OneSmart contract of the most exciting topics to emerge in the blockchainBlockchain ecosystem is the smart contractSmart contract. A smart contract is a computer program whose code is stored in a distributed blockchainBlockchain structure and that directly controls digital assets without relying on a third-party intermediary.

OfConsensus mechanism one blockchain’sBlockchain most prized features is the consistency, hence security, it provides to the stored data. Consistency is achieved through consensus mechanismsConsensus mechanism, the most common of which is Bitcoin’sBitcoin Proof of WorkProof of work (PoW) mechanism.

Chapter 6 introduced the concept of symmetric cryptography. As the name suggests, a symmetric encryptionSymmetric encryption scheme uses the same key for both encryption and decryption; the key is known to the sender and recipient of the message but must remain unknown to anyone else. To communicate secretly, a sender uses an invertible cryptographic function to encrypt a plaintextPlaintext m with the key k and then sends the resulting ciphertext c to the recipient.

A blockchainBlockchain is a ledgerLedger of blocks of information (e.g., transactions, agreements, etc.) that are stored sequentially across a networkNetwork of computers. Rather than a simple algorithm, blockchainBlockchain is a technology construct and an enabling protocol that facilitates a decentralized brokering of data among participants, i.e., its revolutionary properties do not derive from what blockchainsBlockchain do (i.e., store data securely), but from the manner in which they are used and implemented (i.e., trustless and decentralized).

This book provides a comprehensive introduction to blockchain and distributed ledger technology. It includes detailed step-by-step exercises to help readers launch their own Blockchain, and presents shortcut guidance to develop distributed ledger technology applications.

BitcoinAnonymity isBitcoin the world’s most transparent payment method in the sense of transaction traceability: Any transaction that occurs on the networkNetwork can be traced to its origin, and any account that was ever linked to any bitcoinBitcoin or fraction of a bitcoinBitcoin is similarly traceable.

Cryptology is the science of encrypting and decrypting information and the methods employed to those ends. Cryptography (from the Greek “kryptós,” meaning secret or hidden, and “gráphein,” meaning writing) is a subset of cryptology that describes the creation of methods for encrypting information so it cannot be understood by unauthorized parties. Steganography refers to methods for disguising the communication channel over which cryptographically encrypted messages are sent.

We study a supply chain distribution system and investigate experimentally operations of markets where retailers can trade digital claims (tokens) on the supplier’s capacity. Subjects play the role of retailers, have heterogeneous valuations of goods, face random demands, and buy tokens on the supplier’s capacity. Following demand realization, retailers trade tokens with each other in markets implemented as double-sided, single-price, blind, batch auctions. We compare six behavioral treatments, featuring two wholesale prices and three market sizes. As expected, markets reduce leftovers and shortages. Interestingly, market-clearing prices are anchored to wholesale prices and do not signal the value of goods in large markets. Players deploy novel ordering and trading strategies that differ from the transshipment literature. We identify strategies by applying unsupervised machine learning algorithms. In one strategy, players buy a few claims and, after demand realization, use the market to satisfy it. Other players buy more claims than the maximum demand and, once demand is known, sell their excess on the market. Both strategies reduce costs from demand uncertainty but expose players to liquidity and mistakes risks. A third strategy, in which players order from the supplier initially as if expecting the market to be cleared cooperatively, is more profitable. This strategy diversifies demand and market risks. The introduction of markets causes the “pull-to-the-mean” effect and increases order variability. Thus, markets can cause the Bullwhip Effect. Retailers’ and the supply chain’s average profits are higher with markets, but suppliers with low wholesale prices suffer from lower revenues because of the pull-to-the-mean effect. This paper was accepted by Elena Katok, operations management. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.03771 .

Problem definition: The COVID-19 pandemic imposed unprecedented stresses on global supply chains (GSCs), compelling companies to reassess their supply chain structures and strategies. This crisis has also heightened awareness among businesses, consumers, and policymakers about the critical importance and far-reaching implications of GSC design and management. This unique moment presents a generational opportunity for Operations Management (OM) researchers to document and understand the ongoing restructuring of GSCs. Methodology/results: By analyzing microlevel data on U.S. customs import shipments (2019–2021), we uncover shifts in GSC strategies during the COVID-19 pandemic. Firms diversified suppliers within existing sourcing locations and reallocated volumes among them. Whereas dependence on China decreased, imports from other Asian nations like India and Vietnam, as well as North American countries like Canada and Mexico, increased. Industry-specific differences were pronounced, and a notable shift toward lower-frequency, higher-quantity shipments was also observed. Managerial implications: Beyond the challenges of COVID-19, recent years have witnessed other major supply chain disruptions, due to causes such as geopolitical tensions, natural disasters, and port worker strikes. We offer actionable insights for executives designing supply chain strategies to prepare for similar disruptions as they increase in frequency and severity. We identify future research avenues aimed at enhancing the resilience and adaptability of GSCs in a continuously evolving environment. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0879 .

This paper describes the blockchain-enabled token trading game for supply chain management, a web-based supply chain simulation. The game uses blockchain technology’s concepts to create virtual markets for supplier capacity trading among retailers, in which participants take on the role of a retailer, have different valuations for products, and submit an order before knowing their demand; after demand realization, participants trade tokens (claims on the supplier’s capacity) among themselves using virtual markets to maximize their profits. The game provides participants with firsthand experience with how blockchain technology can be used in practice, thus serving as an interactive pedagogical tool. More generally, the game is intended to help supply chain management and blockchain technology students and executives understand the challenges of serving uncertain customer demand and the role of virtual markets in providing an effective remedy for reconciling supply and demand, thus improving supply chain performance. Supplemental Material: The supplemental material is available at https://doi.org/10.1287/ited.2023.0015 . The Teaching Note and PowerPoint presentation are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

Demand forecasting for seasonal products becomes especially challenging in the case of fast innovations, where the product portfolio is upgraded every season. In addition to the problem of forecasting demand without any historical data, companies also have to deal with frequent stockouts, which bias past sales and provide an unreliable anchor for making new forecasts. We show how one can use machine learning models to leverage information on comparable products from the past together with experts’ forecasts to improve forecasting accuracy. A machine learning forecast using only statistical features results in a forecast error reduction of 24%, measured by weighted mean absolute percentage error, compared to a purely judgmental prediction on data from Canyon Bicycles. Better yet, an integrated human-machine forecast leads to a further 14% reduction in forecast error, indicating that experts’ predictions remain essential for forecasting demand for rapidly innovating seasonal products. The combination of the experts’ knowledge of the future and the machine learning algorithms’ ability to leverage historical information works best in this setting.

In times of increasing disruption and scarcity of natural resources, many companies view supply chain resilience (SCRE) and supply chain sustainability (SCS) as primary strategic objectives. However, the connection between these two objectives has not been sufficiently explored. We therefore conducted a large-scale survey of 143 practitioners to gain empirical insights on the intersection of resilience and sustainability from the perspective of supply chain executives. The main results are first that key barriers to both SCRE and SCS are budget constraints and product complexity, and second that implemented resilience strategies affect the degree of sustainability and vice versa. We present a conceptual framework that illustrates the relationship’s identified characteristics and reveals the multiple dimensions of these concepts’ intersection. Approaching each objective with an integrated approach and mindset will help executives overcome the goals’ common barrier – product and partner complexity – and thereby develop a competitive advantage.

Purpose: This paper’s purpose is to deepen our understanding of what drives bottom-up operations strategy formation – that is, continuous improvement activities at the front line – with a particular focus on operations strategy understanding. That way, it aims to contribute to the awareness of management quality in manufacturing – a cornerstone of national competitiveness. Methodology: We examine the antecedents of individual Kaizen generation by frontline employees, drawing on the well-established Motivation-Opportunity-Ability framework and focusing on the dimension of ability – that is, understanding operations strategy. Survey data on 217 frontline employees, working in 17 teams on 11 different production lines, were “triangulated” with their team leader assessments and the plant’s archival records. We tested the hypothesized relationships via analyses that incorporate both structural equation modeling and multiple regression techniques. Findings: Our results suggest that employees typically overestimate their understanding of the plant’s operations strategy and that productivity is driven more by an objective than a subjective understanding of that strategy. We also find that incremental innovation is facilitated by supervisor support, employee engagement, and an employee suggestion scheme; in contrast, neither autonomy nor selected control variables (e.g., age or seniority) has a significant effect. Originality: Our findings and the unique metrics we developed for better management of strategy understanding should help managers increase the productivity of their operations and thus the competitiveness of their respective firms.