Business Intelligence Blog from arcplan

Predictive Analytics: Examples, Advice and Shortcuts


Everyone is throwing around the term “analytics” – about as much as they’re throwing around the term “big data.” While I might put big data on my list of the Most Overused Phrases, analytics gets a pass. As companies realize the amount of insight and value they can glean from their ever growing volumes of data, there has been a surge in analytics initiatives. The goal of these projects is to use data to analyze trends, the effects of decisions, and the impact of scenarios to make improvements that will positively impact the company’s bottom line, improve processes, and help the business plan for the future.

In order for analytics to remain relevant and always provide value, organizations must continually up their game. One way to do this is with predictive analytics, which is becoming more mainstream every day. If you stick around to the end of the article, I’ll tell you a simple way to bypass its complexity and still get the predictions you need.

Gettin’ Predictive With It
Ventana-predictivePredictive analytics involves making predictions about the future or setting potential courses of action by analyzing past data. A 2012 benchmark study by Ventana Research revealed that predictive analytics is currently used to address a variety of business needs, including forecasting, marketing, customer service, product offers and even fraud detection. While predictive analytics used to be in play in only a small number of companies, two-thirds of companies participating in Ventana’s survey are using it, and among those, two-thirds are satisfied or very satisfied. These results indicate the maturity that predictive analytics has undergone over the last few years, as technology has advanced to make it less expensive and more approachable, and therefore easier for more areas of the business to make use of. At this point, it’s safe to say that most Fortune 500 companies are churning out predictive insights on a regular basis, but that doesn’t mean smaller companies without “big data” can’t do the same thing. They can supplement their internal data with external data from social media, government agencies, and other sources of public data to get the insights they need.

In Practice
Let’s take a look at finance institutions, which have predictive analytics down to a science….

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Big Data for Manufacturers: Customer Feedback Should Influence R&D


In 2001, The McKinsey Global Institute published a comprehensive report on big data, Big Data: The Next Frontier for Innovation, Competition, and Productivity, which explores the value that companies across various industries may yield as result of the big data explosion. So far we’ve explored the impact of big data on retail and healthcare companies, but today I’ll explore how big data analytics impact the manufacturing industry.

The manufacturing sector stores more data than any other sector, according to the McKinsey report. Manufacturers will likely get the most benefit from big data analytics since they have so much “raw material” to work with (from machinery metrics to sales systems). Manufacturing is a relatively efficient industry, with many advances made over the last few decades to streamline processes and improve quality through management practices like lean & six sigma (and lean six sigma!). But big data can be the impetus for the next wave of improvements in manufacturing, especially in R&D.

Research and Development
Streamlining the R&D process results in greater efficiency and reduced costs for US manufacturers and is important for products to be competitive in the global economy. But in 2012 and beyond, manufacturers should be going further, leveraging big data to influence design decisions. This means incorporating customer feedback into the process, designing products and adding features that customers actually want. McKinsey calls this “design to value” or “value-driven design.”

Surveys: I’ve taken consumer surveys that ask questions like “How much more would you be willing to pay for x feature?” and I now understand why companies are asking this. They are culling data from consumers about what features are desired and if they are included in the product/service, what is the value, i.e. how much are people willing to pay for it. Gathering concrete insights is one step toward big data analytics influencing R&D. Manufacturers should be listening to what consumers want and refining their designs accordingly. It’s just smart business.

Here’s a concrete example: Domino’s Pizza. You might not think of Domino’s as a manufacturer, but it is – the company is a serious dough manufacturer, producing and distributing dough to more than 5,000 US stores.

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5 Steps to Better Supplier Quality


We work with a number of manufacturing companies around the world that need help staying on top of everything from daily production metrics (like machine utilization and on-time delivery) to complex financial calculations to ensure profitability. But as a business intelligence software provider, one of the most important processes we’ve been tasked with is helping our clients manage supplier quality.

Managing the performance of your suppliers is crucial for controlling costs and improving the quality of your outputs. Experts say that the cost of poor supplier quality may equal more than 10% of an organization’s revenue, so keeping that number under industry standards is simply a smart financial decision. But how do you embark on this journey?

1) Start with a subset of your suppliers.
One company may have potentially hundreds or thousands of suppliers, so determining a subset to begin measuring is imperative. You can roll out your supplier scorecards to every supplier in the future, but for now, let’s get it right with just a few. I suggest ranking your suppliers by how much impact they have on your product. The most critical suppliers that you can’t continue operations without are the ones that you rely on most heavily, so these are a good place to start. In the middle of the list are suppliers who directly impact your product, but you could seek alternatives if the need arises. At the bottom are suppliers that have no direct impact on your product.

2) Set expectations with your suppliers.
Your suppliers may know they have some poor processes that are straining their relationship with you and would relish the chance to improve them. Let your suppliers know that you’re beginning to track metrics that will help establish what needs improvement. This first step – just being honest – goes a long way toward building mutual trust and should force both sides to become invested in the success of the partnership. It is important that both parties communicate and document performance expectations, and have a mutual understanding of those expectations going forward.

3) Determine your metrics for success.
Your management team is probably already measuring a number of things that can be incorporated into your list of metrics. Your finance team is tracking costs; you may have six sigma principles in place to track defective parts per million; and your logistics system probably has metrics related to on-time delivery. I bet you’re even collecting a wealth of data in spreadsheets for monthly reporting. Gather these existing metrics along with any new information you identify as being important – this is the start of your Supplier Scorecards. Here are some sample metrics to consider and how they will help you:

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