exide report

8/20/2019 Exide Report

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EXIDE CASE REPORT

Logistics & Supply Chain Management

SUBMITTED BY:Ayush Bohra (87/51)

Devashi Kesaria (122/51)

Puneet Aggarwal (280/51)

Sammyak Jain (315/51)

Shavir Bansal (337/51)

Vaibhav Kalani (385/51)

Mayank Verma (4021/21)



ANOMALIES IN DATA

The given data had some anomalies and required some cleaning. Following were the discrepencies

with the data:

a)

Production, receipts and sales sheet had many repeat entries. We have considered only uniqueentries in each of the sheets for the analysis.

b) Dispatch data anomalies:

Material To Location Quantity Date_new Week_No

AA1 H406 H406 6 19-05-2012 3

AA1 H406 H406 20 22-05-2012 3

AA1 H406 H406 15 28-08-2012 4

AA1 H503 H503 50 07-11-2012 1

AA1 S429 S429 18 17-10-2012 2

AA3 H406 H406 10 18-05-2012 3

AA3 H406 H406 1 18-12-2012 3

AB1 W42 W42 250 03-08-2012 1

AB3 W42 W42 100 03-08-2012 1

BA3 H406 H406 10 18-05-2012 3

BA3 H406 H406 10 25-06-2012 4

BA3 H406 H406 10 28-08-2012 4

EA3 H406 H406 49 24-07-2012 3EA3 S103 S103 20 30-04-2012 4

EB1 H407 H407 30 12-01-2013 2

FA1 H406 H406 7 28-08-2012 4

The above data had the same ‘from’ and ‘to’ location. We have not considered these entries for

the analysis.



Q1. Identify the fast moving and slow moving batteries on the basis of sales data.

We have considered the weekly sales data for estimating the coefficient of variation of each of the

batteries. Weekly sales data takes into account the risk-pooling effect of the daily variation. The

analysis is summarized in the table below.

We have assumed the following:

a) Coefficient of variation > 0.75 : Slow Moving (S)

b) Coefficient of variation < 0.50 : Fast Moving (F)

c) Products with average sales greater than 1000 are considered for classification as fast or slow.

Product Average Std Dev Coeff. Of variation Type

BA3 1321 466 0.35 F

EA2 16261 5899 0.36 F

AA4 49 22 0.45BB1 445 207 0.47

AA3 46 23 0.51

EA3 15399 8099 0.53

BA2 297 159 0.53

EB2 6390 3517 0.55

FA1 705 414 0.59

AA1 3865 2268 0.59

AB2 26 16 0.60

BA1 235 146 0.62

AA2 52 34 0.64AB3 2382 1545 0.65

DA2 45 30 0.68

DA1 310 220 0.71

EB1 2563 1997 0.78 S

EA1 45 36 0.81 -

AB1 2210 1894 0.86 S

CA1 27 34 1.24 -

Therefore, fast moving products are BA3 and EA2 while slow moving products are EB1 and AB1



Q2. Analyse the weekly sales of the above batteries. Is the weekly sales uniform across

the month?

The following graph and table show weekly sales of batteries across the months:

Month Week 1 Week 2 Week 3 Week 4

Apr 10571 27581 39711 68257

May 28187 41950 36213 70460

Jun 33850 42649 41001 70639

Jul 37319 42685 38493 62754

Aug 30639 43838 43812 76583

Sep 27071 48645 40326 108661Oct 44967 66049 27912 86249

Nov 47728 35233 55375 70808

Dec 47845 49408 52580 109561

Jan 40112 39344 51422 84783

Feb 18907 81300 36791 81089

Mar 36536 43436 46921 92544

Average 33644.33 46843.17 42546.42 81865.67

Std.

Deviation 11375.97 14166.19 7885.27 15327.14

COV 0.34 0.30 0.19 0.19

The weekly sales data is not uniform across the month. 4th week average data is almost double of the

rest of the weeks’ average sales for almost every month. Possible reason is the month end pushing of

sales to achieve the monthly set targets.

Seasonal influence is also seen in sales as sales in winter season are significantly higher than the

summer months.

0

50000

100000

150000

200000

250000

300000

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Weekly Sales in units

Week 1 Week 2 Week 3 Week 4



Coefficient of variation decreases over each week in a month implying that sales prediction gets better

towards end of the month. This is an area of concern for Exide as the prediction is not consistent across

all weeks.

Q3. Analyse the weekly dispathces from the manufacturing plants. Are the weekly

dispatches uniform across the month?

The following graph & table show weekly dispatches across months:

Month Week 1 Week 2 Week 3 Week 4

Apr 97936 98803 115419 132265

May 108697 93995 117915 146566

Jun 110816 100386 122454 125600

Jul 132914 130582 108212 114637

Aug 101653 118840 126979 172993Sep 103129 130863 130229 189816

Oct 168795 184696 108482 192217

Nov 163492 90347 135310 200662

Dec 179643 134401 139504 176056

Jan 174454 134594 106244 121942

Feb 76441 152461 83018 154155

Mar 119433 129439 137699 121109

Average 128116.92 124950.58 119288.75 154001.50

Std.Deviation 34888.22 27095.07 16335.12 31277.51

COV 0.27 0.22 0.14 0.20

The weekly dispatch data is not uniform across the month. 4th week average data is way higher than

the rest of the weeks’ average dispatches for many months. Possible reason is the month end pushing

of products to maintain inventory levels and sales to achieve the monthly set targets.

0

200000

400000

600000

800000

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Weekly Dispatches in units

Week 1 Week 2 Week 3 Week 4



Seasonal influence is also seen in dispatches as sales in winter season are significantly higher than the

summer months.

Coefficient of variation is an area of concern for Exide as the coefficient varies significantly across

various weeks. It will be difficult to handle and improve since there is no set pattern emerging out of

the weekly dispatches data

It seems that nearly 2 months would be the lead time as there is higher correlation between dispatch

and sales data with a lag of 2 months.

Q4. Determine and analyse the inventory of the material at all the tiers.

Methodology:

1) Initially assumed zero starting inventory for each product at each location and calculated the

week-wise ending inventory for each product at each location.2) For each product at each location, calculated the starting inventory that is required so that

there is no negative inventory in any of the 48 weeks.

The tables mentioned below give a consolidated view of the starting and ending inventory of each

of the products at each of the locations for the complete year:

Product

At Factory

Warehouse

At Regional

WarehouseAt Hubs At Spokes

StartingInventory

EndInventory

StartingInventory

EndInventory

StartingInventory

EndInventory

StartingInventory

EndInventory

AA1 4234 4228 2165 4239 944 23578 39 14689

AA2 0 1738 10 26 46 87 0 479

AA3 234 161 105 310 60 708 373 45325

AA4 118 289 162 76 105 304 0 655

AB1 6 37 1146 548 1782 6223 0 810

AB2 23 50 27 158 133 242 1026 8045

AB3 0 680 1549 964 974 6765 918 10823

BA1 0 87 0 657 0 532 573 2366

BA2 160 0 0 198 0 558 66 205BA3 90 11018 1056 2151 720 7876 313 5600

BB1 37 561 1203 883 767 1642 1586 25157

CA1 4 2109 144 409 80 348 6311 13496

DA1 80 160 0 728 0 798 7223 110300

DA2 45 223 12 185 56 170 675 10889

EA1 0 11076 1123 459 549 980 74 223



EA2 6467 119465 41835 19211 18871 70397 126 378

EA3 29706 29495 1457 13007 0 35106 30 181

EB1 5824 14351 6935 2034 7693 9003 28 325

EB2 6573 6034 300 3077 269 11507 79 588

FA1 356 0 105 253 164 2712 440 1160

The cells highlighted in green have huge increase in the aggregate starting inventory and ending

inventory. It shows that proper dispatch and sales levels are not forecasted and there is an issue with

the overall inventory management system.

If we consider average inventory as a percent of dispatch, few products are poorly managed as shown

in table below:

Stage % Benchmark Rationale for choosing these %ages Products that are poorly managed

Factory 10% These percentages have beenchosen according to general practice

in the industry (mot mentioned in

case)

AA2, CA1, EA1Warehouse 10% AB2, EA1

Hubs 15% AA3, AB2, CA1, EA1

Spokes 20% AA4, CA1, AB2, AA3, EA1



Q5. Determine and analyse the total lead times of all the material.

Methodology:

1)

Total lead time = (Total time in Inventory) + (Total time in transport and assembly)

2)

For calculating total time in inventory, we have calculated the inventory turn ratio for each product at

each location using the average inventory and dispatches/sales from those locations and then we have

used the weighted average of these inventory days ( Analysis in the attached excel sheet). The table

below gives a summarized view of the inventory days.

3)

For calculating total lead time in transportation, assumptions mentioned in the case have been used.

Also, we have taken a weighted average of all the transportation time for all the dispatches carried out

in one year. The table below summarizes the total transportation lead time of each of the products.

Lead time for premium products should be less as quality is an important factor for such consumers

and large lead times can lead to battery self-discharge. For eg. lead time for BA3 should be reduced.

Product

Starting

Inventory

Ending

Inventory

Inventory

Days

Transportation

Lead time

(days)

Total Lead

Time (in days)

BA1 0 1755 47 1.90 48.9

DA1 80 2496 54 1.67 55.67

BA2 160 1411 55 1.64 56.64

EB2 7181 35307 58 1.88 59.88

AB3 3441 19232 63 1.89 64.89

EA3 31536 122933 64 1.84 65.84

AB1 3960 14853 66 1.89 67.89

BB1 2580 5452 73 1.89 74.89

FA1 938 8565 81 1.94 82.94AA1 8929 57202 92 1.42 93.42

EA2 74396 319373 104 1.84 105.84

BA3 2541 31934 113 1.61 114.61

EB1 26763 38884 120 1.83 121.83

DA2 187 801 129 1.82 130.82

AA2 122 2056 150 1.97 151.97

AA4 511 1047 160 1.77 161.77

AB2 211 775 227 1.97 228.97

AA3 478 1767 233 1.90 234.9

CA1 258 3047 323 1.36 324.36

EA1 2112 13675 1336 1.85 1337.85



Q6. Map the material flow and check whether the flows are as per the network design.

1) Production:

Location wise production

Material F21 F22 F31 F32 F41 F51 F52 F53 Grand

Total

AA1 48236 35682 66079 85517 235514

AA2 4334 4334

AA3 3559 3559

AA4 2886 2886

AB1 740 116713 117453

AB2 1804 1804

AB3 1044 127308 128352

BA1 6076 6076

BA2 7297 7297

BA3 11586 4658 70644 5596 92484

BB1 24200 24200

CA1 4092 4092

DA1 8432 8432

DA2 2775 2775

EA1 13666 13666

EA2 40782 978362 1019144

EA3 276355 563453 839808

EB1 1346 132600 133946

EB2 99169 193553 292722

FA1 41663 41663

Grand

Total

48236 30090 42124 146891 576677 268221 1867968 2980207

There is no production taking place from F32 which is resulting in unutilized capacity and off-setting

the material flow planning. It is having a direct effect on the production schedules of other factories.

Also, the warehouse in region 3 has to get the product from other factories/warehouses and thus

having a direct implication on transportation costs.

2)

Dispatches from factory:

There are two issues which can be observed from the table shown below:

1) Products being sent to hubs which already have a factory and warehouse in their region

2) Products being sent from factory directly to spokes



As per the network plan hubs/spokes should preferably receive products from their respective

warehouses/hubs. This would lead to increase in transportation costs as the trucks engaged in these

cases would not be plying on the assigned routes.

Location H101 H104 H206 H402 H403 H405 H406 H407 H408 H507 S329 S514

F21

F22 112

F31 90

F41 50 297 395 750 340 1725 545 800 1325

F51 1335 295 750 1000 1015 12354 145

F52 935 1061 490 1490 1200 4160 183

F53 2860 38119

F32

3) Dispatches from warehouse:

Material is being sent from warehouses to hubs which are outside their respective regions. Small

deviations can be possibly explained due to urgent requirements but the network plan should be able

to address these cases appropriately.

For the entries highlighted in red:

These hubs are being served by warehouses of different regions, which are not having factories of their

own. Hence, these warehouses are being served by factory warehouses of some other regions.

Routing products through multiple warehouses is not optimal.

Location H104 H209 H301 H302 H501 H502 H503 H504 H505 H506 H507

W11 110 13395 10497 14728

W12 44057 1500 5334 8937 12413 7769

W21 50

W22 12737

W31 22779 37162

4) Dispatches from hubs:

Material is being sent from hubs to warehouses which is in violation of the network plan. The possiblereasons for this can be -

1. Returns due to damage/deterioration

2. Rerouting to address demand of some other hub/spoke

A significant issue is the transfer of materials from H301 to W31. The quantity transferred is large and

cannot be explained according to the network plan.



Location W11 W12 W21 W22 W31 W32 W33 W41 W42

H103 402

H104 100

H201 1348

H204 60

H206 10

H207 220

H208 240

H209 125

H301 5390

H302 125

H303 24

H305 90

H306 205

H308 23

H309 2

H311 1411

H401 20 300

H404 2838

H407 39

H501 50

H503 40

H504 175

H507 25

5) Dispatches from spokes:

Material is being sent from spokes to hubs which is in violation of the network plan. The

possible reasons for this can be -

1. Returns due to damage/deterioration

2. Rerouting to address demand of some other hub/spoke

A significant issue is the transfer of materials from S502 to H506. The quantity transferred is

large and cannot be explained according to the network plan.

Location H501 H502 H503 H504 H505 H506 H507

S501 78 1

S502 524 15 6030

S503 110 63 878

S504 555 11



S505 185 249 10

S506 172 3

S507 59

S508 190

S510 41 10

S511 235

S512 64

S513 369

S514 44

S515 21

S516 1142

S517 21

S519 114

S520 660

S521 60 5

S522 33 187

S523 11 287

S524 12 84

S525 75 417

S526 466

S527 73

S528 23

Major Areas of Concern

1) Coefficient of variation across 4 weeks for sales and dispatch data shows huge mismatch

2) Production planning of product AA1, AB2, BA3, EA1 and EA2 are not aligned to the demand

shown by Sales data (Benchmark: Sales as the % of Production greater than 80%)

Produc

t

Starting

Inv

End

Inv

Inv

Days

Coeff. Of

variationSales

Productio

n

Sales as % of

production

AA1 8929 57202 92 0.5918553

3235514 79%

EA2 7439631937

3104 0.36

78053

81019144 77%

BA3 2541 31934 113 0.35 63427 92484 69%

EB1 26763 38884 120 0.7812303

7133946 92%

DA2 187 801 129 0.68 2156 2775 78%

AA2 122 2056 150 0.64 2356 4334 54%



AA4 511 1047 160 0.45 2346 2886 81%

AB2 211 775 227 0.60 1268 1804 70%

AA3 478 1767 233 0.51 2204 3559 62%

CA1 258 3047 323 1.24 1311 4092 32%

EA1 2112 13675 1336 0.81 2100 13666 15%

3) Average Inventory as % of Dispatch should be less than following values for respective stages

Stage % Benchmark Rationale for choosing these %ages Products that are poorly managed

Factory 10% These percentages have been

chosen according to general practice

in the industry (mot mentioned in

case)

AA2, CA1, EA1

Warehouse 10% AB2, EA1

Hubs 15% AA3, AB2, CA1, EA1

Spokes 20% AA4, CA1, AB2, AA3, EA1

Supply Chain Strategies

1) For premium products, we should have best service levels in the industry

2) Reduce the loss in battery performance due to increase in lead time. This will give Exide the

competitive advantage, since the quality will not be compromised

Eg. Product EB1 has a lead time of 122 days, which reduces the quality of the product

significantly

3) Improve the correlation of Dispatch and Sales, which leads to improved forecasting, better

inventory management at each stage of supply chain, thus improving the bottom line leading to

higher profit margins

Supply Chain Changes

1) 52% of the products are being dispatched from H206 and H207. So as to minimize this cost our

warehouse W22 should be closer to H206 and H207 as compared to other hubs.

Warehouse Distance Hub Dispatch Sales

% of total

dispatch+Sales

w22 400 H205 12135 121456 10%

w22 500 H206 26656 121456 22%

w22 300 H207 36994 121456 30%

w22 10 H208 33344 121456 27%

w22 350 H209 12327 121456 10%



Similarly, 50% of the products are being dispatched from H405 and H406. So as to minimize this

cost our warehouse W42 should be closer to H405 and H406 as compared to other hubs.

Warehouse Distance Hub Dispatch Sales

% of total

dispatch+Sales

w42 10 H404 85997 333422 26%w42 500 H405 39642 333422 12%

w42 600 H406 125233 333422 38%

w42 450 H407 44975 333422 13%

w42 450 H408 37575 333422 11%

2) Hub H501 caters to a large proportion of the requirements of other hubs in its region and it also

caters to the hubs present in other regions. Due to large dispatches through this hub we

recommend that a warehouse should be setup in that region to better balance the flow of

material.

Optimal Transportation planFor converting the existing the existing supply chain plan into an optimal plan, we need to do the

following:

a) There are many irregularities in the current material flow as discussed in Ques 6. Theses

irregularities have to be addressed.

b) There are supply chain changes that have been suggested by us in the report. If we are able to

make those changes along with addressing the areas of concerns, we can achieve an optimal

transportation plan.

c) We have trucks of capacity 9 tonnes and 16 tonnes which are able to meet the current

transportation requirements but we would want to check the capacity utlization of each of the

truck at each route and would try to optimise the truck capacity accordingly.

exide report

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