cost optimization of supply chain network: a case study of...

International Journal of Research and Innovations in Science and Technology,©SAINTGITS College of Engineering, INDIAwww.journals.saintgits.orgResearch paper

Cost Optimization of Supply Chain Network: A CaseStudy of TMT Bar Manufacturing Company

Sandeep Parida1*, A. B. Andhare2

1P.G. Student, Mechanical Engg. Dept., Visvesvaraya National Institute of Technology, Maharashtra, India2Mechinical Engg. Dept., Visvesvaraya National Institute of Technology, Maharashtra, India

*Corresponding author E-mail: [email protected]

Copyright © 2014 IJRIST. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Now a day’s industries are more competitive for providing the good quality product with minimum cost. Organizationshave ultimate aim maximizing profit, service level and quality, minimizing operational cost in supply chain network.Each manufacturer or distributor has some subset of the supply chain that it must manage and run profitably andefficiently to survive and grow. The present work deals with cost optimization of supply chain network usingnontraditional technique like simple genetic algorithm and multi objective genetic algorithm. The total work is carriedout for comparison of optimized cost with respect to the real cost of manufacturing plant .This report also includes themulti objective optimization method of three objectives i.e. total operating cost, stock level , shortage cost. Whichfacilitate decision makers to develop management policies under a changing environment? The objective of the projectwork is minimization of total operating cost, shortage cost and stock level of inventory.

Keywords: Genetic Algorithm (GA), multi-objective GA, total operating cost, stock level and shortage cost.

1. Introduction

Today’s competitive scenario forces the organization to be more robust. So that every organization think overoptimization of supply chain network. This report emphasizes on cost optimization of supply chain network usingsimple genetic algorithm and multi objective genetic algorithm. The project work is carried out for cost optimization ofsupply chain network using different algorithms. The ultimate objectives of the work are:

a. Minimization of total operating cost of supply chain network.b. Minimization of stock level with respect to total operating cost of supply chain network.c. Minimization of shortage cost by considering three objective functions.

2. Literature Review

According to the research by Lau et al. [1] formulated cost optimization of supply chain network using geneticalgorithm guided by fuzzy logic and compared with various algorithms. The joint cost minimization of supplierselection, lateral transshipment, and vehicle routing in the supply chain network. (a) select one or more suppliers toorder and replenish different types of products in such a way as to minimize the total ordering cost spent by awholesaler (i.e. the sum of total product cost, and total backorder cost related to lead time), (b) maximize the savingson different products, and (c) find the best sequence for delivering various kinds of products to different retailers inorder to minimize the total cost due to the total distance traveled by a vehicle and due to the total time required for avehicle to serve retailers. Both single objective and multi objective approaches are considered in this study. GA withfuzzy logic adjusting the crossover rate and mutation rate after each ten consecutive generations is suggested as the bestway to solve this problem. Danalakshmi et al. [2] described cost optimization of supply chain network using geneticalgorithm. By comparing the algorithm cost and real cost of manufacturing plant. In this work, the optimal solution of

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the problem is obtained by using the non-traditional techniques such as genetic algorithm. Chen et al. [3] describedananalytical model is formulated for the location and allocation of facilities of four-echelon supply chain network for theoptimal facility location and capacity allocation decisions. Fixed location and variable material cost, production,inventory and transportation costs are considered while making strategic decisions. Two objective functions ofminimizing total SC cost and maximizing fill rate are considered. Lopes et al. [4] describe the application ofEvolutionary Algorithms (EAs) to the optimization of a simplified supply chain in an integrated production-inventory-distribution system. The paper presented a comparative study of EAs for the optimization of a supply chain. The supplychain was modeled as a mixed-integer programming problem, encompassing the optimization of costs related tostocking, manufacturing, transportation and shortage. Kalayanmoy Deb [5] described in his book on “Multi objectiveoptimization using Evolutionary Algorithm” Proposed that Evolutionary multi objective optimization (EMO) principleof handling multi-objective optimization problems is to and representative set of Pareto-optimal solutions. Since anEvolutionary Algorithm (EO) uses a population of solutions in each iteration, EO procedures are potentially viabletechniques to capture a number of trade of near-optimal solutions in a single simulation run. And described a number ofpopular EMO methodologies, presented some simulation studies on test problems, and discussed how EMO principlescan be useful in solving real-world multi-objective optimization problems through a case study of spacecraft trajectoryoptimization. Reddy et al. [6] explain supply chain two stage distribution inventory optimization model for adistribution network with multiple ware houses supplying multiple retailers, who in turn serve a large number ofcustomers. This model has taken the distribution and inventory carrying costs into account in the supply chain networkat each period. With the validation of case study using the confectionery industry data, it is clear that the resultsobtained are encouraging and reduced overall system costs. By making retailers to interact and taking a decision onlateral transshipment, the inventory level of different locations at the same echelon is balanced.

3. Supply Chain Management

“Supply chain is define as a group of inter connected participating companies that add value to stream of transformedinput from their source of origin to the end products or services that are demanded by the designated end consumer”

In this definition, there are a number of key characteristics that have been used to portrait a supply chain. First, a supplychain is formed and can only be formed if there are more than one participating companies. Second, the participatingcompanies within a supply chain normally do not belong to the same business ownership, and hence there is a legalindependence in between. Third, those companies are inter-connected on the common commitment to add value to thesteam of material flow that run through the supply chain. This material flow, to each company, comes in as thetransformed inputs and goes out as the value added outputs.

A supply chain is basically a group of independent organizations connected together through the products and servicesthat they separately and/or jointly add value on in order to deliver them to the end consumer. It is very much anextended concept of an organization which adds value to its products or services and delivers them to its customers.Defining the supply chain management can be both dead easy and extremely difficult. It is dead easy because it is sowidely known and widely practiced in almost all businesses. It is also extremely difficult because the definition mustcapture all what supply chain management in practice has reached far and wide. it can be define as “Supply chainmanagement is simply and ultimate business management, whatever may be in its specific context, which is perceivedand enacted from relevant supply chain perspective”.

3.1. Genetic Algorithm

Genetic algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selectionand genetics, a rapidly growing area of artificial intelligence. GAs is inspired by Darwin's theory about evolution -"survival of the fittest". GAs represents an intelligent exploitation of a random search used to solve optimizationproblems. GAs, although randomized, exploit historical information to direct the search into the region of betterperformance within the search space. In nature, competition among individuals for scanty resources results in the fittestindividuals dominating over the weaker ones .Genetic algorithm begins with a set of solutions (represented bychromosomes) called the population.

Solutions from one population are taken and used to form a new population. This is motivated by thepossibility that the new population will be better than the old one.

Solutions are selected according to their fitness to form new solutions (offspring); more suitable they aremore chances they have to reproduce.

This is repeated until some condition (e.g. number of populations or improvement of the best solution) issatisfied.

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International Journal of Research and Innovations in Science and TechnologyVolume 1 : Issue 1 : 2014

4. Case Study: TMT Bar Manufacturing Company

The particular case study is considered on cost optimization of supply chain network using genetic algorithm and multiobjective genetic algorithm. The data used for the project work is taken from thermo mechanically treated (TMT) barmanufacturing company of Maharashtra. The company procures raw material from foreign suppliers and some timefrom domestic suppliers and stores them in scrap yard. The company is manufacturing the variety of product like Lchannels , (Mild steel of strength 500 N/mm2 ,MS500), iron bars with varying diameter like 8mm ,10 mm, 12mm,14mm, 16mm, 24mm, etc. The final product is supplied to the distributors and retailers. The products are distributed tothe western part of India basically Maharashtra, Gujarat, Karnataka, Andhra Pradesh, and Madhya Pradesh.

Though the company’s supply chain network include only flow from supplier to distributor without retailer. The rawmaterial is ordered by the company to suppliers. The raw material is transported through vehicle from Mumbai shipyard to Jalana district of Maharashtra .Company stores these raw material in the scrap yard. The total cost included inthis stage is borne by company .The total cost is direct cost and indirect cost. Direct cost includes transportation costand cost associated with raw material. Indirect cost is labor cost, maintenance cost of vehicle, ordering cost. In thisstudy indirect cost is not considered. Company is manufacturing the final product through different stages. It includeheat treatment of raw material in electrical furnace, tested the constituent of raw material. If there is inadequate amountof any constituent then extra amount is added in the furnace. The TMT bar is made of Mild steel, now heat treatmentprocess is done. In the first stage the billet size of (1 x 1 x 4) m is made .After that through rolling mill the size isreduced to different product like L channels and bars of size 12m length and different diameter. The final product isstored in warehouse near the company. In this process the product is manufactured. The total process includesmanufacturing cost i.e. running of machineries, labor cost and other miscellaneous cost. The company is running 24 x 7throughout year.

According to demand of distributor the final product is distributed to the distributors throughout western India. In thisstage only transportation cost is included Product is transferred through road transportation using vehicles. The trucksused have capacity of 40-60 tons.

The cost detail of company is given below:

Suppliers’ cost per kg(x) = Rs 26Number of unit material is transferred to the company from suppliers per day (ai)= 700tons Manufacturing Capacity of plant per day = 520 tons Demand of product at manufacturer per day(cj) = 480 tons.This detail is considered by looking in to last six months data and forecasted. Cost of transportation per km. per ton (t)= Rs 4 Distributors capacity (cdj) = 550 tons Demand of distributors (dj) = 450 tons Manufacturing Cost per ton (z)= Rs25000 Selling cost at retailer shop to customer per ton = Rs 34000Vehicle Capacity (cvc) = 50 tons Capacity ofinventory at manufacturing plant(IC) = 200 tons

For case study there are two suppliers, one manufacturing plant, and four distributors. There are some assumptionsinvolved like customer demand rate; unit cost of product at retailer zone does not vary with time. In this supply chainthere should not raise uncertainty of demand at customer level. The schematic view of supply chain network (SCN) forTMT bar manufacturing company is shown in the figure 1.

Figure 1: schematic view of SCN for TMT bar manufacturing company.

SUPPLIER-1 SUPPLIER-2

MANUFACTURER

s-1 s-2 s-3 s-4

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From the figure 1 it is clear that the network starts from supplies and ends with distributors. There are two suppliers,one manufacturing company and four distributors. The products are transferred by truck on road. The two echelonsSCN describes the flow of raw material/ final product shown in figure 1. The two suppliers supply product fromMumbai shipyard to jalan district (Maharashtra). The manufacturing industries is at jalan. The finished product aretransferred to 4 distributor centers, situated at different location namely Vodadra (gujarat), Amaravati (Maharashtra),Baramati (Maharashtra) and Ahwa (Gujarat).

A Single Objective Using Simple Genetic Algorithm

Suppliers’ cost : ∑ ai. xConstraints equations : ∑ ai ≥ 480x ≤ 27

Transportation cost : ∑ ai. t+ ∑ cj. tConstraints equations : ∑ cdj ≥ 450∑ ai ≤ 700∑ cvcj ≤ 700

Manufacturing cost : ∑ cj . Z + ∑ dj. ICConstraints equation : ∑ cdj ≥ 450∑ cvcj ≤ 700

dj ≤ 400

The above equations are solved using the GA toolbox in MATLAB. The results of these equations are discussed below.Multi objective function using GA

Objective function 1 : Total Operating Cost

Min∑ ai. x + ∑ ai. t+ ∑ cj. t + ∑ cj . Z + ∑ dj. ICObjective function 2 : Demand to supply ratio

Min (DM+DD) / (∑ ai+∑ cj)Constraint Equation : 0.75 ≤ (DM+DD) / (∑ ai+∑ cj) ≤1.1

Objective function 3 : Stock Level

Min (DM+DD)-∑ ai+∑ cjObjective Function 4 : Min (∑ ai+∑ cj) - (DM+DD))* Z

4.1. Techniques and software usedTechniques Used : Genetic Algorithm and Multi Objective GASoftware Used : MAT LAB 7.11GA Parameters : Crossover fraction = 20

Migration fraction = 0.02Population size = 20Generation = 100

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Figure 2: Solution of objective function for supplier costs Figure 3: Fitness value of supplier costs & bestindividual

Figure 4: Solution of objective function for transportation cost Figure 5: Fitness value of transportation cost & Bestindividual.

Figure 6: Solution of objective function for manufacturing cost Figure 7: Fitness value of manufacturing cost & Bestindividual.

5. Cost Comparison

By looking at the cost table (table 1) it is confirmed that there is a saving in the cost by using optimization method. Thetable shows that there is a saving of about Rs 11 lakhs as compared to the existing actual cost of company. Between thetwo methods used, the cost given by multi objective GA is higher than the cost given by simple GA. This is because insimple GA individual constraints are taken into account while running the solver. However, in multi objective GA allconstraints are considered at a time.

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Table 1: Cost Table

Sl. No. Methods Total Operating Cost(Rs)= Supplier cost +Manufacturing Cost + Transportation Cost

1 Actual cost of company 172,50,2112 Simple Genetic Algorithm (GA) 159,85,3603 Multi Objective GA 160,91,672

The multi objective optimization shows that in different situations D/S ratio is less than one meaning there by that thereis no shortage. This means there will be complete customer satisfaction in terms of product available. But in simple GA,D\S ratio is more than one, hence less customer satisfaction. This is because demand product due to various reason likeretailer and distributor relationship, immediate fluctuation of market rate etc. from the above it can say that in certainsituation multi objective GA gives better result. The Solution for Multi objective GA is shown in the figure 8.

Figure 8: Total operating Cost Vs. Stock Level Vs. Shortage Cost

6. Conclusion

In this paper, an analytical mathematical model is formulated for three stage supply chain network for the optimalsolution of total operating cost, stock level and shortage cost. The total operating cost including suppliers cost,transportation cost, manufacturing cost is evaluated individually and a multi objective cost optimization method isadapted. An optimal solution is obtained within few minutes while running on a standard PC.

The optimization methods used have shown right impact on case study taken though the simple GA show the betterresult than multi objective GA. But in terms of customer satisfaction is least for simple GA. Simple GA shows oneresult only. In multi objective optimization there are many results obtained and decision maker has to decide how muchproduct is stored in inventory with respect to total operating cost, Finally demand to supply ratio is also evaluated.

The results obtained show that the GA, multi objective GA approach not only satisfies the customer’s requirements andcapacity restraints, but also offers a near minimum cost. The best individual of each generation is steadily converging toa near optimal solution with the process of generations. Thus the work has demonstrated use of GA for costoptimization of supply chain network in a practical real life scenario.

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