Using descriptive statistics, you can improve data visibility by creating a snapshot of your product volume, variability, and velocity. Here’s how.
If you are looking to improve your supply chain management systems in 2018, you most likely have asked the common question: How do I assess my current efficiency? This is a good starting point for anyone looking to add functionality or identify loopholes within current processes. There is no single perfect method that meets all needs and has no flaws. However, the good news is, supply chain assessments have proven to be very advantageous for many businesses.
A holiday Christmas story and adaptation of Dr. Seuss’ “How the Grinch Stole Christmas!” Every Manufacturer Down in Manufacturingville liked innovation a lot… Except for the Grinch, who worked north of Manufacturingville The Grinch hated innovation! The whole innovation Season! Now, please don’t ask why. No one quite knows the reason. It could be that
The role of Artificial Intelligence (AI) in business activities has again emerged as a hot topic for 2017 and 2018. In fact, Gartner predicts by 2021, 40% of new enterprise applications will include Artificial Intelligence Technologies, where AI and Machine Learning promise to solve a plethora of problems faced by enterprises today, from better decision making to increased efficiencies and cost savings.
Our world can be defined as a series of interconnections. Interpersonal, international and other relationships dictate the global economy and infrastructure and with the advent of the internet, the threshold of connectivity became limitless.
The use of optimization in supply chain management is widespread, just not in supply planning. Regular use of optimization occurs in inventory management and demand forecasting. “Best-fit straight line” is one of the most common uses of optimization. With this method, you enter or pull into Excel (or your favorite statistics software) a set of “x values” (the independent value e.g. the number of cars in a train) and a set of “y values” (dependent value e.g. the fuel cost for each train), click a few buttons and you get a “best-fit” straight line – a slope (b1), a y-intercept (b0), a measure of goodness, and a straight line drawn through your scatter plot.
An optimization model does the same; it calculates the decisions based on the stated preferences and constraints in the model. That can sometimes have the inadvertent effect of finding new pathways, a road less, if ever, traveled.
Naturally, supply chain optimization in supply planning can feel counterintuitive. Here’s why you should combat that feeling to create the best plan possible.
In recent webinars and presentations, I have been talking about Early Warning Systems within the context of supply chains. The news story above made me think of several examples where a supply chain would use similar concepts to develop early warning metrics.
In this guest blog series titled: “Memoirs of a Black Belt,” Stephen Boyd a Lean Six Sigma Black Belt and 30-year supply chain veteran, shares his insights on achieving higher levels of performance using data from existing systems. All opinions expressed in guest authors may not reflect Arkieva’s view on the subject.