RESULTS: Initiatives   >   NOP-driven Negotiating

  • Vigorous negotiation is a fact-of-life in many business-to-business relationships. The free market foments such competition and makes it difficult for many companies to achieve more than single-digit profit margins.
  • How can companies survive and excel in the often brutal, Darwinian world of B2B? At the negotiating table, insight is everything, specifically, insight into Net Operating Profitability and the variables that drive it.
  • Most businesses have a solid understanding of their Gross Margin, product-by-product, customer-by-customer. However, many do not have a firm grasp on NOP at this same level of detail. Even for those companies that have attempted some sort of profitability initiative to better understand true bottom-line performance, the numbers often lack broad organizational buy-in. This is because those numbers may be driven by arcane rate tables or overly simplistic formulas that fail to capture the true drivers of variability that make one product more or less efficient than another.
  • Consider a hypothetical company that sells 3 products: A, B, and C. Sales and Cost for the prior year are well-understood at the product-level, as depicted in Figure 1. Total Company operating expenses for the prior year totaled $1M, which the finance team apportioned to the Company’s 3 products on the basis of each product’s Sales. This then is the company’s view of each product’s profitability:
  • Figure 1

    Product

    Sls
    (000's)

    Cst

    Qty

    Price

    Gross
    Margin

    Oper Exp

    NOP

    NOP / Unit
     

    A

     

    2000

     

    1500

     

    200K

     

    $10

     

    500 (25%)

     

    667

     

    -167

     

    -$ 0.84
     

    B

     

     700

     

     300

     

     14K

     

    $50

     

    400 (57%)

     

    233

     

     167

     

     $11.93
     

    C

     

     300

     

     100

     

     10K

     

    $30

     

    200 (67%)

     

    100

     

     100

     

     $10.00
     

    Total

     

     3000

     

     1900

     

     

     

     

     

    1100 (37%)

     

    1000

     

     100

     

     
  • Armed with this “insight”, the company enters into a competitive bid situation to earn the business of a new strategic account. Wanting to win the business, but not wanting to lose money on any particular product, the company decides to offer the strategic prospect the following “wholesale” prices:
  • Figure 2

    Product

    Price
     

    A

     

    $11.00
     

    B

     

    $46.00
     

    C

     

    $27.00
  • Now granted, the company is aware that there might be some issues with their OPEX apportionment logic, but knowing that Products B & C have such nice Gross Margins, the company feels safe in making the above offer.
  • However, had the company had the benefit of NOP figures that were the product of Activity-based allocation logic, they might have enterred the fray armed with the following insight:
  • Figure 3

    Product

    Sls
    (000's)

    Cst

    Qty

    Price

    Gross
    Margin

    Oper Exp

    NOP

    NOP / Unit
     

    A

     

    2000

     

    1500

     

    200K

     

    $10

     

    500 (25%)

     

    400 Total OPEX
     65 Unload
     10 Unpack
    125 Putaway
    100 Pick
     15 Pack
     85 Ship

     

    100

     

     $ 0.50
     

    B

     

     700

     

     300

     

     14K

     

    $50

     

    400 (57%)

     

    350 Total OPEX
     45 Unload
     40 Unpack
     90 Putaway
     80 Pick
     60 Pack
     35 Ship

     

      50

     

     $ 3.51
     

    B

     

     300

     

     100

     

     10K

     

    $30

     

    200 (67%)

     

    250 Total OPEX
     40 Unload
     40 Unpack
     45 Putaway
     30 Pick
     50 Pack
     45 Ship

     

    - 50

     

    -$ 5.00
     

    Total

     

     3000

     

     1900

     

     

     

     

     

    1100 (37%)

     

    1000 Total OPEX
     150 Unload
      90 Unpack
     260 Putaway
     210 Pick
     125 Pack
     165 Ship

     

     100

     

     
  • Based on the ABC results, the pricing decisions made in Figure 2 above would be disastrous. Let's explore why:
  • First, note the additional insight ABC provides. ABC decomposes the company’s schmeer of operating expenses into the underlying business activities that support that slice of business, apportioned by detailed drivers which reflect the efficiency of those products in sensible business terms, rather than the overly simplistic assumptions that were the basis of the first NOP apportionment example.
  • Under the ABC approach, Product A reflects high-efficiency for Unpack and Packing activities. Hypothetically speaking, why might this be case? A good example would be a product that is both received and shipped in full pallets, without undergoing any additional handling (maybe simply a barcode scan or application of a label) as it is passed on up the supply chain. The company unloads pallets of Product A by a forklift, puts them in storage bins, and simply reverses the process to ship to the customer in the same simple, efficient manner.
  • Products B and C may be oversized or fragile, or require the company to break down a pallet into cases or individual units to deliver to customers in the smaller volumes the customer can handle, or perhaps these products even require some sort of manual assembly or other special handling.
  • Whatever the case, an overly simplistic allocation system doesn’t reflect these variances in handling, and therefore mis-apportions OPEX.
  • In a highly competitive, low margin business where negotiation is a fact-of-life, the results can be catastrophic. Given the above example, Product A exhibits properties which might classify it as a commodity (high-volume, low-margin, very efficient). Presuming this to be the case, Product A is likely sensitive to small price fluctuations, and could therefore suffer a marked decrease in volume owing to the high-availability of substitutes, even from such a small price increase.
  • Product B, which was profitable at the $11.93 price, now swings into the red, owing to the $4.00 price cut which exceeds its $3.51 NOP, as per the ABC results.
  • Product C, already a money-loser, sucks even more out of the company at the further discounted price.
  • The problem may further compound itself in that the effect of the pricing decisions aren’t merely mathematical – that is, poor pricing may stimulate customer purchasing behavior the wrong way, incenting the customer to buy more of the money-loser, and less of the money maker, leading to a financial death spiral.
  • That is to say, ABC often exposes a subsidizing effect in the product portfolio wherein a few efficient products contribute to the bulk of a company’s profits, while others erode it. Under the ABC approach, the company is armed with better insight to know where to set the line with customers and suppliers, and what bargaining chips are available to get around a pricing impasse. E.g., perhaps a deeper discount for product X can be granted, but in return the company will change the way in which it ships or configures the product to avoid customary handling expenses.
  • The VDDW approach delivers this accurate insight, because an ABC allocation engine sits at the core of the VDDW framework. The VDDW elucidates NOP for all customers, products, and suppliers, along with the efficiency of all supporting business activities, giving a company a leg up at the negotiating table.