As we join the year end recap bonanza, we can’t miss one of our highlights of 2018: a joint Professional Development Event with the APICS Golden Gate chapter. We welcomed members and guests to our San Francisco office in November to discuss “Implementing a Shared Supply Chain Control Tower."

Amelia Hardjasa is a Data Engineer in Alloy's Vancouver office. Previously, she has worked as a data scientist at several different companies, including Boeing Vancouver and Pulse Energy. She holds an MSc from the University of British Columbia.

In October, Alloy was a featured guest on a webinar hosted by APICS Atlanta and Supply Chain Now Radio, “The Power of Connecting to End-Customer Demand.” Through the presentation and Q&A, we defined downstream demand, why it’s important, and how brands can efficiently use this data to their advantage. You can find the full recording of the webinar here, but here’s a quick summary of the key points

When building demand forecasts for consumer goods, there’s a variety of algorithms you can use, from longstanding best practices to cutting-edge methodologies. While each have their pros and cons, at their core, every method is ultimately using historical data to try to predict future demand. The complexity, assumptions, and types of data inputs used in a given model type — and how they are weighted — will vary, but the basic ingredients are similar across the board.

Nick Singarella is an Account Executive in Alloy's SF office. 

He’s originally from Southern California and went to college at NYU (go Violets!).Taking advantage of NYU’s international campuses, Nick lived in both Florence and Buenos Aires while studying international politics. After graduation, he worked in the Los Angeles District Attorney’s office and in finance before making his way to San Francisco with the goal of entering the tech sector. Nick found his way into the industry through sales, and worked at Wiser (e-commerce analytics) before joining Alloy.

More accurate demand forecasts, powered by big data and machine learning, can generate millions in additional revenue for brands.

In our forecasting white paper, we shared the three principles of modern forecasting: use an integrated approach, keep the methodology transparent, and make results actionable. In this blog post, we’ll discuss why the second principle, keeping methodology transparent, is especially important as executives look to make decisions based on demand forecasts.