A paper waste prediction model

The aim of this paper is to develop a model predicting the collected amount of waste paper at the regional level of municipalities. Leaming about the factors that influence the amount of collected paper is a prerequisite for the evaluation and reorganization of collection systems. We hypothesize that the amount of collected paper depends on both, the waste potential and factors which influence the convenience such as the density of collection sites. For this study, we use a sample of 649 municipalities. The data show a high variance in terms of the collected waste paper per person and year between the municipalities. We develop a multivariate regression model providing valuable insights about the relationship between demographic parameters and the amount of collected waste paper. Furthermore, in this novel approach we found a significant positive impact of the convenience of the collection system

A Decision Support System for Efficient Last-Mile Distribution of Fresh Fruits and Vegetables as Part of E-Grocery Operations

Efficient last-mile distribution of fresh fruits and vegetables is a major challenge within e-grocery operations. This work presents a decision support system to jointly investigate the impact of various service offers on customer preferences and logistics operations. Results from a conjoint analysis surveying 531 end consumer are incorporated within an agent-based simulation. Delivery days, fees, time windows and discounts as well as guaranteed remaining shelf life of products at delivery are considered. To model shelf life and schedule deliveries, food quality models and vehicle routing procedures are further integrated within the system. Based on an e-grocery provider operating in Vienna, Austria, computational experiments investigate the impact of the offered delivery service on fulfilled demand, order volume and customer utility. Results indicate the importance of incorporating shelf life data within e-grocery operations and various potentials of considering customer preferences in logistics decision support systems

Is thinking worthwhile? A comparison of corporate segment choice strategies.

The field of strategic marketing has long been identified as fruitful ground for gaining competitive advantage. Ever since the market segmentation concept was introduced in the late sixties, research interest steadily increased, covering issues as e.g. which fundamental segmentation strategy is most appropriate, in which ways can segments be identified or constructed, which algorithm provides optimal data-driven segmentation solutions, which number of segments should be constructed etc.. Interestingly, the issue of segment evaluation and choice has not been emphasised very strongly in the past, although this is of primary interest as soon as it comes to practical implementation. This article tries to fill this gap in an experimental manner: the consequences of different corporate segment choice strategies based on different segment evaluation criteria are investigated under different environmental conditions formalised in a complex artificial consumer market. The results indicate that complex decision models for segment choice do not turn out to be superior in general. Both mass marketers and firms concentrating on particular segments based on an a priori logic can be just as successful under "favourable" market conditions, the most influential condition being the available advertising budget. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science

Individualized Biventricular Epicardial Augmentation Technology in a Drug-Induced Porcine Failing Heart Model

For treatment of advanced heart failure, current strategies include cardiac transplantation or blood-contacting pump technology associated with complications, including stroke and bleeding. This study investigated an individualized biventricular epicardial augmentation technology in a drug-induced porcine failing heart model. A total of 11 pigs were used, for the assessment of hemodynamics and cardiac function under various conditions of support pressures and support durations (n = 4), to assess device positioning and function by in vivo computer tomographic imaging (n = 3) and to investigate a minimally invasive implantation on the beating heart (n = 4). Support pressures of 20-80 mmHg gradually augmented cardiac function parameters in this animal model as indicated by increased left ventricular stroke volume, end-systolic pressures, and decreased end-diastolic pressures. Strong evidence was found regarding the necessity of mechanical synchronization of support end with the isovolumetric relaxation phase of the heart. In addition, the customized, self-expandable implant enabled a marker-guided minimally invasive implantation through a 4cm skin incision using fluoroscopy. Correct positioning was confirmed in computer tomographic images. Continued long-term survival investigations will deliver preclinical evidence for further development of this concept

An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data

Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This paper investigates the suitability of such methods for situations when only binary pick-any customer information (i.e., choice/nonchoice of items, such as shopping basket data) is available. We present an extension of collaborative filtering algorithms for such data situations and apply it to a real-world retail transaction dataset. The new method is benchmarked against more conventional algorithms and can be shown to deliver superior results in terms of predictive accuracy. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science

A critical view on recommendation systems

The literature on recommendation systems indicates that the choice of the methodology significantly influences the quality of recommendations. The impact of the amount of available data on the performance of recommendation systems has not been systematically investigated. We study different approaches to recommendation systems using the publicly available EachMovie data set. In contrast to previous work on this data set, here a significantly higher subset is used. The effects caused by the number of customers and movies as well as their interaction with different methods are investigated. We compare two commonly used collaborative filtering approaches to several regression models using an experimental full factorial design. According to our findings, the number of customers significantly influences the performance of all approaches under study. For a large number of customers and movies, we show that simple linear regression with model selection can provide significantly better recommendations than collaborative filtering. From a managerial perspective, this gives suggestions about the selection of the model to be used depending on the amount of data available. Furthermore, the impact of an enlargement of the customer database on the quality of recommendations is shown. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science

The Emergence of Disruption

We study the influence of technological efficiency and organizational inertia on the emergence of competition when firms decide myopically. Using an agent-based computer simulation model, we observe the competitive reaction of a former monopolist to the advent of a new competitor. While the entrant uses a new technology, the monopolist is free either to stick to his former technology or to switch to the new one. We find that?irrespective of details regarding the demand side?a change of industry leadership occurs only if the new (?disruptive?) technology is not too efficient and organizations are inert. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science

How option thinking can improve software platform decisions

In recent years, the use of option pricing models to support IT investment decisions has been proposed in the MIS literature. In this paper, we discuss the practical advantages of such techniques for the selection of a software platform. First, we argue that traditional quantitative approaches to a cost-benefit analysis give only a partial picture of such decision situations: due to the long planning horizon required because of the time-consuming and resource-intensive implementation process, it is not possible to exactly predict which applications will, in fact, run on the system over time. Thus, the investor is faced with the problem of valuing "implementation opportunities". We then compare different valuation techniques for this task and discuss their respective advantages and drawbacks. The practical advantages of employing such models are demonstrated by describing a real-life case study where option pricing models were used for deciding whether to continue employing SAP R/2 or to switch to SAP R/3. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science

Incentives to cooperate in new product development

The knowledge required for decision making in a firm is distributed across various departments. In practice cross functional teams are used to integrate this distributed knowledge. Incentive schemes are of crucial importance to encourage departments to share knowledge. In this paper, we study different incentive schemes by means of a two stage model. In the first step departments have to choose between learning and sharing knowledge, in the second stage, they bargain about a new product feature. The outcome of the bargaining process in the second stage depends on the capabilities of the agents and their uncertainty about the opponent. The result of the second stage determines the agents' payoffs which in turn influence the time allocation. In a simulation study, we investigate different incentive systems and show to which extent a firm has to reward the sharing of knowledge in order to reach its overall objectives. Furthermore, we are able to derive an analytical solution for the bargaining process under uncertainty and compute Nash equilibria for a discrete set of possible actions. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science