Uncertain Supplier Research Articles

Inventory control strategy: based on demand forecast error

PurposeThis study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.Design/methodology/approachThis study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.FindingsThe study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.Practical implicationsThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.Originality/valueThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Converter Kit Technology Commercialization Strategy as A Solution for The Acceleration of The Electric Motor Vehicle Program

In Indonesia, motorcycle ownership is more than other motorized vehicles. However, the more motorbikes are used, the more vehicle emissions are released. So we need transportation solutions that are more environmentally friendly and energy efficient such as electric motorbikes which can be obtained with a converter kit by still utilizing existing conventional motorbike parts as provided by PT. A B C. However, many challenges were encountered in the commercialization of the converter kit from PT ABC, such as limited and uncertain suppliers, limited introduction to the market, and unpreparedness of Human Resources in product management. This study aims to analyze the constraints on the commercialization process of converter kit products as a basis for designing supply chain engineering proposals and appropriate business engineering. Based on the analysis of TRL and Goldsmith, it shows that the product is at TRL 8 or has fulfilled the requirements, but still has various obstacles. Based on the SWOT analysis, the company still has several shortcomings due to limited market interest, marketing strategies implemented, and price volatility. The results show that there is a business strategy design using the Business Model Canvas method to accelerate technology commercialization, one of which is through the expansion of existing partnership and marketing systems.

Modelling supplier selection and material purchasing for the construction supply chain in a fuzzy scenario-based environment

Mathematical relations between supplier capacities, the resulting material supply shortages, together with the impact of material delays on construction projects are not well defined. In response to this, this paper presents a novel multi-objective mixed integer linear programming model that considers the selection of suitable suppliers, inventory management practices, order quantities and the possibility of splitting a material order as integrated decisions to be optimised. The trade-off between the overall procurement cost and the weighted lateness, a measure of material delay impacts, is optimised. Material prices, supplier capacities, and resulting delays are treated as fuzzy scenario-based parameters. The proposed model is tested on a numerical example and computation experiments validate the model performance. An extensive sensitivity analysis is carried out and results suggest that by considering high variations in uncertain supplier capacities, the model would generate lower procurement cost and show less significant delay impacts. Whereas greater variations in uncertain material prices cause the total procurement cost to grow 55%; greater variations in uncertain delay durations also drastically increase the weighted lateness by over 70%. This highlights the importance of having high quality estimates for uncertain parameters. Additionally, the analysis also indicates that a minimum overall satisfaction level of 0.9338 can be achieved depending on the model user's strategies, and the proposed scenario-adjusted problem outperforms problems modelled under deterministic market conditions. The major contribution of this paper lies in the development of a fuzzy scenario-based model to solve the supplier selection and material purchasing problem in construction supply chains.

Hybrid MCDM and simulation-optimization for strategic supplier selection

Supplier selection for strategic items requires a comprehensive framework dealing with qualitative and quantitative aspects of a company’s competitive priorities and supply risk, decision scope, and uncertainty. In order to address these aspects, this study aims to tackle supplier selection for strategic items with a multi-sourcing, taking into account multi-criteria, incorporating uncertainty of decision-makers judgment and supplier–buyer parameters, and integrating with inventory management which the past studies have not addressed well. We develop a novel two-phase solution approach based on integrated multi-criteria decision-making (MCDM) and multi-objective simulation-optimization (S-O). First, MCDM methods, including fuzzy AHP and interval TOPSIS, are applied to calculate suppliers’ scores, incorporating uncertain decision makers’ judgment. S-O then combines the (quantitative) cost-related criteria and considers supply disruptions and uncertain supplier–buyer parameters. By running this approach on data generated based on previous studies, we evaluate the impact of the decision maker’s and the objective’s weight, which are considered important in supplier selection.

A Hesitant Fuzzy Method for Evaluating Risky Cold Chain Suppliers Based on an Improved TODIM

Enterprises need sustainable development in order to reduce costs and increase income. The cold chain logistics industry needs to promote sustainable supply chains more. As the beginning of the supply chain, the choice of suppliers is particularly important. Considering the risky attitude of decision-makers, an improved hesitant fuzzy TODIM approach is adopted to select suppliers. In order to calculate a more objective indicator weight, the generalized Shapley function of the hesitant fuzzy measure is adopted by analyzing the relationships among indicators. The uncertain supplier evaluation information given by decision-makers is obtained by using hesitant fuzzy information. The improved Interactive and Multi-criteria Decision-Making (TODIM) method based on hesitant fuzzy numbers is used to analyze the psychological behavior of decision-makers under different market prospects and comprehensively rank the candidate suppliers. Finally, a case study of selecting cold chain logistics suppliers is provided to verify the effectiveness and feasibility of the method in this paper.

Inventory systems with uncertain supplier capacity: an application to covid-19 testing

The COVID-19 pandemic has forced governments to impose crippling restrictions on the day-to-day activities of citizens. To contain the virus and lift these restrictions safely, policymakers need to know quickly where the virus is spreading. This has been possible only through widespread testing. Not long after starting largescale testing in the early stages of the pandemic and more recently with a surge of new variants, countries hit a roadblock—the shortage of swabs used in the testing kits due to disruptions in the supply chain caused by COVID-19. This disruption translates to a variable production capacity of the swab suppliers. As a result, when countries order swabs from a swab supplier, their order might not be fully satisfied. Hence, adopting a proper swab inventory management model can help countries better manage COVID-19 testing and avoid widespread shortages of testing supplies. By considering two different swab demand patterns (i.e., stationary and stochastic) and two different production capacity scenarios for the swab supplier (i.e., ample and variable production capacity), we develop four analytical models, in which we consider all combinations of the above demand and capacity scenarios, to derive the optimal swab-procurement policy for a country. Given the rapid change of COVID-19 infection cases and the limited planning period, countries should aim for reactive scheduling. Through a comprehensive numerical study, we also provide guidelines on how countries should optimally react to these changes in the supply and demand of swabs. The research implications for managing inventory with stochastic supplier capacity and uncertain demand in a finite time horizon extend well beyond the application to COVID-19 testing. Supplementary InformationThe online version contains supplementary material available at 10.1007/s12063-022-00308-1.

Targeted evidence collection for uncertain supplier selection

The problem of selecting which suppliers, and how much of different items to order from each, involves multiple, often conflicting, criteria such as costs and delivery times. Within real world multi-criteria supplier selection problems there is inherent uncertainty involved, and consideration of its impacts and mitigation is a current and important research direction going forward within the field of supplier selection.Uncertainty within multi-criteria supplier selection may be in relation to (i) a decision maker’s ambiguous preferences, such as the importance between criteria, (ii) the suppliers’ supply capacities of, and demand for, different items, and (iii) known information about suppliers with respect to the set of criteria, such as each supplier’s delivery times or their average defect ratios. Whilst previous work has explored the first two of these, less work has explored uncertainty pertaining to information about suppliers in terms of the criteria and, specifically, how it could be efficiently reduced. Such uncertainty is an important problem to address, as it may have a large impact upon an order regarding its perceived quality compared to its realised quality, so reducing such uncertainty can have a significant impact.This paper presents a Targeted Evidence Collection (TEC) approach for efficient reduction of uncertainty, pertaining to suppliers, by looking to efficiently collect additional evidence. The approach looks to utilise and gather evidence intelligently and dynamically – by considering both the likelihood that each supplier will be part of a solution, along with a decision maker’s preferences between criteria – to reduce the uncertainty and efficaciously move towards the most appropriate solution given no uncertainty. The approach is able to handle scenarios for which there are both certain and uncertain criteria present, and can take into account any number of criteria.The TEC strategy is evaluated against alternative approaches, including an active learning based approach, for varying numbers of uncertain criteria, numbers of suppliers, and variations in a decision maker’s preferences. The experimentation highlights how TEC efficiently reduces uncertainty, relating to information about suppliers with respect to the set of criteria, requiring up to three times less evidence than its competitors. In this way, TEC helps to effectively mitigate the uncertainty’s adverse effects, and reduce the risks inherent within a supplier selection problem.

Supplier selection and order allocation in CLSC configuration with various supply strategies under disruption risk

Supplier selection and order allocation are important keys for reverse logistics and closed-loop supply chain networks especially with the presence of demand–supply imbalance risks. If such uncertainties and risks are not foreseen in the chain, and corresponding appropriate measures are not taken to handle them, irreparable damages would be expected consequently. The importance of this issue in closed-loop supply chains is more appreciated due to the importance and the effect of this chain on the environment. In this research, the disruption risk and the uncertainties related to the demand, market price, and the number of returned products are simultaneously considered. Purchasing from the backup suppliers and spot market are considered, to take the proper measures in case of uncertainties. Two-stage stochastic programming model is used to express the uncertainty. The decisions on the purchase from the uncertain suppliers, and reserving from backup suppliers are made in the first step. Then, after determining the uncertainties, return decisions (purchasing from the backup suppliers, spot market and the use of returned products) are made. Besides, we develop our model with CVaR risk measurement tool, and assess risk neutral and risk averse models. We also investigate how changes in the key problem parameters can affect sourcing strategies of a firm.

A statistical analysis and simulation based approach to an uncertain supplier selection problem with discount option

The uncertainties existing in today’s manufacturing environment tends researchers to use statistical and simulation approach to address such stochastic problems. The supplier selection problem has attracted growing interest. One of the crucial issues in supplier selection is product outsourcing. The outsourcing improved the service price by decreasing that, also it reduced the waiting time and increased the customer satisfaction. Furthermore, it improved the company’s core competence. In supplier selection problem, some criteria are effective such as: cost, delivery time, quality. During recent years, various approaches are proposed for supplier selection problem. But in one hand, most of them have considered only single criteria, and on the other hand, some stochastic parameters are assumed to be deterministic. In this study, a statistical method based on design of experiment (DOE) and simulation is presented that minimizes cost and lead times while maximizes quality in the existence of discount. Furthermore, model parameters such as demand, lead time, and rejected products percentage are assumed as stochastic parameters. Finally, the model has been solved using weighted objective method and compromise programming and then the results have been analyzed.

Green supplier selection by developing a new group decision-making method under type 2 fuzzy uncertainty

One of the most important issues in green supply chain management is supplier selection. This issue has proven to be one of the most important decision-making processes for production, operations, and purchasing managers. This problem has to be dealt with in both industry and service environments and is essential to help the firms keep their competitive edge over their rivals. However, this vital process has imprecision and ambiguity. It enhances the difficulty of this important decision-making task. Dealing with uncertainty requires appropriate tools and techniques. In this paper, a new method of supplier selection is presented that uses interval type 2 fuzzy sets (IT2FSs) to deal with today’s uncertain environment. To show the importance and the level of knowledge of each decision-maker in the process, the model applies a novel approach that gives the decision-makers a new weight based on their level of knowledge and the gathered judgments. Moreover, the concept of relative preference relation of IT2FSs is developed to address the weight of selection criteria. Eventually, the proposed uncertain supplier selection model develops the concept of multi-objective optimization by ratio analysis plus the full multiplicative form under type 2 fuzzy uncertainty to enhance the capability of the proposed model to function under real-world problems. Finally, to illustrate the capabilities of the introduced approach, first, two existing case studies at the manufacturing system level are taken from the literature and are solved. Then, to present the method in a step-by-step approach, a case study is adopted and solved by the proposed model and the results are presented.

Dynamic pricing and inventory control with delivery flexibility

We study a multi-period inventory system with price-sensitive demand and uncertain supplier, focusing on the advantage of delivery flexibility. The optimal pricing and inventory replenishment decisions are explored. We also investigate the changes of marginal profit, optimal order quantities and optimal prices over time horizon with additive demand noise. By comparing our system with delivery flexibility with the other two traditional systems numerically in different scenarios, we show that the delivery flexibility can improve the total profit and mitigate the supply risk.

Two Stage Procurement Processes With Competitive Suppliers and Uncertain Supplier Quality

This paper considers a sourcing problem faced by a manufacturer who outsources the manufacturing of a product to one of several competing suppliers, whose cost and quality capabilities are unknown. We consider a two-stage sourcing process in which the first stage is the qualification stage, while the second stage is the supplier selection stage. In the first stage, the manufacturer exerts effort to learn about the quality level of each of the suppliers and then must determine the set of qualified suppliers, subject to some tolerance for error. In the second stage, the manufacturer runs a price-only procurement auction, in which the qualified suppliers compete for the manufacturer's business. We model this two-stage sourcing process with the goal of obtaining insights into manufacturer's optimal decisions. We seek to determine the optimal qualification standard, the optimal amount of effort to be exerted in the qualification process and the appropriate tolerance for error in the qualification process, and to understand the interactions between these decision variables. We are particularly interested in understanding how the manufacturer can design the process to 1) ensure the firm only sources from qualified suppliers and 2) encourage competition among the suppliers during supplier selection.

A Note on 'Sourcing Decisions with Stochastic Supplier Reliability and Stochastic Demand'

Burke, Carillo and Vakharia [2009] consider a class of single product sourcing problems with a stochastic demand and multiple uncertain suppliers. Assuming that the demand is independent of the supplier reliabilities and uniformly distributed, they propose to write the expected profit as a quadratic function and derive a closed form expression for the optimal orders. We show that this formula is true only under special circumstances, which are not satisfied in many practical situations of interest. We give an exact formulation and solution procedure, holding under general assumptions. We illustrate our point by a complementary analysis of the numerical examples given in the quoted paper.

Application of decision-making techniques in supplier selection: A systematic review of literature

Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.

Deriving the cost function of an integrated dyadic supply chain with uncertainty in the supply

This paper considers a dyadic supply chain with an uncertain supplier. Both the retailer and the supplier use continuous review policy. The supplier is randomly available for supplying the retailer. The retailer faces Poisson demands. In this system, lead time is a random variable consists of a constant transportation time plus a random delay occurs due to availability of stock at the supplier. In this paper, for the first time in the literature, we consider both the integrity and the uncertainty in the above supply chain. The main distinguishing characteristic of this paper, in comparison with the existing literature, is considering simultaneously two main concepts, namely the integration and unreliable supplier, through modelling the problem. We derive a good approximation for the total cost function of described system, using weighted mean costs of the one-for-one ordering policy. Finally, using simulation studies, we show that absolute errors are significantly ignorable.

Biodiesel: The alternative fuel for new era

The economic development of any developing country depends on its self-reliance in energy needs. The current energy requirement of the country necessitates search for alternative renewable resources also which are renewable safe and non-polluting. The souring of alternative renewable resources also gains to most priority in view of uncertain suppliers and frequent like in price of crude oil in the international market. In recent times, the world has been confronted with an energy crisis due to depletion of resources and increased environmental problems. The situation has led to the search for an alternative fuel, which should be not only sustainable but also environmental problems. The situation has led to the search for an alternative fuel, which should be not only sustainable but also environment friendly. This alternative diesel fuel can be termed as bio-diesel. This fuel is biodegradable and non-toxic and has low emission profiles as compared to petroleum diesel. This fuel is biodegradable and non-toxic and has low emission profiles as compared to petroleum diesel. The study based on secondary data and the study aimed to attempt the environmental impact of Biodiesel and the cost of cultivation for jatropha in Salem district Tamil Nadu. Keywords: Bio-diesel, Jatropha, environmental impact, cost.

Biodiesel: the Alternative Fuel for New Era

The economic development of any developing country depends on its self-reliance in energy needs. The current energy requirement of the country necessitates search for alternative renewable resources also which are renewable safe and non-polluting. The souring of alternative renewable resources also gains to most priority in view of uncertain suppliers and frequent like in price of crude oil in the international market. In recent times, the world has been confronted with an energy crisis due to depletion of resources and increased environmental problems. The situation has led to the search for an alternative fuel, which should be not only sustainable but also environmental problems. The situation has led to the search for an alternative fuel, which should be not only sustainable but also environment friendly. This alternative diesel fuel can be termed as bio-diesel. This fuel is biodegradable and non-toxic and has low emission profiles as compared to petroleum diesel. This fuel is biodegradable and non-toxic and has low emission profiles as compared to petroleum diesel. The study based on secondary data and the study aimed to attempt the environmental impact of Biodiesel and the cost of cultivation for jatropha in Salem district Tamil Nadu.

Sourcing Decisions with Stochastic Supplier Reliability and Stochastic Demand

Supplier sourcing strategies are a crucial factor driving supply chain success. In this paper, we investigate the implications of uncertain supplier reliability on a firm's sourcing decisions in an environment with stochastic demand. In particular, we characterize specific conditions under which a firm should choose a single versus multiple supplier sourcing strategy. In an environment with both uncertain demand and supply, we characterize the total order quantity, the number of suppliers selected for order placement, and the allocation of the total order quantity among these selected suppliers. For deeper managerial insight, we also examine the sensitivity of the optimal sourcing decisions to interactions between uncertainties in product demand and supply reliability. We show that sourcing from a single supplier is an optimal strategy for environments characterized by high levels of demand uncertainty or high salvage values. A numerical analysis based on data obtained from an office products retailer further reinforces our analytical results. In addition, we also find that when minimal order quantities are imposed, there are situations where it is not optimal to place an order with the lowest cost supplier.

Uncertain Switching Costs and Purchase Decisions in Electronic Markets

As advances in information technologies (IT) significantly reduce the time and cost of acquiring and processing product information, some buyers that traditionally work with one or few suppliers have switched to the market environment. The rapid growth of e-commerce has led researchers to believe that a uniform shift to the electronic markets is inevitable. This article examines the issue from the perspective of uncertain supplier’s performance, switching costs, and the value of information. We show that the presence of uncertainty and switching costs favors contractual relationships between buyers and suppliers. As IT makes the market more competitive, the marginal value of information diminishes. Meanwhile, the overall effect of IT on uncertainty and switching costs is fairly limited. As a result, buyers facing high uncertain supplier’s performance and switching costs may find working with a small number of suppliers a better choice.

Oil politics: a modern history of petroleum

The environmental, the economic - and indeed the political - impact of the catastrophic 2010 blowout of BP's well in the Gulf of Mexico has highlighted the central part played by oil in the modern world. The fate of millions now depends on the ever-shifting value of petroleum and on the fortunes of the corporations that deliver it. The story of oil - how it came to play such a dominant role in the world economy, who controls its extraction, pricing and supply - is essential to an understanding of contemporary world politics. In this acclaimed book, Francisco Parra draws on his long experience in the oil world, including as the Secretary General of OPEC, to tell it. Oil Politics surveys the tumultuous history of the international petroleum industry, from its extraordinary growth between 1950 and 1979, presided over by the seven major oil companies, to the price revolution of the 1970s and 1980s, to the re-emergence of Russia as an important but uncertain supplier. Parra charts the changing power dynamics amongst the major oil suppliers and examines their relationships with the major oil importing countries, and how these concerns have impacted on foreign policy. Oil politics in the twenty-first century remain fraught with tensions, and this book offers a uniquely accessible guide to understanding this complex but vitally important subject.