Exploratory data analysis for ORs is the first step – or the ‘scrubbing in’ – towards perioperative analytics for the supply chain.

The vast swathes of data being collected in modern health systems suggest that perioperative analytics (or OR analytics) are at the ready. The truth is that extracting meaningful information from all those data points takes some coordination and a little exploration. Let’s take a look at the exploratory data analysis that underscores analytics, and the different ‘flavors’ of perioperative analytics, from descriptive and diagnostic to predictive and prescriptive.

Imagine yourself in a situation where you are faced with the distress of having to undergo surgery to address an ailment. As a patient, what would be your top-of-mind concerns? You would think about how risky the operation is. How long the recovery will take. Which physician is going to perform the procedure. What the physician’s reputation is. And ultimately, what are your chances of healing from the underlying medical issue you’re getting the surgery for in the first place. That all makes sense. You are one patient facing one concern: your health and wellbeing.

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In the January-March 2018 issue of APICS magazine, APICS CEO Abe Eshkenazi contends that if supply chain leaders bring business success then that makes them business leaders. Mr. Eshkenazi goes on to state “organizations that consider their supply chains as strategic and competitive assets outperform the market”.

Indeed, superior supply chain performance does drive business success in very measurable ways.  How can supply chain justify and measure process improvement initiatives using a metric that finance can relate to?  As cash management is a top priority for finance, sharing the Cash Conversion Cycle (CCC) metric allows supply chain and finance to speak a common language when measuring business success.

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I just read a blog post entitled How do you feel when someone mentions predictive analytics? Well, I feel like it’s a good thing. How about you?

One commenter replied that predictive analytics = forecasting and that it’s just a different label for the same thing. Well, true enough, given that the verb predict is synonymous with the verb forecast.

I submit to you two other synonyms: examine and analyze. An analysis of your historical demand will lead to a better understanding of the numbers. When one understands the elements that drove demand in the past then one can review these elements and assess their validity going forward. The result is a forecast achieved using both quantitative and qualitative methods. This is a very good thing!

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Given that accounts receivable (A/R) are essentially zero interest loans extended to customers, one might be forgiven for considering credit sales a necessary evil. Maybe one day crypto-currencies like Bitcoin will replace all other forms of payment, but in the meantime many companies have a ton of cash tied up in receivables. So how evil is your A/R?

According to a recent D&B study on payment practices in the US, only 53% of companies paid their suppliers within the due date.  The remaining 47% were late with 38% of companies paying within 30 days of the due date and the remaining 9% paying over 30 days late. Interestingly, larger companies are the worst offenders with only 10% paying within the due date.

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A lot of focus has been placed on advanced and predictive analytics, and rightfully so. I have written many posts and have spoken publically on the merits of advanced analytics for several years now.

What I find disorienting and misleading are marketers harping on how important it is to adopt advanced analytics right now. The thing that they just don’t get (or maybe they don’t want to get?) is that an organization will need to transcend a series of analytical maturity levels before they can truly capitalize on the benefits of advanced and predictive analytics.

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Source: MIT Sloan Management Review

In my last post, I introduced the longitudinal study that MIT Sloan Management Review has been conducting over the past five years. From 2010 to 2012 they indicated that 67% of those surveyed believed that analytics gave their organizations a competitive edge. In 2013, that figure stabilized at 66% revealing the so called ‘Moneyball Effect’ where leaders lost their competitive edge that they once enjoyed because followers matured and made analytics core competencies. In 2014, that trend continued, falling to 61%.

But why?

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By now, most of you have probably heard of, or read, the book entitled Competing on Analytics by Thomas Davenport that demonstrates how some of the most successful organizations in the world have made analytics a core capability and integral to their strategic planning. MIT Sloan has been tracking this phenomenon since 2010 echoing Davenport’s findings. From 2010 to 2012 they indicated that 67% of those surveyed believed that analytics gave their organizations a competitive edge. However, in their last installment of their longitudinal study, something interesting happened. Something that I like to call the ‘Oakland Athletics Effect’.

Findings from the annual MIT analytics study

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In 2013, Gartner conducted a survey on Big Data Adoption in Supply Chain Industries and found that adoption has been flat and is lagging behind the overall adoption rate of other industries such as banking, insurance, and retail to name a few. Gartner ascertained that these characteristics pertaining to the Supply Chain industry are attributable to an inherent lack of understanding of what Big Data truly is and a fundamental lack of the required skill sets. This, in essence, is the challenge facing the Supply Chain industry.

In parts one, two and three of this four part series on Big Data, we looked at what makes data “big”, how it can benefit organizations that apply the right analytics, and the implications of doing so, respectively. In the closing segment of this series, we focus our attention on examples of how elements of Big Data can be leveraged. The challenges identified by Gartner translate to opportunities regardless of your organization’s analytical maturity level.

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Big data and big data analytics pose a series of implications and challenges.  Organizations that seek to become analytical competitors must have an established analytics culture consisting of  well trained employees who are using the right enabling technologies.  However, these organizations face challenges maintaining consumer privacy while they collect and use sensitive information.

In parts one and two of this four part series on big data, we looked at what makes data ‘big’ and how it can benefit organizations that apply the right analytics.  In part three, we will look at the cultural, technological, and ethical implications of big data and advanced analytics.

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Big data is all around us.  As we have seen, big data is characterized by its volume, velocity, and variety (the infamous three ‘V’s).  Great, you have a lot of data…now what?  Well, these untapped ‘dark data assets’ give rise to vast opportunities for those organizations that seek new ways to compete.  Studies have shown that organizations that compete on analytics by focusing on their core competencies fare much better than those who do not.  Some have gone so far as to call big data the ‘new oil’.  In part two of this four part series, we will take a closer look at big data analytics.

 
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