Internal control | EPFL Graph Search

Internal control, as defined by accounting and auditing, is a process for assuring of an organization's objectives in operational effectiveness and efficiency, reliable financial reporting, and compliance with laws, regulations and policies. A broad concept, internal control involves everything that controls risks to an organization. It is a means by which an organization's resources are directed, monitored, and measured. It plays an important role in detecting and preventing fraud and protecting the organization's resources, both physical (e.g., machinery and property) and intangible (e.g., reputation or intellectual property such as trademarks). At the organizational level, internal control objectives relate to the reliability of financial reporting, timely feedback on the achievement of operational or strategic goals, and compliance with laws and regulations. At the specific transaction level, internal controls refers to the actions taken to achieve a specific objective (e.g., how

Design of a quality control network for an international food company

Simone Lydia Voldrich, Philippe Wieser, Nicolas Zufferey

In this article, the design of a quality control network is considered in an entire supply chain, in the context of an international food and beverage company, denoted as ABC. For each possible control point of the network, two decisions have to be made: (1) establish a monitoring activity or not; (2) if a monitoring activity is placed, determine its acceptance level with a well-adjusted sampling plan. The objective function to minimize consists of a weighted sum of two conflicting components, namely risk (of having bad units) and processing time (to control the product batch itself). Indeed, for each control point, an accurate monitoring activity reduces risk but augments processing time. Each monitoring activity has a specific cost (if selected) and a budget constraint has to be satisfied for the entire network. Various solutions methods are proposed to tackle this NP-hard problem, ranging from a basic greedy heuristic to advanced metaheuristics involving tabu search as an internal procedure. CPLEX can be used for very small instances (with less than 20 control points), for which a basic tabu search can obtain similar results much quicker (in a fraction of second). The experiments for larger instances highlight the importance of having a good control (in terms of the involved search trajectory in the solution space) on the search process, as the methods with well-tuned diversification mechanisms perform best.

TAYLOR & FRANCIS INC2020

Discrete time stochastic hybrid dynamical games: Verification & controller synthesis

This paper presents a framework for analyzing probabilistic safety and reachability problems for discrete time stochastic hybrid systems in scenarios where system dynamics are affected by rational competing agents. In particular, we consider a zero-sum game formulation of the probabilistic reach-avoid problem, in which the control objective is to maximize the probability of reaching a desired subset of the hybrid state space, while avoiding an unsafe set, subject to the worst-case behavior of a rational adversary. Theoretical results are provided on a dynamic programming algorithm for computing the maximal reach-avoid probability under the worst-case adversary strategy, as well as the existence of a max-min control policy which achieves this probability. The modeling framework and computational algorithm are demonstrated using an example derived from a robust motion planning application. © 2011 IEEE.

2011

Crowd Dynamics and Crowd Capital in Small Teams: Insights from three crowdsourcing exercises in higher education

Christopher Tucci, Gianluigi Viscusi

In this work we aim to study “crowd” dynamics in small teams. To this end, we use the typology of crowds discussed in Viscusi and Tucci [2015] as an interpretive framework, arguing that other characteristics are relevant to identify crowds than the number of participants; thus, the latter is a sufficient, but not necessary condition for crowdsourcing. Consequently, the framework distinguishes different types of crowd dynamics according to their growth tendency and degree of seriality. Density, equality, and goal orientation contribute to further distinguishing the distribution of agents within and between the different types, namely between communities, open crowds (multitudes [Virno 2004] e.g., Twitter users), closed crowds (controlled by intermediaries, such as, e.g., Innocentive that restrict growth and provide self-established boundaries), groups as crowd crystals, potentially leading to any of the others. Another goal of this study is to provide the setting necessary for experiments in business domains aimed at investigating how crowd characteristics may lower or increase “crowd capital,” here defined as the total number of crowd units having a demonstrated effectiveness in idea generation or task achievement [Tucci et al. 2016]. This definition adopts a more outcome-oriented perspective compared to other definitions emerging from this research stream, which aims at conceptualizing crowd capital as a new form of capital of an organization with regard to intellectual, social or economic capital [Lenart-Gansiniec 2016]. In particular, the definition focuses on the internal resources of an organization rather than its inner context such as “structure, corporate culture, and political context within the firm through which ideas for change have to proceed” [Pettigrew 1987, p.657]. Thus, our definition complements the conceptualization by Prpić & Shukla [2013, p.35035] which considers crowd capital “an organizational level capability that is defined by the structure, content, and process of an organization’s engagement with the Crowd” or organizational resources acquired through crowdsourcing for building competitive advantage [Prpić et al. 2015]. Finally, the perspective we propose on crowd capital is also linked to research on how mesolevel structures support effective coordination in temporary groups [Valentine and Edmondson 2014] as well as on frameworks for dynamically assembling and managing paid experts from the crowd through flash teams as sequence of modular tasks that draw on paid experts from the crowd [Retelny et al. 2014].

2017

Crowd Dynamics and Crowd Capital in Small Teams: Insights from three crowdsourcing exercises in higher education

Christopher Tucci, Gianluigi Viscusi

2017