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Each year, Inbound Logistics researches the supply chain challenges of business logistics managers and measures those against the capabilities of technology providers across the industry to develop a list of the Top 100 Logistics IT Providers.

 

Inbound Logistics’ editors place value on choosing providers whose solutions are central to solving transportation, logistics, and supply chain challenges, and whose customer successes are well-documented.

 

TECSYS is honored to once again be included in this prestigious list, selected for its supply chain platform which is designed to flex to the demands of highly-regulated healthcare logistics ecosystems, omni-channel complex distribution landscapes, and tightly-run 3PL operations alike. As supply chains are gaining their foothold as strategic assets and competitive differentiators in increasingly globalized economies, we, as providers, should not underestimate how the data we synthesize is used to support informed decision-making that drives business performance objectives.

 

In a recent Inbound Logistics article on supply chain and logistics decision-making, the author noted that organizations sometimes “throttle their own strategies to fit their current systems. Once you have data, it’s easy to make the business case internally to make the strategic changes that are found in the data.

 

Modern supply chains are vastly intricate, relying on widgets and components manufactured all over the world, and distributing products to an equally complex network. This creates exposure to logistic disruptions at multiple inflection points — the more intricate the chain, the greater the risk of a kink. This means that to provide logistics solutions today, from planning to point-of-use, you cannot have a blind spot in your data-driven visibility.

 

In a shifting distribution management and warehouse management environment, we are proud to have been selected by Inbound Logistics for the ways we use data, our technology platform, and our integrated logistics and supply chain solutions.

 

See www.inboundlogistics.com/cms/top-100-lit for the 2018 list.


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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.

The Metric That Finance and Supply Chain Can Agree On

Per Investopia, the CCC metric “measures how fast a company can convert cash on hand into inventory and accounts payable, through sales and accounts receivable, and then back into cash.”  Actually, the CCC is combined of three separate financial metrics:

  1. Days Inventory Outstanding (DIO); how long it takes to turn inventory into sales.
  2. Days Payable Outstanding (DPO); how long it takes to pay invoices from creditors, such as suppliers.
  3. Days Sales Outstanding (DSO); how long it takes to collect payment after a sale has been made.

All three metrics indicate how long an organization will be deprived of its cash – the lower the number of days, the better. So how can supply chain improve DIO, DPO and DSO?

Days Inventory Outstanding & Just-In-Time (JIT) Replenishment

JIT replenishment has the potential of releasing a ton of capital previously tied up in inventory because goods are received only when needed. With JIT, lead-time demand does not figure into your safety stock calculation (i.e. goods sold/consumed from the time the order is issued until the goods are received) because the system can project the rate of depletion, determine when safety thresholds would be impacted and then back date the replenishment order accordingly.

 

For example, a SKU with a 14 day lead time is projected to reach its safety threshold on May 10th therefore an order must be issued no later than April 26th.   Achieving JIT requires SKU level forecasting and inventory accuracy both of which fall under the domain of supply chain.

Days Payable Outstanding & the Perfect Purchase Order

Achieving the perfect purchase order at the lowest possible cost requires item data quality, automation, vendor engagement and efficient receiving. Quality item data will prevent costly errors – this includes up-to-date vendor pricing.  An automated procurement process allows for a continuous review of inventory levels to protect safety thresholds in support of JIT.  Furthermore the system should look for consolidation opportunities to reduce overall procurement costs.

 

With a truly integrated system, all the information relating to a vendor transaction is available in real-time to procurement, warehousing and finance. With the right tools, supply chain will transform this transactional data into performance metrics that help provide direction on potential optimization opportunities.

Days Sales Outstanding & the Perfect Customer Order

The fastest way to turn a sale into cash is to deliver in full and on time – error-free from start to finish. Again supply chain plays an important role by ensuring the right balance between monies invested in inventory and desired service levels.

 

Data accuracy plays an important role in shortening order cycle times. In fact, data errors are often the reason the wrong product/quantity was shipped.  Picking errors occur for a multitude of reasons; the wrong product in the right bin, a pack was picked instead of an each, the list goes on.

Aiming for the Same Goal

When you think about it, almost all supply chain processes affect either DIO, DPO or DSO in some way. At the end of the day, supply chain and finance both aim for business success – both can and should be considered business leaders.


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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!

 

That said, it is important to measure forecast accuracy both before and after human intervention. Measuring the impact of revisions allows the forecaster to spot bias. Bias exists when forecast accuracy is repeatedly and negatively affected by one or more individuals.

 

In practice, predictive analytics and forecasting should have the same meaning. Professional forecasters don’t blindly predict the future. Beyond looking for trends, they seek to understand the numbers. Nothing new here!  The big difference is that today’s forecaster is equipped with modern technology and fun stuff like graphical reporting. One thing I can tell you for sure is how forecasters feel about modern technology — pretty darn good thank you very much!


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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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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