inlumi blog

What is Sensible Machine learning? I suppose we can’t really start talking about what SensibleML is and how awesome it is, without understanding, at least to an extent, the problem it is trying to solve for.

What is the problem? Have you ever wanted to know what the demand for your product will be in 3 months’ time? Or maybe even a year? Or perhaps ever wondered whether you need to expand your manufacturing line or prioritise certain services within your business today to maximize profits tomorrow?

The technical term given for such problems is Time Series Forecasting and that is exactly why SensibleML was developed.

Now that we have an idea of what the problem is. Let us get back to the question at hand. The short answer is, Sensible Machine Learning is a powerful tool for analysing and making predictions from time series data. By developing accurate and robust models, SensibleML can help us better understand and predict complex systems that evolve over time.

Read the full story below, where we will explore Sensible Machine learning, its benefits, applications and limitations. 

Read here. 

A quick search on Google and you are overwhelmed with the number of articles written about how to motivate and engage your employees. These strategies can form the backbone of company culture – trusting, transparent, people-focused – but can also refer to various perks like private healthcare, team-building, and even breakfast every Friday morning. But one thing is for sure: in today’s digital work environment in which candidates drive the recruitment process, the cost of replacing a top performer who is unhappy is significantly higher than listening to their needs.

As consultants who drive optimisation and digitalisation, our solutions always look different since they are customised for a particular client. Nevertheless, applying a specific methodology will allow you to steer the project as smoothly as possible to ensure you meet the project goals within a specific timeline and budget. Agile and Waterfall are two very distinct —but valuable — methodologies that should be chosen depending on different priorities, teams, resources, and customer needs.

Nowadays we hear more and more about cross-functional partners or cross-functional teams in our business environment. While cross-functional teams have always existed in all industries, where people with different skills and knowledge have contributed to the success of a project, there is now a real increase in the cross-functional partners. This cross-functional partner is taking over the role of a cross-functional team, and in some cases leading these teams to success.

During the Climate Conference (COP26) in Glasgow, 120 world leaders gathered to discuss the urgency of acting on climate change. It became clear that we have a joint responsibility to address climate change to preserve the planet for future generations, with many countries aiming to reduce their carbon footprint significantly before 2030, and to have zero emissions by 2050. The key messages that came from the conference were clear: there is still a lot of work to do, and the time to act is now.

Artificial Intelligence (AI) is taking the world by storm. Enabling machines to simulate intelligent human behaviour, AI unleashes a world of possibilities for businesses to automate and optimise processes, as well as generating insights for better decision-making. Nevertheless, developing this cutting-edge technology relies on historical data generated by humans, and therefore risks reproducing discriminatory practices and harmful stereotypes created by those very humans. How can we ensure that we leverage AI in the right way?

With the advances in technology over the last two decades, Finance leaders can now benefit from a powerful set of tools to collect and analyse data, and to report and gain actionable insights about their businesses. That means both one-off and ongoing IT-run costs are increasing, and in its latest report, Gartner forecasts IT spend to grow a further 5% in 2022 alone.

The discipline of Planning, Budgeting and Forecasting has covered a lot of ground over the past 20+ years, sustaining a steady evolution and maturing into a fully-fledged, crucial element of Management Reporting and Enterprise Performance Management.

Let’s start with something we all know: people dislike change. While it’s easy enough to say ‘change is good’ from an outsider perspective, when it comes to actually implementing a change ourselves we suddenly feel differently. This is because change takes two things that are very difficult to part with: time and effort.