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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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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