Jeudi, 24 mai 2012

DES ENTREPRISES QUÉBÉCOISES HONORÉES

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Photo: Fuller Landau, Gagnant catégorie moyenne entreprise : Danièle Henkel, Kader Mahieddine, Amel Mahieddine (Les Entreprises Danièle Henkel), Michael Newton (Fuller Landau)

Le cabinet comptable Fuller Landau S.E.N.C.R.L. et l’École de gestion John-Molson de l’Université Concordia dévoilent les lauréats du Prix d’entrepreneuriat familial Fuller Landau.

 

Ce Prix, qui en est à sa deuxième édition, récompense les entreprises familiales québécoises qui adoptent des politiques et des pratiques de gestion favorables à la communauté et qui stimulent la croissance économique. Les Entreprises Daniele Henkel se sont illustrées dans la catégorie moyenne entreprise. Société éponyme fondée en 1997, celle-ci se spécialise à la fois dans la fabrication, la distribution, la recherche scientifique et la formation dans le domaine des soins non chirurgicaux pour l’industrie professionnelle de la santé et de la beauté.

Heritage Linencraft, entreprise importatrice et distributrice de literie et de linge de bain depuis plus de 60 ans, se voit pour sa part décerner la palme dans la catégorie grande entreprise. « Le Prix d’entrepreneuriat familial est une excellente occasion pour Fuller Landau de rendre hommage aux entrepreneurs chefs de file du Québec, déclare Michael Newton, associé directeur de Fuller Landau S.E.N.C.R.L. Les candidatures que nous avons reçues démontraient de l’envergure, et nos lauréats représentent éminemment bien les valeurs fondamentales que ces prix soulignent. Félicitations à nos gagnants de cette édition 2012. »

Les candidatures ont été évaluées en fonction des critères suivants : le succès financier de l’entreprise, la qualité de ses liens d’affaires et familiaux, la participation multigénérationnelle, la contribution à la communauté et à l’industrie, ainsi que l’innovation dans ses pratiques ou stratégies d’affaires. Les candidatures en nomination on été examinées par un jury coprésidé par M. Michael Carney, Ph. D. et Mme  Alexandra Dawson, Ph. D. de l’École de gestion John-Molson.

Les cinq jurés votants consistaient en M. Terry Didus, associé chez Heenan Blaikie S.E.N.C.R.L.; Mme Michèle Desjardins, présidente de Koby Consulting et associée principale chez Lansberg, Gersick & Associates; M. J. Sebastian van Berkom, président et chef de la direction de van Berkom et Associés inc.; ainsi que les lauréats de l’an passé, M. Norman Hébert Jr., président et chef de la direction de Groupe Park Avenue Inc. et M. Leslie Machan Jr., vice-président du Centre de planification de la retraite.

« Notre jury n’a pas eu la tâche facile étant donné le haut calibre de l’ensemble des candidatures reçues cette année, précise madame Dawson. La sélection a été plutôt ardue. Les entreprises familiales de la cuvée 2012 qui se sont illustrées nous ont grandement impressionnés. Chacune de ces entreprises jouit d’une prodigieuse feuille de route et d’une solide réputation au Québec. » 

De plus, lors de cette cérémonie, les étudiants d’exception de deuxième cycle n’ont pas été en reste, y recevant également leur prix pour la Meilleure présentation étudiante sur les entreprises familiales. Katty Lara, Santiago Molano et Bethsabeth Paz, du programme MBA, ont soumis une dissertation remarquable, produite dans le cadre de leur cours sur l’entrepreneuriat familial. Les juges ont été séduits par la rigueur de l’analyse de leur rapport sur la maison d’édition Groupe HMH, utilisant de multiples cadres analytiques, tels que les facteurs de succès de l’entrepreneur, l’analyse des parties prenantes et de la chaîne de valeur, ainsi que par les recommandations sur la planification de la succession du leadership.  

Les lauréats ont été annoncés récemment lors de la soirée du Prix d’entrepreneuriat familial Fuller Landau à l’École de gestion John-Molson. Pour plus d’information sur le Prix d’entreprenariat familial Fuller Landau, visitez : tinyurl.com


À PROPOS DE FULLER LANDAU S.E.N.C.R.L 

Fidèles à leur devise « Travaillons fort, jouons dur et amusons-nous! », les professionnels de Fuller Landau S.E.N.C.R.L. – des comptables agréés et conseillers auprès des entreprises – sont constitués de jeunes associés dynamiques et de conseillers d’expérience chevronnés qui se consacrent à faire fructifier l’actif net de leurs clients actuels et futurs. Figurant aux classements des magazines Canadian Business et Maclean’s comme l’une des entreprises qui se sont illustrées pour l’excellence de leur milieu de travail et de leur offre de services, Fuller Landau S.E.N.C.R.L., qui compte plus de 100 employés, offre une vaste gamme de services financiers.

La réussite du cabinet, établi depuis 1963, repose sur les huit principes fondamentaux qui l’animent : l’excellence, la fiabilité, l’innovation, la passion, le leadership, l’esprit d’équipe et l’empathie. Fuller Landau S.E.N.C.R.L. est membre de la Leading Edge Alliance (www.leadingedgealliance.com), un réseau mondial de cabinets conseil et d'expertise comptable indépendants. Pour en savoir davantage sur Fuller Landau S.E.N.C.R.L., visitez  flmontreal.com.

À PROPOS DE L'ÉCOLE DE GESTION JOHN-MOLSON

L’École de gestion John-Molson de l’Université Concordia figure parmi les plus grandes écoles de commerce anglophones du Canada. Elle a pour mission de faire des futurs dirigeants des citoyens responsables, capables de gérer un environnement d’affaires mondialisé, complexe et durable. Elle compte plus de 37 000 diplômés dans le monde entier. Pour en savoir davantage sur l’École de gestion John-Molson, visitez johnmolson.concordia.ca.

Cet article est classé sous :Fuller Landau Entrepreneuriat
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What is Linear Programming in Data Science: Overview

With its rapid expansion, "Data Science" has emerged as a truly multidisciplinary field that draws upon many other disciplines besides computer science, mathematics, data analysis, statistics, etc. The innovations it has brought about have allowed businesses all across the world to make better, more data-driven decisions. Therefore, modern businesses understand the value of their accumulated data.

With the help of cutting-edge software, data scientists evaluate real-world business scenarios by drawing conclusions from existing data, discovering hidden correlations, and spotting meaningful trends. Descriptive analytics is the term for this approach. In addition, data scientists engage in a field of study called Predictive Analytics, in which they investigate causal relationships between several variables.

Predictive analytics is useful for foreseeing potential outcomes since it establishes links between variables. Despite appearances, this is not a simple matter. The existing state of knowledge, limitations, and other factors all add complexity to any organization.

If you want to make a good prediction, you have to factor in all of these factors and find the best possible answer. To solve this problem, we turn to Linear Programming. Data scientists rely heavily on the mathematical technique of linear programming to help them find the best possible answers to a wide range of challenges. Linear Programming takes into account all relevant parameters, equality, and inequality conditions, and arrives at an accurate prediction.

In this section, we'll define Linear Programming and explore the various Linear Programming techniques.
Linear Programming: An Introduction
Before diving into the specifics, it's important to clarify that "programming" in the context of Linear Programming does not relate to coding for a computer or a piece of software. Linear Programming, on the other hand, is an optimization method (Linear Optimization) that can be used to great effect when searching for optimal results in mathematical models. Knowledge of these foundational concepts in Linear Programming is necessary for formulating a linear program:

● Decision Variables: This is shorthand for the variables we haven't been able to pin down yet, the ones we need to find out.
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● Constraints: All the constraints on our decision variable are represented by this set of inequalities or equality constraints.
● Non-negative restrictions: This refers to an important point of constraint in which the values of decision variables cannot be negative.

Now that we have a shared understanding of the fundamental concepts, we can examine the various methods available for resolving a Linear Programming problem.
Linear Programming Methods
To solve a Linear Programming problem successfully, we must adopt:
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● Establishing the parameters of the constraints
● Noting the constraints on non-negative outcomes

However, in this section, we will examine the many different ways in which you can tackle a problem involving linear programming. To choose from, you have four basic options:

● Graphical Method: When faced with a Linear Programming problem with two variables, the graphical technique is the first port of call. It is typically employed when there are just two potential outcomes. To use the graphical technique, you must first construct a series of linear inequalities, and then solve them under the appropriate conditions or constraints. Finally, the viable region is found by graphing all linear equations on the X-Y plane and identifying the region where they intersect. This region represents a model's values and yields the best possible answer.
● Simplex Method: This is an effective strategy for resolving issues with linear programming, and to find the answer that is best for the problem, it employs an iterative process. In this method, the essential variables are changed one at a time until the maximum or minimum value (depending on the requirements) for the primary target function is reached.
● Northwest Corner and Least Cost Method: These are certain types of procedures that are utilized for figuring out the most effective way to convey items or things, and they are employed for transportation challenges. As a consequence of this, the approach of optimization shown here is practical for solving supply-demand problems. The use of this strategy is predicated on the idea that there is just a single product. On the other hand, there are a variety of origins for the demand for this product, and these origins contribute individually and together to the total supply. As a result, the objective of this strategy is to reduce the cost of transportation as much as possible.
● Solving using R: When it comes to statistics and data analysis, R is among the most popular programs available. Using the IpSolve package, optimizing a problem is a breeze in R, requiring only a few lines of code.

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