Abhishek Shah

About Abhishek Shah

Joining Arkieva in 2013, Abhishek works as a Consultant and Project Manager helping various Manufacturing, Chemical, and CPG industry clients. He holds a Bachelor’s degree from IIT Roorkee and a Master's degree from The University of Wisconsin-Madison in Industrial Engineering. At Arkieva he helps in implementing and supporting various supply chain planning modules such as demand, supply, single and multi-echelon inventory. He is also APICS CPIM, CSCP and CLTD certified.

We compared the Accuracy of 4 Different Demand Forecasting Methods; Here’s What We Found.

Over the past few months, we’ve been running simulation tests on different demand forecasting methods: Winter’s additive & multiplicative, seasonal and robust seasonal. Then, we used MAPE to determine the forecast accuracy for each method. Here’s what we found.

What-if Wednesday: Seasonal Model Forecasting with Seasonal Methods

Seasonal method is a regression method that fits a linear trend along with sine and cosine curves. These sine and cosine portions of the regression can fit any seasonal deviations from the linear trend. Robust seasonal method also fits a trend along with sine and cosine curves, however this method uses linear programming to fit a seasonal series in a way that compared to the regular seasonal method is less likely to be thrown off by noisy values that depart from the trend or seasonality.

By |2019-04-13T23:09:23-04:00July 5th, 2017|Forecasting|0 Comments

What-if Wednesday: Forecast Simulation – Changing The Beta Parameter in a Forecasting Method

In our previous blog, we experimented with Alpha - the intercept parameter of the Holt-Winters method - to see how the forecast gets affected as the weight changes. This week we are going to run similar tests with Beta, the trend parameter. Learn how you can use your recent sales trends to improve future forecasts.

By |2019-04-13T23:09:32-04:00February 15th, 2017|Forecasting, What-if Wednesday|0 Comments

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