Description

This downloadable virtual product comprises a deployable web or desktop app that answers the following question: How many and where should I open service facilities with a limited rent expense and investment sum budget? The objective is to maximize sales revenue, subject to investment and rent expense limit constraints. The downloadable zip-file is a virtual product that contains a Shiny Python app for running a app that reads the following input data in pre-defined .csv file templaes: a) market data, i.e. market demand and consumer spending forecast by relevant latitude and longitude coordinate (fine meshed grid of demand points), and b) candidate facility data. The app furthermore requires the user to specify an total investment budget for opening facilities (e.g. renovating the rented facilities, buying equipment and furniture, etc.), as well as a rental expense budget (per relevant period). The app can be deployed on a server, for web-based access – without you ever having to touch the code. If you prefer such a solution, contact us via our contact form for techincal support. See below video for a quick demonstration.

This app can be used for visualizing spatial distribution of forecasted consumer demand and consumer spending budgets by region – for e.g. strategic marketing or sales distribution purposes. The app furthermore considers facility candidate data and spending budgets for running a mathematical optimization program, thereby identifying the optimal facilities for revenue maximization, subject to spending, capacity, and investment constraints.

Composition of the mathematical model and its input data

The app uses the input data for constructing and running a mathematical optimization model.

The objective is to maximize revenue. Revenue is generated when a service facility supplies material, goods, or services to a consumer or client at the service facility. Each facility candidate, specified in the input facility candidate .csv-file, has associated capacity, investment need, and rental expenses. Considering this, the problem is about maximizing revenue while not exceeding the rental expense budget, and while not violating the investment budget.

The consumer demand is forecasted and provided as an input to the facility allocator app in the form of a spatial demand grid. The market.csv template provides the spatial distribution of consumer demand and consumer spending budgets, which in combination determines the sales potential by spatial region.

For revenue maximization the distance between relevant market demand spatial data points and relevant service facility matters. The model assumes a linearly decreasing purchasing probabiliuty within a maximum sales radius. This means: For each consumer, with consumers being represented by market demand points, the probability of paying for a service at a service facility decreases linearly up to a maximum sales radius (e.g. from its office or home), and the probability is 0.0% beyond that sales radius.

See additional details in content overview below.

Detailed downloadable product content overview

This downloadable virtual product contains:

• filetools.py Python file that contains
• maptools.py Python file that contains
• model.py Python file that contains
• app.py Python file that implements the Shiny app with server and user interface implementation
• market.csv file as a template input data file, providing the following input data:
  • column #1: id of spatial market demand data point
  • column #2: latitude coordinate of spatial data point for modeling market demand
  • column #3: longitude coordinate of spatial data point for modeling market demand
  • column #4: projected total demand for the respective spatial data point, for the relevant period
  • column #5: average spending budget per demand unit, in relevant currency (budget)
• facilities.csv file as a template input data file, providing the following input data:
  • column #1: id of facility candidate
  • column #2: latitude coordinate of service facility candidate
  • column #3: longitude coordinate of service facility candidate
  • column #4: capacity (in demand units) for relevant period, for service facility candidate
  • column #5: investment sum required for opening the service facility
  • column #6: rent expense, e.g. monthly rent due for renting the service facility
• requirements.txt file that lists all Python dependencies that you need for running this app

When running the app, the user is furthermore prompted for the following additional model input data:

• Investment limit, i.e. maximum possible investment as a sum for all facilities being opened
• Rent expense limit, i.e. maximum total rent expense for all operated service facilities

You can execute this app directly on your laptop, but you can also deploy as a web-application, running on a web-server. If you need technical support for this, please contact us.

Related SCDA products

If you are interested in this service facility allocator app, you might also be interested in the following SCDA products:

• Link: Geocoder app
• Link: Heatmapping app
• Link: Distance calculator app
• Link: Yogurt supply chain simulation app
• Link: Single warehouse allocator app