Presented by

Group 5 | Section B

PGDM (GM) 2015-16

Dhiraj Kumar [G15078]

Naved Ahmed [G15091]

Pankaj Kr. Goenka [G15096]

Prashant Singh [G15100[

Ved Prakash [G15117]

An Algorithm to

Minimize transportation cost by

Optimizing Vehicle type, Number of Trips & Part Loading for mass manufacturing industries

Case : M/s Super Auto supplying to M/s Bajaj Auto, Chakan, Pune

Background• M/s Super Auto (SAIL), Chakan supplies 10 parts to M/s Bajaj Auto, Chakan, Pune required for

production of various Pulsar / KTM motorcycles. Based on daily production plan the requirement of each of these parts varies from 0 to 3000 Nos.

• These parts are supplied in Reusable Pigeon Hole Plastic Bins of different sizes. Total there are 10 variety of Bins as Bin for each part is exclusive.

• For instance, Bin for Grab Handle holds 10 parts. So for a daily requirement of 200 parts, despatch representative would send 20 bins total. So on and so forth for all the parts.

• Currently to accommodate all the delivery requirement the despatch executive deploys the biggest permissible vehicle, Tata 909 – 2 or 3 in Nos. It is seen that these vehicles are not fully utilized resulting in cost loss.

• M/s SAIL has asked for a transportation cost increase and M/s BAL wants to suggest ways to M/s SAIL to improve vehicle planning & loading in order to minimize the cost. In process, it is required that all existing terms & conditions laid down by M/s BAL be followed.

Approach• Provide M/s BAL & M/s SAIL an easy to use spreadsheet to arrive at optimum number of trips

required for each vehicle type and total number of different Bins to be loaded onto that vehicle which would minimize the total transportation cost.

• Defining the current problem in terms of Input – Process – Output

Input Process Output

• BAL Daily Production Plan*• Part-wise total requirement in

Nos. (calculated)• Part Weight• Bin – LxBxH & Weight• No of Parts / Bin• Vehicle – LxBxH & Permissible

Wt.• Type of Permissible Vehicles

(T909/T709/T-Ace)*Daily/Weekly input, rest are one time inputs

• Define a Linear Program

• Convert Input parameters to a form that can be used in LP

• Execute LP

• Selection of Optimum Vehicle type out of 3 permissible variety

• Total number of trips required for each vehicle type

• Number of Bins of each part to be loaded in each trip

InputsDaily Plan (From Production Plan Sheet) Bin Dimension

Sl. No Part No Tag Part Desc Total Req. Weight

No of Parts per

BinLengt

h Width HeightWt of empty

BinWt per Bin Vol per

BinTotal No of Bins

Unit of Measurement ----> Nos per day

Kg. / part Nos/Bin m m m Kg/Bin Kg/Bin Cu. M /

Bin Nos. / day

1 52DH0177 x1 SF:BALANCER IDLER HOLDER WITH BEAR 2752 0.236 30 0.4 0.3 0.1 0.75 7.84 0.012 92

2 52DH0695 x2 COVER LHRR MOONLIGHT K1 1923 0.725 14 0.6 0.4 0.14 1 11.15 0.0336 137

3 52DJ0412 x3 GRAB HANDLE KIT BLACK LH/RH WITH B 1923 0.585 8 0.6 0.4 0.2 1 5.68 0.048 240

4 52JC0163 x4 LH RR COVER ASSLY K2/K3 MOONLIGHT 829 0.716 14 0.6 0.4 0.14 1 11.02 0.0336 59

5 52JL0013 x5 GRAB HANDLE WITH TPT N WASHER JL 304 0.717 20 0.6 0.4 0.2 1 15.35 0.048 156 52JM0013 x6 COVER LH RR WITH BOLT FOR JM 169 0.762 14 0.6 0.4 0.14 1 11.67 0.0336 127 DH101898 x7 INTAKE MANIFOLD K1(UG5) 1923 0.122 45 0.4 0.3 0.08 0.75 6.23 0.0096 438 DH161410 x8 GRAB HANDLE ASSY 1923 0.433 10 0.6 0.4 0.2 1 5.33 0.048 1929 JG113009 x9 CHANGE LEVER 236 0.220 50 0.4 0.3 0.16 0.75 11.77 0.0192 5

10 JG113800 x10 DUMBLE SPACERS FOR STEPHOLDER 946 0.114 100 0.3 0.2 0.05 0.5 11.89 0.003 9

Vehicle Data

L (m) B (m) H (m) Permissible Wt (in Kg)

Trip Cost Rs/trip Total Vol Cu. M

TATA 909 4.381 2.042 2.13 6000 1800 19.055TATA 709 3.833 2.057 2.13 3000 1500 16.794TATA ACE 2.14 1.43 1.65 1000 1000 5.049

Inputs to Linear Program

Process – Defining the LP

Decision Variables Number of transportation trips for each vehicle type (3 Types of Vehicle) and Number of Bins of each of 10 parts to be loaded in each trip

Objective Function To minimize total transportation cost

Constraints• No production loss at BAL to be ensured.• Upto 5% excess supply can be done over daily plan.• No overloading (by Wt. or by Vol.) of trucks.• Only vehicle TATA 909, TATA 709 or TATA ACE or any combination of these three

vehicles can be used owing to dock constraints.

Process – Defining the LP

Decision Variables

xjyi – x1y1 would mean number of bins of part 1 loaded onto vehicle type 1. 30 such decision variables ( 10 parts x 3 vehicle types) ( j= 1 to 10 and i = 1 to 3)

Yi is the total number of trip of each vehicle type i 3 such decision variables

Objective Function

Min Z = Σ(Ci*Yi) where, Ci is the Cost per trip and Yi is the total number of trip of each vehicle type i, i=1 to 3, for Vehicle type 909, 407 & ACE respectively

Constraints

Weight Constraint

Σ(wj*xjyi) <= Wi*Yi where, xjyi is the total number of bins of type j loaded in vehicle type i. w j is the total wt. of each bin of type j. Wi is the permissible load wt of vehicle type i Total 3 constraints for i = 1 to 3, varying j=1 to 10 each time.

Process – Defining the LP

Constraints (contd…)

Volume Constraint

Σ(vj*xjyi) <= Vi*Yi where, xjyi is the total number of bins of type j loaded in vehicle type i. v j is the total vol. of each bin of type j. Vi is the permissible load volume of vehicle type i Total 3 constraints for i = 1 to 3, varying j=1 to 10 each time.

Total Supply Constraint (Production requirement must be met & tolerance)

Σ(xjyi) >= Total Min. no of Bins required for to be despatched for production – Total 10 constraints for j= 1 to 10, varying i=1 to 3 each time.

Σ(XjYi) <= Total Min. No of Bins required for production*1.05 – Total 10 constraints for j= 1 to 10, varying i=1 to 3 each time.

No of trips Constraint : Σ(Yi)<=100 (i=1 to 3)

Integer Constraint : Yi should be integer

Non-negative Constraint : All decision variables should be non-negative

Objective Function 3300.00(Minimize Transportation cost)

Trip Cost 1800 1500 1000 Truck Type y1 y2 y3 Decision Variable - No of trips of each type 1 1 0

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10

Decision Variable and Constraints pertaining to Truck Type1

x1y1 x2y1 x3y1 x4y1 x5y1 x6y1 x7y1 x8y1 x9y1 x10y1 Decision Variable - No of Bins of each type 92 137 0 0 15 0 43 133 5 0 LHS RHS

Weight Constraint 7.84 11.15 5.68 11.02 15.35 11.67 6.23 5.33 11.77 11.89 3517.047154 <= 6000Volume Constraint 0.012 0.0336 0.048 0.0336 0.048 0.0336 0.0096 0.048 0.0192 0.003 13.34674203 <= 19

Decision Variable and Constraints pertaining to Truck Type2

x1y2 x2y2 x3y2 x4y2 x5y2 x6y2 x7y2 x8y2 x9y2 x10y2 Decision Variable - No of Bins of each type 0 0 240 59 0 12 0 59 0 9 LHS RHS

Weight Constraint 7.84 11.15 5.68 11.02 15.35 11.67 6.23 5.33 11.77 11.89 2585.148941 <= 3000Volume Constraint 0.012 0.0336 0.048 0.0336 0.048 0.0336 0.0096 0.048 0.0192 0.003 16.79394453 <= 17

Decision Variable and Constraints pertaining to Truck Type3

x1y3 x2y3 x3y3 x4y3 x5y3 x6y3 x7y3 x8y3 x9y3 x10y3 Decision Variable - No of Bins of each type 0 0 0 0 0 0 0 0 0 0 LHS RHS

Weight Constraint 7.84 11.15 5.68 11.02 15.35 11.67 6.23 5.33 11.77 11.89 0 <= 0Volume Constraint 0.012 0.0336 0.048 0.0336 0.048 0.0336 0.0096 0.048 0.0192 0.003 0 <= 0 Constaint pertaining to total Number of Bins of each type should be in required range

Total Number of Bins of each type 92 137 240 59 15 12 43 192 5 9

Condition >Min, <Max Min Req. Constaint 92 137 240 59 15 12 43 192 5 9 Max Req. Constaint ( +5% tol.) 96 144 252 62 16 13 45 202 5 10

Constaint on total number of vehicles 2 <= 100(To activate Vehicle selection)

Note :- Non-negative constaint for all decision variable

For Vehicle - Interger constaint

Output - ResultsVehicle Selection

Vehicle Type Tag Total No Trips

TATA 909 y1 1TATA 709 y2 1TATA ACE y3 0

Vehicle Loading Plan

Loading Pattern (Nos of Bins per trip)

Sl. No Part No Tag Part DescNo of Bins

(Min)

No of Bins

(Max)TATA 909 TATA 709 TATA ACE Total

Shipped

152DH0177 x1 SF:BALANCER IDLER HOLDER WITH BEAR 92 96 92 0 0 92

252DH0695 x2 COVER LHRR MOONLIGHT K1 137 144 137 0 0 137

3 52DJ0412 x3 GRAB HANDLE KIT BLACK LH/RH WITH B 240 252 0 240 0 2404 52JC0163 x4 LH RR COVER ASSLY K2/K3 MOONLIGHT 59 62 0 59 0 595 52JL0013 x5 GRAB HANDLE WITH TPT N WASHER JL 15 16 15 0 0 15

652JM0013 x6 COVER LH RR WITH BOLT FOR JM 12 13 0 12 0 12

7DH101898 x7 INTAKE MANIFOLD K1(UG5) 43 45 43 0 0 43

8DH161410 x8 GRAB HANDLE ASSY 192 202 133 59 0 192

9 JG113009 x9 CHANGE LEVER 5 5 5 0 0 510 JG113800 x10 DUMBLE SPACERS FOR STEPHOLDER 9 10 0 9 0 9

Comparison

Before (Adhoc Planning) After (Optimized using LP)

Vehicle Type

Total Trips Cost Vehicle Type

Total Trips Cost

T909 2 3600 T909 1 1800

T709 - T709 1 1500

Ace - Ace -

Total per day

3600 Total per day

3300

Saving of 8% in transportation cost.

Scope of Horizontal Deployment

• The algorithm can be extended to all the vendors supplying parts in mass manufacturing setup for FMCG/ Automobile Industry

• Potential Saving of INR 3.5 Crs per annum if algorithm deployed horizontally at Bajaj Auto Ltd.

Total Part Procurement (Annual) (in Crs) 14000Assuming 25% needs optimization 3500Assuming 2% transport cost on part cost 70Assuming 5% improvement thru' LP 3.5

Scope of Improvement

• Scaling up algorithm for >25 parts and >5 vehicle variants

THANK YOU!

Q & A

Reusable Bin