Supplier Selection and Order Allocation in Smart Manufacturing Paradigm: An ANP-TOPSIS Approach

The sustainable Supplier Evaluation and Selection and Order Allocation (SSOA) problem has received significant attention in supply chain management due to its potential to enhance a company’s performance, improve customer satisfaction, and reduce costs. In this study, an integrated methodology is proposed to address the SSOA problem. The methodology combines multiple techniques to handle the uncertainties associated with supplier evaluation, including a new ranking method based on the concept of Radius of Gyration (ROG) for interval type-2 fuzzy sets. The methodology also incorporates both subjective weights obtained using the Simple Multi-Attribute Rating Technique (SMART) and expert preferences, and objective weights calculated using the Method based on the Removal Effects of Criteria (MEREC) method to determine the weights of evaluation criteria. Some criteria for sustainable development are used to evaluate supplier performance, resulting in type-2 fuzzy sets, which are evaluated using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The ROG-based ranking method is employed to calculate the relative scores of suppliers. Finally, a multi-objective decision-making (MODM) mathematical model is presented to identify suitable suppliers and allocate their order quantities. The methodology is demonstrated in a sustainable SSOA problem and is shown to be efficient and effective, as the ROG-based ranking method allows for more accurate supplier performance evaluation, and the use of the criteria highlights the importance of sustainability in supplier selection and order allocation. The methodology’s practicality is further supported by the analysis conducted in this study, which demonstrates the methodology’s ability to handle the uncertainties associated with supplier evaluation and selection. The proposed methodology offers a comprehensive approach to the SSOA problem that can effectively handle the uncertainties in supplier evaluation and selection and promote sustainable practices in supply chain management.

Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies

In supply chain management practices, supplier selection (SS) is a critical strategic planning activity that usually constitutes an ex ante decision made under uncertainty, whereas order allocation (OA) represents a subsequent operational decision determined ex post, contingent upon both the selected suppliers and actual operational conditions observed during the execution phase—specifically, the realized scenarios of uncertain circumstances. The practical performance of an SS decision inherently depends on its subsequent OA outcomes, while the OA decision itself is constrained by the preceding SS choices. Nevertheless, existing studies typically tackle the SS and OA problems separately or formulate them within a single-stage programming model, failing to adequately capture their sequential interdependence and the impact of OA on SS evaluation. To address this gap, this study develops novel two-stage bi-objective stochastic programming models in which the first-stage SS decisions are evaluated based on two key criteria—total cost and purchasing value—both of which depend on the second-stage OA decisions in response to realized operational scenarios. The stochastic performance of a given SS scheme, arising from adaptive OA decisions under uncertainty, is measured by expected value and conditional value-at-risk. An integrated approach combining weighted-satisfaction sum, linearization, Monte Carlo simulation, and genetic algorithm is developed to solve the models. Computational experiments demonstrate the effectiveness of the proposed methodology and reveal the influence of objective preferences and risk-aversion levels on the optimal supplier selection.

PT. Sekarlima is a woven fabrics manufacturing company. One of the raw materials for fabric weaving process is yarn. To fulfill the demand for woven fabric the company faces some problems in supplier selection and the allocation of raw materials. Supplier selection and the allocation are essential to support the smoothness of the production process. To produce a good quality product the company needs a good planning system and integrated implementation, in accordance with production activities in which it cannot be separated from raw materials. Supplier selection is important in this activity, as it will determine the cost of production. The aims of this research are to determine a set of appropriatesuppliers of yarn using the TOPSIS method and allocate theyarn to selected suppliers using Linear Programming model. TOPSIS is one of the multi-criteria decision methods in which the selected alternative determined by the closest distance of the ideal solution and has the furthest distance from the non-ideal solution using the Euclidean distance (the distance between two points) to determine the relative distance of the alternative. From the results of the analysis, there are 4 suppliers are selected, namely TYF, AGT, DLS, and APC. Five criteria are used in the supplier selection, namely quality, price, delivery, flexibility, and responsiveness. TOPSIS resulted in the order of rank of each supplier TYF, AGT, DLS, and APC is 0.52, 0.48, 0.48, and 0.23 respectively. The order allocation from a linear programming model for each supplier is 1088. 64 kg, 870.91 kg, 526.18 kg, and 54.43 kg.PT. Sekarlima is a woven fabrics manufacturing company. One of the raw materials for fabric weaving process is yarn. To fulfill the demand for woven fabric the company faces some problems in supplier selection and the allocation of raw materials. Supplier selection and the allocation are essential to support the smoothness of the production process. To produce a good quality product the company needs a good planning system and integrated implementation, in accordance with production activities in which it cannot be separated from raw materials. Supplier selection is important in this activity, as it will determine the cost of production. The aims of this research are to determine a set of appropriatesuppliers of yarn using the TOPSIS method and allocate theyarn to selected suppliers using Linear Programming model. TOPSIS is one of the multi-criteria decision methods in which the selected alternative determined by the closest distance of the ideal solution and has the furthest distance from the non-ideal s...

Supplier Selection and Order Allocation in Supply Chain

Integrated linguistic entropy weight method and multi-objective programming model for supplier selection and order allocation in a circular economy: A case study

Supplier selection is essential for any organization , as it plays a significant role in enhancing productivity. This study focuses on a local pharmaceutical wholesaler (PW) company, which places orders with other local PWs to meet its demand. Typically, pharmaceutical companies rely on multiple suppliers to satisfy their needs. However, due to an inadequate evaluation of supplier criteria, a Multi-Criteria Decision Making (MCDM) approach has been implemented to assist the PW in selecting superior suppliers and ensuring an efficient selection process. A key issue in this case study is the lack of a structured method for assessing supplier criteria, resulting in a subjective and lengthy selection process. The criteria for supplier selection encompass quality, flexibility, price, delivery, service, and supplier profile. Furthermore, alongside supplier selection, optimizing order allocation is essential for reducing purchasing costs while maximizing supplier scores. This research proposes a model designed to aid PW in addressing both supplier selection and order allocation challenges. The MCDM framework commences with the Best Worst Method (BWM) to establish the weight of each criterion. These weights then serve as input for the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which ranks and prioritizes suppliers based on their evaluation scores. Subsequently, the results from TOPSIS inform the determination of optimal order allocation through a Multi-Objective Optimization (MOO) method. As part of the system modeling, a sensitivity analysis was performed to explore the effects of specific parameters on the objective function and decision variables, assessing variations in inventory costs, shortage costs, and demand. The findings indicated that only the demand parameter had a significant effect on decision variables, particularly regarding inventory levels and shortages. This research offers a comprehensive solution for the PW to tackle supplier selection and optimal order allocation. By employing MCDM and multi-objective optimization strategies, the company can achieve lower purchasing costs while selecting optimal suppliers based on their evaluation scores. The optimization model presented has dual objective functions: minimizing costs and maximizing total supplier value. Consequently, the model achieved a total purchasing cost of Rp. 340,196,740 and a total supplier value of 5,265,032.

Supplier evaluation and selection on economic, social, and environmental dimensions are crucial for sustaining the pressure of a competitive global supply chain. In this work, a mixed-integer linear programming for supplier selection and order allocation in a single period, multi-supplier, multi-item environment with a prime consideration to the selection of transportation alternatives while delivering items is developed. To capture the real-world situation, the proposed model incorporates no discount and all quantity discount situations considering the bad quality and late delivery disruptions in the supply chain. To reflect a wide variety of operational conditions, two scenarios with two cases have been developed to demonstrate the effect of disruptions and discounts over demand and procurement cost. A real-life case of the automotive sector in central India is studied to validate the proposed model. Also, sensitivity analysis has been performed to understand the trade-offs between different sustainability criteria and the total cost of purchase.

This paper presents a supplier selection and order allocation (SSOA) model to solve the problem of a multiperiod supplier selection and then order allocation in the environment of short product life cycle and frequent material purchasing, for example, fast fashion environment in apparel industry. At the first stage, with consideration of multiple decision criteria and the fuzziness of the data involved in deciding the preferences of multiple decision variables in supplier selection, the fuzzy extent analytic hierarchy process (FEAHP) is adopted. In the second stage, supplier ranks are inputted into an order allocation model that aims at minimizing the risk of material purchasing and minimizing the total material purchasing costs using a dynamic programming approach, subject to constraints on deterministic customer demand and deterministic supplier capacity. Numerical examples are presented, and computational results are reported.

In today's competitive scenario, market demand is highly dynamic and volatile in nature. Production volumes are directly dependent on demand of finished goods which, in turn, determines the order allocation of different parts to suppliers. Demand of finished products varies on continuous basis, making selection of right suppliers and allocation of order quantity of parts a challenging task. In this paper, two independent optimisation approaches for determining product mix and supplier selection and order model are integrated to address the direct effect of market demand on procurement of parts. The paper proposes a multi-objective integer linear program (MOILP) approach for integrated dynamic supplier selection and order allocation with market demand for efficient procurement. The proposed MOILP model optimises order allocation and supplier selection by integrating supplier's capacities, their procurement costs, fixed logistics charges with machine capacity constraints and market demand. The model is evaluated and demonstrated with the help of an illustration and is solved in LINGO 10 using randomly generated data.

A joint supplier selection and order allocation model with disruption risks in centralized supply chain

The main aim of this research is to propose an integrated Multi-Criteria Decision Making (MCDM) and bi-objective mathematical model for supplier selection and order allocation of lean manufacturers. Despite the vast quantity of research on Lean Manufacturing (LM) and its related tools, lean supplier selection and order allocation is less studied in the previous literature. To fill this gap, this study is conducted in four phases as follows. First, an initial list of leanness criteria is extracted from previous studies. Next, using an Analytic Hierarchy Process (AHP), these criteria are examined to be applied in a supplier selection process. Following, a Fuzzy Analytic Hierarchy Process (FAHP) is applied to select the suppliers according to lean supplier selection criteria. Last, a bi-objective mathematical model is developed to determine the optimum order allocation. The developed model is verified, validated, and a sensitivity analysis is conducted to suggest managerial implications according to different values of parameters.

Supplier selection (SS) and order allocation (OA) are strategic decisions that have a substantial effect on a company’s performance. However, order allocation is often neglected, since it results from supplier selection and is considered supplementary: little attention has been paid to its specific nature and complexity. Consequently, the authors conducted a systematic literature review specifically regarding order allocation methods. The research aimed to evaluate how often and when the issue has been dignified with an individual focus, independently of the supplier selection problem. This study conducted a comprehensive examination of the order allocation models and solutions, criteria for order quantity allocation, features of suppliers, items, planning periods, and demand most commonly considered in the literature. Finally, it aimed to discover whether supply chain configurations and trends have been considered in efforts to find a solution to the problem. The scientific contribution of this study is threefold: (i) to expand the review of scientific literature regarding order allocation models, (ii) to identify research gaps and highlight research opportunities, and (iii) to suggest a research agenda for the development of order allocation models according to the requirements of current trends in supply chain management.

The need to gain a global competitive advantage on the supply side has forced businesses to search for effective supply network strategies. The effective selection of suppliers is the key ingredient for the success of supply networks. A variety of analytical methods ranging from simple weighted techniques to complex mathematical programming approaches have been proposed for supplier selection. However, these models are generally aimed at supporting a decision maker (DM) in the final selection phase and they have failed to consider a holistic view of the supplier selection process. Supplier evaluation and selection problems are inherently multi-criteria decision problems. Supply networks are now not only configured by suppliers, but also consist of manufacturers, retailers and customers. Therefore, a holistic and comprehensive approach for evaluating these elements in supply networks is required. We propose a multi-objective mathematical programming approach to select the most appropriate supply network elements. The process performance index (PPI) is used as an assessment tool for the supply network elements and the analytic hierarchy process (AHP) is used to integrate the objectives of the proposed mathematical programme to a single one. The efficacy and applicability of the proposed methodology is demonstrated with a numerical example.

This paper proposes an integrated model of Fuzzy Quality Function Deployment (FQFD) and Multiple Objective Linear Programming (MOLP) for supplier selection and order allocation in global context. Various criteria for global supplier selection are explored through literature review and divided into two categories: buyer attributes and supplier attributes. Some of the criteria for supplier selection incorporated in this paper are very important in global context, which is generally ignored in local supplier selection such as geographical location, money exchange rate and terrorism. The fuzzy-extended Quality Function Deployment (QFD) helps to incorporate the uncertainty in thinking state of decision-makers in establishing the correlation between buyer attributes and supplier attributes and also in supplier rating against various supplier attributes. This paper may help the supply chain managers/purchasing managers in supplier selection in global market and allocate the order among them considering all the constraints under fuzzy environment.