Monthly Archives: December 2009

Rudimentary Heuristics to Support the Concept of Optimization in Negotiations

Today’s post is from Dr. Lloyd M. Rinehart, an Associate Professor of Marketing and Logistics in the College of Business Administration at the University of Tennessee and author of numerous publications including Creating Reality Based Relationships Through Effective Negotiation: Academic Concepts and Research Support, Creating Reality Based Relationships Through Effective Negotiation: Understanding the Negotiation Process, and Effective Negotiation: Understanding the Negotiation Process – A “Road Map” to Successful Sales and Purchasing Negotiation Performance in the Value System. Lloyd can be reached at Rinehart <at> utk <dot> edu.

This post is based on my presentation at the 2009 MPower BPX roundtable and subsequent thoughts that arose out of my resulting discussions. The concepts that I introduced in the session included the definitional parameters of seven relationships that evolve out of negotiations. These seven relationships, which were covered in the doctor‘s review of my presentation in What Relationships Do You Have With Your Suppliers, include:

  • Non-Strategic Transactions,
  • Administered Relationships,
  • Contractual Relationships,
  • Joint Ventures,
  • Specialty Contract Relationships,
  • Partnerships, and
  • Alliances.

I am going to expand on the concept of definitional parameters of relationships in one form, but in order to do so, I am going to consolidate the seven relationships into three relationship categories:

  • Transactionally Driven (Non-Strategic Transactions and Administered Relationships),
  • Contractual / Investment Driven (Contractual Relationships and Joint Ventures), and
  • Relationally Driven (Specialty Contract Relationships, Partnerships and Alliances).

Generally, whether or not it is actually the case, managers perceive that between 30% and 40% of their relationships fall into each of these general categories. Before we continue, Let me define the characteristics of these relationships. They are built on the three dimensions of trust, interaction frequency, and commitment to the relationship. In other words:

     
  • Does the party trust the other party?
  • How much does the party interact and exchange with the other party?
  • How committed is the party to the other in terms of dependence and investment?

Transactionally Driven Relationships are low on trust, low on commitment, but can have a range of interaction and exchange.

Contractual / Investment Driven Relationships are “slightly” higher on the trust, interaction frequency, and commitment dimensions than the Transactionally Driven Relationships.

However, those that are Relationally Driven are significantly higher on the trust dimension, while that other dimensions have a range of values.

That brings the discussion to one of today’s hottest terms in business — “collaboration”! Unfortunately, that term, like many others, means about whatever the author would like it to mean (and, consequently, that leaves the readers to interpret the concept as they desire as well!). Herein, I am going to constrain “collaboration” to be situations in which trust in the other party is HIGH. That means that of the relationships listed above, “collaboration” occurs about 30% to 40% of the time.

Now wait a minute! I said that this post is the result of my thoughts and subsequent discussions, which included a discussion with Michael. My understanding is that some of Michael’s contributions to the space deal with the concept of “optimization” in sourcing and procurement. My definition of “optimization” includes the attempt to minimize or maximize inputs that capitalize on the best outcomes across the integration of the inputs. My first exposure to the concept of “optimization” was in mathematics and micro-economics. The micro-economics applications focused on how companies could optimize the characteristics of their operational inputs and outputs.

However, here we are talking about negotiation, which means that at least two parties, rather than one entity, need be optimized. Here is the problem with the percentages given in this post. Those relationship assessments were originally generated from the perceptions of only one of the parties to the relationship. Therefore, the original data does not actually represent the “dyads” (perceptions of both parties on the relationship.) While most managers view negotiations as being too sensitive to allow external researchers to become involved, we can successfully simulate similar relationship perceptions in contrived environments. The contrived environment allows the opportunity to pair up the parties into “dyads” for dyadic assessments.

Outcomes of those assessments indicate that, in reality, only 13% of the relationships reflect situations where BOTH parties perceive high trust in the other party. In this situation, both parties feel comfortable enough in the negotiation to share information with the other party and work together for the purpose of “optimizing” the joint inputs to the relationship between the “two parties”. That is how I define “collaboration”, and the data indicates that it probably does occur 13% of the time. It is also important to recognize that the process of “working together” in the negotiation process involves a “collaborative” strategy in which the parties are attempting to “optimize” the outcome in a “Win – Win” sense.

However, there is another situation, that constitutes 1% of relationships, where balance in the negotiation occurs. That is when both parties approach the negotiation and relationship from a “competitive” strategy perspective. In this case, both parties are very skilled and effective negotiators and collectively drive each other to outcomes that are similar to the “collaborative” outcomes, but instead reach that position by pushing the other party “hard” to achieve a mutually beneficial outcome. In other words, both parties are approaching the relationship from a Transactionally Driven perspective. Therefore, I believe two diametrically opposite relationship perspectives can lead to similar outcomes, even though the negotiation strategies are very different. However, regardless of the strategy implemented, the parties must thoroughly understand the negotiation process.

Before concluding, one other problem must be identified with this discussion. The 13% of the original 30% to 40% of relationships that were perceived to be “high trust” and the 1% of the relationships that were perceived to be low trust leaves 86% of the relationships unaddressed. Those are relationships that are unbalanced in the level of trust between the parties. If one trusts the other party less, then that party will most probably implement opportunistic strategies which will be “self” beneficial and, of course, at the expense of the other party. Therefore, it is critically important that both parties in a negotiation fully understand the negotiation process and know how various strategies can contribute or detract from the desired outcomes of the negotiation.

I hope these thoughts stimulate discussion (both pro and con) that may advance the quality of decision making in your organization.

Thanks, Lloyd!.

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Asset-Oriented Supply Chains Need Supply Chain Network Optimization

I couldn’t agree more with this recent headline from Supply Chain News that notes that Asset-Oriented Supply Chains Need Supply Chain Network Optimization More Than Ever because, like just about every other type of supply chains, they do. This is because, as IDC Manufacturing puts forward in a recent research report,

  • profitability is closely linked to the cost, and efficient use, of raw materials which are steadily increasing in price while market pressure is driving sales prices down,
  • high operating costs (partly due to the continual increase in the cost of energy and water) are making plant efficiency a key concern, and
  • changing demand patterns are adding additional strain to the supply chain from regional shifts and gaps between production and actual demand.

A good supply chain network optimization tool will allow a company with rising costs and shrinking revenues to understand the costs and benefits of each supply chain network option open to them and answer the following questions:

  • What is the optimal allocation of materials or customers to plants and/or distribution centers (DCs)?
  • What is the best location for new plants and/or DCs to minimize freight, inventory holding, and/or rail fleet costs while maximizing customer service levels?
  • How do we reallocate our capacity so we may close (temporarily or permanently) under-performing plants?
  • What capacity should we build into our plants, production lines, or processes, down to the requirements of specific machines or tools?
  • Based on our inventory levels and production capabilities, what is the optimal product mix, considering co- and byproducts?
  • Based on seasonal demand or production limits, what should we pre-build in inventory?
  • How do we optimize our production and distribution schedules for the desired levels of customer service and profitability?
  • What is the profitability impact of crossing borders – from currency exchange rates, tariffs, or duties?

And in this economic climate, such a tool may well make the difference between riding out the economic downturn and becoming a victim of it.

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The Role of Optimization in Strategic Sourcing – Challenges / Issues

This series discusses the recent report from CAPS Research on the role of optimization in strategic sourcing. The primary goal is to highlight, clarify, and, in some cases, correct parts of the report that are important, confusing, or incorrect to insure that you have the best introduction to strategic sourcing decision optimization that one can have.

This chapter addresses the challenges associated with optimization from a user perspective, which, unfortunately, are numerous. The good news is that they are all easily overcome if you are aware of the issues and address them early on.

User challenges fall into three categories:

  • Increased Knowledge Requirement
    • some people will be afraid of the unknown
        easily addressed with a little education
    • others think optimization is a threat to their job
        easily addressed by explaining optimization is not intelligent, users still need to formulate the models
    • many do not understand the power associated with optimization
        easily addressed by a well-defined pilot
    • utilization can require a significant cultural change
        easily addressed by helping an organization define a change management plan
    • there is a learning curve
        easily addressed through repeated use over time
    • the proper constraints need to be utilized for maximum results
        easily addressed with provider support
  • Measurement of Results
    • it can be difficult to tie cost/benefit to the tool
         but a baseline scenario always provides a good measure
    • market timing can affect the event
        but what-if scenarios can illustrate how much more could be saved/lost with price changes
    • good historical data may not be available
        which means you form a baseline by comparing the unconstrained scenario to the cost of a human-generated solution
    • overuse can lead to “analysis paralysis”
         which means you define the relevant scenarios before you start using the tool, and stop when you have analyzed all of the relevant ones
  • Software Provider Issues
    • tools are still evolving
        the provider needs to explain how current functionality will not be affected, only additional capabilities will be added
    • it’s hard for a novice buyer to select the right solution(s)
         a good provider could recommend that the buying organization engage an external, unbiased, expert for educational purposes before evaluating vendor solutions
    • internationalization — are the right languages supported
         this is tough since no tools allow users to add new languages, but there’s no reason a buying organization could not work with a solution provider to create a new translation if it was needed
    • installed vs. on-demand; supplier-driven vs. buyer driven
        this requires a good understanding of your needs
    • some providers recommend minimum event sizes
        well, this is their problem … if they don’t want your business, what can you do?
    • the provider might not understand the buyer’s business
        the provider simply has to spend some time getting to know the customer

Other points to note are the following statements which are somewhat misleading:

  • the first time you save a lot of money, then returns diminish
    while it is true that the first application on a category will see more substantial returns than subsequent applications, creativity can be used to repeatedly find savings that you might not expect through product substitutions, supplier-defined bundles, and alternative delivery requirements
  • demand collaboration, sales forecasts, and performance management … may be perceived as conflicting with optimization
    while some providers who have these solutions and do not have optimization may position these as “alternatives”, they are not the same thing and they do not conflict with optimization — in fact, they enhance optimization because better forecasts and improved performance give you more reliable data which gives you more reliable models which give you better results

Finally, the chapter summarizes some of the suppliers’ perception of adoption barriers which are worth noting.

  • organizational issues
    • leadership conviction is required for a sale
    • a long term focus is required
    • it’s a hard sell to any organization not already using other e-sourcing tools
    • organizations using other e-tools, including auctions, advanced forecasting, and performance management often see optimization as unnecessary
  • inertia
    • too many organizations are content with costs that are “good enough”
    • others “fear the unknown”
    • optimization is not a well understood term in sourcing
    • there is the fear that optimization can eliminate jobs
  • implementation issues
    • significant resources are often spent on supporting today’s customers, leaving few resources for developing better solutions
    • users rarely have clean data
    • the buyers are not always sophisticated enough to properly structure the problem

In other words, your success is in your hands — you have to see the value, acquire a solution, and get trained if you really want to see optimal results.

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106 Discrete Steps to Global Trade

Stanford University Professors Warren Hausman and Hau Lee recently decided that an end-to-end model was required for global trade management and decided to research the requirements. Analyzing imported goods from Asia to the US in the apparel sector, Hausman and Lee identified 106 discrete steps in the global trade management process. One Hundred and Six. Wow!

They also found ample opportunities for ROI for investment and improvement of global trade processes. Specifically, they estimated that importers actively using Asian sourcing had an opportunity through automation to reduce their supply chain costs by a range of 0.6-2.2% of annual sales. This is a substantial level versus average corporate net profit margins in the apparel sector. For instance, at an average profit margin of about 6%, such a decrease in costs would boost the corporate bottom line by 10% – 37%.

So, if you have IT-enabled global trade management, you:

  • have enhanced efficiency
    as you don’t have to manually execute 106 steps
  • are significantly safer
    the visibility lets you corrupt hiccups before they become costly seven or eight figure disruptions
  • have profits a-plenty
    as you’ve just increased the bottom line by 10% to 40%

So if you don’t have one, go get yourself a GTM solution today! Need a provider listing, start with the resource site.

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Inventory Accuracy Isn’t Rocket Science! Why Are You Still in the Dark Ages?

A recent article in Supply Chain Digest on Measuring Inventory Accuracy started off by nothing that the experts are saying that there’s no clear answer because there are so many ways to calculate inventory. WTF? Did I read that right?

The article then went on to say that the editorial staff at Supply Chain Digest did some informal networking and found that the answers were all over the map. Really? Who did they ask? Cave Trolls? We’re not in the supply chain dark ages anymore or, at least, we shouldn’t be.

So what did the editorial staff find in their investigation for an inventory accuracy calculation which defines the expected variance between book inventory and actual count? They found:

  • Jim Tompkins of Tompkins Associates offered the following formulas:
    • Financial: (Reported/counted Value inventory-System inventory Value)/Expected inventory value
    • Operational: Total inventory UOM Variation/Total Expected Inventory
    • Locational: Number of locations with variances/Total locations
  • Dave Piasecki of Inventory Operations Consulting noted that:
    every accuracy measurement is flawed in itself in that it can’t by itself show you a true picture of your accuracy and that you have to devise an appropriate “composite score”
  • Ken Miesemer of St. Onge recommends:
    cycle counts by location or geographic counts (an aisle or two at a time)
  • Doug Baker of Istoner states that:
    they rely on absolute and net dollar variance as well as unit variances from the cycle count processes

Ugh! I don’t get it. I know each of these experts has heard of RFID and the Internet and should know that this isn’t a hard problem anymore. At a high level, here’s what you do.

  1. Slap an RFID on each shipping unit — be it a unit, box, or palette — as it’s produced and enter it into the system.
       Now you know how much you’ve produced.
  2. Each time it enters or leaves a location, scan it.
       Now you know how much should be at each location.
  3. Use a supply chain visibility solution to link up with your retailer’s POS systems and have them upload a feed of units sold every day.
       Now you know how much is left at the retailers and you instantly know, at any time, the upper limit of how much inventory you have in the chain. Actual inventory is last count minus sales since last POS feed minus theft since last physical count.

Now, if you also use the system to track thefts and calculate average historical theft rate by SKU category (by day) and average daily sales rate, at any time you can produce an inventory count that is expected accurate within the sum of the (daily) theft variance and sales variance. Pretty easy, eh? And all you have to do is use the modern supply chain technology systems you should have been using for at least the last half decade. Any questions?

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