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We study a supply chain where a retailer buys from a supplier who faces financial constraints. Informational problems about the supplier’s demand prospects and production capabilities restrict her access to capital. By committing to a minimum purchase quantity, the retailer can mitigate these informational problems and expand the supplier’s feasible production set. We assume a newsvendor model of operations and analyze the strategic interaction of the two parties as a sequential game. Key parameters in our model are the supplier’s ex ante credit limit, her informational transparency—which conditions the amount of additional capital released by the commitment—and the demand characteristics of the final market. We show that in equilibrium the supplier can benefit from a lower ex ante credit limit or lower informational transparency. The retailer always benefits from an increase in these parameters. We also indicate limits to the commitment approach: under certain conditions, the retailer may prefer to relax the supplier’s financial constraint by adjusting the wholesale price, or a combination of wholesale price and commitment. Our study provides a novel perspective on capital market frictions in supply chains. The e-companion is available at https://doi.org/10.1287/opre.2018.1727 .

We establish a supply chain finance scheme containing a cash-strapped supplier, a creditworthy retailer as well as a financial institution to explore whether the positive or negative salvage value has a crucial impact on the order decisions and financing strategies. Buyer-backed purchase order financing and advanced payment discount (APD) financing are considered to settle the supplier’s fund shortage problem. We found that the positive and negative salvage values affect (1) the retailer’s optimal order quantity. The buyer orders more products with positive salvage value than those with no salvage value and reduces orders for items with negative salvage value; (2) the profits in the supply chain. Ordering items with a positive salvage value can reduce the risk of loss compared to orders with no salvage value, which leads to more gains for the buyer and the whole supply chain, while orders for items with negative salvage increase the losses, resulting in lower profits; (3) the threshold of the retailer’s internal asset level under single financing. The higher salvage value brings more inventory risk to the retailer; hence the retailer should have a higher asset level to ensure that there is sufficient capital to finance the supplier via APD. Finally, we verify the results by numerical experiments and present some managerial implications for different industries.

Recently, equipment replacement and maintenance repair and operation (MRO) optimization have substantially increased owing to the aging and deterioration of industrial plants, such as steel-making factories in Korea. Therefore, plant owners are required to quickly review equipment supply contracts, i.e., purchase order (PO) documents, with suppliers and vendors. Currently, there is inconsistency in the time and quality required for the PO document review process by engineers, depending on their manual skills and practice. This study developed a general provisions question-answering model (GPQAM) by combining knowledge graph (KG) and question-answering (QA) techniques to search for semantically connected contract clauses through the definition of relationships between entities during the review of equipment purchase contracts. The PO documents analyzed in this case study were based on one steel-making company’s general provisions (GP). GPQAM is a machine learning (ML)-based model with two sub-models (i.e., KG and QA) that automatically generates the most relevant answers to semantic search questions through a cypher query statement in GP for the PO engineers. First, based on the developed GP lexicon and its classifying taxonomy to be stored in the Neo4j graph database (GDB), the KG sub-model finds the corresponding synonyms and consequently shows GP-related information in a graphic form. Second, the QA sub-model is a function to find and answer contract information within the KG and applies pattern-matching technology based on the Aho–Corasick (AC) algorithm. Third, nodes with the meaning most similar to the question are selected using similarity measurement if a response cannot be extracted through the pattern-matching process. Forty-five pilot test questions were created and applied to the GPQAM model evaluation. The F1 score was 82.8%, indicating that the unsupervised training methods developed in this study could be better applied to a semantic QA process in plant engineering documents, where sufficient training data are limited and bargained. An expert survey of PO practitioners confirmed that the semantic QA capability of GPQAM might be efficient and useful for their work. As the first case of applying KG technology to semantic QA for plant equipment PO contracts, this study might be a meaningful contribution to the steel plant industry and, therefore, extended to construction and engineering contract applications.

The complexity and age of industrial plants have prompted a rapid increase in equipment maintenance and replacement activities in recent years. Consequently, plant owners are challenged to reduce the process and review time of equipment purchase order (PO) documents. Currently, traditional keyword-based document search technology generates unintentional errors and omissions, which results in inaccurate search results when processing PO documents of equipment suppliers. In this study, a purchase order knowledge retrieval model (POKREM) was designed to apply knowledge graph (KG) technology to PO documents of steel plant equipment. Four data domains were defined and developed in the POKREM: (1) factory hierarchy, (2) document hierarchy, (3) equipment classification hierarchy, and (4) PO data. The information for each domain was created in a graph database through three subprocesses: (a) defined in a hierarchical structure, (b) classified into nodes and relationships, and (c) written in triples. Ten comma-separated value (CSV) files were created and imported into the graph database for data preprocessing to create multiple nodes. Finally, rule-based reasoning technology was applied to enhance the model’s contextual search performance. The POKREM was developed and implemented by converting the Neo4j open-source graph DB into a cloud platform on the web. The accuracy, precision, recall, and F1 score of the POKREM were 99.7%, 91.7%, 100%, and 95.7%, respectively. A validation study showed that the POKREM could retrieve accurate answers to fact-related queries in most cases; some incorrect answers were retrieved for reasoning-related queries. An expert survey of PO practitioners indicated that the PO document review time with the POKREM was reduced by approximately 40% compared with that of the previous manual process. The proposed model can contribute to the work efficiency of engineers by improving document search time and accuracy; moreover, it may be expandable to other plant engineering documents, such as contracts and drawings.

The promotion of low-carbon production is a crucial aspect of sustainable development, and carbon trading has proven to be an effective means of achieving this goal. However, the limited capital hinders small and medium-sized enterprises (SMEs) from achieving low-carbon production. Thus, this paper examines a low-carbon supply chain comprising a financially constrained manufacturer and a well-funded retailer. We propose a game-theoretical model that addresses the financial constraints of the manufacturer through buyer-backed purchase order financing (BPOF) or advance payment discount (APD) with carbon trading revenue-sharing contracts. We investigate the determination of pricing and carbon emission reduction strategies by firms utilizing diverse financing models. We find that the total amount of carbon emission reduction increases with the manufacturer’s carbon trade revenue-sharing ratio. The financing rate of BPOF has a negative impact on the amount of carbon emission reduction, while the discount rate of APD will not affect either the carbon emission reduction or the revenue of the supply chain. Among the two financing strategies, APD is optimal for maximizing profit and promoting low-carbon development throughout the supply chain.

This paper reports a case study of an Indian aerospace industry, XYZ Aeronautics Limited, with an objective to bring reduction in cycle time of outsourcing process. The focus is to identify bottleneck areas and investigate into the improvement opportunities. The paper enriches the present body of knowledge by contributing a methodology and its application for a real-life case study to understand the implications of outsourcing on operational planning. The analysis reported in this research is based on the available knowledge and extensive experience gained while working in the outsourcing department of the case organization for a long period of time. The research integrates action research, longitudinal case study research, and simulation for the detailed and scientific analysis of the problem for a case organization. The study employs a six-step analysis approach for investigating the outsourcing-related issues in case organization. The simulation study has helped to reduce the average cycle time of purchase order placement to 69 days, i.e., improvement by 33.65% over the present 104 days. This would enable the case organization to conduct “what-if” analysis and evaluate the implications of bringing improvements in select bottleneck areas for reducing cycle time of outsourcing process. The investigation is expected to help managers to negotiate on appropriate dimensions with vendors and convince them for the adoption of e-sourcing, e-contract, and e-catalog technologies.

In recent years, environmental protection has been paid more and more attention. Green credit policy (GCP) is one of the significant preferential policies for government to encourage enterprises to vigorously develop green projects. We are interested in the impact of the central bank’s GCP on the profits and optimal strategies of manufacturers and suppliers related to POF (purchase order financing). Specifically, we build a game-theoretical model consisting of a manufacturer, a bank and a green supplier and a non-green supplier. Furthermore, the optimal strategies of the manufacturer and suppliers when the bank or the government sets a carbon emission cap on suppliers are discussed. We come to some important conclusions about a GCP that promotes the development of green projects since it brings higher profits to both the manufacturer and suppliers than the lack of a GCP. Furthermore, the higher the production cost, the better the effect of a GCP. Under the carbon emission restrictions required by the government, the effect of GCP is weakened and the profits of the manufacturer and the suppliers are reduced to zero due to the gradually increasing delivery risks as production costs increase.

[...]it can backfire spectacularly by putting them off retirement plans at a young age. When adults have employee handbooks, school reports, permission slips, purchase orders for supplies, medical invoices, and a myriad of other paper coming at them, it's almost automatic to throw some of those papers into the recycling bin without completely reading them. Human Resource personnel may also know who is having their wages garnished for child support or tax levies, and can ensure those employees realize the significance of this new feature before it adversely impacts them. Nothing gets me angrier than an auto renewal of my Norton subscription for $150+, especially when that money just disappears from my account.

In today’s scenario, blockchain technology is an emerging area and promising technology in the field of the food supply chain industry (FSCI). A literature survey comprising an analytical review of blockchain technology with the Internet of things (IoT) for food supply chain management (FSCM) is presented to better understand the associated research benefits, issues, and challenges. At present, with the concept of farm-to-fork gaining increasing popularity, food safety and quality certification are of critical concern. Blockchain technology provides the traceability of food supply from the source, i.e., the seeding factories, to the customer’s table. The main idea of this paper is to identify blockchain technology with the Internet of things (IoT) devices to investigate the food conditions and various issues faced by transporters while supplying fresh food. Blockchain provides applications such as smart contracts to monitor, observe, and manage all transactions and communications among stakeholders. IoT technology provides approaches for verifying all transactions; these transactions are recorded and then stored in a centralized database system. Thus, IoT enables a safe and cost-effective FSCM system for stakeholders. In this paper, we contribute to the awareness of blockchain applications that are relevant to the food supply chain (FSC), and we present an analysis of the literature on relevant blockchain applications which has been conducted concerning various parameters. The observations in the present survey are also relevant to the application of blockchain technology with IoT in other areas.

Purpose – The purpose of this paper is to summarise and analyse reforms to the compulsory purchase compensation code which have been published for consultation by the Department for Communities and Local Government. Design/methodology/approach – To explain each of the proposals and provide a critical assessment of each of them. Findings – The proposed changes comprise a further small step towards the comprehensive reform of the compensation code which is required. Practical implications – The proposed reforms are to be welcomed but there are a number of areas in which they need refinement following the consultation process. Originality/value – As the proposed reforms were published only in March 2016 there will be few other commentaries available.