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(formerly Mantrala Associates Inc.)
 
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Price Optimization – The B_Line Way

Fashion retailing is emerging as one of the most challenging areas of business activity in the western world. Uncertainties in demand, shrinking selling seasons, lengthening delivery lead times, increasing competition and thinning margins together make the job of the retailer truly daunting.

The fact that fashion goods have a limited shelf life makes procurement decisions especially tricky. Too much stock would lead to unsold inventory at the end of the season (which would be practically worthless) while too less stock would mean missed sales. Retailers use the mechanism of Markdowns to liquidate their stocks as profitably as possible. But then, Markdowns need to be applied with measured discretion for the implications can be significant. Marking down too less and/or too late could lead to unsold stocks at the end of the season causing heavy losses. On the other hand marking down too heavily and/or too early exhausts stocks at unnecessarily low profitability early in the season with massive opportunity losses.

Presently, Retail Buyers do this balancing intuitively based on experience and “gut feel”. Clearly, intuition alone is not adequate to deal with the inherent complexity of the fashion retailing business. Decision-making on critical issues such as procurement and markdown management needs to become a lot more scientific in order to thrive in this demanding environment.

There are thus two related problems that retail buyers need to deal with day in and day out in the fashion retail industry:

a) How much to procure and
b) When and how much to markdown

in order to maximize profitability over the entire selling season.

B_Line answers these questions, taking into account a range of considerations that go into determining the profitability of a retail operation.

Input related to Demand, Prices, Costs and Store Policies etc. is fed to the B_Line Optimizing Engine that uses the stochastic dynamic programming technique to work out profit maximizing procurement and markdown strategies.

The major inputs to B_Line are

  • Permissible price levels for each Period
  • Demand estimates
  • Inventory procurement plan
  • Different types of real and notional costs involved such as
        - cost of goods
        - carrying cost
        - cost of financing
        - promotional cost
        - stock-out cost
  • Inventory shrinkage
  • Other information such as
  • sales returns further broken down into usable returns and unusable returns
  • rate of deterioration and discard

    •         
    Click on image to see screen


    The major outputs from B_Line are

  • Optimal period wise pricing strategy given an inventory procurement pattern
  • Optimal season starting inventory and corresponding optimal pricing strategy     along with price change timings
  • Optimal season starting inventory given a specific pricing strategy.

    •         
    Click on image to see screen

    B_Line backs its recommendations with a wealth of decision support information both at the period (detailed) and season (summary) levels - sales revenues and profits

  • cost analysis
  • cash flow analysis
  • opportunity loss analysis

    Scenario analysis

    B_Line provides for simulation of the worst, likely and best case scenarios so that the user is clear about where the B_Line suggested course of action falls within this bandwidth of possibility.

    Clearly, the quality of the strategy recommended by B_Line depends on the quality of inputs. The dependability of B_Line results is therefore directly determined by the extent to which the inputs reflect the ground reality.

    • Given the uncertain world of fashion, demand estimation is a major challenge. It is to be expected that estimates of demand get better tuned as the season progresses and we get a chance to observe market behavior.

    B_Line has a built in Adaptive Learning Mechanism that enables it to revise demand estimates based on the actual sales data gathered through the POS systems and continuously refine the strategy for the remaining part of the season.

    An established mathematical technique known as Bayesian Updating is used to carry out the demand revision.


    At the start of the season, the demand estimates as available at that time are fed to the optimizing engine and the optimal pricing worked out.

    This pricing is then applied and this leads to certain level of actual sales.

    The original demand estimate and the observed actual sales are then fed back to the Learning Engine, which then generates the revised demand estimates.

    These revised estimates along with the actual inventory position are again fed to the optimizing engine to rework the revised optimal strategy.

    This process is repeated till the end of the season.



    B_Line is thus an interactive decision support mechanism that works with you through the season, learning from actual market experience, suggesting optimal strategies at different times in the season to help you maximize your profits over the entire season.

    © 2003 Markdown Management Inc. and FI Sofex Limited   The B_Line solution  |   The B_Line advantage  |   Case studies