Season Your Data with Theory and Common Sense in Nate Silver's Book, Signal and The Noise

In this essay, the author

• Describes how they attended their second apics central indiana professional development meeting at carmel. bill whiteside is a founder of demand solution northeast, which markets and supports the demand solution suite of forecasting and supply chain management software.
• Explains that the signal and the noise book is about the overwhelming proliferation of data and how so much of that data can produce more noise (garbage) than the signal (truth). nate silver provides practical lessons that are extendable and very applicable to supply chain forecasting.
• Argues that too much data can cause analysis paralysis. we need to use our tools wisely and trust our instincts.

• Describes how they attended their second apics central indiana professional development meeting at carmel. bill whiteside is a founder of demand solution northeast, which markets and supports the demand solution suite of forecasting and supply chain management software.
• Explains that the signal and the noise book is about the overwhelming proliferation of data and how so much of that data can produce more noise (garbage) than the signal (truth). nate silver provides practical lessons that are extendable and very applicable to supply chain forecasting.
• Argues that too much data can cause analysis paralysis. we need to use our tools wisely and trust our instincts.
• Explains that humans do better than computers because they can discern patterns, detect outliers, crunch data, and manage transactions, but they aren't good at nuance or sentiment. human experience can help improve computer-generated forecasts.
• Explains silver's lesson about being resourceful like a fox, so we can be more valuable contributor during the collaborative process.
• Explains that polls become more accurate the closer you get to the election day. the farther you forecast, the less accurate your forecasts will be.
• Explains that forecasts should tell the story of a business, not just the mathematical proof in the data.
• Explains that forecasters face unpredictable events on a regular basis, such as manufacturing and delivery problems, competitive activity, customer issues, and sudden and dramatic weather changes. forecasting process should include disciplined and accountable reviews of forecast accuracy.
• Explains that data is useless without context, so we need to review our forecasts against the perspective of history. a forecast in a vacuum is meaningless.
• Opines that collective wisdom trumps individual brilliance. an experience forecaster's judgment will often improve aggregate forecasts.
• Explains the lesson number nine, "quantify the qualitative," in the supply chain area. they suggest creating a scoring system for this kind of data and incorporating it into forecast reviews.
• Explains lesson number ten is "share your uncertainty". since we know our forecast will be wrong, we need to share uncertainty by share simulation error or forecast ability, vary our expectation by abc code, provide an indication about our confidence level, and provide a range of outcomes.
• Explains that the economic value of forecasts can be related to timing, volume or special events. companies that forecast best are those that treat forecasting as a formal process with discipline, discussion, and accountability.
• Explains that forecasting requires experience, passion, and skill of the forecaster to mold the raw data.
• Introduces justin hendricks, a professional member of apics, who is working in roche as forecasting and planning analyst for distributions between united states of america and mexico.
• Explains mr. whiteside's passion for analyzing situations and finding ways to improve them led him to work in the supply chain area.