300617011 Case Study 2 Chandpur

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

Dae:

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


SQQP 5023 –

1. INTRODUCTION & PROBLEM STTEMENT Numerous administration choices include attempting to mae the best utili!ation of a organi!ation"s assets# Assets commonly incorporate hard$are% $or% cash% time% $arehouse space and crude materials# hese assets might be utili!ed to mae items such as hard$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 maing $ith respect to asset allocation# As taled about in +handpur ,nterprises (imited )+,(*% Steel Di'ision case study% and the organi!ation o'erseeing eecuti'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% Ashay 0ittal% o'erseeing chief of +,( can"t ust utili!e the least epensi'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 & NL"SIS 2#1

here is couple of 'ital focuses should be breaing do$n for better choice maing $hich are

a* 4hat $ould be the best batch that could be maing for one batch5 3


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 %igre 1' Soltion to the bat(h )o*el

#


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

%igre !' Soltion to a )o*el ith bat(h /ariable0 an* linear li)it0 i)plie* b )onthl 0ppl

5


SQQP 5023 –

Alternati'e yields less per batch6 INR @%322# hese sho$s yield more e'ery month by doing more groups% 32 'ersus 321# here are more batches e'ery month this optimi!ation in light of the breaing point on the month to month supply# As a result of this constraint% Sol'er no$ becomes strength to utili!e all the more ecessi'e material rather than less epensi'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 mae benefit5 Is it $orth to disco'er administrati'e endorsement to epand that point of co nfinement5

Ideal ans$ers for (P ha'e hitherto been disco'ered called% deterministic assumptions# Implies% presumption on complete assurance in information and relationship of a issue are characteri!e# Bn the other hand% conditions are continuing changing in certifiable ust in this contetual analysis# hus% to handle the error% significance of seeing ust ho$ touchy that arrangement is to model suspicions and information is essential#

Affectability eamination only for the group $ithout month to month re.uirements in 'ie$ of this case study6

%igre 2' Sen0iti/it anal0i0 for bat(h )o*el ithot 0ppl li)it0 Cinal

Reduced

Bbecti'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>?3F

F#;<@<>?

(ocal Scrap Ra$ 0aterial per /atch )Eg*

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


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


13;1#?>@<

<

<

1#,G3<

13;1#?>@


13;1#?>@<

1#<3;@2F?;

<

1>>1#;F1
13>>#;;;1


@@F#?1@3?;

<

<

1#,G3<

222F#F1@2


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


2?#3@?F;<

<

<

1#,G3<

2?#3@?F;


13;1#?>@<

<

<

13;1#?>@

1#,G3<


13;1#?>@<

<

<

13;1#?>@

1#,G3<


@@F#?1@3?;

-<#@F3;@2F

<

13F#;F2@@

@@?#>;12;


3@#
-<#F3;@2F?;

<

13>>#;;;1

@2#?>F@

<

-2#;313>3

<

1331#@@?;2

<

H+ Ra$ 0aterial per /atch )Eg*

1113#>3
<

<

1113#>3
1#,G3<


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 approimately 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 $ill Ashay 0ittal lose in the e'ent that he should use in any e'ent one unit of a crude material in a clump gi'en or pic not to utili!e that crude material5 his is to stay a$ay from miserable if +,( does not arrange a specific sort of crude material

Crom the sensiti'ity analysis in the case study6

7


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 maimum 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<%
2#>

Corth analysis% Ashay 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>@#

%igre -' Nonlinear )o*el ith bat(h *e(i0ion /ariable0 an* a )onthl ob:e(ti/e

8


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 etreme and only one of numerous nearby maima# 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


SQQP 5023 –

Subect 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:

%igre +' Linear )o*el ith bat(h *e(i0ion /ariable0 an* a )onthl ob:e(ti/e

10


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'#

$ould be applicable for the net ?#? hours# Impro'e the time per month6 a# Hire better maintenance personnel to reduce maintenance time b# Lse better M costlier machinery to reduce breado$n periods c# imely supply of consumables and spare parts to reduce $aiting time )emergency* d# Put in place a better safety plan for $orers 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

300617011 Case Study 2 Chandpur

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