Category Archives: rants

Are You Still Relying on the Mallet and the Carrot?

In the old days, purchasing had two levers in negotiations: the (rubber) mallet, which they used to bonk their suppliers over the head when they did not like the way negotiations were headed, and the carrot, which they used to try and convince suppliers to lower their prices. The particular mallet and carrot would change depending on the negotiation in question, but the goal was always the same — to convince suppliers to offer better terms in exchange for an award (the carrot) or to convince suppliers to offer better terms to prevent a loss of business (the mallet). For example, the buyer might offer the widget supplier part of the gadget buy as well for a 10% decrease in price (the carrot) or might threaten to take the gadget business off the table (if the supplier already had both categories) if a price decrease of 5% across the board was not agreed to (the mallet). A traditional purchaser would alternate between the strategies depending on the supplier, and might even use both in the same negotiation to try and extract the best deal.

However, these days, purchasing has more levers than just the carrot and the mallet, including win-win levers like total cost awards enabled by optimization, new opportunity identification enabled by cutting edge spend and data analysis, and innovation enabled by supplier collaboration and enablement technologies. But many organizations, who are obviously not innovative best-in-class, fast-acting leaders, or even average supply management organizations, still rely almost exclusively on the mallet and the carrot.

Why?

It’s a very good question that needs a damn good answer, especially given today’s economic climate where, thanks to the multiple financial crises, your average supplier is probably in, or about to be in, a credit crisis, if they weren’t already in a crisis with the explosive increase in most commodity costs over the past year and the recent increase in DPO (Days Payable Outstanding) at organizations trying to improve their working capital that failed to see the big picture. (This is one of the three sure-fire finance strategies for supply chain failure.) Now more than ever you need to work with your suppliers to find the best-deal that allows both of you to win … and this means abandoning the time-honored mallet and carrot negotiation techniques of supply management past.

The three techniques identified above will save you millions on their own if you haven’t applied them effectively before:

  • Total Cost Decision Optimization
    It will help you jointly identify savings that can come from better inventory distribution, manufacturing distribution between plants, and raw material cost savings on raw materials that can be bought in bulk, by or on behalf of your supplier, across needs in multiple categories — allowing you to save money without forcing your suppliers to accept unsustainable margins.
  • Spend Analysis
    As pointed out in Opportunity Identification, the savings opportunities that arise just from knowing where you are overspending, and where you can consolidate spend, is significant. Just focussing your efforts on the right buys will save you more than hammering an extra 2% in a negotiation on an insignificant buy.
  • Collaboration and Enablement
    When you work together to help a supply manufacture more efficiently and cost effectively, you can often find significant savings opportunities that would otherwise go undiscovered. Consider a recent client of Apriori who found that a different manufacturing process could reduce the production cost on a $4.80 part to $0.80, a savings of over 80% on a six figure annual buy!

And this just scratches the surface of the innovative techniques for savings that have been covered in this blog over the past few years. So if you really want to succeed in the new supply management economy, throw away the mallet and the carrot before you bonk yourself on the head and bite off more than you can chew.

Why is the Perfect Order So Difficult?

Reading the latest research, you’d think that finding the Holy Grail would be easier than filling a perfect order. According to “Benchmarking the Perfect Order”, a recent study by Kate Vitasek of Supply Chain Visions and Karl Manrodt of Georgia Southern University that was commissioned by the Vendor Compliance Federation, the Perfect Order Index for 2007 was a measly 27.2%, assuming that every order was damage free (due to data unavailability for a proper estimate). Let me say it again — 27.2% at best! That means that three out of every four orders was flawed. That’s performance so bad, that it’s three times worse than the US aviation industry, the poster child for poor performance, where, on average, only one out of four flights was delayed.

After all, how hard should it be to deliver an order:

  • on-time,
  • complete,
  • damage free, and with
  • correct documentation

Think about it:

  • you know the delivery date when you agree to the order,
    and you shouldn’t be agreeing to anything you can’t deliver on
  • you know, line item by line item, what you have to deliver,
    as well as how many units are required
  • you know the fragility of your products,
    and should be packaging and handling them accordingly, and
  • you have to know the documentary requirements, especially if you’re exporting
    as failure to know can result in seized and destroyed shipments at your expense

So what’s the problem?

Well, obviously, you are!

But Why?

That’s the Billion, if not Trillion, dollar question, isn’t it? And the answer is, ultimately, that you’re not prepared for it. Why not? Although it’s hard to say in any individual case, it’s most likely because you haven’t shifted your focus from internal performance to customer delivery. In essence, you haven’t prepared for it. Instead of abondoning outdated software, processes, and metrics that focus on you for newer software, processes, and methods that focus on the customer, and allow you to get everything that really matters right, you’re still using the software, processes, and metrics that you were using 20 years ago during the quality revolution.

Let me explain.

With regards to on-time, most of you are probably still tracking “on-time” as shipped on-time with respect to whatever internal production and distribution schedule you devised. When you ship is irrelevant if the stores need it by Monday for a promotion on Tuesday that’s expected to generate tens of thousands of sales. If on-time to your system is “shipped four days before due date”, but you’re shipping by truck from Texas to Alaska, you’ve got a problem! With limitations on how many hours a driver can drive in a day and border delays, you ain’t gonna make it. You need logistics management software that understands minimum, average, and worst case delivery time requirements (by season) and you need to schedule each shipment to a different location in a large order separately.

With regards to complete, you can’t tackle it on a line-item by line-item basis, split across half a dozen shipments on two different carriers and wash your hands of it when the system says everything’s shipped. It’s only complete if it arrives complete. This means that you have to have an extensive shipment and delivery tracking system in place to insure that everything in a disaggregated order hits the checkpoints that need to be hit when they need to be hit so that part of an order doesn’t get lost. Again, just shipping it “complete” doesn’t make it “complete” if you’re breaking the order up across shipments – because then all shipments have to arrive by the designated date and time for the order to be complete. You need a web-based supply chain visibility solution that can be utilized by your partners to update progress as it happens.

With regards to damage-free, you can’t just package it in accordance with minimally acceptable padding, check a box, dust your hands, and call it a day. You have to ensure that all third parties in your distribution network that handle the product do so with the necessary care and that it passes through each checkpoint undamaged. If one of your distributors screws up and breaks something, you need to get a replacement shipment out, and maybe even expedited, before it arrives broken and useless at the customer site. Again, you need a web-based supply chain visibility solution that can record the order status as it clears each checkpoint.

With regards to correct documentation, you need to make sure that all of the documentation required by each check-point is included before it leaves your facility. These days, if you’re importing or exporting, this requires a Global Trade Management Solution, because it’s almost impossible to manage the dizzying array of requirements otherwise.

In short, unless your key metrics have been defined to be 100% customer-focussed, and you have the proper logistics management, supply chain visibility, and / or global trade solutions in place, you’re not going to be able to achieve the perfect order the majority of the time, and the perfect order will continue to be a “holy grail” when, in actuality, it should be a common occurence. The solution, like the problem, rests with you.

A Tale of Murder and Intrigue in India

The Murder: I just read a short piece in Supply Chain Digest that noted that Lalit Kishore Choudhary, the India CEO of an Italian transmission company, was murdered by an angry mob after dozens of angry laid-off workers pummelled him during a meeting to discuss possible re-instatement.

The Intrigue: Searching for further information, I found this story in Industry Week which quoted India’s labor minister, who declined to criticize the attack, who said it should serve as a warning for management, workers should be dealt with compassion, and the workers should not be pushed so hard that they resort to whatever happened.

WTH?!? As far as I can tell, it sounds like the labor minister is saying that if you get fired for violence, and the discussions to reinstate you don’t go your way, that you can form a mob and kill your former boss. What?!?! You have the right to demand better pay and employment guarantees, but in today’s economy, you can’t expect the latter. If you don’t get what you want, you have the right to leave, and if you get laid off, you often have the right to severance. But you don’t have the right to resort to violence, and you definitely don’t have the right to kill your boss — who may not even have any say in the matter. Even the CEO has to answer to a self-serving Board of Directors!

According to the Industry Week article, a domestic industry body said the incident would hurt India’s international business image. Furthermore, the Federation of Indian Chambers of Commerce and Industry has said that such a heinous act is bound to sully India’s image among overseas investors and deserves our utmost condemnation. All I have to say is that I nominate that as industry statement of the year. If the reporting is accurate, the labor minister effectively said it’s okay to mob and kill your boss if you get fired for violence. Who’s going to want to open an operation in India in that economic climate?

The Return of U.S. Manufacturing?

In my recent piece on Is Your Supply Chain Reversible, I noted that the US is now a low cost country source for (Western) Europe and that those manufacturers ready to take advantage of the situation are going to lead the turnaround in US manufacturing. Shortly after, I found an article in Industry Week that wanted to “welcome back US manufacturing” on the basis that high fuel and energy prices along with rising labor costs in traditionally low-wage markets have some manufacturers rethinking how far they are willing to extend their supply chains. This article caught my attention as it pointed out that some mid-size companies are already bringing manufacturing back home, as they are unable to control shipping costs that are spiraling out of control.

The article mentions Desa LLC, a manufacturer of residential heaters based in Bowling Green, KY, as a case in point. Despite the low production costs in China, the high shipping costs, combined with the recent VAT reductions in China, give local manufacturing a lower TCO. Then there’s the relative price increases in some raw materials in China compared to the US, the falling dollar, and across-the-board energy costs. When everything is put together, the perceived advantages of China-based manufacturing for many (large, bulky) products disappear.

Of course, as the article notes, not all manufacturers are going to return to the US, since labor costs are higher than in other countries, but, as the article notes, many are likely to return to the continent and “near-shore” to Mexico (and, if the dollar rebounds, to Canada for complex products and services). But many are considering the US. A recent AMR survey of manufacturing executives found that 21% are planning to increase US-based manufacturing over the next year and Caterpillar Inc., for example, is investing 1 Billion in a multi-year capacity expansion plan for five Illinois plants.

But when you consider that the smart US manufacturers, like CEI who recently invested in an robotic palletizing system that automated a formerly manual stacking procedure, are investing in better technology that makes production more cost efficient, it’s going to make more and more sense for many manufacturers to return home. After all, it’s all about competitiveness, and those companies who invest money into new equipment, processes, and innovation are always going to have an edge. And considering that the US has been the center-point for innovation over the last few decades, there’s no reason that US manufacturers can’t bring jobs back if they put a bit of effort and investment into it.

Lies, Damn Lies, and Statistics

Hopefully you caught The Brain’s much needed lesson in statistics back in January, as it was very informative. (If you didn’t, you can still go back and read it. Heck, even if you did, it probably wouldn’t be a bad idea to go back and read it again.)

The reason I’m pointing it out again is that I just noticed that Knowledge and Wharton put out a great summary of some of the key points in their article on “The Use — and Misuse — of Statistics: How and Why Numbers Are So Easily Manipulated”. Even though they had to go and use, what is in my view, the waste-of-time, waste-of-print, and waste-of-breath story on Roger Clemens and his alleged (ab)use of steroids as a background, they still made some great points regarding statistics, which need to be reiterated every now and again (because it seems that the vast majority of people who like to do statistical studies and quote statistical results still don’t understand what statistics is really all about).

  • Correlation is not Causation!
    As the article notes, a chain of retail stores may analyze its operations for a set period and find that those times when it reduced its sales prices coincided with times that overall sales fell. The chain might conclude that low prices reduce sales volume when, in fact, it could be the case that stores run semi-annual sales during known down periods. In other words, low sales are causing price declines and not the other way around.
  • It’s much easier to isolate and exclude extraneous data when you have experimental or hard-sciences data.
    In post-activity analysis in a business setting, it’s much more difficult to isolate the effects of a variety of other influences — and any attempt to simplify will most likely lead to incorrect results.
  • Comparing your situation only to those that produced positive effects is selection bias — and it’s wrong! Samples must be random.
    The example the authors use is that the Clemens report tried to prove he didn’t do steroids by noting that there are other examples of professional baseball players, like Nolan Ryan, Randy Johnson, and Curt Schilling, that also enjoyed great success in their 40s. However, that’s atypical behavior. The vast majority of players, and pitchers in particular, steadily improve in their early careers, peak at about 30, and then slowly decline. Clemens started declining in his late 20’s and then rebounded and improved in his 40s.
  • A single, short-term study on a small population is not conclusive! Especially if the population is not representative of the population at large!
    The example given here is a lawsuit filed against the Coca-Cola Company’s marketing for Enviga, it’s caffeinated green-tea drink, that states it actually burns more calories than it provides, resulting in ‘negative calories’. The claim is based on a clinical study of a small group of individuals with an average BMI (Body Mass Index) of 22. However, the majority of the American population has a BMI of 25 or more. Thus, its not statistically reasonable to say that the study would be representative of the population at large.
  • An accounting of the entire testing process is required for proper perspective in interpretation.
    So you found a statistically significant effect, correlation, or difference between some set of variables. If you don’t report the twenty-one insignificant tests you did before you found that one significant result, how do you know it wasn’t a fluke and that the test should probably be repeated?
  • Data-driven studies can’t always tell you the right answer.
    All they can tell you is which answers to eliminate because the data does not support them. The true value of a statistical analysis is that it helps users to properly characterize uncertainty as opposed to a “best guess”, to realize what outcomes are statistically significant, and to answer specific hypotheses.
  • You have to understand what the drivers behind the variables are if you are to have any hope of making a correct interpretation!
    Consider the example of major league baseball outfielders. A hypothesis going into such a study might be that outfielders have a harder time catching balls hit behind them, which forces them to run backwards. You’ll likely find that the opposite was true – that outfielders tend to catch more balls running backwards, even though this seems counter-intuitive at first. However, when you consider the hang-time of the ball, and the fact that balls hit farther are in the air longer, which gives the outfielder more time to catch them, it starts to make sense.
  • The validity of a statistical analysis is only as good as it’s individual components.
    And if even one component is invalid, the whole work is invalid.