Temp 23 19%
Temp 22 38%
Skill 20 55%
skill 19 71%
Skill 18 86%
Other 9 93%
Temp 8 100%

19. Conclusion
• 70 % rejection is from Male operator
• 30 % rejection is from Female operator
Because of Temperature variation
• Quantity Produce VS rejection is high with
Male
• One Female had 8 % of rejection, one male
has 9% of rejection of total
• 38 % defect are because of Temp -

20. Action
• Prefer / recruit Female operator /
• Competency of Male is Lack - Up grade
competency chart for male
• 38 % defect is because of Temperature - SO
control Temp and reduce 38 % error
• 31 % Error is Because of Skill - Train People
with SOP Etc.

21. Trend

24. GUIDELINES FOR
DATA COLLECTION

25. Data collection is nothing but collection of the
required ‘information’ in figures for statistical
analysis of a problem. This provides a sound
basis for decision making and corrective
action.

26. USEFUL POINTS FOR DATA
COLLECTION
• Be clear in mind about the objective.
• Prepare the questions which will make it clear
and precise.
• Collect data relevant to that.
• Use data sheet, check sheet and/or check list
as per the requirement.

27. • Keep them simple and easy.
• Reduce opportunities for error.
• Capture data for analysis, references and
traceability.
• Form should be self explanatory.
• Analyse the collected data for its credibility
and relevance (for whom, when or who
collected the data are important).
• Present data in a way that clearly answers the
question.

28. GUIDELINES FOR
STRATIFICATIONS

29. Stratification is the process of separation of
data into categories. It is normally done for
identifying the categories contributing to the
problem tackled.

30. • Try to have the clarity of the problem. This will
normally help to identify the Stratification
variables to be selected.
• If we have ready information and if they are
reliable they can be straightway used, otherwise,
start collecting data needed.
• Make sure that all Stratification variables are
identified for data collection.
• Data collected may be enormous, which will call
for establishing categories for each stratification
variable.

31. • The categories may be discrete values or range
of values.
• Sort out the data into the categories of
Stratification variables.
• Now summarize the number in each category.
• If they are simple numbers then total them.
• If it is a range of values and needs further
classification provide it.
• Present the result in graphic form to enable
effective communication.

32. • Study the display.
• If you feel that Stratification has to be done in a
different way, go ahead and do it on the same
lines as stated above.
• Sometimes stratified information may need to be
further stratified.
• Once you are satisfied with the stratification,
collect additional information, if needed to
ensure that the interpretation is right.
• Always present the total information of the work
done in an orderly manner to satisfy the
concerned that all aspects connected to the
problem are covered.