National Puzzle Day is January 29th. It is a day Arkieva celebrates because the ongoing challenge of smarter supply chain decisions involves supersized puzzles and games. This year we will focus on probabilistic forecasting using the board game Risk. This blog will show how Monte Carlo Simulation can be used to estimate the average number of “wins”, but critically the range of possible “wins” across some interval.
Having a good forecast is essential to demand planning. It ensures the right goods are produced at the right time and reach the right customers. What’s more is that demand planning is situated at the start of your supply chain, playing an integral part in a streamlined supply chain.
Arkieva prides itself on providing some of the best in supply chain thought leadership. Over 300,000 individuals read our blog articles in 2021. The volatile nature of the supply chain today drove our readers to educate themselves on practical actions they can take to remain competitive.
We are excited to announce that we are rebranding Arkieva to fuel growth, strengthen our position in the market, and continue to be a recognized leader in the supply chain industry. Over the past few years, we have been relentlessly focused on solving the most complex planning challenges through simple, intuitive, end-to-end solutions leveraging our best-in-class data scientists, software developers, and supply chain optimization consultants.
These “key tools” balance a need for simple with a need to handle the complexity of SCM – following the IBM adage – complexity exists whether you ignore it or not, best not to ignore it.
In a previous blog post, we discussed how a high or low value of Coefficient of Variation (CV) impacts the first or second term of safety stock. Today we decided to put this to the test using real customer data - here we will discuss our findings.
In the previous What-if Wednesday previous posts, we experimented with the three parameters of Holt-Winters method, namely; alpha, beta, and gamma. This week we are going to run some tests with the damping factor.
Seasonality forecast simulation: what is the effect of changing the gamma parameter in Holt-Winters forecasting method?
In 1994, the IBM Micro-electronics Division, itself a fortune 100 size firm, put in place a major effort to create best in class supply chain planning process and software including demand planning(DM), central planning, available to promise, et al. I was fortunate to be an original member and had the opportunity to work extensively on
In one of my previous posts, I wrote about using coefficient of variation (CV) as a predictor of forecastability. In this post, I will talk about how it can be used to indicate a sensitivity of lead time towards the safety stock calculations. To quickly remind the reader first: The formula for CV = StdDev