Multiple Suppliers Research Articles

A multi-attribute double auction and bargaining model for emergency material procurement

Emergency material procurement (EMP) is not only a significance segment of the emergency preparedness, but also an important guarantee for disaster relief. The emergency materials have the different quality attributes towards ordinary commodities, so attribute requirements need to be considered in the procurement activity. This research designed a two-stage MADA-B mechanism to study the supply-demand matching of emergency materials with multiple attributes. In the first stage, considering the differentiated needs of multiple buyers (the government and non-governmental organizations) and the differentiated capacity of multiple suppliers, a multi-attribute double auction (MADA) model was constructed. We analyzed the benchmark bidding strategy of buyers and sellers, and then proposed a winner determination model whose objective is maximizing the total bid-ask spreads. In the second stage, considering the buyer's and the seller's discount rate as private information, an infinite bargaining model was established. We analyzed the bargaining process and gave the method to determine the transaction price and quantity. After that, we designed a numerical example to simulate the practice under the background of purchasing multi-attribute emergency ration package (ERP). The research shows that the auction-bargaining mechanism can not only match the attribute between buyers and sellers, but also can scientifically determine the transaction price and quantity according to the wishes of both sides. It has good feasibility and applicability in the emergency management practice. • A multi-attribute double auction and bargaining mechanism is proposed. • It aims to solve the participant matching problem in emergency material procurement. • It can reach an equilibrium on attribute matching, transaction price and quantity. • The strategies of participants in both auction and bargaining stages are given. • The bargaining has no period restriction and is under incomplete information.

Combined pricing and inventory control with multiple unreliable suppliers

We study a general finite horizon, periodic review combined inventory and pricing model with N suppliers and T periods, where both the demands and the supply mechanisms are random. The random supply mechanisms are of a general type that includes most structures encountered in practice. Demands are price dependent according to general, stochastic demand functions. We characterize the optimal combined pricing and ordering policies to all N suppliers. The general results pertain to general independent supply mechanisms. Under random capacities—one of the special random supply mechanisms—they also extend to suppliers that are positively dependent on each other.

Economic sanction and global sourcing complexity: A cross‐country analysis

The supply chain disruption drives the firms to source the same inputs from multiple suppliers in low-cost countries. However, the imposition of economic sanctions may prevent firms from enjoying diversification benefits of global sourcing complexity. Using data for more than 127 countries from 1997 to 2014, we uncover the effects of economic sanctions on the global sourcing complexity. After extensive robustness checks, our main findings reveal that economic sanction hampers global sourcing complexity. These effects are conditional on the level of economic development and the occurrence of financial crises in a particular country. Our results are robust for various forms of economic sanction, alternative estimators, and when controlling endogeneity problems. Our findings suggest important foreign policy and managerial implications for the countries and firms in response to economic sanctions.

The Importance of Corporate Reputation for Sustainable Supply Chains: A Systematic Literature Review, Bibliometric Mapping, and Research Agenda

Corporate Reputation (CR) is essential to value generation and is co-created between a company and its stakeholders, including supply chain actors. Consequently, CR is a critical and valuable resource that should be managed carefully along supply chains. However, the current CR literature is fragmented, and a general definition of CR is elusive. Besides, the academic CR debate largely lacks a supply chain perspective. This is not surprising, as it is very difficult to collect reliable data along supply chains. When supply chains span the globe, data collection is especially challenging, as the chain consists of multiple suppliers and subcontractors, positioned at different tier levels. Recognizing this, the paper examines firstly the current state of CR research through a systematic literature review from a business perspective. The review is combined with a bibliometric mapping approach to show the most influential research clusters, representative of CR research streams and their contributors. This process highlights that the connection between CR and supply chain issues represents a major research gap. Consequently, this paper introduces a research agenda connecting these the two traditionally separated research fields.

Optimization Approaches for Multiple Conflicting Objectives in Sustainable Green Supply Chain Management

Over the years, the global supply chain has evolved into a more extensive interconnected complex network with multiple suppliers, manufacturers, and customers. Since environmental issues have become a burning question in recent years, the focus has shifted to attaining sustainability in supply chain management. The green supply chain or sustainable network is a concept to reduce environmental impacts in the life cycle of a product. However, green supply chain management is often challenged with additional operating costs and difficulty monitoring the implications within the complex network system. Additionally, many stakeholders are unaware of the importance of sustainability analysis, which eventually complicates adopting green cultures in actual applications. Since green supply chain management deals with multiple aspects, such as cost and carbon emission, the multiobjective optimization method is widely used to evaluate supply chain performance. This paper intensively reviews the state-of-the-art literature on applying multiobjective optimization techniques in green supply chain management. The study highlights aspects of green supply chain structures, model formulation techniques considering multiple objectives simultaneously, and solution methods for multiobjective optimization problems. Finally, a conclusion is drawn with the scope of the potential research opportunities for integrating economic and environmental considerations in sustainable supply chain management practice.

What Have We Learned? Implementation of a Shared Learning Agenda and Access Strategy for the Hormonal Intrauterine Device.

In 2015, a global learning agenda for the hormonal intrauterine device (IUD) was developed with priority research questions regarding use of the method in low- and middle-income countries. In addition, members of the Hormonal IUD Access Group aligned on a strategy to expand access in the context of volunteerism and contraceptive method choice. This article synthesizes evidence generated since then and describes steps taken to address demand- and supply-side barriers to access. Findings demonstrated high continuation rates and satisfaction among hormonal IUD users that are comparable to other long-acting reversible contraceptives (LARCs). Across studies, a sizable number of users reported they would have chosen a short-acting method or no method at all if the hormonal IUD were not an option, which suggests that women did not see the hormonal IUD as interchangeable with other LARC options and thus it may fill an important niche in the market. With several countries now poised to scale up the method, resource mobilization will be key. On the demand side, investments in implementation research will be critical to understanding how best to launch and scale the method, while ensuring the sustainability of multiple quality-assured suppliers with affordable public-sector pricing will be necessary on the supply side.

PENELITIAN DESAIN MEBEL BERBASIS PANGKALAN DATA DENGAN METODE DIVERGEN KONVERGEN ITERATIF SEBAGAI STRATEGI R&D & MANUFAKTUR PERUSAHAAN (STUDI KASUS: CORPORATE SPECIALISTS, MALAYSIA & HOMELEGANCE, USA)

Furniture design for mass production & export orientation is complex innature, tend to have a lot of revisions, thus require a lot investment in fund,time, and effort. There are 2 research issue: 1) Mistakes in design proposalsoften occurred (size, construction, style, finishing, price), can not match withfactory production capacity, or buyer’s market target. In this case, factoriesare suppliers of research partner 1 (CS), and buyer is research partner 2(Homelegance). Second issue: 2) Designer’s idea often surrounds in designaesthetic alone, disregard the A-Z aspect in supply & demand chain of amass-produced furniture (production, marketing, packing, shipping, etc).This research uses qualitative research model with many case studies, andapplies 3 methods: 1) divergent convergent iterative design method; 2) 2D &3D database from CS; 3) considering US market response fromHomelegance. This research aims to: 1) providing ways and recommendationfor stakeholder to reduce revisions (time & cost saving), 2) producingdesigns with export oriented quality, 3) providing design insights foracademics about furniture design from early to final phase. Research output(furniture samples) are stored in multiple supplier’s warehouse.

Using fuzzy expectation-based programming for inventory management

Background: Order allocation planning and inventory management are two important problems in manufacturing industries that must be solved optimally to gain maximal profit. Commonly, there are several unknown parameters in those problems such as future price, future demand, etc., and this means decision-making support that can handle this uncertainty is needed to calculate an optimal decision.Objectives: This study aimed to propose a newly developed joint decision-making support to solve order allocation planning and inventory optimisation of raw materials in a production system comprising multiple suppliers, products and review times with fuzzy parameters.Method: The model was formulated as a fuzzy expectation-based quadratic programming with the uncertain parameters approached as fuzzy numbers. This was used to handle the fuzzy parameters involved in the problem. A classical optimisation algorithm, the generalised reduced gradient combined with branch-and-bound embedded in LINGO 18.0 was applied to calculate the optimal decision. Numerical experiments were conducted using some randomly generated data with four suppliers, four raw materials and six review times.Results: Results provided the optimal decision for the given problem, that is, the number of raw materials to be ordered from each supplier at each review time, as well as the corresponding number to be stored in the warehouse.Conclusion: The proposed model successfully solved the given problems and thus can be used by decision-makers to solve their order allocation planning and inventory problems.

Performance implications of peer monitoring among suppliers

PurposeWhat are the performance implications of peer monitoring in a multiple-supplier context? Grounded in agency and social exchange theories, this study aims to examine how, when, and why peer monitoring works as a crucial control mechanism to reduce opportunism among suppliers.Design/methodology/approachA conceptual model and research hypotheses are tested using survey data from 246 respondents in 82 supplier groups.FindingsResults suggest that peer monitoring is related positively to perceived deterrence (as mediator) and negatively to opportunism, whereas the mediated relationship is moderated negatively by generalized reciprocity and positively by balanced reciprocity and negative reciprocity.Originality/valueThis study introduces the application of peer monitoring into business-to-business research and shows how it reduces opportunism. Its findings have implications for manufacturers on how to use peer monitoring to control opportunism among multiple 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.

Balance between breadth and depth in human many-alternative decisions.

Many everyday life decisions require allocating finite resources, such as attention or time, to examine multiple available options, like choosing a food supplier online. In cases like these, resources can be spread across many options (breadth) or focused on a few of them (depth). Whilst theoretical work has described how finite resources should be allocated to maximize utility in these problems, evidence about how humans balance breadth and depth is currently lacking. We introduce a novel experimental paradigm where humans make a many-alternative decision under finite resources. In an imaginary scenario, participants allocate a finite budget to sample amongst multiple apricot suppliers in order to estimate the quality of their fruits, and ultimately choose the best one. We found that at low budget capacity participants sample as many suppliers as possible, and thus prefer breadth, whereas at high capacities participants sample just a few chosen alternatives in depth, and intentionally ignore the rest. The number of alternatives sampled increases with capacity following a power law with an exponent close to 3/4. In richer environments, where good outcomes are more likely, humans further favour depth. Participants deviate from optimality and tend to allocate capacity amongst the selected alternatives more homogeneously than it would be optimal, but the impact on the outcome is small. Overall, our results undercover a rich phenomenology of close-to-optimal behaviour and biases in complex choices.

The Bright Side of Inequity Aversion

Modern consumers are concerned about not only their material payoff, but also the fairness of the transaction when making purchasing decisions. In this paper, we investigate how consumers’ inequity aversion affects a manufacturer who sources inputs from upstream suppliers. We find that, when the manufacturer sources from a single supplier or when consumers observe the manufacturer’s cost, inequity aversion hurts both the supplier’s and manufacturer’s profits. However, when the manufacturer sources from multiple suppliers and consumers do not observe the manufacturer’s cost, inequity aversion reduces both the suppliers’ and manufacturer’s margins, which significantly alleviates the double marginalization problem, increases consumer demand, and improves channel efficiency. As a result, inequity aversion benefits the suppliers, manufacturer, and consumers alike, leading to a “win–win–win” outcome. By comparing cases in which consumers observe and do not observe the manufacturer’s cost, we also find that, when faced with inequity-averse consumers, a manufacturer may find it optimal to withhold its cost information to help secure lower procurement costs from upstream suppliers. This paper was accepted by Dmitri Kuksov, marketing. Funding: This work was supported by the Hong Kong Research Grants Council [Grant 21500920].

Strategic decision for capacity portfolio in supply chain network considering emission permit price and demand uncertainty

This paper develops a modeling approach to investigate supply chain firms’ strategic decisions to select a capacity portfolio in green technologies for their operations. Our approach enables one to study the equilibrium solution of the entire supply chain network with multiple suppliers and manufacturers. The market and policy-based uncertainties in emission permit price and customer demand are considered, while the supply chain firms can purchase or sell the credit for emission on a carbon emission trading market. We propose a stochastic two-stage model in which the technological capacity investment should be determined in the first stage of the game considering both permit price and demand uncertainties. Once the uncertainties are clear in the second stage, supply chain firms will decide upon their quantity and price to their customers. Proposed analytical and numerical results elucidate the uncertainty value of carbon permit price from novel real option perspectives. The result reveals that the uncertainty of carbon price might have a two-sided impact, both as a risk factor and a potential opportunity for investing in green technologies. The numerical case study shows results corresponding to a paradox, resulting in a balance between cooperation and competition between supply chain parties. We further show why the supply chain members should act strategically to get in a suboptimal choice for all parties involved in the network.

Suppliers portfolio and returner incentive decisions in closed-loop remanufacturing systems under multiple stochastic scenarios

ABSTRACT We propose a decision tree model that considers reverse and forward flows in a closed-loop supply chain (CLSC). Based on observations of three CLSCs, the model considers an environment where there is uncertainty in the quantity of returned used components (and new components from suppliers) with the decision being the incentive offered to each return source. Given that there are multiple suppliers, one must determine which supplier(s) to use and the corresponding capacity to reserve, in order to minimise total system costs. An example and a sensitivity analysis are presented to illustrate the model and to investigate multiple scenarios under various conditions. The analysis demonstrates that the supplier portfolio and returner incentive decisions are strongly linked to the supplier reliability, returned quantities, and the costs of not meeting the demand. Furthermore, the analysis suggests that understanding the behaviour of return sources relative to incentives is the most critical variable to implement the model.

A 2019 Outbreak Investigation of Hepatitis A Virus Infections in the United States Linked to Imported Fresh Blackberries.

Globally, hepatitis A virus (HAV) is one of the most common agents of acute viral hepatitis and causes approximately 1.4 million cases and 90,000 deaths annually despite the existence of an effective vaccine. In 2019, federal, state, and local partners investigated a multi-state outbreak of HAV infections linked to fresh blackberries sourced from multiple suppliers in Michoacán, Mexico. A total of 20 individuals with outbreak-related HAV infection were reported in seven states, including 11 hospitalizations, and no deaths. The Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), and Nebraska State and Douglas County Health Departments conducted a traceback investigation for fresh blackberries reportedly purchased by 16 ill persons. These individuals reported purchasing fresh blackberries from 11 points of service from September 16 through 29, 2019 and their clinical isolates assessed through next-generation sequencing and phylogenetic analysis were genetically similar. The traceback investigation did not reveal convergence on a common grower or packing house within Mexico, but all of the blackberries were harvested from growers in Michoacán, Mexico. FDA did not detect the pathogen after analyzing fresh blackberry samples from four distributors, one consumer, and from nine importers at the port of entry as a result of increased screening. Challenges included gaps in traceability practices and the inability to recover the pathogen from sample testing, which prohibited investigators from determining the source of the implicated blackberries. This multi-state outbreak illustrated the importance of food safety practices for fresh produce that may contribute to foodborne illness outbreaks.

Exact analysis of a push–pull system with multiple non identical retailers, a distribution center and multiple non identical unreliable suppliers with supply disruptions

Exact analysis of a push–pull system with multiple non identical retailers, a distribution center and multiple non identical unreliable suppliers with supply disruptions

Pulled in opposite directions: A joint consideration of supply and demand uncertainty in supply chain decision‐making

AbstractSupply chain inventory management decisions are complicated by the presence of both downstream (demand) and upstream (supply) uncertainties. Prior research shows that each type of uncertainty leads to specific decision biases. Demand uncertainty induces a pull‐to‐center bias, wherein orders are drawn away from the optimal ordering decision in the direction of expected demand. Supply uncertainty elicits a diversification bias, wherein inventory managers tend to source from multiple suppliers, even when sole sourcing is optimal. Our research employs a controlled laboratory experiment to study these biases when both types of uncertainty—that is, supply and demand uncertainty—are present. Our results show that the presence of both supply and demand uncertainty has a more nuanced effect on decision biases than does either type of uncertainty alone. Demand uncertainty can elicit and influence the diversification bias, even in the absence of supply uncertainty. Moreover, supply uncertainty affects the magnitude of the pull‐to‐center bias. Our work reveals the need for further attention from supply chain academics and managers on how supply and demand uncertainty jointly affect inventory managers’ decisions.

Developing supply chain resilience: a robust multi-criteria decision analysis method for transportation service provider selection under uncertainty

ABSTRACT This study examines critical success factors (CSFs) for supply chain resilience regarding the selection of transportation service providers. Due to extensive global sourcing, many companies are dependent on multiple suppliers and service providers. Using a Delphi approach, this article identifies the CSFs relevant to the resilience of transportation service providers in supply chains. A novel approach using Multiple Criteria Decision-Analysis (MCDA) is then developed to rank a group of CSFs. In particular, the Best-Worst Method (BWM) and Multi-attributive Border Approximation Area Comparison (MABAC) methods are used to rank resilience-related CSFs for transportation service providers in uncertain environments using Hesitant Fuzzy Sets (HFS). Based on our results, hybrid MCDA methods can be applied in order to develop an effective method for determining resilience-related CSFs when selecting transportation service providers in uncertain environments.

Robust supplier-selection and order-allocation in two-echelon supply networks: A parametric tolerance design approach

In this paper, we study the integrated supplier-selection and order-allocation problem in a two-echelon make-to-order supply chain motivated by the belt conveyor industry. In our setting, the Original Equipment Manufacturer (OEM) can source its single product from multiple suppliers in the first echelon who themselves source the sub-components of the product from multiple other suppliers in the second echelon. Within the second echelon, the suppliers fall into clusters of product supply networks based on their capability to produce a part. OEM in our setting is interested in determining the optimal tolerances for its supply network members to operate so as to minimize the variability introduced into the total supply chain costs. To obtain the optimal supplier-selection and order-allocations under a periodic review demand policy, we model the problem as a mixed-integer non-linear programming (MINLP) formulation. We further incorporate the Taguchi Method of Tolerance Design (TMTD) into our model, leading to robustness of our solutions while maintaining optimality in supplier-selection and order-allocation decisions. We test our model using a real-world case study and derive critical insights. Our numerical results demonstrate a 51% improvement in coefficient of variation of the objective function when our method is applied to the real life case study. Additionally, we show that as the level of confidence of the decision-maker on the range for a critical parameter increases, the gains from our methodology significantly increase.

The Implications of Strategic Inventory for Short-Term vs. Long-Term Supply Contracts in Nonexclusive Reselling Environments

Problem definition: Although it is well known that a reseller’s ability to hold strategic inventory under a short-term supply contract can potentially benefit both the reseller and a supplier, existing research on strategic inventory focuses almost exclusively on exclusive reselling environments. However, in practice, multiple suppliers often sell to the same nonexclusive reseller, and it is not uncommon for suppliers to ask for future commitments to order quantities from resellers in return for their own commitment to wholesale prices. We investigate how the possibility of strategic inventory influences competing suppliers’ choices between short-term transactional and long-term commitment contracts to a nonexclusive reseller. Methodology/results: Using a game-theoretic model, we consider the interactions between two partially substitutable suppliers and a single, nonexclusive reseller over a two-period horizon in which demand is deterministic. We demonstrate that in nonexclusive reselling environments, where more than one supplier sells its product through the same reseller, the use of strategic inventory under short-term contracts intensifies the price competition between suppliers. We show how this effect can be mitigated when one or both suppliers offer a long-term contract. Moreover, we show that long-term contracts can arise as an equilibrium outcome, particularly when products are more substitutable or holding costs are large. Managerial implications: By considering the interactions between two partially substitutable suppliers and a nonexclusive reseller in a multiperiod setting, we contribute to the literature on strategic contracting and long versus short-term contracts. Our research provides managers with a new explanation, beyond the need to encourage idiosyncratic investments or eliminate the possibility of hold-up, for why long-term contracts may benefit suppliers in practice, who face competition while selling through a nonexclusive reseller.