Let’s say, hypothetically, that you’re a big fan of baking, and every so often your officemates are happy to act as guinea pigs by trying out whatever inventive confections you dream up. The one problem here is that your business involves a lot of travel and a lot of semi-remote workers, so it can be difficult to estimate how many people are going to be in the office on a given day—i.e. it's tough to know how large a batch of cookies to bring in when you decide to bake for your coworkers. You might simply base your batch sizes on past demand levels, assuming that because the last time you baked there were X number of people in the office, similar numbers are likely to hold true again, but this strategy has the potential to miss the mark drastically.
Blockchain. Big Data. Industry 4.0. Smart Factories. Terms like these get thrown around with some regularity whenever the future of manufacturing is under discussion. Often, it’s difficult to cut through the jargon and get to the matter that’s most relevant to you, i.e. what is this new technology and how will it impact your business? It’s a strange new world in modern manufacturing, and we at the flexis blog are committed to demystifying the terms that you may be hearing more and more frequently as you evaluate the ongoing health of your supply chain. To that end, let’s talk about machine learning, intelligent manufacturing, and the difference between the two.
Topics: Big Data
As the world of manufacturing becomes ever more competitive, many are trying to stay ahead of the competition by driving toward a lean supply chain. While this approach is no doubt a boon for identifying potential cost savings and supply chain optimizations, it can often leave companies more vulnerable to supply chain risk. As inefficiencies and redundancies are pared down, manufacturers can become less insulated against possible uncertainty and disruptions. As a result, supply chain managers are now more than ever searching for ways to combat risk and preserve the value-added improvements of their lean supply chains. Even in a market filled with unpredictable externalities, risk management can have a tremendous impact on the bottom line, but for variant-rich industries managing risk is often easier said than done. Let’s take a look at some of the biggest hurdles companies face in combating supply chain risk.
It’s safe to say Big Data is here to stay. Since its introduction in the manufacturing landscape in the early 1990’s, Big Data has demonstrated its value proposition in the capacity for grouping, sorting, and analyzing large and complex data sets into executable actions, provides planners and managers the capability to apply predictive analytics and other forward-looking logistic strategies to increase the efficacy, efficiency, and cost-effectiveness of planning and production programs.
Big Data has since found a home working in tandem with other supply and manufacturing movements such as Industry 4.0, Advanced Analytics, and The Internet of Things (IoT). Alongside these technological developments and platforms, Big Data has helped companies gain increased insight and visibility into a number of critical planning and production functions such as forecasting, modeling, data analysis, and the implementation of integrated sales and manufacturing principles for a more streamlined production cycle.