how to help in adoption of DMS in Mars incorporated

1. Metrics To
Measure The
Adoption
Adoption Time
Point of
parities with
old
Application
Consistency
Features
meeting
expectation
User Satisfaction
Clear
Instruction
Support
Easy
Interface

2. Adoption time: This measures the amount of time it takes for
users to reach to his/her normal efficiency
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100 150 200 250 300 350 400 450 500
Average
time
spent
per
SKU
line
(Y)
Cumulative SKUs line (X)
X Variable 1 Line Fit Plot
Y
Predicted Y
Expon. (Y)
Result
Days required to reach the old
efficiency = 26 or ~One Period
Current Input Rate (Tejas) = 1SKU/21.6sec
Previous Input Rate (Compass) = 1SKU/7.7sec

3. Average Customer Satisfaction grade:
6.5/10
User Satisfaction:
This measures how satisfied users are with the application

4. Most used Feature of the Application
Trade Coverage
Reports

5. Trade Coverage
SKUs Searching
Number Key
Editable Option in the end
Tedious number of steps

6. PepsiCo SFA: 5SKUs Old SFA: 6SKUs Current SFA: 3SKUs
Comparative analysis of applications

7. • The average SKU lines for MM per bill cut is 10-12
• The average different category/ brand per bill cut is 5-7
• Habitual of using the "T9“ key layout
(3x3 grid number layout)
• Touch area is also less, especially for
the DSRs with 5 inches or less display
screen.
Reasons:
1. Instead of the current virtual keyboard,
we can use the phone keyboard.
2. Small popup screen to fill the data like
in the old version
Potential Solution:
• It is less complicated and user friendly
• More display areas for SKUs, will help
DSRs to find the product a little faster
Benefits of Potential Solution:
It is possible to present five SKUs
on a single display, or something
similar, in order to increase the
DSRs' productivity by decreasing
the exponentially longer search
time for the product compared to
the old SFA.
Current GUI Recommended GUI
Recommended Keyboard

8. Current GUI
Recommend Filters
Make it editable here itself it will be
very convenient for the DSRs.
Current Filters
Recommend GUI
This will display what
that outlet ordered
most recently.

10. Step 1 Step 2 Step 7
4 Steps: Filtration

13. Contribution SKU Lines: Average number of SKU lines per order that are fulfilling the
minimum order Qty
Bangalore, Mangalore, Mysore
Branch BKR BKS CHMST FS GNRLS GRCL GRCS OTH PAAN SAMT SPK SUBDWS-GEN WS-SPE Grand Total
Mysore 262 47 283 84 305 49 591 68 16 101 2 80 1888
Banashankari 384 51 1040 850 29 108 1126 46 27 109 57 1 3828
Kammanahalli 887 28 1456 460 31 125 1859 18 43 332 110 3 5352
KudluGate 1093 18 983 959 48 68 1349 29 22 332 1 96 4998
Nagarbhavi 374 49 1279 675 16 100 1164 14 12 146 110 8 3947
Vidyaranyapura 409 36 645 466 6 83 846 28 18 129 41 2707
Madikeri 1 1 2
Mangalore 368 12 231 173 290 106 717 72 4 73 56 2102
Puttur 231 8 56 68 1 39 138 13 8 37 44 643
Udupi 108 2 65 85 8 12 224 18 25 56 2 1 27 633
4116 251 6038 3820 734 690 8014 306 175 1315 5 3 621 12 26100
All calculations are w.r.t Snicker bar during P5
Total Stores Total stores placed order Total Stores Placed Snicker Bar order Order Qty less than 12 % age
26100 7592 969 446 46.02683
South
Total Stores Total stores placed order Total Stores Placed Snicker Bar order Order Qty less than 12 % age
98840 52226 17340 8395 48.41407
Bangalore, Mangalore, Mysore Retailers Segmentation
Highlighted segmentations are responsible for 70-80% of non-contributing SKUs line

14. The Sales Executive(MM)
visits between 180 and
200 stores per week.
Each store is visited four
times per month, so the
total number of visits per
month is between 700
and 800.
In which only 60% are
useful( 750X0.6=450).
Therefore, if we can
increase our efficacy to 75-
80%, we can achieve the
same results by visiting
600 stores per month.
In a month we have 150 (750-600) unproductive
visits by SE(MM) and around ~430 by SE(F4)
Reasons behind it:
HIGH-CLOSING STOCK AT THE RETAILERS. FEWER SALES AT THE RETAILERS.
Problem statement: DSRs and ISRs are currently experiencing ZERO orders and ZERO collections from
multiple retailers.
CURRENT AVERAGE
EFFICIENCY 60-65 %
MM
Bill cuts
30x0.9x0.7=19
Efficiency
19/0.3= 63.3%
Total Time spent
(19x15)/60= 4.75hrs
Assumptions:
1. 90% of Stores open
2. 70% Gives Order
3. Average time spent 15-20 mins
4. 30 Stores visited
F4/F2
Bill cuts
50x0.9x0.6=27
Efficiency
27/0.5= 54%
Total Time spent
(17x15)/60= 4.25hrs
Assumptions:
1. 90% of Stores open
2. 60% Gives Order
3. Average time spent 5-8 mins
4. 30-50 Stores visited

15. MYSORE CHENNAI COIMBATORE
MADURAI KARNATAKA KERALA

16. Start
Cluster
Visited =2
Last Visit
=
yesterday
Calculating Clusters
Score
C1 > C2
C1 > C3 C2 > C3
Cluster 1 Cluster 3 Cluster 2
Store Checklist
Last Visit
> 14 days
Order
Frequency
>2
Order Value based
Ranking( Starts with 3)
Clusters with
outlet information
Sorting of outlets
basked on Ranking
Top 40 outlets
Based on the attendance
location calculate nearest outlet
and use route name to
segregate outlets into 2 clusters
Rank wise Trade
Coverage
End
Yes
Yes
Yes Yes Yes
No
Yes
No
No
No
No
Leave
No
No
No
Rank 2 Yes
Rank 1
Flow Chart for Dynamic Route/Beats Allocation

17. MYSORE
1 2
3
Clusters Score Calculating Parameters
1. Maximum TOP outlets
2. Average Ordering Frequency (Cumulative of
all periods)
3. Average Ordering value per store
(Cumulative of all periods)
OF1 OF2 OF3 OF4
Outlets 43.86% 32.91% 16.23% 7.00%
OF2 Outlet Current Billcuts in Yr per Outlets Billcuts Potential Minimum bill cut amount Potential increase in Revenue
31930 21 27 300 57474000
OF2 Outlet Current Billcuts in Yr per Outlets Billcuts Potential Minimum bill cut amount Potential increase in Revenue
24253 21 27 300 43655400
Considering 32.9% of outlets as OF2
Considering 25% of outlets as OF2

18. How to
Achieve
Dynamic
Routing
DATA COMPILATION OF ALL SE-
COVERED RETAILERS
DIVIDE THEM INTO TINY
CLUSTERS BASED ON A
VARIETY OF CONSTRAINTS.
E.G. AREA, TYPE OF STORE,
BILLING AMOUNT, ETC.
ANOTHER CONSTRAINT SUCH
AS A RETAILER PRIORITY LIST,
BASED ON THE FREQUENCY OF
ORDERS.
IDENTIFYING THE TREND OF
EACH RETAILER'S ORDERING
AND PAYMENT FREQUENCY
USING THE TREND AND OTHER
CONSTRAINTS, DETERMINE
THE OPTIMAL ROUTE AND
RETAILERS TO VISIT ON A
GIVEN DAY.
EFFICIENCY ACHIEVED

19. Data Requirement
Report of Weekly Route
covered by each SE
Coordinates of the
outlets on these routes
Order report of each
retailer.
Sales order report of
retailer.

20. Things to be worked upon
1. Training
2. Overall effect of dynamic route allocation
3.