Semantic data integration for supply chain management: with a specific focus on applications in the semiconductor industry

Supply Chain Management (SCM) is essential to monitor, control, and enhance the performance of SCs. Increasing globalization and diversity of Supply Chains (SC)s lead to complex SC structures, limited visibility among SC partners, and challenging collaboration caused by dispersed data silos. Digitalization is responsible for driving and transforming SCs of fundamental sectors such as the semiconductor industry. This is further accelerated due to the inevitable role that semiconductor products play in electronics, IoT, and security systems. Semiconductor SCM is unique as the SC operations exhibit special features, e.g., long production lead times and short product life. Hence, systematic SCM is required to establish information exchange, overcome inefficiency resulting from incompatibility, and adapt to industry-specific challenges. The Semantic Web is designed for linking data and establishing information exchange. Semantic models provide high-level descriptions of the domain that enable interoperability. Semantic data integration consolidates the heterogeneous data into meaningful and valuable information. The main goal of this thesis is to investigate Semantic Web Technologies (SWT) for SCM with a specific focus on applications in the semiconductor industry. As part of SCM, End-to-End SC modeling ensures visibility of SC partners and flows. Existing models are limited in the way they represent operational SC relationships beyond one-to-one structures. The scarcity of empirical data from multiple SC partners hinders the analysis of the impact of supply network partners on each other and the benchmarking of the overall SC performance. In our work, we investigate (i) how semantic models can be used to standardize and benchmark SCs. Moreover, in a volatile and unpredictable environment, SC experts require methodical and efficient approaches to integrate various data sources for informed decision-making regarding SC behavior. Thus, this work addresses (ii) how semantic data integration can help make SCs more efficient and resilient. Moreover, to secure a good position in a competitive market, semiconductor SCs strive to implement operational strategies to control demand variation, i.e., bullwhip, while maintaining sustainable relationships with customers. We examine (iii) how we can apply semantic technologies to specifically support semiconductor SCs. In this thesis, we provide semantic models that integrate, in a standardized way, SC processes, structure, and flows, ensuring both an elaborate understanding of the holistic SCs and including granular operational details. We demonstrate that these models enable the instantiation of a synthetic SC for benchmarking. We contribute with semantic data integration applications to enable interoperability and make SCs more efficient and resilient. Moreover, we leverage ontologies and KGs to implement customer-oriented bullwhip-taming strategies. We create semantic-based approaches intertwined with Artificial Intelligence (AI) algorithms to address semiconductor industry specifics and ensure operational excellence. The results prove that relying on semantic technologies contributes to achieving rigorous and systematic SCM. We deem that better standardization, simulation, benchmarking, and analysis, as elaborated in the contributions, will help master more complex SC scenarios. SCs stakeholders can increasingly understand the domain and thus are better equipped with effective control strategies to restrain disruption accelerators, such as the bullwhip effect. In essence, the proposed Sematic Web Technology-based strategies unlock the potential to increase the efficiency, resilience, and operational excellence of supply networks and the semiconductor SC in particular

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Stochastic regret minimization for revenue management problems with nonstationary demands

We study an admission control model in revenue management with nonstationary and correlated demands over a finite discrete time horizon. The arrival probabilities are updated by current available information, that is, past customer arrivals and some other exogenous information. We develop a regret‐based framework, which measures the difference in revenue between a clairvoyant optimal policy that has access to all realizations of randomness a priori and a given feasible policy which does not have access to this future information. This regret minimization framework better spells out the trade‐offs of each accept/reject decision. We proceed using the lens of approximation algorithms to devise a conceptually simple regret‐parity policy. We show the proposed policy achieves 2‐approximation of the optimal policy in terms of total regret for a two‐class problem, and then extend our results to a multiclass problem with a fairness constraint. Our goal in this article is to make progress toward understanding the marriage between stochastic regret minimization and approximation algorithms in the realm of revenue management and dynamic resource allocation. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 433–448, 2016Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135128/1/nav21704.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135128/2/nav21704_am.pd

Optimal and Heuristic Lead-Time Quotation For an Integrated Steel Mill With a Minimum Batch Size

This paper presents a model of lead-time policies for a production system, such as an integrated steel mill, in which the bottleneck process requires a minimum batch size. An accurate understanding of internal lead-time quotations is necessary for making good customer delivery-date promises, which must take into account processing time, queueing time and time for arrival of the requisite volume of orders to complete the minimum batch size requirement. The problem is modeled as a stochastic dynamic program with a large state space. A computational study demonstrates that lead time for an arriving order should generally be a decreasing function of the amount of that product already on order (and waiting for minimum batch size to accumulate), which leads to a very fast and accurate heuristic. The computational study also provides insights into the relationship between lead-time quotation, arrival rate, and the sensitivity of customers to the length of delivery promises

Matching Spatially Diversified Suppliers with Random Demands

A fundamental challenge in operations management is to dynamically match spatially diversified supply sources with random demand units. This dissertation tackles this challenge in two major areas: in supply chain management, a company procures from multiple, geographically differentiated suppliers to service stochastic demands based on dynamically evolving inventory conditions; in revenue management of ride-hailing systems, a platform uses operational and pricing levers to match strategic drivers with random, location and time-varying ride requests over geographically dispersed networks. The first part of this dissertation is devoted to finding the optimal procurement and inventory management strategies for a company facing two potential suppliers differentiated by their lead times, costs and capacities. We synthesize and generalize the existing literature by addressing a general model with the simultaneous presence of (a) orders subject to capacity limits, (b) fixed costs associated with inventory adjustments, and (c) possible salvage opportunities that enable bilateral adjustments of the inventory, both for finite and infinite horizon periodic review models. By identifying a novel, generalized convexity property, termed (C1K1, C2K2)-convexity, we are able to characterize the optimal single-source procurement strategy under the simultaneous treatment of all three complications above, which has remained an open challenge in stochastic inventory theory literature. To our knowledge, we recover almost all existing structural results as special cases of a unified analysis. We then generalize our results to dual-source settings and derive optimal policies under specific lead time restrictions. Based on these exact optimality results, we develop various heuristics and bounds to address settings with fully general lead times. The second part of this dissertation focuses on a ride-hailing platform's optimal control facing two major challenges: (a) significant demand imbalances across the network, and (b) stochastic demand shocks at hotspot locations. Towards the first major challenge, which is evidenced by our analysis of New York City taxi trip data, the dissertation shows how the platform's operational controls--including demand-side admission control and supply-side empty car repositioning--can improve system performance significantly. Counterintuitively, it is shown that the platform can improve the overall value through strategic rejection of demand in locations with ample supply capacity (driver queue). Responding to the second challenge, a demand shock of uncertain duration, we show how the platform can resort to surge pricing and dynamic spatial matching jointly, to enhance profits in an incentive compatible way for the drivers. Our results provide distinctive insights on the interplay among the relevant timescales of different phenomena, including rider patience, demand shock duration and drivers' traffic delay to the hotspot, and their impact on optimal platform operations

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Modeling and analyzing a health care supply chain system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A Thesis presented on Improving and Redefining the Supply Chain in Healthcare, defining existing problems with current Supply Chain applications, and reviewing current applications and trends in the Supply Chain culture within the Manufacturing Industries and Healthcare Industries. Research of successful applications of new, and improvements to existing supply chain methodologies are presented. The concept of a future Supply Chain Management System, extending the boundaries of conventional Healthcare Supply Chain to include both conventional customer (Healthcare Materiel and Capital Assets Requestors and Distributors) and the nonconventional (the Healthcare Beneficiary), is presented in detail. Final discussions and conclusions of this concept are offered as a review of this concept for the reader of this manuscript