Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime” models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches

Towards a Value-based Method for Risk Assessment in Supply Chain Operations

This paper proposes a risk assessment framework as a research road-map, with the aim of developing a protocol that integrates the risk management requirements from the perspectives of the business and the government. We take the viewpoint of value modeling and interpret the risk management problem as a control problem. Four steps of risk assessment are identified in the framework, forming the risk management cycle

The five information technology blind spots of economists

On August 9, 2007, the European Central Bank (ECB) decided to organize a conference call with the twenty largest banks in Europe. The problem was that these banks were no longer lending each other money. Banks were in urgent need of money and as one of the bankers made it clear: “We need three things: we need cash, we need a lot, and we need it now” (NRC Handelsblad , 2012). The same day, the ECB started to inject 95 billion euro, followed the next day by the Federal Reserve System (FED) in the United States (US) with an injection of 24 billion US dollars. It was the first sign that something was going wrong. However, very few people understood the significance of it. [...

The five information technology blind spots of economists

On August 9, 2007, the European Central Bank (ECB) decided to organize a conference call with the twenty largest banks in Europe. The problem was that these banks were no longer lending each other money. Banks were in urgent need of money and as one of the bankers made it clear: “We need three things: we need cash, we need a lot, and we need it now” (NRC Handelsblad , 2012). The same day, the ECB started to inject 95 billion euro, followed the next day by the Federal Reserve System (FED) in the United States (US) with an injection of 24 billion US dollars. It was the first sign that something was going wrong. However, very few people understood the significance of it. [...

Function Approximation Using Probabilistic Fuzzy Systems

We consider function approximation by fuzzy systems. Fuzzy systems are typically used for approximating deterministic functions, in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems i

An Online Learning and Optimization Approach for Competitor-Aware Management of Shared Mobility Systems

An important trend in mobility is the consumption of mobility as-a-service heralding in the age of freefloating vehicle sharing (FFVS) systems. In many markets such fleets compete. We investigate how realtime competitor information can create value for operators in this context. We focus on the vehicle supply decision which is a large operational concern. We show empirically that local market shares directly depend on the share of available vehicles in a location, which underlines the value potential of competitor awareness. We leverage this insight by proposing a novel decision support system for optimal management of FFVS systems under competition. We proceed in two phases, (1) a predictive phase and (2) a prescriptive phase. In phase (1), we compile a spatio-temporal dataset based on Car2Go and DriveNow transactions in Berlin, which we supplement with temporal, geographical and weather data. We partition the city into hexagonal tiles and observe vehicle supply per tile at the start of each period. We train machine learning models to predict vehicle inflows and vehicle outflows during the next period to derive total supply and demand. We find that inflows and outflows can be predicted with high accuracy using similar models. We test different temporal and spatial resolutions and find that spatial resolution incurs larger performance penalties. In phase (2), we formulate a myopic mixed integer non-linear programming model with a margin-maximizing objective function. The model trades off additional market share gains against the cost of re-locating vehicles, which enables operators to assign vehicles optimally across the service network. Our numerical studies on the case of Car2Go and DriveNow demonstrate that this competitor-aware model is capable of profitably improving market share by up to 1.4% or 3.4% for human-based and autonomous relocation respectively in a prefect foresight scenario and by up to 0.8% and 1.8% respectively when using predicted values

Designing Intelligent Software Agents for B2B Sequential Dutch Auctions: A Structural Econometric Approach

We study multi-unit sequential Dutch auctions in a complex B2B context. Using a large real-world dataset, we apply structural econometric analysis to recover the parameters governing the distribution of bidders’ valuations. The identification of these parameters allows us to simulate auction results under different designs and perform policy counterfactuals. We also develop a dynamic optimization approach to guide the setting of key auction parameters. Given the bounded rationality of human decision makers, we propose to augment auctioneers’ capabilities with high performance decision support tools in the form of software agents. Our paper contributes to both theory and practice of auction design. From the theoretical perspective, this is the first study that explicitly models the sequential aspects of Dutch auctions using structural econometric analysis. From the managerial perspective, this paper offers useful implications to business practitioners for complex decision making in B2B auctions

Knowledge Sharing in Non-Knowledge Intensive Organizations: When Social Networks do not Matter?

Considerable attention has been paid to the network determinants of knowledge sharing. However, most, if not all, of the studies investigating the determinants of knowledge sharing are either focused on knowledge-intensive organizations such as consultancy firms or R&D organizations, or knowledge workers in regular organizations, while lesser knowledge intensive organizations or non-knowledge workers are rarely explored. This is a gap in the literature on social networks and knowledge sharing. In this paper, the relations between network determinants and actor determinants of knowledge sharing are empirically tested by means of a network survey in a less knowledge intensive organization, specifically employees of a Dutch department store chain. The results show that individual-level variables such as departmental commitment and enjoyment in helping others are the major determinants of individuals’ knowledge sharing behavior, but none of the social network variables play a role. The results thus present an important boundary condition to social networks effects on knowledge sharing: social networks only seem to play a role in knowledge sharing for knowledge workers, not for blue-collar workers