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

Transcription

1 , SAINTGITS College of Engineering, INDIA Research paper Cost Optimization of Supply Chain Network A Case Study of TMT Bar Manufacturing Company Sandeep Parida1*, A. B. Andhare2 1 P.G. Student, Mechanical Engg. Dept., Visvesvaraya National Institute of Technology, Maharashtra, India 2 Mechinical Engg. Dept., Visvesvaraya National Institute of Technology, Maharashtra, India *Corresponding author sandeep2010parida97@gmail.com 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. Organizations have 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 and efficiently to survive and grow. The present work deals with cost optimization of supply chain network using nontraditional technique like simple genetic algorithm and multi objective genetic algorithm. The total work is carried out for comparison of optimized cost with respect to the real cost of manufacturing plant.this report also includes the multi objective optimization method of three objectives i.e. total operating cost, stock level, shortage cost. Which facilitate decision makers to develop management policies under a changing environment? The objective of the project work 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 over optimization of supply chain network. This report emphasizes on cost optimization of supply chain network using simple genetic algorithm and multi objective genetic algorithm. The project work is carried out for cost optimization of supply chain network using different algorithms. The ultimate objectives of the work are a. b. c. 2. Minimization of total operating cost of supply chain network. Minimization of stock level with respect to total operating cost of supply chain network. Minimization of shortage cost by considering three objective functions. Literature Review According to the research by Lau et al. [1] formulated cost optimization of supply chain network using genetic algorithm guided by fuzzy logic and compared with various algorithms. The joint cost minimization of supplier selection, lateral transshipment, and vehicle routing in the supply chain network. (a) select one or more suppliers to order and replenish different types of products in such a way as to minimize the total ordering cost spent by a wholesaler (i.e. the sum of total product cost, and total backorder cost related to lead time), (b) maximize the savings on different products, and (c) find the best sequence for delivering various kinds of products to different retailers in order to minimize the total cost due to the total distance traveled by a vehicle and due to the total time required for a vehicle to serve retailers. Both single objective and multi objective approaches are considered in this study. GA with fuzzy logic adjusting the crossover rate and mutation rate after each ten consecutive generations is suggested as the best way to solve this problem. Danalakshmi et al. [2] described cost optimization of supply chain network using genetic algorithm. By comparing the algorithm cost and real cost of manufacturing plant. In this work, the optimal solution of 42

2 Volume 1 Issue the problem is obtained by using the non-traditional techniques such as genetic algorithm. Chen et al. [3] describedan analytical model is formulated for the location and allocation of facilities of four-echelon supply chain network for the optimal 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 of minimizing total SC cost and maximizing fill rate are considered. Lopes et al. [4] describe the application of Evolutionary Algorithms (EAs) to the optimization of a simplified supply chain in an integrated production-inventorydistribution system. The paper presented a comparative study of EAs for the optimization of a supply chain. The supply chain was modeled as a mixed-integer programming problem, encompassing the optimization of costs related to stocking, manufacturing, transportation and shortage. Kalayanmoy Deb [5] described in his book on Multi objective optimization using Evolutionary Algorithm Proposed that Evolutionary multi objective optimization (EMO) principle of handling multi-objective optimization problems is to and representative set of Pareto-optimal solutions. Since an Evolutionary Algorithm (EO) uses a population of solutions in each iteration, EO procedures are potentially viable techniques to capture a number of trade of near-optimal solutions in a single simulation run. And described a number of popular EMO methodologies, presented some simulation studies on test problems, and discussed how EMO principles can be useful in solving real-world multi-objective optimization problems through a case study of spacecraft trajectory optimization. Reddy et al. [6] explain supply chain two stage distribution inventory optimization model for a distribution network with multiple ware houses supplying multiple retailers, who in turn serve a large number of customers. This model has taken the distribution and inventory carrying costs into account in the supply chain network at each period. With the validation of case study using the confectionery industry data, it is clear that the results obtained are encouraging and reduced overall system costs. By making retailers to interact and taking a decision on lateral 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 transformed input 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 supply chain is formed and can only be formed if there are more than one participating companies. Second, the participating companies within a supply chain normally do not belong to the same business ownership, and hence there is a legal independence in between. Third, those companies are inter-connected on the common commitment to add value to the steam of material flow that run through the supply chain. This material flow, to each company, comes in as the transformed 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 services that they separately and/or jointly add value on in order to deliver them to the end consumer. It is very much an extended 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 so widely known and widely practiced in almost all businesses. It is also extremely difficult because the definition must capture all what supply chain management in practice has reached far and wide. it can be define as Supply chain management is simply and ultimate business management, whatever may be in its specific context, which is perceived and enacted from relevant supply chain perspective Genetic Algorithm Genetic algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and 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 optimization problems. GAs, although randomized, exploit historical information to direct the search into the region of better performance within the search space. In nature, competition among individuals for scanty resources results in the fittest individuals dominating over the weaker ones.genetic algorithm begins with a set of solutions (represented by chromosomes) called the population. Solutions from one population are taken and used to form a new population. This is motivated by the possibility 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 are more chances they have to reproduce. This is repeated until some condition (e.g. number of populations or improvement of the best solution) is satisfied. 43

3 Volume 1 Issue Case Study TMT Bar Manufacturing Company The particular case study is considered on cost optimization of supply chain network using genetic algorithm and multi objective genetic algorithm. The data used for the project work is taken from thermo mechanically treated (TMT) bar manufacturing company of Maharashtra. The company procures raw material from foreign suppliers and some time from domestic suppliers and stores them in scrap yard. The company is manufacturing the variety of product like L channels, (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 to the 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 raw material is ordered by the company to suppliers. The raw material is transported through vehicle from Mumbai ship yard to Jalana district of Maharashtra.Company stores these raw material in the scrap yard. The total cost included in this stage is borne by company.the total cost is direct cost and indirect cost. Direct cost includes transportation cost and cost associated with raw material. Indirect cost is labor cost, maintenance cost of vehicle, ordering cost. In this study indirect cost is not considered. Company is manufacturing the final product through different stages. It include heat treatment of raw material in electrical furnace, tested the constituent of raw material. If there is inadequate amount of any constituent then extra amount is added in the furnace. The TMT bar is made of Mild steel, now heat treatment process 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 is reduced to different product like L channels and bars of size 12m length and different diameter. The final product is stored in warehouse near the company. In this process the product is manufactured. The total process includes manufacturing cost i.e. running of machineries, labor cost and other miscellaneous cost. The company is running 24 x 7 throughout year. According to demand of distributor the final product is distributed to the distributors throughout western India. In this stage only transportation cost is included Product is transferred through road transportation using vehicles. The trucks used have capacity of 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)= 700 tons 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)= Rs Selling cost at retailer shop to customer per ton = Rs 34000Vehicle Capacity (cvc) = 50 tons Capacity of inventory at manufacturing plant(ic) = 200 tons For case study there are two suppliers, one manufacturing plant, and four distributors. There are some assumptions involved like customer demand rate; unit cost of product at retailer zone does not vary with time. In this supply chain there should not raise uncertainty of demand at customer level. The schematic view of supply chain network (SCN) for TMT bar manufacturing company is shown in the figure 1. SUPPLIER-2 SUPPLIER-1 MANUFACTURER s-1 s-3 s-2 s-4 Figure 1 schematic view of SCN for TMT bar manufacturing company. 44

4 Volume 1 Issue 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 echelons SCN describes the flow of raw material/ final product shown in figure 1. The two suppliers supply product from Mumbai shipyard to jalan district (Maharashtra). The manufacturing industries is at jalan. The finished product are transferred 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 Constraints equations Transportation cost x 27 Constraints equations Constraints equation ai 480 ai. t+ cdj 450 cvcj 700 cdj 450 Manufacturing cost ai. x cj. t ai 700 cj. Z + dj. IC 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 Objective function 2 ai. x + Demand to supply ratio Min (DM+DD) / ( Constraint Equation 0.75 (DM+DD) / ( Objective function 3 Stock Level Min (DM+DD)- Objective Function 4 cj. t + ai. t+ Min ( ai+ ai+ cj) ai+ cj ai+ cj) 1.1 cj) - (DM+DD))* Z 4.1. Techniques and software used Techniques Used Software Used GA Parameters Genetic Algorithm and Multi Objective GA MAT LAB 7.11 Crossover fraction = 20 Migration fraction = 0.02 Population size = 20 Generation = cj. Z + dj. IC

5 Volume 1 Issue Figure 2 Solution of objective function for supplier costs Figure 3 Fitness value of supplier costs & best individual Figure 4 Solution of objective function for transportation cost Figure 6 Solution of objective function for manufacturing cost 5. Figure 5 Fitness value of transportation cost & Best individual. Figure 7 Fitness value of manufacturing cost & Best individual. 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. The table shows that there is a saving of about Rs 11 lakhs as compared to the existing actual cost of company. Between the two methods used, the cost given by multi objective GA is higher than the cost given by simple GA. This is because in simple GA individual constraints are taken into account while running the solver. However, in multi objective GA all constraints are considered at a time. 46

6 Volume 1 Issue Table 1 Cost Table Sl. No. Methods Total Operating Cost(Rs)= Supplier cost + Manufacturing Cost + Transportation Cost Actual cost of company Simple Genetic Algorithm (GA) Multi Objective GA 172,50, ,85, ,91,672 The multi objective optimization shows that in different situations D/S ratio is less than one meaning there by that there is 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 like retailer and distributor relationship, immediate fluctuation of market rate etc. from the above it can say that in certain situation 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 optimal solution 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 is adapted. 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 better result than multi objective GA. But in terms of customer satisfaction is least for simple GA. Simple GA shows one result only. In multi objective optimization there are many results obtained and decision maker has to decide how much product 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 and capacity restraints, but also offers a near minimum cost. The best individual of each generation is steadily converging to a near optimal solution with the process of generations. Thus the work has demonstrated use of GA for cost optimization of supply chain network in a practical real life scenario. References [1] [2] [3] [4] H. C. W. Lau, T. M. Chan, W. T. Tsui, and G. T. S. Ho Cost Optimization of the Supply Chain Network Using Genetic Algorithms. IEEE, pp , C. S.Dhanalakshmi and G. M. Kumar Optimization of supply chain network using genetic algorithm. IEEE winter simulation conference, pp , Jason C.H. Chen and Cheng-Liang Chen Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Journal of Computers and Chemical Engineering 28, pp , Marco Aurelio Falcone, HeitorSilverio Lopes and Leandro Dos Santos Coelho Supply chain optimization using Evolutionary Algorithms,International Journal of Computer Applications in Technology,

7 Volume 1 Issue [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] Kalayanmoy Deb Multi Objective Optimization Using Evolutionary Algorithm, First Edition, John Wiley & sons (Asia) Pte Ltd ISBN K. Balaji Reddy, S. Narayanan and P. PandianSingle-Echelon Supply Chain Two Stage Distribution Inventory Optimization Model for the Confectionery Industry, journal of Applied Mathematical Sciences, Vol. 5, 2011, no. 50, pp Chang, Ying-Hua, Yu, Fang-Pei, Kao and Hui-Sung Applying co evolutionary genetic algorithms to solve the supply chain network design problems---base on the textile industry in Taiwan, International Journal of Industrial Engineering, Vol. 12, No. 3, pp S. AfshinMansouri, Davidgallear and Mohammadh.Askariazad Decision support for build-to-order supply chain management through multi objective optimization Int. J. Production Economics David E.Goldberg Genetic Algorithms in search, optimization and machine learning. Pearson Education (Singapore) pvt.ltd Sunil Chopra and Peter Meindl Supply Chain Management, Strategy, Planning and Operation, Third Edition, Pearson Education, Prentice Hall publishers, ISBN , B. Latha Shankar, S. Basavarajappa, And Jason C.H. Chen and Rajeshwar S. Kadadevaramath Location and allocation decisions for multi-echelon supply chain network A multi-objective evolutionary approach, Journal of Expert Systems with Applications 40, pp , Viswanathan, S. and R.Piplani, Coordinating supply chain through common replenishment epochs, European Journal of Operational Research, 129, pp Sue A.H. Network design in supply chain management, International Journal of Agile Management Systems, Vol. 1, No. 2, pp J. Gheidar-Kheljani, S.H. Ghodsypour, and S.M.T. FatemiGhomi Supply chain optimization policy for a supplier selection problem a mathematical programming approach Iranian Journal of Operations Research Vol. 2, No.1, pp , Syarif, A., Y.Yun and M.Gen Study on multi-stage logistics chain network a spanning tree-based genetic algorithm approach, Computers and Industrial Engineering, 43, pp CemalettinKubat and BarisYuce A hybrid intelligent approach for supply chain management system, Journal of Intel Manufacturing 23, pp Ellram, L. M The supplier selection decision in strategic partnerships, Journal of Purchasing Material Management,26(4), pp Liu Xiao-Feng and Zhang Meng Cost Optimization Model of Distribution Systems in Supply Chain under Stochastic Demand, IEEE, R. Ganesha Managing supply chain inventories a multiple retailer, one warehouse, multiple supplier model, International Journal of Production Economics, Vol.59, No.6, pp , J. J. Gao, Y. J. Wang, Y. J. Guo Minimum cost model of distribution systems under demand uncertainty, Journal of Northeastern University (Natural Science), Vol.23, No.1, pp.87-90, E. B. Diks, A. G. Kok, and A. G. Lagodimos Multi-echelon systems a service measure perspective, European Journal of Operational Research, Vol.95, No.2, pp ,