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Analytics Case Study # 2 – Demand Elasticity, MRS, Efficient Inputs

Company “XYZ” is an early stage specialty bakery products company seeking to open multiple locations throughout metropolitan NYC. They are entering a market saturated with individually owned neighborhood bakeries throughout a population of over 8 million.  Given the competitive nature and cost of living in NYC, their belief is that they can gain a large market share by delivering enhanced “up market bakery products” at a better price point.  Beyond advertising, the two key hurdles for success are consumer demand considerations (local loyalty and customer preferences) and XYZ company’s production function for their business model; labor (specialized skills), land (high cost for space rental) and capital (perishable inventory).  Their challenge is to produce more efficiently, thereby offering lower price points while still providing upscale specialty-baked products.  If they produce more efficiently, they will not only yield price points with a greater elasticity (increased quantity responsiveness to price changes), but they will be able to more accurately determine customer preferences that directly affect surpluses and shortages. Through Analytics, both of these challenges can be met: lower costs of production and more efficient inventory management. To date there have not been any chain specialty bake shops in the NYC metropolitan area successfully competing against single unit neighborhood bakeries.


Utilizing consumer preferences, marginal rates of substitution for product consumption and differentiation, XYZ Company can efficiently manage perishable inventory.  This provides lower costs for “Upscale Products” and with a better shelf ratio that addresses perishable inventory.   Inventory specialization, they believe, will lead to increased efficiency, which implicitly brings higher productivity, yielding even further price point reductions.


While two of their three production functions are relatively fixed: labor requires skilled bakers, wages are unionized, and rental space (land) is market priced, the marginal rates of substitution by weather Analytics will assist the inventory management for lower price points.  XYZ now has a competitive edge over existing specialty bakeries by using Analytics to manage their inventory.  They have built their business plan with a more effective use of their capital against the perishable nature of bakery goods, to allow them to more efficiently compete with better shelf ratios against the neighborhood single unit bakeries.


Case Questions:

1.   What are the variables associated with consumer behavior and demographics that affect revenue growth?

2.   How does Analytics enhance XYZ’s business plan beyond conventional financial modeling tools based on traditional accounting methods?

3.   To what extent will inventory efficiency of perishable products enhance profits?


The above questions related to this real world example are easily considered by applying Analytics to Demand Preferences based on weather.   The Analytics of weather patterns offer enhanced shelf ratio inventory, and in turn yield lower price points through more efficient capital materials allocation.  By understanding that sunny days yield higher sales in sweet items, verses higher sales of savory items on inclement days, this small early stage company is able to allocate and optimize its procurement of inventory materials (capital) to produce goods more efficiently.  Based on preferences beyond traditional financial models, forecasting cash flow for material inventory is more efficient.  This Analytic data on weather provides trends on Consumer Demand, including but not limited to: tastes, preferences, moods, demographics, geography of neighborhoods, and locations relative to arteries of transportation.  By inserting substitute product categories (savory items vs. sweets) into a formula for Cross Elasticity of Demand, using SAS, a company can produce a more efficient use of its capital, resulting in greater competitiveness and profitability.