Dynamic Supplier Research Articles

Dynamic Supplier Selection and Its Optimal Strategy Considering Full Truck Load and Fuzzy Demand using Fuzzy Expected Value-Based Programming

This article proposes a linear integer optimization model incorporating fuzzy parameters to find the optimal solution for a dynamic supplier selection problem with uncertain demand. The uncertain demand value is represented using a fuzzy variable. A fuzzy expected value-based linear optimization solver is used to address the optimization problem, to minimize the total cost under fuzzy demand values. Several computational experiments were conducted to evaluate and analyze the model. The results show that the proposed model effectively identifies the optimal suppliers for each product. Additionally, the model determines the optimal purchase volumes for each product type from the selected suppliers, leading to the minimal total expected cost.

Resources Price Adjustment in Cloud Computing for Load Balancing and Fairness Between Users and Suppliers

Resource price adjustment is a challenging issue when building a cloud computing system. For this purpose, we present an approach that adopts a market-driven price adjustment model as an economic incentive model and ensures the balance between supply and demand. We show how this model may be adapted and applied to an infrastructure as a service (IaaS) in cloud computing. We place ourselves in the context of the virtual machines market. To achieve competitive equilibrium, prices are adjusted according to a tâtonnement-like process. The prices are computed by a third party, the auctioneer. However, this process requires several assumptions to ensure the existence and stability of competitive equilibrium, not all of which are verified by cloud computing systems. The atomicity and convexity of preferences are not guaranteed by cloud computing systems. These questions will be the subject of our empirical study. We find empirically that the change in the sign of excess demand, indicating the existence of equilibrium, occurs between two similar prices. The experimental results show that the proposed dynamic price adjustment approach is more profitable than fixed-price pricing, allowing dynamic suppliers to become important market agents.

A robust optimization to dynamic supplier decisions and supply allocation problems in the multi-retail industry

The research focuses on multi-retail distribution with a strategic distribution network. Challenges include intense competition, logistical and transportation complexities requiring robust infrastructure like warehouses and efficient supply chain management, as well as operational inefficiencies and distribution costs. To address these issues, a model is applied to make strategic decisions, such as determining the necessary number of facilities to minimize total supply chain network operational costs and infrastructure for retail distribution. The outcome is a model that introduces a novel approach to enhancing supply chain efficiency and effectiveness from production to distribution stages, thereby reducing system costs, including ordering costs and inventory handling. Costs and loss costs resulting from remaining products produced can be minimized by considering networks, multiple suppliers, multiple warehouses, Distribution Centers (DC), multiple retailers, and multiple products, factoring in the distances between facilities in the network. Subsequently, comprehensive testing of inspection, distribution, and retail parameters is conducted, with a focus on specific periods and product types. When applying this model, certain characteristics need to be considered regarding the importance of selecting efficient suppliers of goods, such as procurement, performance improvement, and the number of supply chains and supply chain systems. This research introduces novelty in production methods that can lead to increased customer satisfaction, sales, market share, profit margins, more effective brand advertising, and revenue streams. In this process, the research undergoes a training, testing, and validation process in forming a strategic multi-retail distribution network model, spanning a total of 52 epochs. This process yields accuracy values for training at 90 %, testing at 92 %, and validation at 94 %

Dynamic selection and order allocation of resilient suppliers based on improved fuzzy multi-criteria decision method

ABSTRACT A complex supply chain can improve the economic benefits of an enterprise to a certain extent, whereas its vulnerability also occurs. How to select resilient suppliers to improve enterprise risk resistance has become hot issue in supply chain management. Furthermore, due to the complexity of the supply chain and the instability of the market, the supplier selection in different periods and customer demands is dynamic. We use the best-worst approach to model the initial selection of suppliers taking into account risk factors. Based on this result, our study applies the fuzzy multi-criteria decision method to establish a multi-objective model that aims to solve the problem of dynamic supplier selection considering resilient criteria. We apply the improved wolf pack algorithm to measure the optimal order allocation strategy between profit and pollution. The supplier selection and order allocation of new energy vehicle manufacturing companies are used as a case study for verification. The results show the multi-objective model of supplier selection considering risk and resilience factors is feasible, and the dual-objective model of supplier selection and order allocation considering different demands in different periods are effective. Compared with the original algorithm, the improved Wolf pack algorithm has better comprehensive performance.

Dynamic Supplier Selection Based on Fuzzy Cognitive Map

In this paper, the principle of fuzzy cognitive map is adapted to identify the main important criteria for a given period of time and then to select suppliers dynamically. The main idea come from the fact that a given criterion, like for example “a supplier who is ecofriendly”, could be described and decomposed by various sub-criteria or factors, like “low emission of pollutants”, “low emission of carbon dioxide”, “low energy consumption”, “respectful of biodiversity”, “recyclable and/or recycled products”, noted that these factors can go in the same direction or on the contrary be contradictory. Furthermore, an important factor for the selection of suppliers at a given period could be less interesting at another time because of dynamical changes and crises (economic, wars, national or international politics, competition...). Therefore, a timely prediction of important criteria based on a fuzzy cognitive map associated with nonlinear Hebbian algorithm is proposed. The fuzzy cognitive map is defined with the help of experts and tested for a simple pharmaceutical supply chain. Results reveal the performance of the proposed method.

Dynamic supplier competition and cooperation for buyer loyalty on service

A firm’s service-driven always-a-share behavior may have a significant effect on the competitive and cooperative inventory policies of its suppliers. To explore this effect, we consider a model of a repeat buyer (she) sharing her patronage among two heterogeneous newsvendor-type suppliers over an infinite horizon. To enjoy the best service advantage, the buyer plays one supplier (him) against the other by rewarding product availability with repurchase (loyalty) and punishing stockouts with switching (disloyalty) in the next period. Faced with this behavior, the optimal ordering policy of each supplier is a basestock policy with a non-negative “active” basestock level when the supplier has the buyer’s loyalty and a zero basestock level when he does not. Under competition, the optimal active basestock level of each supplier is greater than his myopic basestock level and increases in the other supplier’s active basestock level. Under a mild condition, the active basestock levels of both suppliers have at least one pure-strategy Nash equilibrium solution. If the suppliers cooperate, the optimal active basestock level of the supplier with the highest/lowest myopic profit is greater/smaller than his myopic basestock level. To better comprehend these results, we apply them to the case where the buyer’s demand is exponentially distributed. This allows us to obtain exact expressions for the optimal active basestock levels and payoff functions, which we then use in a numerical sensitivity analysis. We conclude with a discussion of the extension of the results to more than two suppliers.

Switchover to industrial additive manufacturing: dynamic decision-making for problematic spare parts

PurposeIntroducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.Design/methodology/approachIn this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.FindingsThe practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.Originality/valueThe dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

Dynamic Supplier Competition and Cooperation for Buyer Loyalty on Service

Dynamic Supplier Competition and Cooperation for Buyer Loyalty on Service

Revisiting employer perceptions of skill mismatch: the case of the machine manufacturing industry in Vietnam

ABSTRACT There are growing claims that increasing skill mismatch, or skill deficit, is impeding further economic growth and industrialisation in Vietnam. Previous studies often attributed it to the failure of supply-side initiatives to meet increasing skill demand. However, it is still uncertain whether skill demand is increasing in Vietnam, as the supply-side approach presumes. Through questionnaires and in-depth qualitative interviews, this research investigated employer perceptions of skill mismatch at the intermediate occupation level in the machine manufacturing industry. This sector is expected to lead higher value-added industrialisation. It found that employers are not uniformly concerned about large skill shortages because many of them do not require large numbers of skilled workers due to intensified supply chain competition, small domestic markets, and incremental technological progress. In order to achieve upskilling and further industrialisation, Vietnam needs an integrated skill formation strategy which not only improves skill supply but also stimulates the dynamism of skill demand, as the demand-side approach proposes. The research findings suggest that one possible way to evolve the demand-side approach in Vietnam is to develop supporting industries. These industries typically contain some dynamic suppliers which have high aspirations for ascending value chains and higher demand for intermediate workers than assemblers.

Effective purchasing reallocation to suppliers: insights from productivity dynamics and real options theory

Continuous supplier selection, evaluation and reselection are among buying firms' key processes to improve their overall performance. This paper aims at increasing knowledge on effective and dynamic supplier evaluation and management by answering the following research question: Do performance decomposition technique and real option theory represent appropriate mechanisms to understand structural change in supply chain and subsequent performance improvements? By taking insights from productivity dynamics in economics literature and real options theory from strategic investment context, we apply a method to measure buying firm's overall supply chain performance through reallocation of purchases among exiting, surviving, and entering suppliers based on their past performance. The method is tested and illustrated with the case of a Finnish contractor and its 535 suppliers in 269 construction projects during 2013–2016. This study provides new insights into the dynamic business relationships of a large buyer in an uncertain environment where supply chain performance is improved through continuous changes in the supplier network. The novelty of this study lies in adopting concepts from productivity decomposition literature in microeconomics in the context of supplier selection and management to quantify the overall performance development of all suppliers, and break it down to the performance components of exiting, surviving, and entering suppliers as well as reallocation of purchases among survivors. Furthermore, real options theory helps in interpreting the entering, surviving, and exiting supplier groups as real options exercised, maintained, and abandoned, respectively.

A model integrating environmental concerns and supply risks for dynamic sustainable supplier selection and order allocation

Environmental factors are increasingly being considered in supply chain risk management (SCRM), which itself represents a growing trend. Accordingly, dynamic supplier selection and order allocation have become very important. Both qualitative and quantitative factors should be considered in the selection of eligible suppliers. In this study, a novel two-stage comprehensive mathematical model is developed for selecting a set of suppliers and assigning an order quantity to each supplier. The first stage involves a primary selection of alternative suppliers according to the risk value, which is determined using qualitative and quantitative methods based on the best–worst method, and the second stage involves establishing a multiobjective mathematical model for dealing with dynamic supplier selection and order allocation. The proposed approach, which helps enterprises manage uncertainty in SCRM, is applied in this study to the new energy vehicles industry.

Dynamic supplier selection strategy towards negotiation process in beef industry using K-means clustering

Dynamic supplier selection strategy is the actual condition to get the best supplier and selecting supplier based on company condition, in conditions of uncertain demand for beef. The key criteria when choosing suppliers include the Term of Payment, price and quality factor; these criteria are used by the company during the negotiation process. The purpose of this research is to get the most appropriate and profitable supplier, then obtain proof that K-means clustering can be used as a strategy to simplify the negotiation process in supplier selection. The method used for clustering is K-means. From the analysis, the company gets the right cluster supplier to facilitate the process of selecting suppliers and negotiate the price and time offered. The clustering process using K-means shows which supplier is the most profitable with characteristics that are almost the same as the specified centroid. Dynamic demand changes can be solved by changing the number of clusters to make it easier for companies and still earn profitable suppliers. Companies can negotiate with suppliers in clusters; thus, the suppliers can meet the standards given by the company. Further research is suggested to do clustering with more variety of attributes such as lead time of delivery, service, and capital owned by suppliers.

Dynamic supplier selection through optimal ranking under two-echelon system

PurposeWith the increasing competition among the industries, they remain under pressure as how to select the best set of suppliers for the competitive edge. Often, it has been challenging to develop an effective set of suppliers due to varied and asymmetric mode of criteria. The purpose of this paper is to develop a responsive chain under original equipment manufacturer (OEM).Design/methodology/approachThis study proposes a responsive chain under a two-echelon system (TES) of OEM, which needs to collaborate with a set of suppliers at each echelon through an integrated methodology of AHP and TOPSIS. According to the OEM’s criteria, demands and suppliers’ capacity vary with time, therefore they are not static for a longer period. Hence, supplier selection (SS) problem possesses dynamicity in real practice. For this, MILP is used for finding optimal order quantities based on the optimal ranking at each echelon in the multi-period scenario. Subsequently, sensitivity analysis (SA) is conducted through Taguchi method of parameter design (TMPD) to achieve an optimal ranking in the TES.FindingsThis study suggests optimal criteria’s weight, percentage contribution, and flexibility for the suppliers and manufacturers involving through maximum demand strategy at each echelon of OEM. It also provides robust group of suppliers and manufacturers in the TES through optimal ranking and simultaneously in the order allocations. Furthermore, it restricts the number of suppliers and manufactures at each echelon through proposed methodology to obtain the solution in a very short running time.Originality/valueTo validate this model, a real data set for the case of chain conveyor company is used. This adopted methodology can suggest the organization that how the approach should be implemented.

A two-stage stochastic optimization model for reverse logistics network design under dynamic suppliers’ locations

This paper presents a two-stage stochastic programming model for reverse logistics network design (RLND) under uncertainty with dynamic supply sources’ locations. The primary objective of the optimization model is to maximize the expected profit generated by selling recycled materials to the secondary markets and to make the landfilling option less attractive. In comparison with the state-of-the-art stochastic optimization models in this area, which mainly focus on the expected optimal value, this paper emphasizes the role of source separation of recyclable materials to increase the productivity level at the collection centers (CC). Indeed, the quantity of materials collected from the supply sources and the recycling rates at the CC are the primary sources of uncertainty considered in this study. A Sample Average Approximation (SAA) procedure is developed to solve the stochastic model and perform sensitivity analyses on the number of supply sources, the sample size and the level of uncertainty targeting the random parameters. Managerial implications are discussed through a case study from the demolition industry in the province of Quebec, Canada. Although the use of source separation centers (SSC) improves the network performance in both rural and urban zones, the additional flexibility provided by these platforms reaches its best efficiency in the case of high-density urban areas. Finally, the results suggest significant RLND adjustments that lead to an increase in the average profit by 17.6% and recycle around 29% of additional building materials waste from demolition.

Dynamic supplier selection approach for mining equipment company

PurposeThis paper aims to address the supplier selection (SS) problem under dynamic business environments to optimize the procurement cost of spare-parts in the context of a mining equipment company (MEC). Practically, involved parameters’ value does not remain constant as planning periods due to fluctuation in the demand and their market dynamics. Therefore, dynamicity in the parameter is considered as an important factor when a company forms a responsive chain through most eligible suppliers with respect to planning periods. This area of study may be considered for their complexities to the approaches toward order-allocations with bi-products of unused and repair spare-parts.Design/methodology/approachAn integrated methodology of analytic hierarchy process (AHP) and mixed-integer non-linear programming (MILP) is implemented in the two stages during each planning periods. In the first stage, AHP is used to obtain the relative weights with respect to each spare-parts of each criterion and based on that, the ranking is evaluated in accordance with case considered. And in the second stage, MILP is formulated to find the allocations of each spare-part with two distinct approaches through Model-1 and Model-2 separately. Moreover, Model-1 and Model-2 are outlined based on the ranking and efficient parameters-value under cost, limited capacities, quality level and delay lead time respectively.FindingsThe ranking and their optimal order-allocation of potential suppliers are obtained during consecutive planning periods for both unused and repair spare-parts. Subsequently, sensitivity analysis is conducted to deduce the key nuggets with the comparison of Model-1 and Model-2 in the changing of capacity, demand and cost per spare-parts. From this analysis, it is found that suppliers who have optimal parameter settings would be better for order-allocations than ranking during the changing planning period.Practical implicationsThis paper points out the situation-specific approach for SS problem for a mining industry which often faces disruptive supplying environments. The managerial implication between ranking and parameters are highlighted through Model-1 and Model-2 by sensitivity analysis.Originality/valueIt provides useful directions for managers who are involved in the procurement of spare-parts in the mining environment. For this, suppliers are selected for order-allocation by using Model-1 and Model-2 in the dynamic business environment. The solvability of the model is presented using LINGO 17. Furthermore, the case company selected in this study can be extended to other sectors.

A mixed integer linear programming model for dynamic supplier and carrier selection problems

The dynamic supplier selection problem (DSSP), which occurs when no supplier can fulfil the buyer's total demand due to various limitations, has received attention from academics. However, to the best of our knowledge, there is no publication that addresses the DSSP and carrier selection simultaneously. Given that products are sourced from different suppliers, the choice of carrier is important in order to satisfy demand at the minimum total cost. In this paper, we present a model that integrates the DSSP and carrier selection under uncertain delivery performance; that is, a portion of orders are not delivered on time and a portion of delivered goods are imperfect and hence are rejected by the buyer. We propose a mixed integer linear programming (MILP) model and apply it to a case where a buyer is sourcing multiple items from multiple suppliers and the decision has to be made on a multi-period planning horizon. A numerical example has been tested to show the effectiveness of the model. We assume that backlogging is allowed and the effect of demand fluctuation is analysed.

A mixed integer linear programming model for dynamic supplier and carrier selection problems

The dynamic supplier selection problem (DSSP), which occurs when no supplier can fulfil the buyer's total demand due to various limitations, has received attention from academics. However, to the best of our knowledge, there is no publication that addresses the DSSP and carrier selection simultaneously. Given that products are sourced from different suppliers, the choice of carrier is important in order to satisfy demand at the minimum total cost. In this paper, we present a model that integrates the DSSP and carrier selection under uncertain delivery performance; that is, a portion of orders are not delivered on time and a portion of delivered goods are imperfect and hence are rejected by the buyer. We propose a mixed integer linear programming (MILP) model and apply it to a case where a buyer is sourcing multiple items from multiple suppliers and the decision has to be made on a multi-period planning horizon. A numerical example has been tested to show the effectiveness of the model. We assume that backlogging is allowed and the effect of demand fluctuation is analysed.

Mathematical model for dynamic suppliers’ selection strategy in multi-period supply planning with lead-times uncertainty

Abstract In the supply planning, the management of uncertainties is the most difficult task but becomes increasingly important in the constantly changing business environment. In literature, most paper examine the uncertainty demand under the assumption of the constant order lead-times. However, lead-time is rarely constant in real time because of unpredictable events in the supply system such as machines’ breakdowns, transport delays, quality issues, etc. In fact, stochastic lead-time has been provoking much more difficulties to supply chain managers than expected. Therefore, managing lead-time uncertainties plays a more and more important role in production planning and inventory control. Moreover, this context pushes companies to diversify their suppliers for outsourcing and/or procurement of raw materials or semi-finished products. The aim of the paper is give a mathematical formulation for dynamic suppliers’ selection strategy in multi-period supply planning for finished products under stochastic lead-times.

A Dynamic Supplier Selection and Inventory Management Model for a Serial Supply Chain with a Novel Supplier Price Break Scheme and Flexible Time Periods

A new supplier price break and discount scheme taking into account order frequency and lead time is introduced and incorporated into an integrated inventory planning model for a serial supply chain that minimizes the overall incurred cost including procurement, inventory holding, production, and transportation. A mixed-integer linear programming (MILP) formulation is presented addressing this multi-period, multi-supplier, and multi-stage problem with predetermined time-varying demand for the case of a single product. Then, the length of the time period is considered as a variable. A new MILP formulation is derived when each period of the model is split into multiple sub-periods, and under certain conditions, it is proved that the optimal solution and objective value of the original model form a feasible solution and an upper bound for the derived model. In a numerical example, three scenarios of the derived model are solved where the number of sub-period is set to 2, 3, and 4. The results further show the decrease of the optimal objective value as the length of the time period is shortened. Sufficient evidence demonstrates that the length of the time period has a significant influence on supplier selection, lot sizing allocation, and inventory planning decisions. This poses the necessity of the selection of appropriate length of a time period, considering the trade-off between model complexity and cost savings.

Dynamic supplier selection and lot-sizing problem considering carbon emissions in a big data environment

Selection of the right suppliers with a view of overall reduction in the procurement cost as well as carbon emissions is an important task for the buyers to sustain in the fierce competitive environment. The rising concerns about carbon emissions due to severe climatic changes globally have forced supply chain managers to restrategize about controlling their carbon emissions. This paper proposes a dynamic supplier selection model incorporating the carbon emissions under the carbon cap-and-trade scenario. Carbon emissions caused due to ordering, holding the inventory, production and handling and transportation have been considered in the paper. The proposed mathematical model is a mixed-integer-non-linear-program (MINLP). Validation of the proposed MINLP has been done using two randomly generated datasets having the essential parameters of Big Data, i.e. volume, velocity, and variety.