Amanda Setili shares a few practices designed to help you excel at both long-term and short-term thinking.
Every one of us knows well the constant tug-of-war between long and short-term thinking. You want to lose weight, but you also deeply desire that double chocolate cake. You want to put a new roof on your house, but you also have your heart set on a restorative summer vacation.
Long-term big thinking is the foundation of all major accomplishments. From the interstate highway system to the automobile manufacturing plants that populated it, such accomplishments are the result of decision makers’ choice to invest—sometimes painfully–now to create impact across many decades. But they also are the result of countless individuals working one day at a time.
So—as you already know—the trick is to excel at both short as well as long-term thinking, each in its place. But that is harder to do than it sounds. Here are a few practices I’ve found helpful:
Key points include:
- Incent short-term behaviors that have long-term benefits
- Work on both fronts at once
- Progress of all sizes
Read the full article, Navigating the Constant Tension Between Long and Short-Term Thinking, on LinkedIn.
Umbrex is pleased to welcome Odd Utgård with The Real Consultancy. Odd spent two years with McKinsey in Oslo following his graduation from the London School of Economics. After McKinsey, he spent several years working with high tech startups and spin-offs from research institutes and universities, before founding the leading Norwegian tech incubator and seed fund – StartupLab. Odd’s key expertise is in growth and strategy, with a keen eye for business improvement opportunities and analytics.
He lives in Oslo, Norway and works primarily in the Nordics (but is happy to work globally once the pandemic allow him to!) Always interested in discussing business problems or opportunities and football over a coffee.
Umbrex is pleased to welcome Niara Phillips. Niara spent two years as a management consultant with Bain & Co and two years as a strategy and operations consultant with Booz Allen Hamilton. She has been operating as independent consultant since January 2020. Prior to Bain, Niara was a senior advisor (appointee) within the federal government under the Obama Administration. Niara has particular expertise in stakeholder engagement, project management operations and execution,and has led major transformations in IT, industrials, and agribusiness. She recently relocated to Portland, Oregon and previously called New York City home. Niara is happy to collaborate on remoted projects involving strategy and operations.
This article from Karthik Rajagopalan’s company blog introduces a type of machine learning that may provide solutions for some traditional optimization problems such as inventory optimization and supply chain optimization.
The field of artificial intelligence is slowly beginning to permeate our lives. Computers and other machines are being endowed with intelligence through a process called machine learning. One particular type of machine learning that is of interest to us here in Paramis Digital is Reinforcement Learning (RL), which has been quite successful in a limited number of applications like teaching computers how to play video games and teaching robots how to perform certain activities.
Supervised learning uses data with input and labeled output and learns the relationship between them. Unsupervised learning, on the other hand, works on unlabeled data and is generally used for pattern detection. It is not uncommon to find applications that use a combination of the two methods. In reinforcement learning an agent, say a robot, learns to perform activities in an environment to accomplish a specified goal. The agent learns by performing various activities, collecting feedback from the environment in response to the activities and evaluating the feedback. The key part of this learning method is the reward mechanism, a mathematical construct, which rewards the agent for performing activities that will move it towards and eventually accomplish the goal. The agent is also punished for performing activities that detract it from accomplishing its goals.
Read the full article, Reinforcement Learning, on the Paramis Digital website.