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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.

 

How long does it take your company to turn credit sales into cash?  According to NAW’s Institute for Distributor Excellence, the typical distributor’s Days Sales Outstanding (DSO) is 46 days. As one of three measures required to compute a company’s cash conversion cycle, DSO is key in evaluating financial health.

 

Let’s review how DSO is calculated with an example using a monthly timeframe.  Company XYZ made $10.5 million in credit sales in January. At month end, their A/R balance is $12 million. There are 31 days in January, so Company XYZ’s DSO for January is a little over 35 days: 31*( $12,000,000 / $10,500,000 ) = 35.43

 

A more precise calculation of DSO would involve using the average accounts receivable in January rather than the balance at month end. If your company experiences seasonal business cycles, I recommend a yearly timeframe. As an alternative, trend the DSO by month {period} and compare the DSO of any given month to the same month of the prior year.

 

Assuming that collection policies are in line with industry norms, if your company’s DSO is higher than industry average then finding the underlying causes may surface performance improvement opportunities beyond collection activities. Supposing that product quality is not an issue, all business processes falling under the order-to-cash cycle need to be examined.

 

Evil A/R is a threat to cash flow! Fortunately you don’t need superpowers to tackle this villain.


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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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Forecastability is an important word in demand planning.  Oddly, the word forecastability is not listed in the Merriam-Webster dictionary nor is it found in Wikipedia.  However Wiktionary describes forecastability as “A measure of the degree to which something may be forecast with accuracy”.  That something could be an item used in the production of a finished good or the finished good itself.

 

In healthcare that something could save a life. Prudent healthcare providers must ensure the availability of products that play a critical role in a hospital setting. This is good news for the patient. Unfortunately, the bad news is that the strategy for achieving desired fill rates ties up huge amounts of capital. In fact, there is so much overstock that a company called Hospital Overstock has made a business out of buying excess inventory from hospitals and clinics. Sadly a lot of inventory is lost, damaged, expired or becomes obsolete.

 

It is estimated that billions of dollars are unnecessarily tied up in inventory not just in healthcare but in inventories everywhere. Demand planning and forecasting is the answer to this problem. The demand planning process helps organizations achieve the right balance between service levels, inventory investment and operating costs. Ideally it is a collaborative process whose output is a shared operational forecast used in production, procurement, logistics and financial planning. The forecast numbers are then monitored for accuracy as plans will need to be adjusted should actual demand be significantly higher or lower than anticipated.

 

With hundreds of items to manage, it is essential for planners to rate their assigned items. Along with rating items based on their forecastability, planners must rate items based on their importance as well. What are the costs and/or consequences in the event of a shortage?  High value or high impact items have a higher level of importance than low value or low impact items. More effort should be invested on important items with a low degree of forecastability and much less effort on items of lower importance and a high degree of forecastability.

 

Nobody can ever accurately predict the future yet planning for the future is essential. When conditions remain unchanged, the future demand for an item will probably be very similar to its past demand.  However nothing stays the same for long in today’s world therefore both the importance and forecastability of an item must be periodically reassessed.

 

A famous American author wrote “No sensible decision can be made any longer without taking into account not only the world as it is, but the world as it will be.”  Never have these words been more relevant.


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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With the advent of big data, organizations are beginning to recognize the impact that big data and analytics can have on their ability to compete in their respective industries.  In a recent study by MIT and the SAS Institute, 67% of leading organizations firmly believe that analytics give them a competitive advantage.  This recognition has revealed that it is not only about the volume, velocity and variety of the data at hand, but having the right culture, skillsets, and technologies in place, while respecting the privacy of consumers.  This post will be the first of a four part series aimed at demystifying the term ‘big data’, and touching on opportunities, implications and challenges related to big data.


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In my July post, I introduced the ‘Hierarchy of Supply Chain Metrics’, which is a framework of supply chain metrics conceived by Gartner, the world’s leading information research and advisory company.  The model provides 3 tiers of integrated metrics to assess, diagnose, and correct supply chain performance, and is a great example of what constititutes a supply chain scorecard.

 


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