Quantifying and understanding the supply and demand of goods and services in your trade area is foundational to evaluating the performance of existing facilities and also planning for future expansion. The Trade Area Solutions core expertise is being able to estimate a network of trade areas using our spatial economic framework. Its major advantage is that it ensures a complete and accurate supply and demand picture by accounting for the growing portion of producer purchases between trade areas. Please click here for a complete explanation of our retail trade area framework and its major benefits.
Trade Area Solutions also provides "quick and dirty" trade area definitions such as circles and customer point-of-sale derived polygons. We believe, however, that these conventional trade area techniques are false economy. Click here for a background as well as the major economic problems associated with conventional trade area analysis.
Supply & demand analysis
  
Short and long-term forecasting The detailed farm level supply and demand data by rural municipality and by major grains and oilseeds forms the foundation for short and long-term forecasting at a detailed regional level. This is ideal for "what if" scenarios in policy analysis and revenue planning. Trade Area Solutions have extensive experience in supply and demand forecasting and agriculture/rural policy analysis. Grain handling & transportation analysis The Spatial Grain Flow Model is ideally suited for monitoring changes and analyzing the grain handling and transportation system. Its major advantage is that in addition to complete supply and demand data by rural municipality and by major grains and oilseeds, the system also estimates grain flows by truck into or out of rural municipalities and/or grain delivery trade areas in western Canada. The model is also well suited to forecasting and analyzing "what if" scenarios in many different areas including: • the impact of changes in the livestock & poultry sector on grain flows; • the impact of changes in transportation freight rates and/or multi-car freight rate incentives on grain flow patterns; • the impact of structural changes such as branch line abandonment, the construction of new high-through-put elevators and the closure of elevators on grain flow patterns; • market share analysis: forecast changes in shares given elevator openings and closures; • accurate grains & oilseeds supply/demand analysis and short and medium-term forecasts at a very detailed regional level: for each rural municipality or each grain delivery point; • analyzing the performance of elevators at each delivery point; • estimating the volume of grains & oilseeds trucked out of or into each rural municipality and/or each delivery point in western Canada; • quantifying and forecasting the impact of grain flow patterns on the road network; and • monitoring the impact of changes in grain transportation policies.    
Performance metrics
The reality of agri-business is that stores in different locations will have different market shares for a number of reasons including the impact of competition. Market share statistics, however, don't include the impact of competition. As a result, Trade Area Solutions also calculates an untapped market potential performance metric. Untapped market potential is defined as target sales - actual sales. Target sales are calculated as an important byproduct of the indices developed in the supply/competitive analysis.
The end result is is untapped market potential metric that includes the full impact of competitors within a trade area and allows for a direct comparison between a network of existing facilities, and new potential sites by individual goods and services despite very different competitive profiles.
The calculation of untapped market potential for both existing facilities and new potential sites through the use of the supply analysis (destination data) is an important advantage of our market research framework. This often removes the need to spend additional resources on further site evaluation modeling or analysis such as a spatial interaction models (based on consumer origin data) to better forecast sales and/or calculate untapped market potential.
Forecasting sales is one of the natural end products of trade area analysis. Our advantage is that we don't forecast sales in isolation from major competitors. We forecast sales for all major competitors with information from the supply/competitor analysis which ensures a much higher level of accuracy.
Although there is no perfect prediction tool given the numerous factors and the potential for future change, the key is a high level of understanding of the supply and demand within the trade area. Since our framework builds a complete supply and demand picture, sales forecasting is a natural byproduct of this process. We also have extensive forecasting experience and have the ability to use a variety of intuitive, econometric, and GIS solutions when necessary. Often the best and most accurate forecasting method is also the simplest.
The sales forecasting process utilizes the profiles and indices developed in the supply and demand analysis. The key supply and demand drivers are identified and analyzed, which can then be fed directly into a firm's marketing strategy, as well as site selection studies. We can also perform a complete probabilistic risk assessment of the sales forecasting results.
Trade area profiling/facility
sizing
Site
selection/network
planning For organizations with multiple outlets, retail network optimization remains poorly understood. The current conventional network optimization models are called location-allocation models. These models have two components: the location portion examines the location while the allocation component is not unlike a spatial interaction model (SIM) which predicts purchase behavior. The major problem is that the trade area for location-allocation models is often an input into the model and often assumed to be a relatively simple conventional trade area. This creates a major data gaps because the foundational supply and demand picture is incomplete and inaccurate. Economics 101 provides background as well as the major economic problems associated with conventional trade area analysis.
The Trade Area Solution site selection and network planning services employ much of the analysis framework outlined above. Our framework advantage is that it is a whole-of-system approach, which offers a seamless market analysis solution to locations with facilities (validated with customer point-of-sale data), new potential sites and network planning. It ensures a complete picture and understanding of the market economics by filling in the missing regional supply and demand data gap. This framework allows a direct comparison of existing facilities with potential expansion sites and a ranking of these site in terms of untapped market potential. In addition, the framework allows us to analyze and model dynamic "what if" scenarios in uncertain business environments where the market potential and competitor actions or reactions are critical to the business plan. Our business advantage provides a more complete explanation of our trade area framework and its major benefits.
Trade Area Solutions offers a wide range of detail for site selection and network planning projects. We can build a site model that includes the key supply and demand drivers to better forecast sales and quantify the impact of the above internal trade area dynamics of customer purchase behavior relative to competitors. Although there is no perfect prediction tool given the numerous factors and the potential for future change, the key is a high level of understanding of the supply and demand within the trade area. We have the ability to use a variety of intuitive, econometric, and GIS solutions when necessary. Often the best and most accurate forecasting method is also the simplest. We provide site selection and network planning at any scale from a single store with a single product to a nationwide network with a large basket of goods and services.
Producer profiling/segmentation
Yield modeling Trade Area Solutions is currently beta testing a yield model for US corn and soybean crops. The yield model combines daily real time weather, weekly near real time satellite remote sensing data, and daily short and long-term weather forecasts using advanced statistical and GIS modeling techniques. The result is a model capable of producing daily yield estimates throughout much of the growing season, which can explicitly incorporate virtually all available historical and forecast data on any given day. Preliminary results suggest a viable commercial product with an extremely high forecast accuracy rate. The US yield model project began as a natural extension of our modeling expertise. The objective was to build the most accurate yield model possible for the major US, crops which could be run on a daily basis throughout the growing season. An examination of the industry revealed that there are few quantitative models currently in use. And the ones in use are not capable of incorporating all available information including remote sensing and weather data. In addition, these competitor models often have accuracy rates that are too low for them to be of significant commercial value. This yield model is a first in that it combines daily real time weather, bi-weekly near time satellite remote sensing data, and daily short and long-term weather forecasts using advanced statistical modeling techniques. The result is a model capable of producing daily yield estimates throughout much of the growing season which explicitly incorporates virtually all available historical and forecast data on any given day. Major
strengths Data • Area, yield, production statistics for corn, soybeans,
cotton, winter wheat, spring wheat, durum wheat, barley, sorghum,
flaxseed and oats for each county, crop reporting district (CRD)
and state from 1990 to present. Source: NASS. Output Accuracy As expected, forecast accuracy improves as the crop matures. Note that the corn model begins (July 16th) with an average accuracy of about 85 percent compared to over 90 percent for the soybean model. Although these graphs display average accuracy from 1990 to 1999, it is important to note that if weather conditions in July, August and September are relatively average, then the forecast accuracy improves dramatically. For example, in 1995 when weather conditions were relatively average during the growing season, the model predicted both soybean and corn yields within 1 percent on July 16th. The greater the weather deviates from normal during the growing
season, the higher the forecast error is during the beginning of
the crop year. For example, during the 1993 drought year, corn yields
are forecasted to be 25% higher than actual on July 16th. The final
1993 estimate, however, ended up being 100% accurate by Nov 5th.
Custom
mapping
Other
research and
analysis
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