Selection of Optimum Supplier through Mathematical Modeling in the Supply Chain

             Selection of Optimum Supplier through Mathematical Modeling in the Supply Chain

¹ Kashif Ayaz (Industrial Engineering) ² Dr Muhammad (Business Administration)

¹ University of Engineering and Technology ² Laureate Folks International

https://laureatefolks.blogspot.com

laureatefolks@gmail.com, WhatsApp: +923334446261

 

1           INTRODUCTION

Purchasing is one of the most strategic operations in a business since it allows you to cut costs while also improving the quality of the raw materials. Furthermore, effective procurement decisions have a good impact on the manufacturer as well as the overall supply chain. Choosing the best suppliers is a crucial organizational planning decision that has impacts across an organization. Supplier purchases account for a large part of many firms' total costs.

There are suppliers and customers in every commercial venture, and there are ties between them. These links are of processes, facilities, and activities; SCM is in charge of managing these connections in order to provide the most value to the customer at the lowest time and expense to the supplier. (Basu and Wright 2010). SCM is crucial to managing supply chain activities to get optimum benefit. However, in today’s world, only economic performance is not required from the global supply chain. Corporate stakeholders demand organizations to develop a supply chain model with sustainability and to achieve sustainability there are three dimensions to consider, social, economic, and environmental (Peng, Ablanedo-Rosas et al. 2016). The economic and environmental dimensions are highly considered and given more attention in SCM

A lot of functions are needed to consider enhancing supply chain sustainability. One of them is outsourcing. An organization uses strategic sourcing in procurement intending to enhance partnership with the suppliers, reduce expenditures, simplify the procurement process, and reduce the total costs of ownership of strategic products or services(Jilani 2018). The goal of outsourcing is to enhance the flexibility of the firm and company able to focus on its core activities, as in today’s environment it is harder to fulfil customer requirements and achieve their trust. (Hila and Dumitraşcu 2014). Previously, only non-core components were outsourced; however, the trend has changed, and now any activity of a company, whether core or non-core, can be outsourced, including parts, operational processes, information and technology, production and marketing practices, and client service operations. (Kroes 2007).

Keywords: Supplier Selection, Modelling Supply chain, Lead Time, Optimum Order allocation

1.1         Research Questions

·         Achieving Optimum Supply chain results through Mathematical Modeling

·         Outsourcing is the core strategy of the Supply chain

·         Modeling for Best supplier selection

1.2         Objectives

·         Modeling of supply chain management

·         Considering insourcing as well as outsourcing

·         Selection of Optimum Supplier

·         Order allocation to optimize overall supply chain cost

1.3         The Motivation of the Research

Several significant factors influence the choice to select a supplier. These include: (1) a rapid rise in the price of purchased products as a proportion of total earnings for an enterprise. A quicker pace of technological changes with the lower life cycle of a product; and an increase in foreign purchasing. One efficient technique to assure the stability of a manufacturer's supply chain is to implement multiple sourcing strategies. A buyer can buy the same final products from numerous suppliers through multiple sourcing. Demand requirements are shared among numerous suppliers if the volume is significant enough. Most purchasing experts believe that when purchasers employ many suppliers for a product, the buying business will be safe when there is a shortage. Because of the economic relevance and inherent complexity of buying decisions, supplier selection is an attractive issue for using quantitative analysis. Mathematical programming approaches are well suited to giving optimal solutions to a variety of problem formulations.

1.4         Contribution of the Research

This research is beneficial for every firm involved in the supply chain, whether it be Supplier, manufacturer, retailer. However, mostly it is converging towards manufacturer, service provider, and retailer. Because it tends to improve, the Decision strategies in SCM and research focus is on Supplier selection. It will greatly benefit industries that need procurement from their suppliers. Our concerned industry is the spare parts manufacturing industry. Therefore, any other spare part industry can take benefit from it. Suppliers must be selected not only on the pricing but also on their ability to satisfy a lot of diverse needs. The needs of each company are varied. The logistic material department of Philips Electronics Industries Ltd.'s Consumer Electronics business, for example, has quality, cost, and delivery, flexibility, and responsiveness requirements for supplier selection. Mostly there is a tradeoff between multiple factors of Supplier selection. To draw better decisions, a Mathematical model is established for the real scenario.

2           LITERATURE REVIEW

Choosing the ideal supplier is difficult since it incorporates a number of factors and criteria, like pricing, performance, technical abilities, and services, a lot of work has gone into developing decision models for the supplier selection problem that consider several of these diverse features and factors into consideration. The problem of supplier selection has been solved using a lot of mathematical programming techniques. The following are the different research papers that have utilized different optimization strategies to solve the proposed models: multi-criteria optimization, linear programming, and mixed-integer programming

(Mendoza and Ventura 2012) This article focuses on inventory management for commodities acquired from a select group of suppliers, As a result of this method; firms might save money, which can have a knock-on impact across the SCM. Despite the fact that the models provided were created for a single-stage system (several vendors, one producer), they give a good foundation for future research into multi-stage SCM. (Jayaraman, Srivastava et al. 1999) In this article mixed integer programming is used for the selection of suppliers and how much quantity is to be allocated to the selected suppliers. (Amin, Razmi et al. 2011) shows how much order should be allocated to the selected suppliers through fuzzy linear programming. (Aissaoui, Haouari et al. 2007) also proposed model about purchasing decisions of Single and multiple items, as well as periods, are used in their proposed classification., (Moheb-Alizadeh and Handfield 2019) has introduced a multi-objective mixed-integer linear programming model for supplier selection and order allocation.

The order allotment strategy, which is part of the strategic purchasing decision, has an influence on the firm's connection with its vendors. (Nazari-Shirkouhi, Shakouri et al. 2013). (Mendoza and Ventura 2012) During the supplier evaluation and order allocation procedure, Kashif suggested that inventory management be considered. There have been two models presented. The first method was used to analyze order amounts, while the second model has been used to assure that almost all of the quantities were the same size. (Shen, Olfat et al. 2013) introduced the concept of Triple Bottom Line For the selection of the best supplier in which they considered the three economic, social, and environmental factors. (Nazari-Shirkouhi, Shakouri et al. 2013) has used fuzzy TOPSIS for supplier evaluation. This is a fuzzy two-phased approach with a multi-objective linear programming model. (Safaeian, Fathollahi-Fard et al. 2019) suggested the Zimmermann fuzzy technique for converting the proposed fuzzy model into a single objective form.

The supply chain is the integration of firms, that add value to the final product delivered to the customer. These firms focus on the efficiency of network and logistics processes and create value for the downstream customer. Firms achieve this value by increasing product benefit for the customer, cost reduction, service delivery improvement(Mangan and Christopher 2005). Now less obvious dimensions are considered as more valuable like low-risk supply chain(Wagner and Bode 2008), better visibility across the supply chain(Sundarakani, De Souza et al. 2010), green supply chain(Neto, Bloemhof-Ruwaard et al. 2008). The SCM means not only internal in a company but external between companies the material and information flow, as well as logistic activities, are controlled and planned(Cooper, Lambert et al. 1997). Supply chain management aims to improve products and services by reducing production time and cost without compromising product quality. This is achieved when management work cooperatively with other supply chain organization (Handfield and Nichols, 1999).

It is realized that competition is no longer be company against the company but SC against SC (Mazlan and Ali 2005). SCM aims to provide value to the end customer. Production planning and inventory control are important processes of SC. Inventory management aims to give two answers to question (1) the optimal order size. (2). when to order? The economic lot size models were developed to answer the above question. Traditional lot size models are economic order quantity EOQ, economic production quantity EPQ, newsvendor, and joint economic lot size JELS. Recently environmental aspects are considered in economic lot size modelling mostly EOQ and Newsvendor is used to find lot-sizing considering the environment (Su, Xiao et al. 2016, Tao, Guiffrida et al. 2017).

In this research, we will evaluate our supplier selection through the cost of Transport, Lead time, and Product cost as given in fig 1. The proposed model will give optimal order allocation for best-suited suppliers. The model is nonlinearly constrained multi-objective.

Figure 1 Model for Supplier Selection through Transportation cost, Lead time and Product cost

3           MATERIAL METHODS

Mathematical modelling approaches have long been utilized to get the best results from real-world scenarios. These techniques are more capable and less expensive. We will utilize the procedures below to simulate SCM and outsourcing.

1.      Literature review

2.      Problem statement

3.      Proposal

4.      Detail literature review

5.      Mathematical modelling

                 i.            Notation

               ii.            assumption

             iii.            Model formulation

6.      Data collection and data analysis

7.      Solution and methodology

8.      Compiling the result

9.      Documentation and Submission

3.1         Flow Chart

Figure 2 Research workflow chart

4           DISCUSSION

On-time delivery of client demand (customer service level) is a critical criterion, especially in made-to-order manufacturing systems where profit margins are higher. In a rapidly changing market environment, flexibility can help a company respond more quickly to supply chain concerns and facilitate on-time delivery of consumer demand. It is very important to select and implement flexible suppliers to contribute to the whole supply chain's flexibility. A good supply contract between a manufacturer (buyer) and a supplier is critical to the supply chain's long-term viability and integration.

The results of the multi-objective analysis done on the developed model proved that the best suppliers were chosen based on each objective's criteria. Furthermore, the study shows that Transportation Cost, product cost, and delivery lead time are critical elements in delivering finished items to clients on time. As a result, contracting with flexible suppliers who have shorter delivery lead times improves the overall performance of a supply chain and ensures that customer orders are delivered on time.

5           REFERENCES

Aissaoui, N., M. Haouari and E. Hassini (2007). "Supplier selection and order lot sizing modeling: A review." Computers & operations research 34(12): 3516-3540.

Amin, S. H., J. Razmi and G. Zhang (2011). "Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming." Expert Systems with Applications 38(1): 334-342.

Basu, R. and J. N. Wright (2010). Total supply chain management, Routledge.

Cooper, M. C., D. M. Lambert and J. D. J. T. i. j. o. l. m. Pagh (1997). "Supply chain management: more than a new name for logistics."  8(1): 1-14.

Hila, C. M. and O. Dumitraşcu (2014). Outsourcing Within A Supply Chain Management Framework. Proceedings of the 8th International Management Conference “Management Challenges for Sustainable Development.

Jayaraman, V., R. Srivastava and W. Benton (1999). "Supplier selection and order quantity allocation: a comprehensive model." Journal of Supply Chain Management 35(1): 50-58.

Jilani, P. A. (2018). "Indirect Procurement Strategies for Supply Chain Sustainability."

Kroes, J. R. (2007). Outsourcing of supply chain processes: Evaluating the impact of congruence between outsourcing drivers and competitive priorities on performance, Georgia Institute of Technology.

Mangan, J. and M. J. T. I. J. o. L. M. Christopher (2005). "Management development and the supply chain manager of the future."  16(2): 178-191.

Mazlan, R. M. R. and K. N. Ali (2005). Relationship between supply chain management and outsourcing, Heriot-Watt University.

Mendoza, A. and J. A. Ventura (2012). "Analytical models for supplier selection and order quantity allocation." Applied Mathematical Modelling 36(8): 3826-3835.

Moheb-Alizadeh, H. and R. Handfield (2019). "Sustainable supplier selection and order allocation: A novel multi-objective programming model with a hybrid solution approach." Computers & industrial engineering 129: 192-209.

Nazari-Shirkouhi, S., H. Shakouri, B. Javadi and A. Keramati (2013). "Supplier selection and order allocation problem using a two-phase fuzzy multi-objective linear programming." Applied Mathematical Modelling 37(22): 9308-9323.

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Peng, Y., J. H. Ablanedo-Rosas and P. J. M. P. i. E. Fu (2016). "A multiperiod supply chain network design considering carbon emissions."  2016.

Safaeian, M., A. M. Fathollahi-Fard, G. Tian, Z. Li and H. Ke (2019). "A multi-objective supplier selection and order allocation through incremental discount in a fuzzy environment." Journal of Intelligent & Fuzzy Systems 37(1): 1435-1455.

Shen, L., L. Olfat, K. Govindan, R. Khodaverdi and A. Diabat (2013). "A fuzzy multi criteria approach for evaluating green supplier's performance in green supply chain with linguistic preferences." Resources, Conservation and Recycling 74: 170-179.

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Sundarakani, B., R. De Souza, M. Goh, S. M. Wagner and S. J. I. J. o. P. E. Manikandan (2010). "Modeling carbon footprints across the supply chain."  128(1): 43-50.

Tao, Z., A. L. Guiffrida and O. F. J. A. J. o. M. Offodile (2017). "Carbon Emission Modeling in a Two Stage Supply Chain."  17(1).

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