In manufacturing today, data analysis tools can give management the information it needs to make better decisions in areas such as maintenance and labor. Unfortunately, however, many data analytics systems require large sets of historical data to generate accurate and useful results.

According to Rebecca Grollman, a data scientist at Bsquare, anomaly detection is different. These algorithms can begin generating useful information without needing to be trained on historical data. Although simple, anomaly detection can be used for applications such as detecting machine stoppage, sensor malfunctions, tracking production output, and more. Engineering.com recently spoke with Grollman about this solution. 

How essential is historical data in typical data science applications?

Is your enterprise dominated by passive thinking and prescribed routines? Or is it one that generates fresh thinking and unlocks insights into the future?

The viral popularity of TED Talks—with more than a billion views to date—highlights the innate hunger we have for discovering breakthrough ideas.

When it comes to making that high-stakes decision or tackling the most pressing challenges facing your firm, whose experience, inspiration, and insights do you seek? Just as important, why do you look up to those particular individuals or organizations? What do they possess that draws your attention?

What if this wisdom and intelligence resided in your own organization? What does it take to become a thought leader within one's firm?

Do you know what a retailer and a tightrope walker have in common? They both have to balance. For the tightrope walker, the logic is clear. But what’s the balance that a retailer is looking for?

A typical dilemma of shortages vs. storage costs

Although the dilemma of shortages vs. storage costs is applicable to any product category, it’s much more painful with perishables. If their quantity can’t meet the demand, retailers should be ready to see a frown from an unhappy customer who didn’t find her favorite dairy, fruit, or vegetable on the shelves.

However, staying on the safe side by ordering more perishables is hardly a cost-effective solution. Perishable products require special storage conditions, and their shelf life seldom exceeds a couple of days, which means retailers must address disposal issues. So, it’s easy to understand why retailers, by all means possible, try to find the optimal balance between storing too much and too little.

Current business conversation often focuses on data and big data. Data are the raw information from which statistics are created and provide an interpretation and summary of data. Statistics make it possible to analyze real-world business problems and measure key performance indicators that enable us to set quantifiable goals. Control charts and capability analysis are key tools in these endeavors.

Control charts

Developed in the 1920s by Walter A. Shewhart, control charts are used to monitor industrial or business processes over time. Control charts are invaluable for determining if a process is in a state of control. But what does that mean?