No one doubts we are fully immersed in the Age of Information. A few years ago, David Russell Schilling (@davidrschilling) reported, "Buckminster Fuller created the 'Knowledge Doubling Curve'; he noticed that until 1900 human knowledge doubled approximately every century. By the end of World War II knowledge was doubling every 25 years. Today things are not as simple as different types of knowledge have different rates of growth.
In an Amazon-dominated world, blockchain provides brands with paths to both partner more effectively with retailers and, more importantly, to sell directly to consumers.
For brands, blockchain is a big deal.
Artificial Intelligence (AI). AI is a big field which includes things like cognitive computing and machine learning and the hype about AI seems to grow each year. Chris Brandt explains, "There are two very popular words in science and technology nowadays — artificial intelligence and machine learning. For a lot of people who might not be aware of the difference, they are just the same. It might come, therefore, as a surprise that these two are different.
One hundred percent of businesses are concerned about the possibility of a disruption in their supply chain; yet, 40% of "companies do not analyze the source of the disruption, according to the Supply Chain Resilience Report 2016 by Business Continuity Institute and Zurich Insurance Group." With 70% of businesses experiencing some sort of disruption last year, the fact that 2 out of 5 businesses don't have a deeper understandi
Over a dozen years ago, Steven Spielberg’s movie Minority Report introduced viewers to a future in which marketing and advertising is so interactive that personalized sales pitches are aimed at potential customers as they walk through a mall. The film was set in the year 2054. A lot has changed since the film was released including the decline of malls and the rise of e-commerce. One prediction the film got right was the increased use of artificial intelligence (AI) in the marketing field.
Robotic Process Automation (RPA) is attracting a lot of attention both for its ability to generate great efficiency as well as the peril it represents to numerous jobs. Leslie Willcocks, a professor of technology, work, and globalization at the London School of Economics, defines RPA as taking "the robot out of the human." She explains, "The average knowledge worker employed on a back-office process has a lot of repetitive, routine tasks that are dreary and uninteresting. RPA is a type of software that mimics the activity of a human being in carrying out a task within a process.
Yesterday, I facilitated a group of business leaders attempting to drive innovation in supply chain practices. The last five years were tough for the group. With five serial years of Draconian cost-cutting efforts and downsizing, the team has been heads-down trying to survive. For many years, the focus was overcoming day-to-day hurdles. In addition, the company, for the first time in a decade, is struggling to grow. As a result, there has been little time for conferences, external training, or educational forums.
You hear and read a lot about the importance of manufacturing for ensuring a healthy U.S. economy. I agree with that assessment. There is good news on that front.
When I joined the world of software as a business analyst from manufacturing, I was naive. How so? I never fathomed the amount of money that commissioned software sales personnel make selling software. This high level of compensation drives extreme behavior.
There seems to be a growing consensus among business analysts that organizations organized around principles developed during the industrial age must transform into digital enterprises to survive in the Information Age. Former IBM executive Irving Wladawsky-Berger notes, "Firms came into being to make it easier and less costly to get work done." That objective has not changed; but the means for achieving it have.
For consumer goods manufacturers and retailers, machine learning can be a powerful ally.
In the design of planning systems, it is important to think through the elements. These include data inputs, data model fit, engines, outputs, visualization, ease of use, workflow/what-if analysis, master data, and integration. The least problematic element is integration. However, and ironically, integration fears drove many decisions in the past decade.
A Closer Look at Forecasting
"Supply chains keep making headlines around the world for the wrong reasons," insists Boris Felgendreher (@FelgenTweets), Senior Marketing Director for EMEA at GT Nexus, He offers a few examples of supply chain failures and then asks, "Are global supply chains becoming more fragile and if so, why?" In answer to his first question — are supply chains becoming more fragile — he believes they are.