I always tell people what I did 50 years ago (as a forgerer) is now 4,000 times easier to do today.  Technology breeds crime and it always has and it always will. There’s always going to be people willing to use technology in a negative, self-serving way.  So today it’s much easier, whether it’s forging checks or getting information. …I don’t think we’ve done a lot to really protect consumers.  If they [the government] wanted to really help the consumer from being a victim, they would be doing a lot more trying to educate the consumer…Back then [in the 1960’s] when I forged checks I had to find a Heidelberg printing press.  That press was $1 million.  Today, one simply sits down, opens a laptop and says “Who’s my victim today?”

  • Frank Abagnale, Jr., professional forgerer and scam artist, now working with the FBI teaching about cybercrime, identity theft, and fraud.  Abagnale, Jr. was the subject of the movie “Catch Me If you Can”.  Source:  Wall Street Journal, September 25, 2017, “It’s Easier Than Ever to Be a Swindler –and Victim”, p. R8.

The above quote, made by one of the world’s most famous forgerers and scam artists, reflects the challenges facing supply chain executives in today’s environment.  Concern about cybersecurity, counterfeit products, and authenticating everything from food to drugs to airplane parts, is becoming an increasing concern.  One of the possible solutions identified in this area is block chain technologies.

As noted in previous blogs I’ve written, block chain is a technology that is emerging but has not yet been widely adopted except in pilots.  However, multiple industries are very interested in it, including life insurance companies, aircraft manufacturers, pharmaceutical companies, food and tobacco, and electronics companies, to name a few.

One of the experts in the area of block chain and IOT, and one of the highlights of the CSCMP conference I attended two weeks ago was Shari Diaz, of IBM.  Shari is the Ecosystem and Innovation Leader at IBM, and is leading the charge on exploring how block chain will change the ecosystem within the Watson Supply Chain operating model.

What is blockchain?  It is a shared ledger, digital record of events that is immutable, and can’t be changed.  It is an append-only distributed system of record which is shared across a network.  Think for a moment, how many times do supply chain partners get into disputes over contractual obligations, rebates, invoicing.  Shari recalled that when she worked in purchasing, she would have to manually monitor transactions to ensure quantity discounts were reached.  “I negotiated a rebate with a supplier, but had to manually monitor how much we spent with them, and did we hit the threshold?  And when I thought we hit it, I called the supplier but it took 6 months to get that check.”  Block chain provides an immutable, secure, shared version of the truth to drive unprecedented efficient.  Combined with smart contract business terms embedded in transaction databases, block chain has the potential to monitor transaction execution, monitor much did we bought, and authorize the payment automatically when the quantity discount threshold is reached.  That is the promise of block chain.

The promise is powerful.  One of the most important is the promise of privacy, ensuring appropriate visibility, and secure transactions that are authenticated and verifiable.  It enables, for the first time, authenticated trust.  All parties agree to network verified transactions.  There is a high level of cryptography, as well as agreed-on consensus requirements.  Consensus requirements involves all parties agreeing that  new identities are validated.  “We agree as a block chain that someone who wants to post or publish an event must have consensus agreement, which means that half of us have to agree that that person is who they say they are.  If we know each other better through relationship development over time, we can dial that down to perhaps 30%.  But if a lot of third parties we don’t know are involved, perhaps we need to dial it up to 75%, which drives up the authenticity of the event.  Was it delivered?  Consensus requirements will become an important element of block chains.

IBM has been working on a prototype block chain for the global financing business.  Shari notes that “we built a prototype for our global financing business, largely becaue we are the largest financing company on the planet!  But we would invoice a third party, and they would dispute it, and it would go into a set of email disputes!  So we put all those events on a block chain, and there was now no question on when it is appropriate for an invoice –and disputes went down 75%!  In addition, the time to solve a dispute went down from 39 days to 10 days.  Think about the working capital savings, and in a business the size of IBM, we realized how powerful this technology could become!  We also realized that it needs to be open sourced, so we worked with Linux, and the HyperLedger project was born. Hyperledger is not a platform, it is a fabric – and we have over 900 developers working with it, and a large consortium is evolving quickly.  Fabric 1.0 came out in August.

Several pilots have begun to explore block chain applications.  Maersk is working to create a global trade digitization that can digitize customs processes using block chain.  This has the potential to improve the import/export exchanges that occur at port crossings, to speed up the movement of goods, improve the financing of freight forwarding transactions, and speeding up the supply chain. Walmart and IBM are also working to use blockchain for ensuring food safety, focusing specifically on pork imports from China.   A London-based company called Everledger has placed more than 1.6 million diamonds on a blockchain. Entries on the digital record include dozens of attributes for each diamond, including the color, carat, and certificate number, which can be inscribed by laser on the crown or girdle of the stone.

We are likely to see more and more pilots on block chain emerging.  Our student teams in the supply chain program at NC State are working on block chain research, and will have an update to present at our upcoming Supply Chain Resource Cooperative meeting.  Stay tuned!

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I had the opportunity to listen to Missy Cummings, a Professor in the Pratt School of Engineering at Duke University.  Missy shared some insights on automation in the logistics sector, at a conference that was awash with discussions on block chain, digitization, and new technology in logistics.

Missy noted that “I was one of the US Navy’s first female fighter pilots, and Yossi Sheffi from MIT who I worked with (who was also a fighter pilot with the Israeli air force).  One of the toughest things we did was land a jet fighter on an aircraft carrier – and it opened my eyes to the future.  One of the things you learn quickly in the landing is to turn the computer on, and keep your hands off the controls.  The computer handles it, but you may take over only if it gets tricky.  In fact, when you take off, the pilot has to show their hands in the air and show them to everybody, to reassure everyone that you are NOT touching anything.  And only then do they fire you off the carrier.  If you touch anything, a human can create pilot-induced oscillations off the front of an aircraft carrier, and there were many fatalities in this case.  This started me thinking:  ‘I am so good I am not allowed to touch anything!’  But where is logistics  going, if pilots can’t touch the planes on takeoff?  That motivated me to go back to school at UVA, and started at MIT as an assistant professor, and then Duke hired me, and I now have a Humans in Automation Lab.”

Professor Cummings then gave the audience a brief history of humans and automation.  At the turn of the century, horse and buggies were entrenched as the primary form of road transportation.  There was a real battle among people, many who insisted we would never get rid of the horse and buggy.  Not only have we moved to cars, but we are now moving to driverless cars.  Next, on Wall Street – the human interactions on the trading floor have been replaced by computers.    Most of the financial industry today (fin-tech) is done by computers.  In aviation – the old jets are replaced by UAV drones with no pilots.  So we have achieved that level of automation technologically, but we won’t have full replacement of pilots, primarily due to regulatory issues.

Cummings works on human supervisory control.  A human operator is trying to control a computer vehicle, and it mediates the commands that are interpreted by actuators into a drone.  So humans sit on the loop (not IN the loop) –as they are not directly controlling the systems.  We have sensors and displays that are used to balance humans in the automation.  But should humans be given stop buttons, or what is the nature of their interactions?  Computers work best in automation when involved in complex, time-pressured high risk domains, require embedded automation/autonomy with human supervision.  Think of mining trucks in Australia, or nuclear power grids –there is a lot going on and a lot of information being taken in.  Humans are increasingly moving from the mechanics of operating equipment, to the cognitive interpretation of what is happening.  Every commercial aircraft is basically an automated drone today.  The pilot coujld be doing their job from a Starbucks coffee shop!  Pilots touch the stick about 3.5 minutes out of an entire flight, and that is on take-off only.  That is due to the regulatory structure, (and it will soon go away).  Even NASA let the astronauts have too much control, and it resulted in suboptimal landings for the first few trips to the moon.  Today, humans are only involved in case something goes wrong.  If I can get rid of the human capital, labor and retirement costs will go down.  But there must be a balancing act somewhere.  In China, we see cheap labor, and even then they have decided to start investing in robots.  There is a balance between humans and machines, and if I can automate all these jobs, is there a potential human cost?

Cummings explores this in her paper on skills, rules, knowledge, and expertise that explores the way you do a complicated task.  (A figure from this paper is shown below).  You start with skills based reasoning, for instance, involving how to keep the plane balanced.  You spend a year as a pilot learning those skills, and they become automatic, the same way you learn how to drive a car and stay between the white lines.  Then once you learn a skill, you have cognitive rasoning for rules based – procedures.  For example, you learn that “If something happens, do this”.  Even the medical community is using procedures.  Then you free up cognitive reasoning to do knowledge-based reasoning.  Because as you get into situations with more uncertainty, you have to make guesses about what to do.  The best example of this in aviation is “the miracle on the Hudson”.  Sully had to make a guess about the right place to land.   “Can I make it to Teeterborough or put it in the water?”  His experience allowed his mind to make fast simulations, and he learned that he wouldn’t be able to make the landing.  The record shows that had he turned the aircraft as soon as the birds struck the engine – he could have made it back.  But he would have had to instantly make the decision, and he had to first consult the procedure manual, which took time, and rendered the decision moot.

So how does automation work?  Automation has to be able to assess the situation, understand what is going to happen, and it has to focus on skills-based reasoning first.  Over time as it learns more and more situations that leads to expertise.  But humans are the only machines that truly have the highest level of expertise.

Of the transportation domains, rail has the lowest uncertainty, as you are traveling in a one-dimensional vehicle which is akin to skill-based reasoning.  Next up in terms of uncertainty are drones, which are the easiest to automate.  Drone technologies are mature, have been around for 30+ years, and are rules-based.  Airplanes are also rules-based, and because there are three degrees of freedom, there is also more room for error and less likelihood of head-on collisions.  But the most dangerous, and the most uncertain technologies, are driverless vehicles.  There are many, many more uncertain conditions occurring, and the trajectory is only in two dimensions, so there is much much less room for error, compared to aircraft which has 3 dimensions.  Driverless cars require expert knowledge and reasoning.  “Are there kids playing soccer, what about animals, and how do I react?  That is pure expert-based reasoning, and we are simply not there yet with computers to handle this level of uncertainty.

Unfortunately, these technologies are being introduced in reverse!!  We are primitive in automated rail.  There are 3rd world countries that have more successful and more advanced automated rail systems.  Drones are coming very slowly, with FedEx and UPS moving slowly, but there no technological reasons why they will not evolve.  (There are some sociotechnical concerns however).  But driverless cars!!  Dr.Cummings is working with groups in Washington DC on proposed legislation  to put 1 M driverless cars on the roads in the next year.    In her opinion, “We are not ready for it, but that is the power of lobbying markets.  And car companies are cutting deals with Uber, Lyft – and there is a big business out there, but it is much too early in the evolution of technology for driverless vehicles.”

Think about the following story. A navy seal loved his Tesla and put 20 you tube videos about it on the web.  He was going down a divided highway – and a tractor trailer turned in front of him and he should have seen it, or the Tesla should have seen it, but in any case he was killed.  This happened because he was bored, only held the wheel for 25 seconds of a 37 minute ride, and did not read the PDF manual with the Tesla.  If he had, he would have known that this is NOT a driverless car – and that it has problems with the camera vision.  The truck was turning broadside and the color of the truck made it unrecognizable.  The technology is still very immature –and complacency, boredom, and a lack of understanding of the autonomy are common reasons why more than one person has died this way.

We have human strengths and limitations we need to understand.  AI is not perfect.  Computers still don’t understand inductive reasoning, the contents of the world around them.  Machine learning and pattern recognition still requires a lot of training and teaching that has to go on – and to take higher level relationships doesn’t work well because computers are deductive, and make rigid decisions, making them very vulnerable.

In one case, a bunch of students put graffiti on stop signs, in the form of four black and white stickers.  This simple trick was able to fool a driverless car that the stop sign was interpreted as a 45 mile an hour speed limit.  If hackers can fool automated cars by putting stickers on stop sign, that is a very serious safety concern.

Let’s focus instead on automating trains!  That is an area that would definitely benefit public transportation, and help us to drive true progress in safe, efficient, automated transport.



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I had the incredible opportunity to fly in a B-17 bomber this past weekend, a replica of the Memphis Belle that survived 25 tours of duty in Europe.  I even got to sit in the front position of the bombardier, in the glass bubble at the front, and look down on the ground below.  This experience made me even more aware of the bravery of the men who flew in the “Flying Fortresses”, and how they risked their lives for the better good.

One of the interesting elements that caught my attention was the primitive nature of navigation and accuracy that existed on the B-17.  Many of the signals pilots, gunners, and bombardiers relied on were primitive in nature, and the risk of a Messerschmidt dive-bombing them from out of nowhere was always on their minds.  In the early stages of their deployment, 80% of B-17 flights never made it home.  Everyone on the plane had to operate as a team, and no one was allowed to leave the plane if it was hit without ensuring that all parties were able to escape.  I had the chance to chat with some of the old WWII vets who were at the event as well.  One of them told me that “Everybody had a role, and every one of us had to be mentally alert, watching in all directions, and communicating with one another constantly.”


For managers to make sense out of different signals in equivocal situations requires effective communication and interconnectivity with others in their environment. Intelligence is a process of interconnectivity or what management guru Karl Weick describes as “heedful interrelating”.  A collective mind emerges as a capacity in an on-going activity stream when activities among people are tied together ascontributions that are subordinated to a joint system. An inability to make such connections in periods of high uncertainty can be disastrous.

Weick (2001)notes that an example of “less heedful interrelating” is Winston Churchill’s reconstruction of why he failed to see that Singapore was vulnerable to land invasion in the Second World War. A good illustration of the awareness of multiple causality  may be found in Churchill’s response to his horrified discovery that Singapore, rather than being impregnable, proved to be highly vulnerable to a Japanese land invasion (Allinson, 1993). Churchill noted afterward that “I ought to have known. My advisors ought to have known and I ought to have been told, and I ought to have asked”. These four lapses are lapses of interconnection in gathering information about unexpected events (Weick, 2001).

What does this have to do with supply chains?  It comes down to paying attention to the signals, and working with your suppliers as a team.

Supplier managers who establish regular risk-based performance reviews with suppliers are more likely to adjust their payment terms to improve cash flow for suppliers facing problems. Examples of communication involve meetings to identify the impact of economic conditions on supplier cash flow, regularly scheduled information sessions to exchange data on a supplier’s financial conditions, discussions on contingency planning and business continuity plans in advance of downward forecasts.  In addition, research is needed on emerging technologies that provide real-time event notification through online procure to pay systems, emerging block chain technologies, and joint platforms for collaboration and sharing information.

Although over half of the companies in a research study we just completed were in regular communication with suppliers (60 per cent) and were discussing business continuity contingency planning (53 per cent), less than half were renegotiating contracts (43 per cent). Also, over a third of firms had suppliers requesting improved payment terms, yet most buyers failed to act on these signals.

Our results support the idea that communication flows can lead to a buyer’s improved understanding of their supply network’s financial health, and can also lead to tangible contractual re-negotiation activity that can help counter the effects of financial distress and reduce financial supplier disruption risks.  Teamwork, paying attention to signals, and communication are key.  Just ask one of the WWII vets who flew in the B-17.

Allinson, R.E. (1993), “Global disasters: inquiries into management ethics”, Business Ethics Quarterly, Prentice Hall, New York,NY.

Weick, K.E. (2001), Making Sense of the Organization, Blackwell Publishers, Malden, MA.

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Procurement is always thinking about cost savings, year over year.  But is this really a true measure of competitiveness? So procurement will have a budgeted amount for, say 4.5 cents per unit for a product, when the current price is 4 cents.  They are able to hammer the supplier and get them down to 4.2 cents, so procurement declares victory and says they saved their organization a lot of money – .3 cents per unit! But is this really savings?

Vel Dhinagaravel, CEO of Beroe, thinks this is the wrong way to measure procurement.  In fact, over lunch, on a recent visit to NC State, we both agreed that in our collective experience of over 50 years working in procurement, we have never met a procurement person who has ever missed a cost savings target!




Instead, he feels procurement should be focused on the competitive advantage they deliver to their company, vis a vis their competition.  This idea extended back to a meeting Vel had with a Chief Procurement Officer and his Chief Financial Officer.  At an informal dinner, the CPO half jokingly asks – “sales guys get all the limelight.  Here I am delivered more than $1B of savings and I don’t get the same respect.  Why is that?”

The CFO had a great answer.  Sales organizations are always measured on revenue growth and achievement – but they always have a second dimension, which is market share.  If you grow your top line by 30% but your market share drops, someone else is growing faster then 30%.  Procurement will only get respect if you have a dimension which is the equivalent of market share.

So  “Savings” is the same as revenue growth – However, what is procurement’s equivalent of “market share”?  Saving $100MM is great and that might have represented a 3% reduction in the company’s cost structure – But what if the competition had been able to achieve a significantly higher reduction in that same year? Would that not erode the competitive advantage of the company?

But how can one find out how much competitors are spending?  From a benchmarking perspective – every company would love to know exactly what their competitors spend on each raw material / service.  This is the “holy grail” – but as the saying goes, don’t let great be the enemy of good – and should we strive to benchmark the unattainable?

Beroe came up with a way to benchmark cost savings, through a measure of the COGS – Cost of Goods Sold.  This includes all production items, including Packaging, Raw materials, manufacturing costs, logistics etc.  In most cases, COGS can be derived from annual reports, and can be done on an industry basis.  Let’s look at a simple industry, beer.  If we extract COGS from annual reports over a period of time, say from 2011-2015, and compare them across competitors, then the chart shown above is produced.

If you take 2011 as the baseline year – Each of the companies would have a different COGS/Revenue ratio, but let us normalize this and assume everyone starts at the exact same point – 100.   As you can see, some companies end up below 100 in 2014 and some end up above. If revenue had stayed constant, three of these companies are actually spending more on COGS (per $ revenue) on 2014 than they did in 2011! However, the annual reports of these three companies has a write up where they talk about the massive savings delivered by procurement. This dichotomy leads to cynicism on the true impact of procurement.

This same analysis can be applied across industries, and in general, Beroe has found that half are doing really well, and half are not.  It is not the intent to explain the differences – the world is changing in many different ways, and markets change, and some firms may be impacted more – but everyone is operating in the same world.  Ultimately, if sales is measured on relative advantage – (generally market share in marketing), then procurement should be measured on cost competitiveness as well relative to the market.

This can actually drive some improvements in supplier relationships, if best procurement practices are followed.  If you move to a model where the supplier makes a decent margin, cost management can be a much more open model.  Japanese procurement organizations rarely change suppliers – they have long-term relationships, and while they certainly hold the supplier’s feet to the fire to get a competitive price, but it is open book and there is a  guarantee that the suppliers will always be making a decent margin, and that the supplier will invest in productivity and pass on the margins.  This competitive advantage is what procurement needs to be focused on.

In Vel’s opinion, procurement is still too focused on price – but the biggest issues are specifications and demand.  The price you pay per unit, how many units you buy, and what kinds of units you buy are the key parameters.  By focusing too much on price – procurement is not paying attention to whether they are buying the wrong things and are buying too much of them, which will impact cost.  Every time one company went to the market to get the best prices on a product, they found they had the lowest price.  But their COGS competitiveness was low.   This was because they had 10 times the number of skus per $1B of spend – making it difficult for suppliers to work with them.  Procurement needs to think about local optimal vs. global optima.  One bad spec, and you can get a local optima – but the real cost savings comes with the right cost specifications!  COGS benchmarking can help expose these types of issues.


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RACI Charts have been around for years, yet are often discounted.  RACI stands for Responsible, Accountable, Consulted, and Informed – which refers to the relative governance structure that exists in an organizational relationship.  Although RACI “charts” are often viewed as irrelevant and bureaucratic, they can provide an important clue regarding the “decision-rights” for procurement and contracting decisions.  This is particularly true for sales people.

An important foundational issue for sales personnel to understand is the relative level of procurement maturity within the organization, and the extent to which the organization has developed a category management structure.  This involves developing deep insights into the target account’s internal organizational structure.  In less mature organizations, the sourcing decision will be more ad hoc, will be typically led by decision-makers in the business unit, and the textbook approach of working through business stakeholders at a local level makes sense.  However, more and more organizations are going through procurement transformation initiatives, and are seeking to progress towards a more centralized, structured procurement organization. If the company has a strong centralized category structure, it is important to understand the role of procurement in the organization throughout the contracting cycle, and how much influence they have in the supplier selection decision.

  • What is the organizational hierarchy in procurement – category manager for the category, director of sourcing (transportation and related services), buyers, and CPO?
  • Who are the members/roles of the cross-functional team responsible for implementing strategic sourcing for that target area of spend?
  • Do they have a formal strategic sourcing program?
  • Are they organized around spend categories?
  • How much do they spend on the category?
  • Is the spend category fragmented or consolidated?

Understanding the governance structure and the procurement relationship with business unit stakeholders can provide important insights that can lead to more successful sales strategies.  First, the RACI chart can lead to appropriate discussions with the right people in the procurement organization structure beyond the immediate stakeholder, to help educate and inform others in the organization about the supplier’s capabilities.  Second, it can lead to improved understanding of the governance structure for upcoming contractual negotiations, and the size of the “prize” that exists across the network.  Third, it can lead to important insights on business priorities and critical success factors, that can lead to the right operational metrics that become important during the contracting cycle.

Generally speaking, RACI charts will show that procurement has a category team that is very engaged in the early stages of setting up stakeholder requirements, writing a statement of work, and establishing the suppliers to receive the RFQ/RFP, as well as operating the RFP.  Sales needs to understand details on who is running it, as well as the criteria for “winning” the bid.  This is not always clearly stated, or if it is, there is an “official” and an “unofficial” set of requirements.  By building relationships with procurement ahead of a bid, greater market intelligence can yield a lot of dividends in preparing a winning proposal.


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This week, my former student Vel Dhinagaravel, and founder/CEO of supply market intelligence company Beroe, came to my class to speak.  Listening to him reminded me of the fun we had starting up the first ever company to produce supply market intelligence focused on the procurement space.  Today, Beroe is the most successful procurement intelligence company, and continues to lead the pack in this space.  Here is a summary of how it all got started, in Vel’s own words!

I started at GM India after my undergraduate degree was completed in mechanical engineering.  A week in, the head of logistics procurement quit – and I became the new head of logistics procurement, managing over 300 truck movements. I learned that to manage logistics you have to manage relationships with people in the supply chain, and I also learned that there is an informal network of relationships that had to be managed in the supply chain…  this was in 2002.   By then, I had became interested enough in supply chain that I wanted to study it more formally, and decided to pursue a graduate degree in the United States.

I learned about  a program at NC State and this guy, Rob Handfield, that had started something called the SCRC…I think I sent him 27 emails before he responded.  I came over as a student, and we started working on a lot of projects, including the supply chain simulation, now used in Rob’s Supply Chain Relationships class.  Underlying the tool, it is sophisticated in terms of the algorithms we built.  We began to work with a lot of companies who were interested in building external market intelligence in the markets they were working in, including companies like Shell, Valeant, GSK, Cardinal Health, and others.  NC State has strong linkages to companies in the supply chain space and we started to work full time together.

One of the things I decided early in my career is that I never wanted a formal job!   I liked working on different things, and so working with Rob involved always working on new and emerging problems.    In 2005 a large software company gave us a project:  They wanted to enter the ERP space but the cost for an end to end tool would be enormous, and involved too much risk.  So their idea was to start by developing a tool for procurement, and this would offer a way for them to eventually get into finance.  Also, procurement is a nerve center for companies, and is connected to other companies that could help them build their platform out.

They hired Rob and I to go out and speak to about 50 companies and gauge their interest in investing in a new enterprise program focused on procurement.  What we found was that in the top ten things companies wanted, a procurement ERP system was NOT in their top 10.  The number one thing they talked about as a challenge was that they believed that procurement didn’t understand the supply markets as well as the suppliers.  Procurement has to go out and get the best possible price, and need to understand the market dynamics better then suppliers.  But the heads of procurement said they weren’t confident their markets were well understood.  We dug deeper – and found that procurement executives wanted data to help compare how much they were spending to market information, in order to understand their supply markets.

Think about it for a moment:  Major companies spend a lot of money buying cans, logistics, marketing services, whatever, spending billion of dollars.  But  how much do they spend on understanding supply markets and dynamics?  We went to figure out how much they were spending – and contrasted it with how much suppliers were spending to understand their customer markets.  We went to 50 companies, and asked them:  What is the ratio that procurement was spending on intelligence vs. what suppliers are spending on market information?  We were astounded to discover that the ratio was 1 to 105.  The same results applied to different regions and different industries markets.   In effect, we discovered that at that time procurement was a backwater when it came to understanding markets – but it has evolved a great deal since then.  At the time, however, we told ourselves that if procurement was spending $1 to the $105 that suppliers were spending, there were massive gaps in knowledge that could lead to massive savings for companies!

We did find one chemical company, Englehard that really understood this gap, and they were a forward thinker, and wanted to hire analysts and focus on market information.  Rob and I asked them:  What if we did it for you and set up a company and did your market research for you as a third party?  One of their managers calls me and says – we will take you up on your offer.  I need to send you a contract, and what is the name of your company?   I didn’t have a name for a company.  Rob came up with the idea of calling the company Beroe, who was the goddess of Beirut, who according to legend would travel around the world and tell people what she had seen.  This was a great metaphor for a market intelligence company.

Later, (after we formed the company), we discovered that Beroe was talking a lot about what she saw, but it was about the wrong type of stuff!  She was the chief gossip – more like People magazine.  Beroe is also the name of a jelly fish and a Turkish football club!   At any rate, we started Beroe in 2006 and the company gave us a single category for a pilot study, and it went well.  And we started growing.  In 2006  – we had a simple outsourcing model.  Instead of having analysts in Chicago, they were in India.  By 2008 we had grown to 25 people.  Business was good and profitable – and we never raised any external money.  Our initial total investment was $9000 and I ran the business…

I started to think about how our analysts were working.  By this time, Englehard had been absorbed by BASF.  Each company had five analysts, and the idea was that a company could ask them for any research they wanted.  They were in Chennai – but every analyst was being asked to work on a variety of things, including logistics one month, research on plastics, the next, or whatever was on their radar.  But Beroe wasn’t building depth in any market area – even though procurement wasn’t that demanding yet.  It was still the era of quick wins, and procurement wasn’t yet starting to ask tough questions that our analysts couldn’t answer.  So we moved to a model of specialists – metals, chemicals, indirect services, etc.  In this model, we decided that we will answer your question based on the right team who specializes in that market, who builds a network of subject matter experts, and who really understands the details of what is going on in that market.  This worked exceptionally well – so that by 2013 we had grown to 300 analysts.  We then focused on 9 industries – healthcare, FMCG, mining, materials, etc., and asked the question:  what do they spend money on?   What is the top 90% of their spend – and this led us to a total of 310 categories, with about one analyst per category.  Today we are at 450 categories, which is more than a 1 to 1 map to our analysts.

In this model, a procurement manger could speak to an expert in virtually every space in the global supply market.  You name it:  eggs and milk, chemicals,  freight in Eastern Europe, craft labor in Canada, you name it!  It was very granular –but it wasn’t an easy way to do the business.  Our specialist model was difficult to operate, especially around load balancing!  We would get four questions on eggs all coming in the same week, which would all fall on one analyst.  But if a company had five analysts assigned who were generalists, it would be easier to manage.  It was the most important shift we made.

IN 2013 we grew to 300 people, and most of our revenue was in a time and materials model.  But inflation was catching up with us in India – between 2006 – 2013, our wage bill had gone up 200%, but the billing rate had stayed flat.  You can’t go to a US customer and ask for a 20% wage increase – you are lucky if maybe you get 2%.  We were still profitable but the only reason for this was exchange rates.  The dollar went from 30 rupees to 60 rupees.  So while India  had inflation, the exchange rate helped to ensure we were still profitable, but working on the idea that the dollar we appreciate by 100% every seven years is not sustainable.  At least you can’t bet the business on it.  So we had to de-link our revenues from our inputs, and had to become more productive.

Time and materials is the most inefficient approach for running a business, and so we decided to move to an output based model.  We created a model Beroe Infinity, which involved the following proposition:  Pay us a fixed feed per spend category, and use us in an unlimited way.  Throughout the year you can ask us any number of questions.  We are getting the same amount of revenue, regardless of the amount of input.  Customers could have overrun us with this model,  and we could have been overloaded.  But then I discovered an important lesson.

Let me illustrate this with a story first.  Food in colleges is horrible in India.  When I came to the US and people complained about the food at NC State back then, I told them I thought it was amazing compared to what we had experienced in college!  (Today almost all college food, including NC State, is excellent!)  But at my university in India, breakfast consisted of  had bread, butter and jam which was pre-plated:   four slices of bread, a slice of butter and a spoon of jam.  People would complain about it every day.  Finally, the guy who ran the cafeteria  got sick of the complaints, and one day he has brought out all the bread upfront on the counter, along with a 25 kg jar of jelly, and a massive slab of butter.  “Take whatever your want!” he said.  And people went crazy eating bread and jelly and butter.  After two weeks of this passed, however, a note was posted in the cafeteria:   the average consumption after things had settled down was 4 slices of bread, a slice of butter, and a spoon of jam.

And for Beroe, I also recognized that people will only ask for research they can consume!  Beroe Infinity was a great move for us…it worked out.  We moved to 425 analysts in 2014.  It is about how much revenue per employee you can generate, and productivity levels doubled in 2016 and we continued to see productivity going up. We raised external capital for the first time in 2016.  And we just recently released Beroe LIVE, which is providing basic MI for free to everyone, with additional insights paid for on a customized basis.  And it is working great!

In the next post, Vel shares his insights on how procurement needs to be more competitive by benchmarking savings versus competitors!



Hurricane Harvey hit the Gulf Coast and Houston with driving rains that left many businesses struggling with flooding, with little advance warning.  Entire warehouses full of products, as well as significant links in the global supply chain could be affected, as well as transport and other nodes. This raises the issue of supply chain risk once again, and the only statement that can be made with confidence in such cases is that it is really difficult to predict the impact of such events. What is the role of analytics in creating managerial insights into such events?  There are three major areas:  preparation for risk, business recovery, and loss of limitation.

An interview I had today with a senior executive at a major global contract manufacturer helped to shape some ideas on this question of preparation for risks such as Hurricane Harvey, which is making a big mess in Houston. Houston is a center for plastic resin due to the string of refineries in Texas and Louisiana, and the kind of commodities that are impacted include all plastic parts manufactured in the US.  The refineries are also a center for chemicals in the tier 3 or 4 range for these commodities. The suppliers  in these areas are very much disrupted and managers don’t know yet what the impact will be. “We know there is a risk – but we don’t even know their impact.” The challenge for analytics is to be able to map supply chains that are dependent on a single geography, and this would be important to prepare for risk. And the moment you get an inkling of a weather or other disruption, managers could make  steps (e.g. stocking up with safety stock) and apply analytical insights to identify which parts are dependent on the geography.   Preparation for risk.

Business recovery is another area of risk. What do we do after Harvey to ensure our continuity of supply? Organizations have to be able to detect which sites are impacted, what is stuck in the port, and other problems that occurred because of the event, using advanced analytics. This is a problem that could be attacked with Machine Based Learning.  Assuming one could have enough knowledge about previous events, would it be possible to develop a model to help managers priorities areas of their product flows that are under immediate pressure. This would entail the ability to scan dependencies based on bills of material, and identify a multitude of impact points and parts and products affected. Perhaps through pattern analysis – through a multitude of escalation points – the analytics could detect those which require the highest attention, using analytics to obtain this intelligence.  Because managers are under time pressure to act quickly at the moment of the disaster, rapid insights into these issues is critical during a disaster.

A third area for analytics is loss of limitation.  After the event has already happened, and I have done everything I could to recover, is there still an impact that I don’t know about yet?   There are things in the supply chain which could multiply the impact of a disaster for several weeks. For instance, when Typhoon Hato hit South China last week, several electronic suppliers were impacted. In one case, a supplier to the contract manufacturer was supplying a critical part.  The team recognized the situation and obtained as many parts as they could get – but it also became clear that this supplier would not be able to manufacture for several weeks.  MRP systems that plan production are not able to detect that a single part will be bottlenecked for several weeks, but if planning does not take this into consideration, it could impact revenue because of customer shortages.   In addition, the MRP planning system will continue to pull in the other 99 positions on the BOM for this part – and for these 99 parts the inventory would start to pile up in the warehouse and tie up cash flow, because the product could not be produced without the single critical part from the impacted supplier.  This managerr noted that “I can’t do much about revenue loss, but I could do something about cash flow impact if I get some mitigation ahead of time, and stop the system from continuing to order parts that are stocking up!”  When we know the impact will be there in a supply chain, how do we limit losses on all other frontiers. It could be extra freight and workforce overtime and probably other categories of cost which would accrue as an associated loss risk to any event.

Hurricanes are going to continue to happen, and evidence suggests they are going to continue to occur.  But so will other natural and man-made disasters.  Japan will be prone to earthquakes and tsunami. South China will be exposed to hurricanes, and South Korea which is next North Korea (and the location of production of 50% of the world’s semiconductors) is in a precarious situation.  Analytics are needed to help managers deal with the preparation, recovery, and loss of limitation for more disasters that are inevitable..

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Graham Givens, a  recent graduate from our MBA program started his new role over the summer, and provided some great information and feedback on some of the lessons learned that he is experiencing based on his first month on the job.  This is hot off the press, and provides  good feedback for our faculty, students, and partner companies who are kicking off projects with companies this semester.  Being aware of these requirements can be of great benefit to current students, and also to those who are looking for an MBA that will get them off to a great career in SCM:
-Tracking down information: I would say that 60-80% of my job is tracking down information. Whether that is internally or externally, I spend a significant amount of time digging for data and requesting better information. My work in SCRC projects and in my scholar projects has been invaluable in this part of my job.
Dealing with messy data: Almost all of the data I receive is not in the format that I need it to be in. A lot of our data comes in pdfs and ERP excel outputs so I really have to manipulate and input data in the correct form. My work with CEFS and farmers definitely gave me the know how to tackle these issues.
Would have been helpful: 
-Dealing with large data sets: I am always working in large data sets that usually have around 10,000 plus lines of data. Even though I have my data analytics certificate, I feel like I never really learned the basics in breaking down large data sets in excel. This includes pivot tables, vlookup equations, sumif equations, and standard sorting and filtering. I learned how to breakdown data sets using expensive software packages but no one really uses these. I would suggest giving all scholars the opportunity to take some basic Excel classes and dig into data sets larger than 5,000 lines of data.
Once again, Excel skills boils to the top.  But the important of research-based skills, especially in market intelligence, is also a core component of all of our practicum classes in the MBA, and will be invaluable are a lifelong career skills.  Our business librarian, Jennifer Garnett, did a great job again this past week in explaining the many resources we have to offer at NC State for data collection and research.  Thanks Jennifer!


Nowhere are disruptions more prevalent than in Low Cost Countries (LCC’s), which have become the focal point for outsourced contract manufacturing for major apparel brands such as Nike, Under Armour, Gap, Apple, and others. As organizations have expanded their supply chains globally, they have moved to outsourced manufacturing that reduces cost, but which also exposes their brand to greater risk. “Instead of brand versus brand or store versus store, it is now suppliers—brand—store versus suppliers—brand—store, or supply chain versus supply chain,” as Lambert and Cooper[1] highlighted in a seminal research article in 2000. This shift has led to increased division of labor across the supply chain concentrating on core business activities, leading to outsourcing of processes that can be done more efficiently by suppliers and suppliers’ suppliers in any country in the world. Faced with stiff price competition in their home markets, the apparel industry was one of the first to adopt the outsourced supply chain concept (Wieland and Handfield, 2013; 2014). By deploying the “fast fashion” business model, the apparel industry began to outsource garment manufacture to suppliers and subcontractors in low-cost countries, such as China, India, Vietnam, Malaysia, Pakistan, and Bangladesh. This has allowed global brands to create extremely responsive supply chains and bring lower priced apparel to store shelves. Further, the time to design and delivery of new garments to the market was reduced from more than one year to just a few weeks. More efficient processes, cheaper products, and happier consumers – a winning combination.

But these highly efficient supply chains had a downside that executives at first chose to ignore. By chasing cheap labor, Western retailers were putting tremendous cost pressure on suppliers who, in turn, were willing to minimize capital investments to keep costs low. Adapting what was considered to be common local standards, many suppliers from low-cost countries realized that to compete on price, they decided to forgo investments and labor practices that would dodge Western labor codes. For example, in Bangladesh, mostly women and often children are exposed to risks from lacking safety standards in garment factories. And this poses a major risk to apparel brands, who do not want to be seen as exploiting labor and forcing unsafe working conditions.

The most obvious way to create social responsibility might be just to avoid sourcing markets with low social standards. However, cutting off imports from developing countries will usually not help people in these countries. In some cases it was shown that wages and working conditions in sweatshops are superior to the workers’ prior employment wages (Clark and Powell, 2012). A recent article (Chu, 2012) noted that low cost countries all have the same problems that factories in Bangladesh do. But as any buying agent who has visited Bangladesh knows, things aren’t much better when you visit factories in other parts of Asia, including Myanmar, Pakistan, China, Indonesia, or India. The same unsafe working conditions prevail, and you are just as likely to encounter the problems with capacity and subcontracting that were discovered in Bangladesh. It appears that apparel companies, in particular, are not about to stop sourcing from Low Cost Countries, and that the need for building greater supply chain resilience in the face of these conditions is required.

But monitoring risk and improving factory working conditions is easier said than done. Although management research on Corporate Social Responsibility has proliferated in recent years, and most companies have adopted CSR codes of conduct, comprehensive reviews of current states in Low Cost Country factories suggests that “CSR policies have neither prevented nor could they in themselves prevent the death and injurity of workers in this particular place and era” (Ross, 2016). Companies often emphasize that they audit the factories where these accidents occur have no relationship to the actual occurrence of incidents, and that companies need to move away from checklist monitoring to contractual obligations (Esbenshade, 2016).   And the major risks such as Rana Plaza are infrequent, but there are often daily “issues” that occur such as work stoppages, union protests, and other issues that pop up on a frequent basis. A company cannot monitor “all” risks and/or issues at once, so a “cone of plausibility” approach is needed to be able to focus on key issues that require monitoring, which could escalate and become full blown risks. It is also important to track external risks in countries identified as having a higher probability of incidents. Often companies rely on a qualitative or “gut heavy” process to identify plausible risks. The creation of a quantitative process by which the risks can be identified and amalgamated into a risk management index, would improve the ability of companies to more accurately identify possible supply disruption risks.  This is a project that several of our graduate students are working on, and we hope to be discovering these insights soon.

[1] Lambert, D.M. and Cooper, M.C. (2000): Issues in Supply Chain Management. Industrial Marketing Management 29, 65–83.

Ross, R. 2016. “The Twilight of CSR: Life and Death Illuminated by Fire”, pp. 70-94, Achieving Workers’ Rights in the Global Economy. Edited by Richard Appelbaum and Nelson Lichtenstein, London: ILR Press.

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Fall semester at the Poole College of Management kicked off this week, and a new group of MBA and engineering students walked into our hallowed halls.  In my MBA 541 class on supply chain relationships, I spent some time introducing the students to some of the topics we will cover this semester.  Although some may think the class is only about “procurement”, it is really much more about building effective relationships that drive collaboration, value, and innovation that is mutual to both parties.  The types of projects we will be working on in this class with our Supply Chain Resource Cooperative partners reflects a wide array of challenges these companies face.  A common theme in all of these is the need for assessing risk, creating insights through analytics, and exploring new technologies and their impact on the digital supply chain.  Students taking this class will enjoy a “hands-on” education, that will undoubtedly lead them into internships and full-time jobs in these exciting new areas.  Some of the projects these students will be engaged in are listed below.

Acquiire:   Performing research on new grant reform rules identifying the key aspects of the rules & their impact on procurement.  This will lead to some important new developments with this supply chain technology provider.

Bayer:  Research on best practices in the energy and utility sector.  The energy market is changing quickly, and insights on approaches to managing energy is central to Bayer’s competitiveness.

Cisco:  Students will be working on improvements in Cisco’s Global Procurement Services (GPS) digital transformation, with a focus on Cisco’s indirect procurement categories.

Cheniere Energy:  Our newest partner will be working with students on opportunities to replace older Master Service Agreements with newer contracts that will further reduce contract and operational risk in the business.

Lenovo:  Students will complete in-depth research on blockchain principles and present several use cases of implementing blockchain within Lenovo DCG supply chain.

Mitsubishi Bank:  Our students will work on our newest partner to utilize available probability management techniques to develop a robust and useful tool for identifying and analyzing vendor portfolio risk.

American Red Cross:  Teams will work on best practices in managing IT Contracts, using available resources such as IACCM insights.  In addition, another team will review current Travel policies and best practices.

Restaurant Supply Chain Solutions (Yum Brands):  Another new partner!  The team will work in designing tools to measure the competitive advantage of a supply chain organization. The team will utilize strategic thought leadership to create a measurable benchmark that assesses the performance of a restaurant supply chain.

These project teams will of course be supported by our faculty advisors, John Zapko, Tom Donahue, Walt DeGrange, and Craig Demarest, who have a combined experience of over 120 years of supply chain experience and knowledge!  This is an exciting beginning to an exciting semester.

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