Incorporating machine learning (ML) into complex operations can seem daunting, but it's a powerful way to enhance decision-making. To integrate ML effectively:
- Identify repetitive tasks where ML can automate processes, saving time and reducing errors.
- Invest in quality data sets, as ML algorithms rely heavily on data accuracy for optimal performance.
- Train your team to understand the basics of ML, ensuring smooth implementation and maintenance.
How have you used machine learning to improve your operations? Share your experiences.
-
Identificar as tarefas repetitivas onde o ML pode automatizar processos, economizando tempo e reduzindo erros. A interface automatizada agrega tempo, custo e a eliminação do retrabalho.
-
It's also important to understand that ML cannot be used in all situations to support complex operational decision making. To incorporate ML, it's important to have good quality data. However it has been observed that, many organisations lack data maturity and hence to use ML, it's important to digitise the processes first.
-
El uso de técnicas de machine learning las uso para hacer predicciones de variables con tendencia y estacionalidad, para esto es muy importante la recolección de datos históricos. Los modelos de ML permiten tener resultados más precisos y ahorro en el tiempo de análisis, permitiendo hacer foco en la calidad de los datos con el que se alimenta el modelo y en los resultados para responder las preguntas del negocio basada en datos. Esto se conoce como “Data Driven”
-
ML can help derive new insights which is then fed into the operational engine as new data points to optimise decision making. For example, in a resource allocation problem, ML algorithms can be used to predict the performance output of the targets, then the allocation of resources can be adjusted to optimise the overall output. In a scheduling problem, ML methods can be employed to predict availability of resources, job completion time or future demands. The estimation is then used as inputs to formulate and solve the scheduling optimisation problem.
-
My favourite tool is the effort-benefit matrix, which we've extensively used at Generali Česká pojišťovna. It helps us prioritise ideas at every stage: from initial scoping to proof of concept (POC), and then again for features within those POCs. It's beneficial for teams lacking advanced skills, fostering valuable discussions and aligning everyone involved. Initially, estimates are based on intuitive "t-shirt sizing," later, they become more precise, with data-backed benefits for specific features.
Rate this article
More relevant reading
-
AlgorithmsWhat are the most effective divide and conquer algorithms for faster problem solving?
-
Industrial EngineeringHow do you optimize the hyperparameters of SVM for industrial classification problems?
-
Job AnalysisHow do you conduct a task analysis for a complex human-machine system?
-
Analytical SkillsHow can probabilistic reasoning improve your arguments?