BiGSEM Colloquium (Management)

Wednesday, May 7 at 2 pm in U3-140

Speaker: Robin Weisner
Title: Entrepreneurship and mindfulness – (im-) possible?

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Wednesday, July 2 at 2 pm in U3-140

Speaker: Theresa Elbracht
Title: tba

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Wednesday, July 16 at 2 pm in U3-140

Speaker: Daniel Alayou
Title: tba

Wednesday, November 6 at 2 pm in U3-140

Speaker: Theresa Elbracht (supervised by Christian Stummer)
Title: It does not have to be a bell: An agent-based simulation of unusual innovation adoption curves 

Abstract:
During the past five decades, studying the market diffusion of innovations has been a vivid field of research. The models that describe the diffusion process over time typically result in a bell-shaped adoption curve and an s-shaped diffusion curve. However, some innovations do not show this typical adoption pattern due to certain market conditions, which innovation managers should be aware of. Research – mostly from the beginning of the 2000ies and the 2010ies, but also more recent work – indicated extraordinary adoption patterns from sales data, and refer to them as saddles, slides, or a combination of slide and bell.

In our early-stage research endeavor, we use agent-based simulation experiments as a means for identifying (and analyzing) market characteristics that yield to such unusual diffusion patterns. In particular, we investigate (i) the dual market hypothesis, and (ii) the effects of variations in the social network and the heterogeneity of consumer agents.

 

Wednesday, June 26 at 2pm in U3-140

Speaker: Robin Weisner (supervised by Sabrina Backs)
Title: A Closer Look at Entrepreneurial Ecosystems: An Agent-Based Simulation Approach [45 minutes]

Abstract: Entrepreneurial ecosystems are a well-established phenomenon to explain a high concentration of entrepreneurial activity in a certain geographical area. They consists of six key elements that interact with and influence each other. Universities play a key role in this context, as they have numerous opportunities, especially in the course of the third mission, such as initiating cooperation and creating spin-offs, in order to stimulate the entrepreneurial ecosystem. We would like to use agent-based simulation to take a closer look at what these are in detail and how they can affect the entrepreneurial ecosystem by modeling such an entire ecosystem, representing the individual actors with their decisions and on both the micro and macro levels to investigate interaction. The aim of this research project is to develop recommendations for action for the actors, if necessary also the region and the state, which allow to establish "what-if" relationships that can be applied to different entrepreneurial ecosystems.

Speaker: Corinna Hesse (supervised by Peter Limbach)
Title: Capital Allocation -  Empirical Evidence and Literature Review [30 minutes]

Abstract: Companies frequently decide on the use of their capital – a process called capital allocation. This process covers two primary categories: investments, such as spending cash on capital expenditures and research and development, and financing, such as corporate payouts and debt repayments. While extant literature focuses on the individual components of the capital allocation process, this research agenda is concerned with the general strategic allocation of firms’ capital, incorporating all its elements simultaneously. Data on capital allocation strategies and objectives is not available in standardized format. However, U.S. corporations increasingly report about capital allocation in their earnings calls and, to a lower extent, in their annual reports (10-Ks). As a starting point, we conduct a manual analysis of earnings call transcripts and 10-Ks to identify statements capital allocation, translating them into statistically exploitable variables. To provide large-sample evidence, we employ a self-learning algorithm that annotates sentences within the aforementioned documents. The first paper of our research agenda aims to understand several questions that are at the heart of corporate finance and governance: (1) Which firms voluntarily disclose information about capital allocation and what information is reported? (2) How does capital allocation disclosure and strategy relate to corporate governance and CEO characteristics? (3) How does capital allocation vary in the cross-section of firms as well as over time? (4) Do firms with a capital allocation strategy exhibit superior accounting and stock-market performance?

Speaker: Frederik Tolkmitt (supervised by myself)
Title: Innovation diffusion in the presence of opposing information sources: An agent-based simulation approach [45 minutes]

Abstract:  The market introduction of radically new products and their respective innovation diffusion can considerably be affected by consumers’ increased uncertainty due to diverging information received from opposing sources. Such information can originate, for example, from strong general attitudes being prevalent in certain consumer groups (e.g., regarding the pros and cons of integrating AI capabilities in products) or it may even be spread by a competitor employing a negative-word-of-mouth campaign (e.g., when questioning the eco friendliness of electric vehicles). In such a setting, consumers receive ambiguous signals, which should increase their uncertainty and potentially delay product adoption. However, commonly used belief updating models (such as Bayesian learning), in a counterfactual manner, assume a decrease of uncertainty whenever new information is received even if this information substantially deviates from previous information. In our research, we demonstrate this effect by means of a computational simulation experiment based on a straightforward agent-based model of innovation diffusion and we propose a novel approach that more realistically captures consumer uncertainty with respect to insufficient or ambiguous information.

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Wednesday, May 22, 2-3pm, in U3-140

Speaker: David Winkelmann

Title: Subscription-Based Inventory Planning for E-Grocery Retailing

Abstract:
The growing e-grocery sector faces challenges in becoming profitable due to heightened customer expectations and logistical complexities. This project addresses the impact of uncertainty in customer demand on inventory planning for online grocery retailers. Given the perishable nature of grocery products and intense market competition, retailers must ensure product availability while minimizing overstocking costs. We propose introducing subscription offers as a solution to mitigate these inventory challenges. Unlike existing literature focusing on uniform subscription models that may harm profitability, our approach considers the synergy between implementing product subscriptions and cost savings from improved inventory planning. We present a three-step procedure enabling retailers to understand uncertainty costs, quantify the value of gathering additional planning information, and implement profitability-enhancing subscription offers. This holistic approach ensures the development of sustainable subscription models in the e-grocery domain.

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24th of April 2024

Speaker: Mohsen Nafar
Title: "A New Dynamic Variable Ordering for Compiling Decision Diagrams for the Maximum Independent Set Problem"

Spearker: Elias Schede
Title: "Anytime Algorithm Configuration"

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08.11.2023

Jakob Schulte

"Innovative Matheuristic for Large-Scale Optimization: A Case Study from ROADEF 2022"
Abstract:
The ROADEF challenge 2022 involves RENAULT's supply chains, which span over 40 plants in 17 countries and involve 1500 suppliers. Every week, 6000 trucks deliver parts from suppliers to plants. The filling rate of these trucks is critical since the inbound transportation annual budget is several hundred million euros. The objective is to pack a set of items from suppliers into stacks and load the stacks onto trucks for delivery to the plants, with the goal of minimizing (a) the number of trucks used and (b) the inventory in the plants due to early deliveries. Items have a delivery time window ranging from 1 to 5 days. It is possible to deliver the items early (e.g., at their earliest arrival time) to better fill the trucks, but early deliveries generate inventory costs for the plants. Inventory costs are highly sensitive at Renault, as they represent several hundred million euros. Therefore, the best solution is to deliver items to the plants at the latest possible time while minimizing the number of trucks used. In terms of data volume, a large instance can contain up to 260,000 items and 5,000 planned trucks, spanning a horizon of 7 weeks.

In this paper, we introduce an innovative matheuristic designed for optimizing large-scale assignment and container loading problems. Our algorithm effectively showcases its capabilities by yielding new globally optimal solutions, which have the potential to generate significant cost savings for RENAULT's supply chain operations, potentially amounting to millions of euros. By tackling the intricate optimization challenges posed by the ROADEF Challenge 2022, our approach offers a promising avenue for elevating supply chain management, improving cost efficiency, and enhancing overall operational performance within a vast industrial context.

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17.01.2024

Peter Limbach and Christian Stummer

"Two editors' experiences on the dos and don’ts in academic publishing"

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07.02.2024 - in U3-140 - it will starts at 3pm (instead of 2pm)

Henning Witteborg

"Using Stochastic Programming for Surgery Scheduling under Uncertainty”

Abstract:

The scheduling of elective surgeries highly depends on the available capacities of beds in the postoperative intensive care ward. While these capacities, with their high demands on equipment and staff, are limited, the development of the occupancy of the intensive care units is subject to various factors of uncertainty, such as the individual length-of-stay of a patient or emergency arrivals. Despite this fluctuating demand, we aim to optimize the utilization of capacities in the intensive care unit after a surgery.

For this purpose, we present a model that proposes a scheduling of elective patients for their stay in the ward while considering the mentioned factors of uncertainty. We model this problem as a stochastic programming approach with a planning horizon of one week, in which we approximate the uncertain and decision-dependent evolution of the ICU occupation in scenarios generated by methods from Machine Learning and simulation techniques. The model aims to schedule as many patients as possible one week in advance while respecting the risk of exceeding critical occupancy levels as chance constraints and therefore avoiding outcomes that would be considered unacceptable in practice.

In cooperation with a hospital in Bielefeld, the model's architecture is based on real-world implications, using historic data from patients who were admitted to the intensive care unit. To demonstrate the applicability of the model, we created instances that represent real-world planning problems and typical patient characteristics. Our results show the potential for significant improvements in surgery scheduling by optimizing the utilization of the necessary capacities in postoperative care. Additionally, the conceptualization as a stochastic programming model with chance constraints allows the implementation of risk-oriented policies for the scheduling and admission of patients.