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trucks by reporting availability of trucks ahead of when they actually were. This not only

results in additional inventory carrying cost, but also resulted in additional loss of control

over the tractors on the part of the company. More over the percentage of tractorsdamaged during transportation is also questionable. Transit/storage resulting in

repair/replacement/replenishment (70% of tractors received a 'yellow' card on receipt at

dealers - implying not ready for sale. 75% of these were set right in the first week. Theremaining sometimes got complicated in 'investigations', resulting in non settlement of

claims/dues even upto four years).

Forecasting Technique for Inventory Planning

The key concerns in inventory planning were to enable high service levels to the dealers,

and at the plant to respond to seasonality

Forecasting:

Forecasting can be done at three levels as follows:

(i) Company level:This should enable aggregate and seasonality planning.

(ii) Regional office level:This is required for cross-checking the periodic consolidated dealer forecasts for

placing orders from the factory. An important factor is choosing a suitable

established forecasting model and to validating the model using standard

techniques like Root Mean Squared Error.

(iii) Dealer level:

This should result in enabling regions to position inventory in the stockyards andplace orders from the factory. At the dealer level, developing models for the

disaggregate level of forecasting would have been difficult. However, it became

clear that tracking of potential customers would be a reasonably robustmechanism for assessing demand since a customer went through many predictable

stages of the buying process, before finally purchasing a tractor. The model wouldalso have to incorporate marketing decisions.

Inventory Planning:

The monthwise sales at an aggregate level could be forecasted with a high level of

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uniform production plan rather than the costs of the demand-driven production plan.

Exhibit TN-1 (also refer Exhibit 5 of case) gives the inventory because of seasonality

under the context of a uniform production plan. The seasonality inventory would cleartowards the end of June, which is at the end of the AprilMayJune peak. The overall

inventory cost because of seasonality was Rs 47.4 million, which amounted to Rs 790 per

tractor. It was the managements judgement that the cost because of the demand-drivenproduction plan would be higher, especially since it involved overtime, which often had

long-term fixed cost implications. Consequently, no attempt was made to quantify the

costs because of the demand-driven production plan.

Central Dispatch Yard

The problem of extended period of 'lack of control' and poor delivery quality could besolved by a central despatch yard. As there was no space adjacent to or near the existing

plant for such expansion, the possible location of the yard was at a suitable highway

location, 20 km away from the plant.

The analysis for the economics of a central despatch yard indicates that at an inventorysaving of two days per tractor, the annual savings would be Rs 12 million. The annualoperating cost would be Rs 2 million (including the additional transportation cost to move

the tractors to the central despatch yard rather than the payment to the transporters to

move the tractors to their godowns), thus offering a net saving of Rs 10 million per year.This was very good compared with the investment cost of Rs 15 million. There were

issues as to whether the two days would be entirely saved, just because the allocation

would now be made after physically seeing the truck that would transport the tractors.

The qualitative benefit of the increased flexibility in allocation and reduction in lossesbecause of being able to inspect the transporting truck were considered significant. It was

also felt that the transporters would welcome this move, since they would save on the

storage space in their godowns, while of course giving up the margin on the payment formoving the tractors to their godowns.

Mathematical Programming Model for Stockyard Location Analysis

Stockyard location analysis can be effectively considered as a typical linear programming

transportation problem. Transportation problem deals with optimal transportation andallocation of resources where there are sources with a supply of some commodity is

available and destinations where the commodity is demanded.

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throughput did not influence the cost structure (although such cost structures can be

negotiated), since volumes were expected to be at levels where the minimum manning at

the stockyards would suffice. Data are provided for the five potential stockyard locations,monthly operating costs (as specified by the third party) and distance from factory

(Exhibit TN-2, also refer Exhibit 6 of case), and location of the 19 dealers of the

company in Gujarat, along with the expected monthly demand and distances from thepotential stockyards (Exhibit TN-3, also refer Exhibit 7 of case). The total Gujarat

demand was expected to be 500 tractors per month.

The mathematical programming model for Gujarat had five zero-one variables to decideon the stockyard locations and 95 zero-one variables to decide on dealer stockyard

assignment. The objective function optimized the total relevant cost consisting of the

primary and secondary transportation costs and the stockyard operating costs. There were19 constraints to ensure that each dealer was assigned to a stockyard, and five constraints

to ensure that a stockyard was open, if required for being assigned to a stockyard. There

could be a few additional constraints, depending on stockyard capacities, minimumthroughput volumes (for outsourcing) and limitations for control, etc. Since this model

facilitated a tactical decision, it would be run whenever there were significant changes in(i) demand patterns within a state, (ii) stockyard location costs or (iii) ability to servicethe dealers with appropriate service levels. In general, this was not expected to occur

within say two years.

Mathematical programming model

Variables

i = 1, 2, . . . , s potential stockyard locations (s is typically 45 in a state),j = 1, 2, . . . , n dealer locations ( j is typically 1520 in a state).

Yi = 1 if stockyard i is selected

= 0 otherwiseYij =1 if dealer j is served via stockyard i

= 0 otherwise

Inputs

pi= primary transport cost per tractor km from factory to stockyard isij= secondary transport cost per tractor km from stockyard i to dealer j

di= distance from factory to stockyard idij= distance from stockyard i to dealer j

Dj= demand at dealer j per month

Ci = cost of operating stockyard i per month

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level. Of the scenarios considered, the locations at Valsad and Ahmedabad were

preferred. This was also driven by (i) the convenience of retaining the existing location

and (ii) expected opportunities for growth in the markets near Valsad.

When similar models were run for other states, the recommendations yielded a total

saving, across the states where stockyard locations were revised, of about Rs 1 millionper month, i.e. Rs 12 million per year. The final recommendations for stockyard locations

of the major states, based on the model output and implications in terms of the criteria

considered, are given in Exhibit 5. The model indicates a shift in the location of the

stockyards and the number stockyards. The locations are to be shifted towards Mumbaiand the number of stockyards should also be increased. This indicates that there is greater

emphasis on transportation costs rather than warehousing costs.

Apart from the specific recommendations, one of the greatest benefits of the modeling

exercise was in convincing the organization that a variety of factors can be considered for

analysis, often leading to counterintuitive solutions. Also, the scenario analysisdemonstration prompted the in-company logistics team to carry out a sensitivity analysis

by examining marginal violations of desirable parameter values by considering morescenarios by varying parameter values.

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Exhibit TN 1: Inventory because of seasonality, with uniform production policy

Month Demand Production Inventory due to

uniform productionJanuary 5,000 5,000 1,100

February 4,000 5,000 2,100

March 4,500 5,000 2,600

April 6,000 5,000 1,600

May 5,900 5,000 700

June 5,700 5,000 0

July 4,500 5,000 500

August 4,000 5,000 1,500

September 4,500 5,000 2,000

October 5,500 5,000 1,500

November 5,400 5,000 1,100

December 5,000 5,000 1,100

Total 60,000 60,000 15,800

Average

per month

5000 5000 1317

Inventory cost (at Rs 200,000 per tractor and 18% per annum inventory carrying cost)

1317 x 200,000 x 0.185 = Rs 47.4 million per annum

Exhibit TN 2: Inventory Because of Seasonality, with Uniform Production policy

Potential stockyard locations (i), operating cost (Ci) and distance from factory (di)

Sr No Stockyard

Location

Operating

Cost per

Distance from

Thane (Kms)

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8

Exhibit TN-3: Dealer Location, Demand and Distance

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Dealer Location

Amreli

Anand

Bardoli

Bharuch

Bhavnagar

Dharampur

Dholka

Godhara

Himmatnag

ar Jamnagar

Junagadh

Nadiad

Mehsana

Morbi

Palanpur

Patan

Porbandar

Rajpipla

Surendrana

gar

No of Tractors/

Month

35 30 25 40 25 20 20 20 35 20 30 45 20 20 20 30 20 25 20

1 Valsad 633 272 62 163 514 32 385 315 424 616 630 293 419 647 491 456 715 204 4312 Surat 566 205 31 96 447 109 318 248 357 549 563 226 352 570 424 389 648 141 364

3 Vadodara 399 38 125 71 280 266 151 81 190 382 396 59 185 403 257 238 481 82 200

4 Ahmedabad 258 73 225 182 200 377 40 136 79 313 327 52 74 292 146 125 412 195 116

5 Rajkot 105 255 492 365 175 560 162 321 304 88 102 234 299 67 371 255 187 255 111

* All distances are in Kilometers.

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Exhibit TN 4: Scenario Analysis for Gujarat: Total Relevant Cost and Stockyard Sites

(Rs)

Cost/tractor/km Current

Secondary

Distance

limit:None

Secondary

distance limit:350 kms

Secondary

distance limit:500 kms

Secondary

distance limit:

None

Minimum no of tractors to beserviced by a stockyard:

200/month

Secondary

distance limit:

500 kmsMin. no of tractors to be serviced by

a stockyard: 200/month

Primary: 2.5 10,28,999 8,73,533 8,78,209 8,75,454 8,75,484 875,484Secondary: 3.5

Ahmedabad Valsad Valsad Valsad Valsad Valsad

Rajkot Ahmedabad Ahmedabad Ahmedabad Ahmedabad

Rajkot

Primary: 3.0 11,19,855 8,22,880 9,43,085 8,87,380 8,22,800 8,99,080Secondary: 3.0

Ahmedabad Valsad Valsad Valsad Valsad Valsad

Ahmedabad Vadodara VadodaraRajkot

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Exhibit TN 5: Recommendations for Stockyard Locations

State Existing Yard(s) Optimal Locations

Andhra Pradesh Hyderabad

Hyderabad

Vijaywada

Tamil Nadu ChennaiHosurTrichy

Karnataka BangaloreBelgaum

Davangere

Gujarat AhmedabadValsad

Ahmedabad

Madhya Pradesh BhopalIndore

Raipur

RajasthanJaipur

SriGanganagar

Kota

Jodhpur

SriGanganagar

Punjab Jalandhar Patiala

Haryana Karnal Gurgaon