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Between 2005 and 2019, U.S. business applications rose 40 percent while conversion to employer firms fell by nearly half. We study whether boundary redrawing helps explain this pattern. Structured routine-cognitive work can be governed through deliverables and thinner buyer and supplier interfaces. When such work remains place-bound, outsourcing creates demand for domestic specialist suppliers. Across 722 commuting zones, a one percentage-point higher baseline routine employment share raises applications by 27.8 per 100,000 residents. Realized entry concentrates in micro-establishments, with no startup quality gains. Contract and industry evidence point to local supplier entry, not routine-manual displacement.

Abstract A growing number of studies and evidence from industries suggest that, besides managing the relationship with its suppliers, a buyer needs to proactively manage the relationships between those suppliers. In a buyer–supplier–supplier relationship triad, the buyer, as the contracting entity, influences the suppliers’ behaviors and the relationship between them. By considering the relationships in such a triad, we are able to gain a richer and more realistic perspective of buyer–supplier relationships. In this study, our goal is to examine supplier–supplier relationships in buyer–supplier–supplier triads, focusing on how such relationships impact the supplier performance. We frame the supplier–supplier relationship as co‐opetition—one in which competing suppliers work together to meet the buyer's requirements. We investigate the role of the buyer on such relationships, and how the buyer and co‐opetitive supplier–supplier relationships affect supplier performance. We find mixed empirical support for our hypotheses. However, we are able to demonstrate the dynamics of supplier–supplier co‐opetition in the buyer–supplier–supplier triad. We point out the need for further studies in this area.

The research explores and examines factors for supplier evaluation and its impact on process improvement particularly aiming on a steel pipe manufacturing firm in Gujarat, India. Data was collected using in-depth interview. The questionnaire primarily involves the perception of evaluation of supplier. Factors influencing supplier evaluation and its influence on process improvement is also examined in this study. The model testing and validation were done using partial least square method. Outcomes signified that the factors that influence the evaluation of the supplier are quality, cost, delivery and supplier relationship management. The study depicted that quality and cost factors for supplier evaluation are insignificant. The delivery and supplier relationship management have the significant influence on the evaluation of the supplier. The research also depicted that supplier evaluation has a significant influence on process improvement. Many researchers have considered quality, cost and delivery as the factors for evaluating the suppliers. But for a company, it is quintessential to have a good relationship with the supplier. Hence, the factor, supplier relationship management is

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for international shipments. Our approach integrates trade relationships between industry sectors, transportation costs, and supplier rating model adapted from existing literature. The model's core objective is to minimize the discrepancy between modeled and observed commodity flows while ensuring a close match to regional shipping distance distributions. Implemented and tested across four major U.S. metropolitan areas, Atlanta, Chicago, Dallas-Fort Worth, and Los Angeles, the model demonstrates high fidelity in replicating observed freight patterns. Key findings reveal consistent alignment with national shipping distance trends and highlight significant spatial variations in commodity trade assignments and demand across the study regions. This behaviorally informed and transport-sensitive framework is designed to approximate real-world deci

In the Priority $k$-Supplier problem the input consists of a metric space $(F \cup C, d)$ over set of facilities $F$ and a set of clients $C$, an integer $k > 0$, and a non-negative radius $r_v$ for each client $v \in C$. The goal is to select $k$ facilities $S \subseteq F$ to minimize $\max_{v \in C} \frac{d(v,S)}{r_v}$ where $d(v,S)$ is the distance of $v$ to the closes facility in $S$. This problem generalizes the well-studied $k$-Center and $k$-Supplier problems, and admits a $3$-approximation [Plesník, 1987, Bajpai et al., 2022. In this paper we consider two outlier versions. The Priority $k$-Supplier with Outliers problem [Bajpai et al., 2022] allows a specified number of outliers to be uncovered, and the Priority Colorful $k$-Supplier problem is a further generalization where clients are partitioned into $c$ colors and each color class allows a specified number of outliers. These problems are partly motivated by recent interest in fairness in clustering and other optimization problems involving algorithmic decision making. We build upon the work of [Bajpai et al., 2022] and improve their $9$-approximation Priority $k$-Supplier with Outliers problem to a $1+3\sqrt{3}\appro

Considering a supply chain with partial vertical integration, we attempt to seek answers to several questions related to the cooperation competition based friction, abundant in such networks. Such an SC can represent a supplier with an inhouse production unit that attempts to control an outhouse production unit via the said friction. The two production units can have different sets of loyal customer bases and the aim of the manufacturer supplier duo would be to get the best out of the two customer bases. Our analysis shows that under certain market conditions, an optimal strategy might be to allow both units to earn positive profits particularly when they hold similar market power and when customer loyalty is high. In cases of weaker customer loyalty, however, the optimal approach may involve pressurizing the outhouse unit to operate at minimal profits. Even more intriguing is the scenario where the outhouse unit has a greater market power and customer loyalty remains strong here, it may be optimal for the inhouse unit to operate at a loss just enough to dismantle the downstream monopoly.

Supply chain optimization is key to a healthy and profitable business. Many companies use online procurement systems to agree contracts with suppliers. It is vital that the most competitive suppliers are invited to bid for such contracts. In this work, we propose a recommender system to assist with supplier discovery in road freight online procurement. Our system is able to provide personalized supplier recommendations, taking into account customer needs and preferences. This is a novel application of recommender systems, calling for design choices that fit the unique requirements of online procurement. Our preliminary results, using real-world data, are promising.

Detecting fraud and corruption in public procurement remains a major challenge for governments worldwide. Most research to-date builds on domain-knowledge-based corruption risk indicators of individual contract-level features and some also analyzes contracting network patterns. A critical barrier for supervised machine learning is the absence of confirmed non-corrupt, negative, examples, which makes conventional machine learning inappropriate for this task. Using publicly available data on federally funded procurement in Mexico and company sanction records, this study implements positive-unlabeled (PU) learning algorithms that integrate domain-knowledge-based red flags with network-derived features to identify likely corrupt and fraudulent contracts. The best-performing PU model on average captures 32 percent more known positives and performs on average 2.3 times better than random guessing, substantially outperforming approaches based solely on traditional red flags. The analysis of the Shapley Additive Explanations reveals that network-derived features, particularly those associated with contracts in the network core or suppliers with high eigenvector centrality, are the most imp

Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficiency fully. Traditionally derived from financial portfolio management, the Black-Litterman (BL) model offers a new perspective for the TSSA scenario by balancing expected returns against insufficient supply risks. However, its application within TSSA is constrained by the reliance on manually constructed perspective matrices and spatio-temporal market dynamics, coupled with the absence of supervisory signals and data unreliability inherent to supplier information. To solve these limitations, we introduce the pioneering Deep Black-Litterman Model (DBLM), which innovatively adapts the BL model from financial roots to supply chain context. Leveraging the Spatio-Temporal Graph Neural Networks (STGNNS), DBLM automatically generates future perspective matrices for TSSA, by integrating spatio-temporal dependency. Moreover, a novel Spearman rank correlation distinctively supervises our approach to address the lack of supervisory signals, specifically designed to navigate through the complexities of supplier risks

This paper proposes a human-centered conceptual model integrating lean and Industry 4.0 based on the literature review and validated it through a case study in the context of an advanced automotive first-tier supplier. Addressing a significant gap in existing research on lean Industry 4.0 implementations, the study provides both theoretical insights and practical findings. It emphasizes the importance of a human-centered approach, identifies key enablers and barriers. In the implementation process of the case study, it is considered at group level and model site level through operational, social and technological perspectives in a five-phase multi-method approach. It shows what effective human-centered lean Industry 4.0 implementation look like and how advanced lean tools can be digitized. It highlights 26 positive and 10 negative aspects of the case and their causal relation. With the appropriate internal and external technological knowhow and people skills, it shows how successful implementation can benefit the organization and employees based on the conceptual model that serves as a first step toward lean Industry 5.0.

We present approximation algorithms for the Fault-tolerant $k$-Supplier with Outliers ($\mathsf{F}k\mathsf{SO}$) problem. This is a common generalization of two known problems -- $k$-Supplier with Outliers, and Fault-tolerant $k$-Supplier -- each of which generalize the well-known $k$-Supplier problem. In the $k$-Supplier problem the goal is to serve $n$ clients $C$, by opening $k$ facilities from a set of possible facilities $F$; the objective function is the farthest that any client must travel to access an open facility. In $\mathsf{F}k\mathsf{SO}$, each client $v$ has a fault-tolerance $\ell_v$, and now desires $\ell_v$ facilities to serve it; so each client $v$'s contribution to the objective function is now its distance to the $\ell_v^{\text{th}}$ closest open facility. Furthermore, we are allowed to choose $m$ clients that we will serve, and only those clients contribute to the objective function, while the remaining $n-m$ are considered outliers. Our main result is a $\min\{4t-1,2^t+1\}$-approximation for the $\mathsf{F}k\mathsf{SO}$ problem, where $t$ is the number of distinct values of $\ell_v$ that appear in the instance. At $t=1$, i.e. in the case where the $\ell_v$'s a

Abstract As firms increasingly emphasize cooperative relationships with critical suppliers, executives of buyer firms are using supplier evaluations to ensure that their performance objectives are met. Supplier evaluations, one type of supplier development program (SDP), are an attempt to meet current and future business needs by improving supplier performance and capabilities. The purpose of this study was to determine how suppliers perceive the buying firm’s supplier evaluation communication process and its impact on suppliers’ performance. Three communication strategies (indirect influence strategy, formality and feedback) were tested separately and one in unison (collaborative). Using structural equation modeling (SEM) and data collected from 139 first‐tier North American automotive suppliers, the results of this research have shown that, contrary to the SDP literature from the buying firm’s perspective, the supplier’s perceptions of the buying firm’s communication does not directly influence suppliers’ performance. Specifically, the supplier evaluation communication process does not ensure improved supplier performance unless the supplier is committed to the buying firm. Buying firms can influence the supplier’s commitment through increased efforts of cooperation and commitment. The results also indicate that when a buying firm utilizes collaborative communication, the supplier perceives a positive influence on the buyer–supplier relationship.

Currently, wind energy is one of the most important sources of renewable energy. Offshore locations for wind turbines are increasingly exploited because of their numerous advantages. However, offshore wind farms require high investment in maintenance service. Due to its complexity and special requirements, maintenance service is usually outsourced by wind farm owners. In this paper, we propose a novel approach to determine, quantify, and reduce the possible conflicts of interest between owners and maintenance suppliers. We created a complete techno-economic model to address this problem from an impartial point of view. An iterative process was developed to obtain statistical results that can help stakeholders negotiate the terms of the contract, in which the availability of the wind farm is the reference parameter by which to determine penalisations and incentives. Moreover, a multi-objective programming problem was addressed that maximises the profits of both parties without losing the alignment of their interests. The main scientific contribution of this paper is the maintenance analysis of offshore wind farms from two perspectives: that of the owner and the maintenance supplier.

This paper presents evidence on the granular nature of firms' network of foreign suppliers and studies its implications for the impact of supplier shocks on domestic firms' performance. To demonstrate this, I use customs level information on transactions between Argentinean firms and foreign firms. I highlight two novel stylized facts: (i) the distribution of domestic firms' number of foreign suppliers is highly skewed with the median firm reporting linkages with only two, (ii) firms focus imported value on one top-supplier, even when controlling for firm size. Motivated by these facts I construct a theoretical framework of heterogeneous firms subject to search frictions in the market for foreign suppliers. Through a calibration exercise I study the framework's predictions and test them in the data using a shift-share identification strategy. Results present evidence of significant frictions in the market for foreign suppliers and strong import-export complementarities.

This paper studies the duopoly competition between renewable energy suppliers with or without energy storage in a local energy market. The storage investment brings the benefits of stabilizing renewable energy suppliers' outputs, but it also leads to substantial investment costs as well as some surprising changes in the market outcome. To study the equilibrium decisions of storage investment in the renewable energy suppliers' competition, we model the interactions between suppliers and consumers using a three-stage game-theoretic model. In Stage I, at the beginning of the investment horizon, suppliers decide whether to invest in storage. Once such decisions have been made, in the day-ahead market of each day, suppliers decide on their bidding prices and quantities in Stage II, based on which consumers decide the electricity quantity purchased from each supplier in Stage III. In the real-time market, a supplier is penalized if his actual generation falls short of his commitment. We characterize a price-quantity competition equilibrium of Stage II, and we further characterize a storage-investment equilibrium in Stage I incorporating electricity-selling revenue and storage cost. Count

Given a metric space $(V, d)$ along with an integer $k$, the $k$-Median problem asks to open $k$ centers $C \subseteq V$ to minimize $\sum_{v \in V} d(v, C)$, where $d(v, C) := \min_{c \in C} d(v, c)$. While the best-known approximation ratio of $2.613$ holds for the more general supplier version where an additional set $F \subseteq V$ is given with the restriction $C \subseteq F$, the best known hardness for these two versions are $1+1/e \approx 1.36$ and $1+2/e \approx 1.73$ respectively, using the same reduction from Max $k$-Coverage. We prove the following two results separating them. First, we show a $1.546$-parameterized approximation algorithm that runs in time $f(k) n^{O(1)}$. Since $1+2/e$ is proved to be the optimal approximation ratio for the supplier version in the parameterized setting, this result separates the original $k$-Median from the supplier version. Next, we prove a $1.416$-hardness for polynomial-time algorithms assuming the Unique Games Conjecture. This is achieved via a new fine-grained hardness of Max-$k$-Coverage for small set sizes. Our upper bound and lower bound are derived from almost the same expression, with the only difference coming from the well-

The coordination of actions and the allocation of profit in supply chains under decentralized control play an important role in improving the profits of retailers and suppliers in the chain. We focus on supply chains under decentralized control in which noncompeting retailers can order from multiple suppliers to replenish their stocks. Suppliers' production capacity is bounded. The goal of the firms in the chain is to maximize their individual profits. As the outcome under decentralized control is inefficient, coordination of actions between cooperating agents can improve individual profits. Cooperative game theory is used to analyze cooperation between agents. We define multi-retailer-supplier games and show that agents can always achieve together an optimal profit and they have incentives to cooperate and to form the grand coalition. Moreover, we show that there always exist stable allocations of the total profit among the firms upon which no coalition can improve. Then we propose and characterize a stable allocation of the total surplus induced by cooperation.

Production networks arise from supply and customer relations among firms. These systems are gaining growing attention as a consequence of disruptions due to natural or man-made disasters that happened in the last years, such as the Covid-19 pandemic or the Russia-Ukraine war. However, data constraints force the few, available studies to consider only country-specific production networks. In order to fully capture the cross-country structure of modern supply chains, here we focus on the global automotive industry as represented by the MarkLines Automotive dataset. After representing this data as a network of manufacturers, suppliers, and products, we perform a pattern-detection exercise using a statistically grounded validation technique based on the maximum entropy principle. We reveal the presence of a significantly large number of V-shaped and square-shaped motifs, indicating that manufacturing firms compete and are seldom engaged in a buyer-supplier relationship, while they typically have many suppliers in common. Interestingly, generalist and specialist suppliers coexist in the network. Additionally, we unveil the presence of geographical patterns, with manufacturers clustering

Due to the limited permissions for upgrading dualside (i.e., server-side and client-side) loss tolerance schemes from the perspective of CDN vendors in a multi-supplier market, modern large-scale live streaming services are still using the automatic-repeat-request (ARQ) based paradigm for loss recovery, which only requires server-side modifications. In this paper, we first conduct a large-scale measurement study with up to 50 million live streams. We find that loss shows dynamics and live streaming contains frequent on-off mode switching in the wild. We further find that the recovery latency, enlarged by the ubiquitous retransmission loss, is a critical factor affecting live streaming's client-side QoE (e.g., video freezing). We then propose an enhanced recovery mechanism called AutoRec, which can transform the disadvantages of on-off mode switching into an advantage for reducing loss recovery latency without any modifications on the client side. AutoRec allows users to customize overhead tolerance and recovery latency tolerance and adaptively adjusts strategies as the network environment changes to ensure that recovery latency meets user demands whenever possible while keeping ove