What statistical tools can you use to evaluate decision-making strategies?

Tools are important but often I think they become the primary focus. Part of what defines leadership is that it’s a human element, a gut feeling, something that is subjective that sometimes cannot be quantified. I think tools are important to help understand trends, predict future performance but should always be used in the context of sound judgment.

Pour évaluer les stratégies de prise de décision, plusieurs outils statistiques peuvent être utilisés. Voici quelques-uns des plus courants : 1. Analyse SWOT : Cet outil permet d’identifier les forces, faiblesses, opportunités et menaces d’une stratégie. Il offre une vue d’ensemble de la position actuelle de l’entreprise.

That's purely theoretical. I think any attempt to evaluate the rate of success is a waste of time. The environment is too dynamic What I found useful is to have a set of parameters, for example, budget, and time, and choose the option that is the best in all the parameters and then take the necessary steps to make it happen. Higher Probability is not a guarantee. Determined action is.

Assessing decision-making strategies typically entails the examination of data to determine how well different approaches are performing in achieving desired outcomes. I can list few tools for this analysis: * Regression Analysis * Monte Carlo Simulation * A/B Testing * SPC * Data Visualization

When it comes to evaluating decision-making strategies, there are several statistical tools you can use. One commonly used tool is statistical analysis, which involves analyzing data to identify patterns, trends, and correlations. You can also use probability theory to assess the likelihood of different outcomes. Additionally, decision trees and regression analysis can help you model and evaluate different decision scenarios. These tools provide valuable insights to inform and improve decision-making strategies. It's all about using data and statistics to make more informed choices!

I found that the method of decision-making should be chosen depending on the person who has to make the decision. Ask what will be the best way for them. Some like visual, some table. Don't assume that your way fits all. An efficient process is when all stakeholders agree on the tools

Un árbol de decisión puede ser usado para explicar y delegar acciones que no han quedado claras en los procesos o que son desviaciones, excepciones que pensamos son reglas

Whenever possible, I use Monte Carlo simulation in my assessments. I'd like to share two cases where the simulation worked exceptionally well and produced great results: Portfolio Optimization: By modeling variables such as asset return rates and market conditions, the simulation aids in understanding the probability of different financial outcomes. It proved effective in optimizing investment portfolios. IT Capacity Planning: In response to an HR demand to optimize server capacity planning, various variables such as network traffic, workload, and potential failures were considered. The simulation provided insights into the optimal number of servers to maintain performance and reliability.

Analisando mais sobre está análise percebo que ela tem um grande potencial de ser utilizada nas análises de documenta??es, ou a falta deles, para cria??o de cenários de teste. Se removermos a equa??o do desafio e usarmos como base o objetivo da mesma, podemos ent?o determinar a funcionalidade futura através da nossa experiência com o sistema legado existe e através do conhecimento profundo da persona do nosso cliente e do avan?ado de tecnologias e necessidades atuais, conseguimos criar possíveis cenários que est?o "cobertos" na funcionalidade atual em quest?o.

é essencial a aplica??o do pensamento científico nos processos decisórios das organiza??es tanto públicas quanto privadas. Uma das formas mais simples é através da cria??o de hipóteses sobre as quais se buscar?o informa??es para desafiá-la ou corroborá-la. Podemos fazer isso através do cruzamento de dados obtidos por ferramentas qualitativas e quantitativas, bem como desenhar experimentos tendo em mente a cria??o de um grupo de controle e um (ou mais) de interven??o.

Testes A/B s?o muito utilizados por QA que se encontram em agencias publicitárias para testar qual banner é mais efetivo para uma campanha. Nestes testes diversos altera??es podem ser realizadas, desde fontes, cores, pessoas na imagem, frases de CTA, entre outros. Isso tudo acontece por que um mesmo público alvo pode ter comportamentos, pensamos ou atividades diferentes dependendo da sua localidade por exemplo, e com isso os testes A/B se tornam efetivos para determinar o sucesso de uma campanha.

Regression analysis can be used to model the relationship between decision-making strategy and its outcomes. This information can be used to identify factors that are associated with better or worse outcomes Classification & regression trees (CARTs): CARTs are a type of machine learning algorithm that can be used to predict outcomes based on a set of input variables. CARTs can be used to evaluate decision-making strategies by identifying the factors that are most important in predicting success or failure A/B testing is a method of testing two or more versions of a decision-making strategy to see which one performs better. A/B testing can be used to evaluate the effectiveness of different decision-making strategies in real-world settings

The method of making decisions is highly dependent on the field you work in. For some fields a data data-driven, quantitative methods are essential, for other areas, more qualitative methods apply. In some cases, when the risks are high you should use a mix of tools to cover all the bases.

The selection of a statistical tool is contingent upon several factors, including the particular circumstances surrounding the decision-making process, the nature and quality of the available data, and the objectives of the analysis being conducted. It is of paramount importance to carefully and judiciously opt for the most fitting statistical method or technique that aligns with the unique characteristics and complexities of the decision-making issue you are endeavoring to resolve.At times, descriptive statistics alone can encapsulate the core insights inherent in the dataset

1. Descriptive Statistics: Such as mean, median, mode, standard deviation, and percentiles, provide a summary of data, helping decision-makers understand the central tendencies and variability in their strategies’ outcomes. 2. Hypothesis Testing: Including t-tests, chi-squared tests, and analysis of variance (ANOVA), used to determine if there are statistically significant differences between groups or conditions. 3. A/B Testing: It is a common method for evaluating decision-making strategies by comparing two or more variations to determine which one performs better in achieving a specific goal. 4. Chi-Squared Goodness of Fit: This test assesses whether observed data fits a theoretical distribution or expected values.

Tener en cuenta la experiencia, el haber trabajado en una gran superficie que multiplica por 10 su venta en menos de 30 días , y luego baja de la misma manera, te lleva primero a veces a prever con un a?o de anticipación los posibles quiebres de stock causados por malos pedidos de los importadores. Segundo a negociar anticipadamente el riesgo al término de la Zafra, ir corrigiendo a medida que se van dando las ventas. Tercero estar atento a los pedidos de los clientes Extranjeros que son un soplo de aire fresco al compartir sus historias en otras latitudes, caso que recuerdo es un chef italiano que no conseguía pi?ones importados, integrarnos al Turismo entendiendo que somos un eslabón muy importante. Tuve que compactar la anécdota