Case Study 2_Chandpur

CASE STUDY 2 CHANDPUR ENTERPRISES LIMITED, STEEL DIVISION

Name:

SYED ABDUL RAHMAN BIN SYED AHAMED 816025 KHALIDUL ANWAR BIN ISHAK 818573 NOOR HANIM BINTI MD ISA 818933

UUM CASE STUDY 2 DECISION ANALYSIS

Date:

SQQP 5023 –

10 JANUARY 2015

Table of Content Chapter Title

Page

1.0

Introduction & Problem Statement

3

2.0

Analysis & Discussion

3-11

3.0

References

11

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UUM CASE STUDY 2 DECISION ANALYSIS

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1. INTRODUCTION & PROBLEM STATEMENT Numerous administration choices include attempting to make the best utilization of a organization's assets. Assets commonly incorporate hardware, work, cash, time, warehouse space and crude materials. These assets might be utilized to make items such as hardware, furniture, sustenance or apparel or benefits, for an occasion, plan for aircrafts or creation, publicizing arrangements or venture choices. Linear programming (LP) is a generally utilized scientific demonstrating method designed to help supervisors in arranging and choice making with respect to asset allocation. As talked about in Chandpur Enterprises Limited (CEL), Steel Division case study, and the organization overseeing executive needs to settle on the crude materials requirement for August creation at his steel plant. Because of lower and upper limits on the measures of every crude material in a

batch

and changing measures of power and time devoured for distinctive crude materials, Akshay Mittal, overseeing chief of CEL can't just utilize the least expensive crude material. A linear program and Excel's Solver enhancement capacity will give the ideal amounts that meet the imperatives.

2. DISCUSSION & ANALYSIS 2.1

There is couple of vital focuses should be breaking down for better choice making which are; a) What would be the best batch that could be making for one batch? 3

UUM CASE STUDY 2 DECISION ANALYSIS

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b) What is the profit associated with this batch?

Decision variables:

x i = kilograms of raw materials i to order per batch

Related variables: fi = recovery i *

x i = finished goods tons of raw material

i The optimization is: Max [Revenue – Cost of RM – Electricity Cost – Consumables Cost – Salary Cost] Where, per batch, Revenue = 29000 * Cost of RM =

∑ ifi /1000

Rate per Ton∗x i /1000 i¿ ∑¿

Electricity Cost = 4.30 * [700 *( Consumables Cost = 2000 *

ixi ¿ /1000+1200 ¿¿ ∑

∑ ifi /1000

Salary Cost = 3000 a)

Constraint on batch size of 4,000 kg Figure 1: Solution to the batch model

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UUM CASE STUDY 2 DECISION ANALYSIS

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So, to optimize a batch without any constraint related to monthly limits, profit per batch will be INR5, 421. b)

Batch optimization with limits implied by monthly supply

Figure 2: Solution to a model with batch variables and linear limits implied by monthly supply

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UUM CASE STUDY 2 DECISION ANALYSIS

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Alternative yields less per batch: INR 5,322. These shows yield more every month by doing more groups, 328 versus 321. There are more batches every month this optimization in light of the breaking point on the month to month supply. As a result of this constraint, Solver now becomes strength to utilize all the more excessive material rather than less expensive material. This enhances the general proficiency and in a roundabout way diminishes the time of one bunch.

2.2

Second analysis, will the administrative requirement of 4,000 kg for each batch of finished product hamper the capacity to make benefit? Is it worth to discover administrative endorsement to expand that point of confinement? Ideal answers for LP have hitherto been discovered called, deterministic assumptions. Implies, presumption on complete assurance in information and relationship of a issue are characterize. On the other hand, conditions are continuing changing in certifiable just in this contextual analysis. Thus, to handle the error, significance of seeing just how touchy that arrangement is to model suspicions and information is essential. Affectability examination only for the group without month to month requirements in view of this case study: Figure 3: Sensitivity analysis for batch model without supply limits

Variable Cells Name Tasla Raw Material per Batch (Kg) Rangeen Raw Material per Batch (Kg) Sponge Raw Material per Batch (Kg) Local Scrap Raw Material per Batch (Kg) Imported Scrap Raw Material per Batch (Kg) HC Raw Material per Batch (Kg)

Final

Reduced

Value

Cost

Objective Coefficient

Allowable Increase

Allowable Decrease

1391.788450 1391.788450 556.715379 835.073069 0.000000 1113.430760

0 0 0 0 0 0

2.67 3.37 2.14 2.37 0.18 1.24

0.72127846 1.E+30 0.56473868 0.65316276 2.93138483 1.E+30

0.56199723 1.00457297 6.98050847 4.65367232 1.E+30 0.49234907

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UUM CASE STUDY 2 DECISION ANALYSIS

Pig Iron Raw Material per Batch (Kg)

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278.357690

0

2.24

0.80967742

13.9610169

Constraints Tasla Raw Material per Batch (Kg) Rangeen Raw Material per Batch (Kg) Sponge Raw Material per Batch (Kg) Local Scrap Raw Material per Batch (Kg) Imported Scrap Raw Material per Batch (Kg)

Final Value

Shadow Price

1391.788450 1391.788450 556.715379 835.073069 0

0 1.03952679 0 ` 0

Constraint R.H. Side 0 0 0 0 0

Allowable Increase 1.E+30 1441.96107 1.E+30 1.E+30 1.E+30

Allowable Decrease 1391.78845 1344.98991 2226.86152 3618.64997 4453.72303

HC Raw Material per Batch (Kg) Pig Iron Raw Material per Batch (Kg) Tasla Raw Material per Batch (Kg) Rangeen Raw Material per Batch (Kg) Sponge Raw Material per Batch (Kg) Local Scrap Raw Material per Batch (Kg) Imported Scrap Raw Material per Batch (Kg) HC Raw Material per Batch (Kg) Pig Iron Raw Material per Batch (Kg) Total Finished Product per batch (Kg)

1113.430760 278.357690 1391.788450 1391.788450 556.715379 835.073069 0 1113.430760 278.357690 4000

0.57320807 0 0 0 -0.56395268 -0.63952679 -2.93138483 0 -0.80347947 3.39526792

0 0 0 0 0 0 0 0 0 4000

1751.31349 1.E+30 1391.78845 1391.78845 1386.96255 1344.98991 1331.55792 1113.43076 276.243094 1.E+30

956.36581 278.35769 1.E+30 1.E+30 557.491289 852.878465 0 1.E+30 280.504909 4000

Discussion: i. ii. iii.

Batch size is a big constraint on profits If increase the batch size by 1 kg, profit increase per batch by INR3.40 If increase batch size by ~320 batches per month, profit increase to

iv.

INR109, 000 (~6.25%) If it requires approximately INR1, 300,000 in capital and time investment to increase the batch size by just 100 kg, will able to recover that cost in less than 12 months

2.3

Third analysis, what amount of benefit will Akshay Mittal lose in the event that he should use in any event one unit of a crude material in a clump given or pick not to utilize that crude material? This is to stay away from miserable if CEL does not arrange a specific sort of crude material From the sensitivity analysis in the case study:

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UUM CASE STUDY 2 DECISION ANALYSIS

i.

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Row 13 indicates, Imported Scrap is the only raw material not being used in the current optimized plan which is the maximum profit per batch

ii.

without any monthly limit constraint. Row 31 shows, CEL would losing INR2.93 per additional kilogram if use

iii.

Imported Scrap. Suggest buying Imported Scrap if necessary and the price must below INR20, 070 per ton.

2.4

Forth analysis, Akshay Mittal must know the suggestions from ideal batch from question 2.1 on month to month commitment. At the point when run Solver for boosting the benefit every month, benefit every month shows INR1, 788,705 which is much higher than the benefit every month assessed in question 2.1, INR1, 739,245. In the meantime, benefit per clump INR4, 873 dropped essentially from inquiry 2.1 INR1, 739,245.

Figure 4: Nonlinear model with batch decision variables and a monthly objective

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UUM CASE STUDY 2 DECISION ANALYSIS

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The past methodology finishes up a shabby and ease crude material such as HC great in cluster plan and might incorporated in with the general mish-mash. Be that as it may, subsequent to this is a nonlinear model, there is probability that this enhancement may not produce a worldwide most extreme and only one of numerous nearby maxima. Along these lines, nonlinear model required to check if worldwide optima have. Nonlinear model need to use at many distinctive beginning stages to see dependably wind up at same ideal arrangement. An approach to detail a straight month to month model is to utilize month to month crude material choice variables and include a choice variable for the quantity of batches. Month to month enhancement: y i = tons of raw material i to order per month b = number of batches in a month Revenue = 29000 * Cost of RM =

∑ igi

Rate per Ton∗ yi i¿ ∑¿

Electricity Cost = 4.30 * 700 *( Consumables Cost = 2000 *

iyi ∑ ¿+1200∗b ¿

∑ igi

Salary Cost = 3000 * b

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UUM CASE STUDY 2 DECISION ANALYSIS

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Subject to min and max constraint for each i, constraint on batch size of 4,000 kg, batch size limit and hours available per month. Monthly optimization: Max [Revenue – Cost of RM – Electricity Cost – Consumables Cost – Salary Cost]

Figure 5: Linear model with batch decision variables and a monthly objective

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UUM CASE STUDY 2 DECISION ANALYSIS

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Discussion: i. ii. 2.5

Profit per month is same as profit per month for nonlinear monthly model. Nonlinear model did provide a global optimal

Last analysis, what are the suggestions to improve profits? Based on sensitivity analysis (Fig 3): i. ii.

Find other sources for Rangeen to increase supply. Negotiate a deal with supplier and pay an amount up to an additional INR919 per ton of supply for each ton over the current limit of 500 tons.

iii.

Improve time per month from 600 hours to higher. Every one hour increase in time will result profit by INR2, 981. This additional profit

iv.

would be applicable for the next 7.7 hours. Improve the time per month: a. Hire better maintenance personnel to reduce maintenance time b. Use better / costlier machinery to reduce breakdown periods c. Timely supply of consumables and spare parts to reduce waiting time (emergency) d. Put in place a better safety plan for workers to reduce time in related activities.

3. REFERENCES i.

Render B., Stair, R.M., & Hanna, M.E. (2006). Quantitative Analysis for

ii.

Management. Prentice Hall. (2011), Chandpur Enterprises Limited, Steel Division: Teaching Note

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