Category Archives: Best Practices

10 Things I Learned at INFORMS 2006

I attended as many talks as I could manage in the three and a half days that I spent at the worlds largest Operations Research / Management Science conference (separated by meetings with some local companies), and as a result I learned the following:

  1. Under the right model:
    • centralized models of production, decentralized make-to-stock models of production, and decentralized make-to-stock models of production can all be highly profitable
    • sole sourcing is not always the right option
    • but sometimes sole sourcing is the right option
    • when supply chain participants collaborate and share processes, higher customer satisfaction can result
    • better information leads to lower costs
    • shifting inventory to the right supply chain participant can save everyone money
    • large distribution networks are often bloated and inefficient
    • increased flexibility often leads to increased cost and profit loss
    • lean supply chains can be very profitable
    • but sometimes modular supply chains with more inventory are more profitable
    • a complete characterization of potential supply chain risk is challenging
  2. Abstract, strategic, “big picture” thinkers often solve problems faster and better than concrete, tactical, “current crisis” thinkers.
  3. Decreasing customer returns increases profit.
  4. OEMS can profit greatly from secondary markets and those that try to shut them down might be severely jeopardizing their business.
  5. Good supply chain planning is key to good disaster readiness planning.
  6. 1 in 5 outsourcing relationships are doomed to failure because they favor the client at the expense of the vendor from the outset
  7. Highly skilled individuals prefer solutions with moderate amounts of complexity.
  8. The benefits of centralization realized depend on commitment levels.
  9. If you want to sell your solution, focus on the benefits, not the features.
  10. Manufacturers benefit from innovative customers.

Well, as you probably guessed, I did not actually learn the above, but I did learn that academics now have solid mathematical models that explain why us practitioners have observed each of the above “teachings” offered by various talks that I attended. Don’t worry, I’m not going to bore or confuse you with the models, but simply point out why each of these is true from a “common sense” viewpoint.

    • different production models can be highly profitable
      it really depends on how lean your supply chain is
    • sole sourcing is not always the right option
      it is often a risky proposition
    • sometimes sole sourcing is the right option
      since dual sourcing can often be costly
    • higher customer satisfaction results from collaboration
      do you pick out your wardrobe with your eyes closed?
    • better information needs to lower costs
      better forecasting alone saves you money
    • properly placed inventory saves money
      it costs money to move improperly placed inventory around
    • large distribution networks are often bloated and inefficient
      if JC Penney needs less than 10 DCs in the US, how many do you need
    • increased flexibility often leads to increased costs and profit loss
      the more versions of a product you have, the less likely you are to sell a large quantity of any particular unit, and profits, like economy, come with scale (and each different variation has its own setup and teardown production costs)
    • lean supply chains can be very profitable
      in fact, they can be more profitable than you think
    • sometimes modular supply chains with more inventory are more profitable
      if you have to shut part of your supply chain down waiting on inventory, you’re losing money – the right amount of safety stock at each location can prevent this
    • complete characterization of potential supply chain risk is challenging
      you can never come up with and plan for more than a finite number of possibilities but in real life, an infinite number of things can go wrong
  2. abstract, strategic, “big picture” thinkers are better problem solvers
    a “big picture” thinker is less likely to sacrifice better opportunities tomorrow for good opportunities today
  3. decreasing customer returns increases profit
    customer returns decrease profits, so reducing them increases profit
  4. OEMS can profit greatly from secondary markets
    considering how much today’s high-tech equipment costs, a company is a lot more likely to invest in a solution that has a decent resale value
  5. good supply chain planning is key to good disaster readiness planning
    if you do not know what is critical to your operations, then you do not know what to prepare for
  6. 1 in 5 outsource relationships is doomed to failure from the outset
    considering the less than stellar returns from many outsourcing projects, this should not be a surprise
  7. highly skilled individuals prefer solutions with moderate amounts of complexity
    after all, using a simple solution does nothing to demonstrate your capabilities
  8. benefits of centralization realized depends on commitment levels
    an unsupported initiative never works (and center-led is probably more effective anyway)
  9. focus on the benefits, not the features, in solution selling
    with the exception of those few individuals who have to use the solution significantly in their daily tasks (who are usually not the decision makers), no one really cares how cool it is to use – they care about how effective it is at solving their business problem and saving money
  10. manufacturers benefit from innovative customers
    innovation helps everyone

CombineNet IV: BoB’s Unique Talents

Disclaimer: This blog, including this post, is not sponsored by CombineNet (acquired by Jaggaer). The author is not employed by, contractually engaged with, or affiliated with CombineNet. Any and all opinions expressed herein are solely those of the author. Furthermore, the opinions expressed herein should be contrasted with the opinions of other educated professionals before the reader forms his or her own opinion. Finally, the author is neither endorsing nor dissenting the use of CombineNet’s products or services – merely trying to spread awareness on the importance of optimization and the relative uniqueness of an offering like that of CombineNet. This disclaimer holds true for each post in this multi-part series and will be repeated.

Warning: This is a lengthy post.

In my last post, I outlined in some detail a problem that I felt not only required BoB (Best of Breed) but required CombineNet in particular for an optimal solution. What I did not convey is that not only are there other problems out there that I could have chosen, but there are a significant number of supply-chain related problems that often require BoB.

Today I am going to discuss six problems that generally require a BoB solution. This does not mean that you would necessarily require CombineNet (there are some other optimization vendors that can tackle a few of these), but that you would require a best of breed optimization solution (similar to that offered by CombineNet) to tackle these problems and be assured that the solution you achieved was optimal.

The problems I am going to discuss are:

  1. Distribution Network Design
  2. Large Combinatorial Problems
  3. Large Non-Homogenous Logistics Problems
  4. Non-Traditional Sourcing Problems
  5. Very Large (Traditional) Sourcing Problems
  6. Regret Minimization Problems

Distribution Network Design

Most large retailers or distributors have large distribution networks – often dozens of locations throughout a single country or region. However, this is generally not optimal. For example, in a talk at INFORMS given by a practitioner at APL Logistics, they described how they analyzed the distribution network used by JC Penney that had almost 60 DCS (and cost over 330M / yr to operate) and using in house proprietary meta-heuristic optimization algorithms, they deduced an optimal distribution network that had only 8 DCs, saved over 30M dollars, and, on average, shaved over a day off of standard delivery times! What the presentation did not dwell on (since INFORMS is an OR conference) is that these problems are usually humongous and insanely difficult to model, yet alone solve with your average off the shelf optimization problem (as bad as the multi-level make vs. buy problem discussed in my last post) and without a best of breed solution, your chances of finding the truly optimal solution are often slim.

Large Combinatorial Problems

Large pure combinatorial problems are much, much harder than large pure linear problems (which optimizer’s like IBM iLog’s CPlex can often cut through like a hot knife through butter on today’s high end machines) and significantly harder than general MIP problems. The reason is that these problems contain very large numbers of binary variables, and the best generic domain-independent techniques available are generally no better than greedy branch and bound, and for even a thousand binary variables, that could be 21000, or over a trillion evaluations. An example of a large combinatorial problem is a large marketplace auction where all the participants bid on fixed size lots which are non-decomposable. In other words, CombineNet’s (original) definition of an exchange.

How much better are best of breed solvers on large combinatorial problems? Let’s consider CombineNet’s best of breed solution customized for exchanges. According to CombineNet: “The resulting optimal tree search algorithms are often 10,000 times faster than the state-of-the-art general-purpose MIP solvers on the hard instances of real-world market clearing.” As I have expressed to CombineNet personnel directly, I doubt that this is the average case, but I know beyond a doubt that well defined algorithms can easily shave a factor of 100 or more off of solution time, and that this can be scaled up to almost 1000 on a multi-core machine with a smart parallel implementation. In other words, don’t always expect the best case, but the average case performance of BoB on these problems will demonstrate significant improvements.

Large Non-Homogeneous Logistics Problems

This is similar to a variation of the multi-level make vs. buy problem discussed in the last post. However, in this situation you are trying to optimally bundle your deliveries across product, and sourcing categories and choose the optimal carriers and distribution network independent of your suppliers. Given the non-uniform quotes (weight, volume, LTL, FTL), lot sizes, and various charges and surcharges often imposed by freight carriers, forwarders, loaders, unloaders, warehousers, etc., this problem can become really surly really fast on a large buy. Moreover, unlike the sourcing case where it often makes up a low percentage of your spend and a high order approximation is more than sufficient, when you aggregate your logistics across multiple categories, even a fraction of a percentage point can become significant.

Non-Traditional Sourcing Problems

Most Platform Optimization Engines are optimized for traditional sourcing problems – this means that they are generally not optimized for non-traditional sourcing problems. (Why should they be? Most of the problems you face are traditional everyday sourcing problems.) But every now and again you might have a non-traditional sourcing problem. One example – cell phone plan optimization. Cell phone plans are expertly crafted to be as confusing as possible to make sure the carrier maximizes profit at your expense. If you’re a small company, it probably isn’t worth the hassle trying to figure it out and standardize on a common carrier plan – the costs of manpower and resulting therapy costs will probably outweigh the savings, but if you are a large company, you can save hundreds of thousands of dollars, if not millions, with the right company wide plan (which will probably consist of different sub-plans for different groups and individuals, but all on the same corporate contract). That’s why Soligence has a solution just centered around cell phone plan optimization.

Another example, as conveyed to me by Paul Martyn himself (CombineNet’s Chief Marketing Officer and premiere evangelist on the CombineNotes blog) is energy utilization optimization. If you are a large corporation that produces energy for your production operations and consumes it from the grid, whether you realize it or not, you have a sophisticated form of an energy trading problem. If you can produce enough extra energy to add to the grid, should you, and when? If you have the potential to store energy, then adding energy to the grid at peak hours and only siphoning off extra energy at non-peak hours could slash digits off of your energy bill. Furthermore, shifting your operations so that your maximum energy utilization only occurs at non-peak hours could also save you bags of money. Considering this isn’t a traditional energy trading model, traditional production model, or traditional operations research planning model, its easy to see why you might have to call on BoB.

Very Large (Traditional) Sourcing Problems

POE, especially a well designed and implemented POE that uses real optimization technology as its underpinnings, excels at traditional sourcing problems. It’s what POE was built for. That being said, POE is built on off-the-shelf optimizers, and off-the-shelf optimizers are designed for general purpose needs. That means that they will hit their breaking points before a custom designed best of breed solution, even though they improve every year. For example, leading solvers can easily crunch MIP problems with hundreds of thousands of variables and pure LP problems with millions of variables, but once your MIP problems contain millions of variables and your LP problems tens of millions of variables, you’ll start to notice a performance degradation, which could be rapid and considerable when you get close to the underlying solver’s breaking point. When your encounter the odd problem that is this large, a best of breed solution that also integrates domain intelligence can save you a lot of time and rapidly increase your chance of finding the optimal business solution in the finite timeframe that you have to make a decision.

Regret Minimization Problems

Most of the time you know the problem you need to solve, the associated constraints, and the associated costs. But every now and again you don’t. For example, you need to rationalize your supply base but do not know the optimal number of suppliers. In most products, you have to choose a number or run multiple scenarios with different numbers and then take the best one. Although this approach can be effective, it doesn’t help you understand why a certain solution is best or guide you to the best solution. A best of breed solution that manages the search algorithm can not only guide you to a potentially optimal solution but inform you of nearby solutions that are invalidated by your soft or uncertain constraints. This is very difficult for a platform optimization engine – since it generally cannot guide the search. The best it can do is run different scenario formulations, show you nearby answers, and identify constraint conflict sets. It’s a good approach, but for a strategic problem, the more you know, and the faster you know it, the better you are.

By now, you’re probably asking does BoB have the upper hand? Well, even though BoB can solve a slew of problems that POE cannot and it’s always theoretically possible to tone down a solution, whereas it’s not always theoretically possible to built up a solution, the reality is that, as I’ve said before, when it comes to optimization, one size does not fit all and using a sledgehammer on a finishing nail is not always effective. Furthermore, these are not your everyday problems. It often takes years to realize the savings from distribution network redesign, reverse auctions and sealed bid negotiations are generally not combinatorial exchanges, you do not redesign your transportation network after every sourcing event, most of your sourcing problems are traditional, most of your sourcing problems are of a manageable size with proper strategic sourcing, and if you don’t know what your constraints should be on a regular basis, you have deeper problems you need to solve first. That’s why an upcoming post will focus on POE, the everyday hero, in more detail. When combined with this post, you should have a better understanding of where each technology can be helpful to you.

Disclaimer II: Although this blog, including this post, is not sponsored by CombineNet and the author is not employed by, contractually engaged with, or affiliated with CombineNet, the author is going to report, in full disclosure, that CombineNet did allow the author to use one of their free registrations at INFORMS (of which they were a sponsor), as well as buying the author lunch.

Best Practices from Lessons Learned

One of the best practice-oriented talks at the 2006 Informs Annual meeting was William Hefley’s talk on Identifying Issues in Sourcing: Informing Development of Best Practices. In this talk, William Hefley of the IT Services Qualification Center at CMU discussed lessons learned from dealing with client organizations. The fifteen lessons learned identified point to best practices that you can adopt to improve your sourcing organization.

Lessons Learned

  1. Clients often make decisions to source without considering:
    • fit with broader strategy
    • short term organization performance impact
    • appropriateness
    • risk of losing internal expertise
  2. Clients tend to rely on consultants to conduct source selection without consideration of consequences.
  3. Some client organizations establish special sourcing projects named to convey popular images to investors.
  4. “Distress outsourcing” leads to more distress.
  5. Most clients do not baseline existing operations or benchmark desired states.
  6. Clients tend to abdicate entire responsibility to providers after a deal is signed.
  7. Clients negotiate better deals when internal team bids are involved.
  8. Both clients and service providers are challenged in SLA (Service Level Agreement) interpretation.
  9. Most client and service provider teams interpret scope differently.
  10. Client organizations often have difficulty with expectation management.
  11. Clients typically do not execute communication plans for internal or external audiences.
  12. Buy in of management, power brokers, and other key stakeholders is important for any project.
  13. Clients are seriously challenged by internal and external change management.
  14. Clients need to know skill sets and competencies required to manage services internally and externally.
  15. Clients need to retain, develop, and deploy appropriate technology and management skills to manage, oversee, and coordinate with service providers.

Implied Best practices

  1. Determine appropriate sourcing strategies and anticipated results before considering outsourcing to a service provider. Outsourcing doesn’t always add value.
  2. Bringing in consultants is a great way to augment your expertise, but only if you work with the consultants and actively participate in the process.
  3. Don’t initiate a sourcing project just to please investors – initiate a sourcing project because you expect benefits.
  4. Handing your problems over to someone else will not solve them – identifying the causes and resolutions will. Outsourcing improves efficiencies, it is not a miracle cure.
  5. You can not measure improvement without a baseline, nor can you define an appropriate SLA without a reasonable understanding of expected improvements.
  6. Outsource providers are good at managing their operations – not yours. You need to define your overall sourcing strategy, manage the relationship, and make sure the services provided are in-line with your goals.
  7. Understand your costs before you attempt to negotiate a deal. After all, the goal of outsourcing is to add value, and a service is generally not valuable if it costs you more to outsource the process than to manage it in house.
  8. Adopt a common language consistent with customer service goals, not hard-to-measure quality metrics.
  9. Again, adopt a common language consistent with your organization’s customer service goals and make sure both parties understand the scope of the deal on both sides before it is signed.
  10. Define your outsourcing goals up front and make sure a senior, experienced professional is in charge of managing the relationship.
  11. It’s important to communicate your intent before the deal is inked, gather feedback from the appropriate stakeholders, and update them regularly on results.
  12. Without buy-in, it will be hard to gather the internal support required to make your project a success.
  13. Hire or retain a change management expert and have the team responsible for managing service delivery work with the expert to define and implement appropriate processes.
  14. Seek external training for any skill sets and competencies that your organization is weak on. Consider academic programs and professional programs (such as Next Level Purchasing’s, now the Certitrek NLPA SPSM program).
  15. Define appropriate processes and employ technology to ensure those processes are consistently employed in a best-practice fashion. Strive for six sigma quality.

Six Sigma IV: Achievement

The recent Insight from Aberdeen Group, “Technologies Enable Six Sigma” (now Aberdeen Access Members Only) does a great job at demonstrating why we need Six Sigma, or 99.9997% accuracy, and why 99.9% accuracy is not good enough. At a 99.9% quality level, in the United States alone, this would mean:

  • no electricity for nearly an hour each month
  • unsafe drinking water once per week
  • 2,000 lost articles of mail per hour
  • 2 short or long landings at most airports each week
  • 20,000 wrong drug prescriptions per year
  • 500 wrong surgical procedures a week

The insight also makes it a point to note that Six Sigma is not just for manufacturing, since the metrics can apply universally. Whether you measure bad parts coming off an assembly line, or errors made in processing orders, a process is a process and a defect is a defect.

Based on a recent study, the insight notes an interesting fact – those companies that measure PPM (parts per million) and DPMO (defects per million opportunities) achieve better performance than those that simply measure defect rates in terms of %good or %defective. Although none of the Best in Class performers achieved true six sigma quality, those that measured PPM and DPMO were very close to five sigma performance (99.98%).

Why? I think it is partly due to the lessons offered in the Aberdeen Insight, “what gets measured, gets managed” and Six Sigma relies on accurate and accessible data which requires the proper technology, and partly due to psychology. We’ve been trained by the media’s overuse of statistics to accept 99% as very good and 99.9% as excellent and 99.99% as absolutely fantastic. After all, Ivory is 99.99% pure, and we’re supposed to accept that to be as good as it gets, but the reality is this is barely five sigma, which we know to be unacceptable in any critical operation.

As implied in the lessons learned, Aberdeen found a direct correlation between the application of technology and Best in Class performance. In all but one category of IT solutions (non-conformance reporting), adoption rate increases with performance. Although this does not decisively prove cause and effect, there is a strong correlation between top performers and supporting technology, and what’s more important – a detailed cause and effects analysis or being in the “better” pool?

Of course, six sigma is more than just measuring defect rates. Six Sigma employs a structured approach to problem-solving and requires the management of projects which have the potential of having significant impact on the business. To maximize the benefits of these projects, it is necessary to provide an infrastructure that facilitates the adoption of Six Sigma across the organization, manage individual projects, share information effectively and manage financial information in such a way as to gain acceptance on an enterprise-wide basis. While, strictly speaking, tools such as spreadsheets and generic desktop tools, and even pencil and paper, can assist in early stages of Six Sigma implementation, standardization and collaboration are necessary to achieve the next level of benefits.

By implementing project management tools that support collaborative efforts and provide workflow automation, Six Sigma practitioners are able to focus on the analytical aspects of the methodology to drive to true results.

For more information on Six Sigma, I refer you back to parts I through III of the series which appeared on e-Sourcing Forum [WayBackMachine] back in September.

  • I: An Introduction
    II: Innovative Quality
    III: Value Based Strategic Sourcing

Your Supply Chain is NOT Secure! Part II.

Yesterday we discussed how the article Nine Cautionary Tales in the September (2006) issue of IEEE Spectrum makes it abundantly clear that no matter what you think, your supply chain is NOT secure – regardless of how safe you think your supply chain is or what voluntary security initiatives you might subscribe to.

Today we are going to discuss some ways to mitigate the risks that are, more-or-less, out of your control. What is more important is that many of these risks are not just terrorist risks (where you literally have no control), but natural disaster risks as well (where you may not be able to take any reasonable precaution).

We’ll discuss each scenario in turn.

Bomb in a Box

Scenario: A bomb is detonated in a shipping container somewhere in a major port city. Hundreds, if not thousands, of shipping containers (which now contain 90% of international cargo) are destroyed or damaged and the port is shut-down for weeks during investigation and recovery.

Mitigation: There’s nothing you can do to stop this, but you can insure it does not devastate your business. In addition to mitigating supply risk by using two suppliers, you should also mitigate delivery risk of key shipments (critical direct materials or high-demand, low supply consumer goods such as those Sony PS3’s that are going to fly off the shelves) by using two logistics carriers or insuring that your logistics carrier splits shipments across ports, containers, and trucks. Make sure you use multiple ports as part of your regular operations, and can re-route shipments quickly if one port gets considerably backed up (or temporarily shut down due to a natural disaster, terrorist attack, or strike).

ElectroShock

Scenario: Terrorists take out part of the power grid and a whole city, state, or even region goes dark – taking out your operation with it.

Mitigation: Critical operations, which for most companies today revolve around data-centers, should have their own back-up generators. Your communications network should also have its own back-up generators. Even if you can’t work, you should still be able to keep in constant communication with your supply chain so that you can recover quickly when the power comes back on. (And cell phone batteries only last so long!)

Toxic Train Wreck

Scenario: A terrorist blows a hole in the side of a tank car transporting toxic chemicals, such as chlorine gas. This scenario is more dangerous than you think – most railway lines go through major cities near densely populated areas. And this could also be caused by a de-railing, which could be caused by a downed tree (due to a lightening strike), also putting this risk in the natural disaster category.

Mitigation: Make sure you have evacuation plans for all of your offices and plants and the ability to hot-swap your operations to a remote location.

Crude Attack

Scenario: A highly trained commando squad blows up a refinery. A very expensive processing plant is destroyed, toxic smoke fills the air, oil supply drops, and energy prices skyrocket.

Mitigation: Have evacuation plans in place if your offices or plants are close to refineries, power-generation stations, or chemical manufacturing or processing plants that could cause a significant hazard if something goes wrong. Make sure your critical back-up power centers can run basic operations on alternate sources of energy – wind power, solar power, biofuels, etc. Consider geo-thermal heating and cooling. You might not be able to meet all of your power needs this way, but the less gas you need to keep going, the less an oil-based energy crisis will affect your business.

Agro-Armageddon

Scenario: A small group of terrorists infect small groups of cows with mad-cow disease in geographically remote parts of the country and in order to contain what appears to be a burgeoning epidemic, hundreds of million of cattle are slaughtered across the country. (The virus that causes this disease is harmless to humans.)

Mitigation: The real danger here is if your business relies on beef – distributor or steak-house. The mitigation is to make sure you are set-up to import beef from multiple countries at any one time.

Black Christmas

Scenario: Terrorists blanket shopping malls with open containers of mercaptan (the highly volatile and noxious-smelling chemical ordinarily used to signal the presence of propane gas) and postal offices with anthrax stimulants, scaring consumers away from shopping malls and shutting down the largest delivery service. Sales plummet.

Mitigation: First of all, don’t bet your business on a single holiday season. If you are in the business of seasonal novelties, diversity and attack all the holidays. Secondly, make sure you are set up to work with multiple delivery carriers, local and national. Standard courier rates are quite high, but some companies will give you great deals on volume, which will allow you to use them instead of the post-office at only a slightly higher cost. (This is critical especially if the bulk of your sales are low dollar goods. Most people will not want to pay a 50%+ shipping premium. For example, I’ve never ordered a single 11.99 CD at 7.99 next-day courier shipping.)

Star Struck

Scenario: A group of highly trained activists takes over a prestigious televised event with a number of important people present.

Mitigation: This sort of endeavor would take months and months of up-front planning and infiltration into all of the appropriate service organizations. There are two potential approaches here. The first is to move the event around and not select your service organizations too far in advance. However, if you are holding your event at a high profile venue in a city where resources need to be booked months (and months) in advance, this is not feasible. Make sure that all of the organizations you use are establish, trusted, and cognizant of best-practice security procedures. Make sure they do background checks on all new employees and that the security firm you hire does a complete, up-to-date, risk assessment, even if it’s worked the venue before.

A Farmer’s Fury

Scenario: A group of angry activists make truck-bombs using their unrestricted access to ammonium nitrate fertilizer, drive them up to a building, walk away, and detonate them using a remote detonator.

Mitigation: Restrict parking near critical facilities. If you feel this is a real threat, manually inspect all large vehicles entering your premises.

Too Much – Or Too Little

Scenario: In the future, airline security has lapsed to pre-9/11 levels as the urgency to protect the homeland has subsided with reduced terrorist attacks and a new government to the point where someone could walk on the plane with a shoe-bomb. (This also has an accidental equivalent, the plane crashes.)

Mitigation: The time-tested “don’t put all your executives on the same plane (, bus, or boat)”.