Operational research

1. “INVENTORY & FORECAST”
FOR
PLYBOARDS
(CENTURY PLYBOARDS INDIA LTD.)
KARNAL (HR)
Submitted by Ashok Kumar

2. CPIL, Karnal unit came into existence in May 1998 as a
result of foresightedness of the managing director
visionary Mr. Prem Kumar Bhajanka.
The 17 acres production complex is located at Ganger,
Tarawari near Karnal, Which is nearly 130 km far from
Delhi in west direction.
All the grades of Plyboards, Block boards, Flush doors &
Decorative Plyboards are produced at the plant.
COMPANY PROFILE IN A GLANCE

3. Used hard wood, imported timber like
Dipterocarpus (Gurjan) imported from Burma.
 Beech imported from Europe.
Eucalyptus(Keruing) imported from Malaysia.
The annual consumption of this organisation of
plantation products during last financial years was nearly
2500 m3.

4.  There are 18 types of products manufactured at this plant:
TYPES OF PRODUCT MANUFACTURED:
BWR PLYWOOD
IS 710 PLUS
BWR
WIN MR
PLYWOOD
ARCHITECT
WIN STAR MR
PLYWOOD
BWP BLOCK
BOARD
BWP B/B
CLUSTER
TEAK MR
BLOCK BOARD
WIN MR
BLOCK BOARD
REJECTED
PLY
FLUSH DOOR
FLUSH DOOR
TEAK & DECO
TECHNOPINE
FLUSH DOOR
FLUSH DOOR SHARON
BURMA TEAK MR REJECTED PLYWOOD
RECON
WIN STAR MR PLYWOOD

5. DATA COLLECTION
FORECASTING
ABC
ANALYSIS
USING SPSS
PROCEDURE FOR
PROCUREMENT
WAGNER
WHITIN
METHOD
RECOMMENDA
TIONS

6.  “To forecast the frequent items of plywood and suggest the optimal
procurement”
 DATA COLLECTION:-
Data provided by company.
 ABC Analysis:-
According to ABC Analysis the following items are in A & B category.
 BWR PLYWOOD
 BWP BLOCKBOARD
 WIN MR BLOCKBOARD
 WIN MR PLYWOOD
 ARCHITECT
 FLUSH DOOR
RESEARCH OBJECTIVE

7. 1. BWR PLYWOOD
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
1 2 3 4 5 6 7 8 9 10 11 12
DEMAND
BWR PLYWOOD
2013-14
2012-13
2011-12

8.  Fit Statistic Mean SE Minimum Maximum Percentile
5 10 25
 Stat. R-squard .640 . .640 .640 .640 .640 .640
 R-squared .987 . .987 .987 .987 .987 .987
 RMSE 6155.921 . 6155.921 6155.921 6155.921 6155.921 6155.921
 MAPE 1.957 . 1.957 1.957 1.957 1.957 1.957
 MaxAPE 5.730 . 5.730 5.730 5.730 5.730 5.730
 MAE 4380.789 . 4380.789 4380.789 4380.789 4380.789 4380.789
 MaxAE 15198.439 . 15198.439 15198.439 15198.439 15198.439 15198.439
 Normalized BIC 17.749 . 17.749 17.749 17.749 17.749 17.749
MODEL FIT(WINTERS’ADDITIVE)

9. Apr 14 May 14 Jun 14 Jul 14 Aug 14 Sep 14
Forecast 224147 303536 308774 368537 279477 284696
UCL 236672 318141 325197 386597 299038 305650
LCL 211623 288932 292350 350477 259916 263742
FORECASTED VALUES

10.  Inventory Carrying Charge (I) = 13% per year
= 0.13/12 = 0.011 per month
Unit Cost (C) = 1290 per cbm
 Inventory Carrying Cost (IC) = 1290*0.011= 14.19 = 14.2 per month
 Ordering Cost (A):
Salaries & Other Costs = Rs {(1, 80,000 + 20,000) + (3, 00000 +
45,000)}
=Rs 5, 45, 000
 No of Orders = 10
 A = 5, 45, 000/ 10 = Rs 54,500
PROCUREMENT FOR BWR PLYWOOD

11. First-period problem
C1(1) = Co + A+ C*(r1) + IC*{(r1)/2}
C(1) = 290,795,573.7
Two-period problem
C2(1) = Co(0) + A+ C* (r1 + r2) + IC*{(r1)/2 +( 2*r2)/2}
= 686,667,224.9
C2(2) = C1(1) + A + C*(r2) + IC*{(r2)/2}
= 684,566,619.3
C(2) = min {C2(1), C2(2)} = C2(2)

12. Three- period problem
C3(1) = Co + A+ C*(r1+r2+r3)+IC*{(r1)/2+(2r2)/2+(3r3)/2}
= 1,091,562,571
C3(2)= C1(1)+A+C*(r2+r3)+IC*{(r2)/2+(2r3)/2}
= 1,087,269,670
C3(3)= C2(2)+A+C*(r3)+IC*{(r3)/2}
= 1,085,131,875
C(3) = min{C3(1), C3(2) & C3(3)} =C3(3)

13.  Four-period problem
 C4(1)=
Co(0)+A+C*(r1+r2+r3+r4)+IC*{(r1)/2+(2r2)/2+(3r3)/2+(4r4)/2}
= 1,577,441,752
 C4(2) = C1(1)+A+C*(r2+r3+r4)+IC*{(r2)/2+(2r3)/2+(3r4)/2}
= 1,570,532,238
 C4(3) = C(2)+A+C*(r3+r4)+IC*{(r3)/2+(2r4)/2}
= 1,565,777,830
 C4(4) = C(3)+A+C*(r4)+IC*{(r4)/2}
=1,563,215,718
C(4) = Min {C4(1), C4(2), C4(3) & C4(4)} = C4(4)

14.  Five-period problem
 C5(1) =
Co(0)+A+C*(r1+r2+r3+r4+C5)+IC*{(r1)/2+(2r2)/2+(3r3)/2+(4r4)/2+(5r5)/2}
= 1,954,465,402
 C5(2)= C(1)+A+C*(r2+r3+r4+C5)+IC*{(r2)/2+(2r3)/2+(3r4)/2+(4r5)/2}
= 1,938,994,715
 C5(3) = C(2)+A+C*(r3+r4+C5)+IC*{(r3)/2+(2r4)/2+(3r5)/2}
= 1,932,256,020
 C5(4) = C(3)+A+C*(r4+C5)+IC*{(r4)/2+(2r5)/2}
= 1,927,709,621
 C5(5) = C(4)+A+C*(C5)+IC*{(r5)/2}
= 1,925,779,835
C(5) = Min{C5(1), C5(2), C5(3), C5(4) & C5(5)} = C5(5)

15.  Six-period problem
 C6(1)=Co(0)+A+C*(r1+r2+r3+r4+r5+r6)+IC*{(r1)/2+(2r2)/2+(3r3)/2+(4r4)/2+(5r5)/
2+(6r6)/2}
= 2,327,275,255
 C6(2)=C(1)+A+C*(r2+r3+r4+r5+r6)+IC*{(r2)/2+(2r3)/2+(3r4)/2+(4r5)/2+(5r6)/2}
= 2,316,359,572
 C6(3) =C(2)+A+C*(r3+r4+r5+r6)+IC*{(r3)/2+(2r4)/2+(3r5)/2+(4r6)/2}
= 2,307,598,899
 C6(4) = C(3)+A+C*(r4+r5+r6)+IC*{(r4)/2+(2r5)/2+(3r6)/2}
= 2,301,031,516
 C6(5) = C(4)+A+C*(r5+r6)+IC*{(r5)/2+(2r6)/2}
= 2,297,080,363
 C6(6) = C(5)+A+C*(r6)+IC*{(r6)/2}
= 2,295,113,515
C(6) = Min { C6(1), C6(2), C6(3), C6(4), C6(5) & C6(6)} = C6(6)

16. 2.BWP BLOCKBOARD
0
20,000
40,000
60,000
80,000
100,000
120,000
1 2 3 4 5 6 7 8 9 10 11 12
D
E
M
A
N
D
BWP BLOCKBOARD
2013-14
2012-13
2011-12

17.  Fit Statistic Mean SE Minimum Maximum Percentile
5 10 25
 Stat. R-squard .688 . .688 .688 .688 .688 .688
 R-squared .961 . .961 .961 .961 .961 .961
 RMSE 2854.427 . 2854.427 2854.427 2854.427 2854.427 2854.427
 MAPE 3.305 . 3.305 3.305 3.305 3.305 3.305
 MaxAPE 9.840 . 9.840 9.840 9.840 9.840 9.840
 MAE 2302.875 . 2302.875 2302.875 2302.875 2302.875 2302.87
 MaxAE 6617.534 . 6617.534 6617.534 6617.534 6617.534 6617.534
 Normalized BIC 16.212 . 16.212 16.212 16.212 16.212 16.212
MODEL FIT(WINTERS’ADDITIVE)

18. Apr14 May 14 Jun 14 Jul 14 Aug 14 Sep 14
 Forecast 97317 98998 98341 114239 77144 75026
 UCL 103124 105500 105469 121943 85384 83769
 LCL 91509 92496 91213 106534 68904 66282
FORECASTED VALUES

19.  Unit Cost (C) = 1150 per cbm
 Let Co(0) = 0
 First-period problem
 C(1) = 112,219,569
 Two-period problem
C2(1) = 226,955,465.9 C2(2) = 226,383,344.2
 C(2) = min {C2(1), C2(2)} = C2(2)
 Three- period problem
C3(1) = 341,549,915.9 C3(2) = 340,355,344.6
 C3(3) = 339,787,394.9
 C(3) = min{C3(1), C3(2) & C3(3)} =C3(3)
PROCUREMENT FOR BWP B/B

20. Four-period problem
C4(1) = 475,452,931.1 C4(2) = 473,534,959.9
C4(3) = 472,243,610.5 C4(4) = 471,574,710.6
C(4) = Min {C4(1), C4(2), C4(3) & C4(4)} = C4(4)
Five-period problem
C5(1) = 566,242,722.3 C5(2) = 563,836,902.5
C5(3) = 562,057,704.4 C5(4) = 560,900,955.8
C5(5) = 560,467,607.2
C(5) = Min{C5(1), C5(2), C5(3), C5(4) & C5(5)} = C5(5)

21. Six-period problem
C6(1) = 655,003,965.5 C6(2) = 652,123,746.4
C6(3) = 649,870,148.8 C6(4) = 648,239,000.9
C6(5) = 647,331,252.9 C6(6) = 646,911,353.6
C(6) = Min { C6(1), C6(2), C6(3), C6(4), C6(5) & C6(6)}= C6(6)

22. The result obtained from applying the procurement-
scheduling model. We conclude that the optimum
procurement policy for the products lies in placing the
order for the products (According to the forecasted
demand) at the start of every month then to keep the
inventory for a longer period.)
RECOMMENDATIONS