CASE STUDY 2 CHANDPUR ENTERPRISES LIMITED, STEEL DIVISION
Name Name::
SYED SYED ABDUL ABDUL RAHMAN AHMAN BIN SYED SYED AHAMED AHAMED 816025 KHALIDUL ANWAR BIN ISHAK 818573 NOOR HANIM BINTI MD ISA 818933
UUM CASE STUDY 2 DECISION ANALYSIS
Dae:
SQQP 5023 –
10 !ANUARY 2015
Table of Content
Chapter Title
Page
1"0
Introduction & Problem Statement
3
2"0
Analysis & Discussion
3-11
3"0
References
11
2
UUM CASE STUDY 2 DECISION ANALYSIS
SQQP 5023 –
1. INTRODUCTION & PROBLEM STTEMENT Numerous administration choices include attempting to mae the best utili!ation of a organi!ation"s assets# Assets commonly incorporate hard$are% $or% cash% time% $arehouse space and crude materials# hese assets might be utili!ed to mae items such as hard$are% furniture% sustenance or apparel or benefits% for an occasion% plan for aircrafts or creation% publici!ing arrangements or 'enture choices#
(inear programming )(P* is a generally utili!ed scientific demonstrating method designed to help super'isors in arranging and choice maing $ith respect to asset allocation# As taled about in +handpur ,nterprises (imited )+,(*% Steel Di'ision case study% and the organi!ation o'erseeing eecuti'e needs to settle on the crude materials re.uirement for August creation at his steel plant#
/ecause of lo$er and upper limits on the measures of e'ery crude material in a
batch
and changing measures of po$er and time de'oured for distincti'e crude materials% Ashay 0ittal% o'erseeing chief of +,( can"t ust utili!e the least epensi'e crude material# A linear program and ,cel"s Sol'er enhancement capacity $ill gi'e the ideal amounts that meet the imperati'es#
!. DISCUSSION & NL"SIS 2#1
here is couple of 'ital focuses should be breaing do$n for better choice maing $hich are
a* 4hat $ould be the best batch that could be maing for one batch5 3
UUM CASE STUDY 2 DECISION ANALYSIS
SQQP 5023 –
b* 4hat is the profit associated $ith this batch5
Decision 'ariables6 x i 7 ilograms of ra$ materials i to order per batch Related 'ariables6 fi =
recovery i *
x i = finished goods tons of ra$ material
i
he optimi!ation is6 0a 8Re'enue 9 +ost of R0 9 ,lectricity +ost 9 +onsumables +ost 9 Salary +ost:
4here% per batch% Re'enue 7 2;<<< =
∑ ifi / 1000
Rate per Ton∗ x i / 1000 i¿ +ost of R0 7 ∑¿
,lectricity +ost 7 >#3< = 8?<< #$ +onsumables +ost 7 2<<< =
ixi
∑ ¿ /1000 + 1200 ¿ ¿
∑ ifi / 1000
Salary +ost 7 3<<<
a)
Constraint on batch size of 4,000 kg %igre 1' Soltion to the bat(h )o*el
#
UUM CASE STUDY 2 DECISION ANALYSIS
SQQP 5023 –
So% to optimi!e a batch $ithout any constraint related to monthly limits% profit per batch $ill be INR+, -!1.
b)
Batch optimization with limits implied by monthly supply
%igre !' Soltion to a )o*el ith bat(h /ariable0 an* linear li)it0 i)plie* b )onthl 0ppl
5
UUM CASE STUDY 2 DECISION ANALYSIS
SQQP 5023 –
Alternati'e yields less per batch6 INR @%322# hese sho$s yield more e'ery month by doing more groups% 32 'ersus 321# here are more batches e'ery month this optimi!ation in light of the breaing point on the month to month supply# As a result of this constraint% Sol'er no$ becomes strength to utili!e all the more ecessi'e material rather than less epensi'e material# his enhances the general proficiency and in a roundabout $ay diminishes the time of one bunch#
2#2
Second analysis% $ill the administrati'e re.uirement of >%<<< g for each batch of finished product hamper the capacity to mae benefit5 Is it $orth to disco'er administrati'e endorsement to epand that point of co nfinement5
Ideal ans$ers for (P ha'e hitherto been disco'ered called% deterministic assumptions# Implies% presumption on complete assurance in information and relationship of a issue are characteri!e# Bn the other hand% conditions are continuing changing in certifiable ust in this contetual analysis# hus% to handle the error% significance of seeing ust ho$ touchy that arrangement is to model suspicions and information is essential#
Affectability eamination only for the group $ithout month to month re.uirements in 'ie$ of this case study6
%igre 2' Sen0iti/it anal0i0 for bat(h )o*el ithot 0ppl li)it0 Cinal
Reduced
Bbecti'e
Allo$able
Allo$able
alue
+ost
+oefficient
Increase
Decrease
asla Ra$ 0aterial per /atch )Eg*
13;1#?>@<
<
2#F?
<#?212?>F
<#@F1;;?23
Rangeen Ra$ 0aterial per /atch )Eg*
13;1#?>@<
<
3#3?
1#,G3<
1#<<>@?2;?
Sponge Ra$ 0aterial per /atch )Eg*
@@F#?1@3?;
<
2#1>
<#@F>?3F
F#;<@<>?
(ocal Scrap Ra$ 0aterial per /atch )Eg*
3@#3
<
2#3?
<#F@31F2?F
>#F@3F?232
<#<<<<<<
<
<#1
2#;313>3
1#,G3<
1113#>3
<
1#2>
1#,G3<
<#>;23>;
Variable Cells
Name
Imported Scrap Ra$ 0aterial per /atch )Eg* H+ Ra$ 0aterial per /atch )Eg*
6
UUM CASE STUDY 2 DECISION ANALYSIS
Pig Iron Ra$ 0aterial per /atch )Eg*
SQQP 5023 –
2?#3@?F;<
<
2#2>
<#<;F??>2
13#;F1<1F;
Cinal
Shado$
+onstraint
Allo$able
Allo$able
alue
Price
R#H# Side
Increase
Decrease
asla Ra$ 0aterial per /atch )Eg*
13;1#?>@<
<
<
1#,G3<
13;1#?>@
Rangeen Ra$ 0aterial per /atch )Eg*
13;1#?>@<
1#<3;@2F?;
<
1>>1#;F1
13>>#;;;1
Sponge Ra$ 0aterial per /atch )Eg*
@@F#?1@3?;
<
<
1#,G3<
222F#F1@2
(ocal Scrap Ra$ 0aterial per /atch )Eg*
3@#3
<
1#,G3<
3F1#F>;;?
<
<
<
1#,G3<
>>@3#?23<3
H+ Ra$ 0aterial per /atch )Eg*
1113#>3
<#@?32<
<
1?@1#313>;
;@F#3F@1
Pig Iron Ra$ 0aterial per /atch )Eg*
2?#3@?F;<
<
<
1#,G3<
2?#3@?F;
asla Ra$ 0aterial per /atch )Eg*
13;1#?>@<
<
<
13;1#?>@
1#,G3<
Rangeen Ra$ 0aterial per /atch )Eg*
13;1#?>@<
<
<
13;1#?>@
1#,G3<
Sponge Ra$ 0aterial per /atch )Eg*
@@F#?1@3?;
-<#@F3;@2F
<
13F#;F2@@
@@?#>;12;
(ocal Scrap Ra$ 0aterial per /atch )Eg*
3@#3
-<#F3;@2F?;
<
13>>#;;;1
@2#?>F@
<
-2#;313>3
<
1331#@@?;2
<
H+ Ra$ 0aterial per /atch )Eg*
1113#>3
<
<
1113#>3
1#,G3<
Pig Iron Ra$ 0aterial per /atch )Eg*
2?#3@?F;<
-<#<3>?;>?
<
2?F#2>3<;>
2<#@<>;<;
-555
2.26+!786!
-555
1.E925
-555
+onstraints
Imported Scrap Ra$ 0aterial per /atch )Eg*
Imported Scrap Ra$ 0aterial per /atch )Eg*
Total %ini0he* Pro*(t per bat(h $3g4
Discussion6 i# ii# iii#
/atch si!e is a big constraint on profits If increase the batch si!e by 1 g% profit increase per batch by INR3#>< If increase batch si!e by J32< batches per month% profit increase to
i'#
INR1<;% <<< )JF#2@K* If it re.uires approimately INR1% 3<<%<<< in capital and time in'estment to increase the batch si!e by ust 1<< g% $ill able to reco'er that cost in less than 12 months
2#3
hird analysis% $hat amount of benefit $ill Ashay 0ittal lose in the e'ent that he should use in any e'ent one unit of a crude material in a clump gi'en or pic not to utili!e that crude material5 his is to stay a$ay from miserable if +,( does not arrange a specific sort of crude material
Crom the sensiti'ity analysis in the case study6
7
UUM CASE STUDY 2 DECISION ANALYSIS
i#
SQQP 5023 –
Ro$ 13 indicates% Imported Scrap is the only ra$ material not being used in the current optimi!ed plan $hich is the maimum profit per batch
ii#
$ithout any monthly limit constraint# Ro$ 31 sho$s% +,( $ould losing INR2#;3 per additional ilogram if use
iii#
Imported Scrap# Suggest buying Imported Scrap if necessary and the price must belo$ INR2<% < per ton#
2#>
Corth analysis% Ashay 0ittal must no$ the suggestions from ideal batch from .uestion 2#1 on month to month commitment#
At the point $hen run Sol'er for boosting the benefit e'ery month% benefit e'ery month sho$s INR1% ?%?<@ $hich is much higher than the benefit e'ery month assessed in .uestion 2#1% INR1% ?3;%2>@# In the meantime% benefit per clump INR>% ?3 dropped essentially from in.uiry 2#1 INR1% ?3;%2>@#
%igre -' Nonlinear )o*el ith bat(h *e(i0ion /ariable0 an* a )onthl ob:e(ti/e
8
UUM CASE STUDY 2 DECISION ANALYSIS
SQQP 5023 –
he past methodology finishes up a shabby and ease crude material such as H+ great in cluster plan and might incorporated in $ith the general mish-mash# /e that as it may% subse.uent to this is a nonlinear model% there is probability that this enhancement may not produce a $orld$ide most etreme and only one of numerous nearby maima# Along these lines% nonlinear model re.uired to chec if $orld$ide optima ha'e# Nonlinear model need to use at many distincti'e beginning stages to see dependably $ind up at same ideal arrangement#
An approach to detail a straight month to month model is to utili!e month to month crude material choice 'ariables and include a choice 'ariable for the .uantity of batches# 0onth to month enhancement6
y i 7 tons of ra$ material i to order per month
b = number of batches in a month Re'enue 7 2;<<< =
∑ igi
Rate per Ton∗ yi
+ost of R0 7
i¿ ∑¿
,lectricity +ost 7 >#3< = ?<< #$ +onsumables +ost 7 2<<< =
iyi
∑ ¿+ 1200∗b ¿
∑ igi
Salary +ost 7 3<<< = b
9
UUM CASE STUDY 2 DECISION ANALYSIS
SQQP 5023 –
Subect to min and ma constraint for each i% constraint on batch si!e of >%<<< g% batch si!e limit and hours a'ailable per month#
0onthly optimi!ation6 0a 8Re'enue 9 +ost of R0 9 ,lectricity +ost 9 +onsumables +ost 9 Salary +ost:
%igre +' Linear )o*el ith bat(h *e(i0ion /ariable0 an* a )onthl ob:e(ti/e
10
UUM CASE STUDY 2 DECISION ANALYSIS
SQQP 5023 –
Discussion6 i# ii#
2#@
Profit per month is same as profit per month for nonlinear monthly model# Nonlinear model did pro'ide a global optimal
(ast analysis% $hat are the suggestions to impro'e profits5
/ased on sensiti'ity analysis )Cig 3*6 i# ii#
Cind other sources for Rangeen to increase supply# Negotiate a deal $ith supplier and pay an amount up to an additional INR;1; per ton of supply for each ton o'er the current limit of @<< tons#
iii#
Impro'e time per month from F<< hours to higher# ,'ery one hour increase in time $ill result profit by INR2% ;1# his additional profit
i'#
$ould be applicable for the net ?#? hours# Impro'e the time per month6 a# Hire better maintenance personnel to reduce maintenance time b# Lse better M costlier machinery to reduce breado$n periods c# imely supply of consumables and spare parts to reduce $aiting time )emergency* d# Put in place a better safety plan for $orers to reduce time in related acti'ities#
2. RE%ERENCES i.
Render /#% Stair% R#0#% & Hanna% 0#,# )2<
ii.
0anagement# Prentice Hall# )2<11*% +handpur ,nterprises (imited% Steel Di'ision6 eaching Note
11