Innovative Technology at the Interface of Finance and Operations

We introduce blockchains and distributed ledgers and study their intra- and interoperability. Blockchain intraoperability allows one to swap different assets defined on the same blockchain supporting smart contracts. Blockchain interoperability enables one to exchange or move assets residing on different blockchains. Finding practical mechanisms for intra- and interoperability is of paramount importance for the ultimate success of blockchain technology. We recommend using automated market makers for intraoperability and gateways and atomic swaps for interoperability.

Information-sharing facilitates the coordination of supply chains and can be essential for enterprises’ survival. Studies have shown that supply chains benefit from the availability of aggregated information (e.g., inventory levels), as that would facilitate planning, inform decisions, and limit the emergence of operational distortions that result from the bullwhip effect and inefficient inventory allocation. Despite this, the degree of supply chain transparency within and across companies remains deficient at best, even as advancements in information and communication technology have facilitated secure information-sharing. What is more important is that companies have a direct incentive not to share certain operational attributes (e.g., inventory levels or sales, order sizes, wholesale prices). The issues resulting from technology limitations and irrational ordering- and allocation processes were brought into acute focus during the 2020 COVID-19 (C-19) pandemic, manifesting as persistent and global disruptions in supply operations and delivery delays. This chapter outlines promising approaches and methodologies for resolving these chronic shortcomings by leveraging both distributed ledger (“Blockchain”) technology (DLT) and the newly emerging capabilities of fully homomorphic encryption (FHE). By employing zero-trust information-sharing, we present an approach that combines DLT with FHE to bridge information-sharing limitations in hyperconnected supply chains without compromising data control. The chapter also introduces real-world applications of both DLT and FHE to illustrate their transformative potential. We conclude by highlighting the practical benefits of implementing both technologies, as well as the implications for broader adoption.

This tutorial builds on the earlier work by Babich and Hilary (Found Trends Technol Inf Oper Manage 12(2–3):152–172, 2019) and (Manuf Service Oper Manage 22:223–240, 2020) and updates the status of various applications of blockchain technology to supply chains. We present a revised framework for analyzing the value of blockchain applications, based on five strengths and the corresponding five weaknesses. We describe industry efforts to take advantage of these strengths and to address these weaknesses. We outline the main academic research themes and discuss examples of research questions.

The blockchain technology has recently started to gain traction in supply chain management. Along with the smart contract which automates payments following a pre-defined protocol, the information recorded in the blockchain can be used to hold the failure-causing suppliers accountable for their own faults and allow the buying firm to pay the suppliers contingently. This could change supply chain quality contracting for industries where supplier accountability is difficult to achieve under traditional technologies (e.g., agri-food and pharmaceutical). In this work, we study the impact of accountability in a multi-sourcing supply chain, where a buying firm procures from multiple suppliers who belong to the same tier of the supply chain. We find that in a multi-sourcing supply chain, a critical value of accountability is that it guarantees cash flow feasibility for the buyer when he offers first-best quality contracts to the suppliers, hence improving the implementability of first-best quality contracts in practice. We further find that the value of accountability is strengthened as the supply chain becomes more complicated, while weakened when suppliers face limited liability constraints.

The paper proposes an architecture for implementing the Bank of England’s basic principles for the design of a CBDC with a focus on real-time high value, often cross border, enterprise transactions. The use of digital ledger technology makes innovative use of chaincode – smart contracts specifying how transactions are to be conducted – and tokens – providing a convenient mechanism for executing transactions and providing the CB monetary policy management. The proposed approach minimizes counterparty risk, eliminates settlement risk, and enhances transaction efficiency, thereby improving global productivity.

We provide guidance for decision makers to quantify risk/value trade-offs under uncertainty in complex systems. We develop a framework integrating market, operations, and financial modules. The market module includes price, demand and variable cost, the operations module considers production constraints, manufacturing yields and schedules, while the financial module provides proforma income and profit statements considering fixed cost. Special emphasis is given to the use of stochastic simulation, optimization, and real options valuation as the technology choices while the key challenges of integrating financial risk management with operational modeling and IoT-driven execution are discussed. We utilize the integrated models in a real-world setting where process improvements continuously improve return on investment. We determine the cost of uncertainty and conclude with highly promising results obtained from a prior industrial project where building on the integration of financial risk management, operational modeling and IoT-driven execution was the key determinant of success.

Markets and their corresponding equilibrium concepts have traditionally been used as very powerful building blocks to find allocations and prices. This chapter provides examples of the use of Fisher markets in the technology industry. We focus on Internet advertising auctions, fair division problems, content recommendation systems, and robust abstractions of large-scale markets. After introducing these markets, we describe how these models fit the relevant application domains and what insights they can generate, exhibiting the most important theoretical and computational results from the recent literature on these topics.

We present our work with a global online fashion retailer, Zalando, as an example of how a global retailer can utilize massive amount of data to optimize price discount decisions over a large number of products in multiple countries on a weekly basis. Given demand forecasts under a collection of discrete prices, Zalando’s objective is to set discount levels to maximize total profit over the entire selling horizon while taking into account both local and global business constraints. Local constraints refer to single product level requirements, where Zalando needs to balance sales across different countries and over different weeks while adhering to a first-come-first serve policy. That is, as long as product inventory exists, a customer is served independent of the customer’s origin country or time of arrival. Global constraints refer to specific targets set by management for different product categories and each country. We address these challenges by applying a three-step process. In the first step, we cluster products into groups that behave similarly and solve the aggregated problem in a way that allows us to decouple the problem into a problem for each product category. Each product category includes thousands of individual products (SKUs) and the various markets where products are sold, each of which with its own target sales and margins. In the second step, we decompose this problem using Lagrangian relaxation into a problem for each product (SKU) and provide an efficient way to identify the Lagrange multipliers. Finally, in the last step, we optimize decisions for individual products and also address local business constraints. For this new approach, which was implemented as part of Zalando’s price discount decision process, we provide results from offline tests and field experiments to demonstrate its benefit.

Empirical research has shed little light on the nature of bank formation as a banking behavior in an unregulated setting, due to the lack of observational data. On the other hand, recent years have witnessed the increasing popularity of peer-to-peer lending platforms which connect borrowers to lenders. An interesting observation is that some users are conducting micro banking activities, freely performing dual roles as both borrowers and lenders. They are referred to as microbanks. The microbanks face few regulatory restrictions or supervisory powers. Seizing this opportunity, we empirically examine the dynamics of free entry behaviors, using a sample of unregulated microbanks from one of the largest online peer-to-peer lending platforms in China. In particular, we explore the formation of microbanks at monthly intervals. Further, we create a quasi-experiment by leveraging the fact that the exact date to receive a repayment is exogenous to the microbanks. We find that a positive liquidity shock is positively associated with microbank formation.

After a brief overview of FinTech, this survey paper focuses on two timely topics in econometrics related to privacy and transparency issues: (1) Econometrics for sensitive financial data with privacy preservation in the era of big data. (2) The wisdom of the crowd and prediction markets, in the presence of new information from anonymous individual level trading data.

The very nature of technology can affect the timing of investments and the combinations of equity and debt used to finance them. These decisions are also impacted by the assets in place and how they are financed. This chapter explores how the flexibility built into the technology impacts the timing and financing decisions. The models developed are all based on real option valuation where interest rates on all debt are endogenously determined. We not only explore whether operational flexibility induces earlier or later investment and more or less use of debt, but we also explore how bond priority rules affect decisions, how operating leverage affects decisions and whether financial contracts can be designed so as to facilitate first best outcomes. Overall our results suggest flexibility is accompanied with less use of debt and earlier adoption of new investments.