Company Background

Souper Bowl (''the company'') is a privately held business headquartered in Maine, and has a fiscal year-end of December 31. The company has been in business since 2008 and prides itself on offering creative soups at a reasonable price and that are made with locally sourced ingredients. The most popular soups include sweet potato corn chowder, curried root vegetable and lentil, and maple-roasted butternut squash. Souper Bowl typically experiences increased sales during winter months since soup hits the spot on a cold and snowy day. To further encourage sales on days when customers often avoid venturing outside, the company provides a delivery service and guarantees that soup can be delivered to anyone no matter what the weather.

The company found this strategy to be particularly successful in 2015 when New England (including Maine) experienced record snowfall during February and March. Souper Bowl sells their soup at several restaurant locations throughout Maine. The company employs three regional managers that direct the day-to-day operations for a group of stores that are organized by approximate geographic region: northern Maine (Store Type 1), mid-Maine (Store Type 2), and coastal Maine (Store Type 3). Each regional manager knows their local market well and has the flexibility to advertise and offer promotions with the overall goal of increasing sales year over year. If total sales at the end of the year exceed total sales from the prior year for that manager's set of locations (i.e., ''Store Type''), then the manager earns a monetary bonus from the company.

An audit of the company is required to comply with debt covenants related to a large bank loan that the company entered into when it began operations. Specifically, Souper Bowl must provide audited annual financial statements to the bank within 90 days of the fiscal year-end. The company must also provide unaudited quarterly financial statements to the bank within 45 days of the end of each quarter. The debt contract includes a financial covenant that requires pre-tax income in each quarter to be greater than zero. If not met, the bank has multiple remedies at its disposal, including calling the loan such that the entire balance is due immediately, seizing the company's assets that are posted as collateral, or providing a waiver for the violation.

You have been working for a recently formed accounting analytics team at DEKP LLP. In the short time your team has been together, you have already started building a very good reputation regarding your data management and predictive analytics skills. The partner in charge of the Souper Bowl audit Nancy Bosch was particularly impressed with two of your team's recent assignments (Bibitor inventory management and NewPub tax case) and specifically requested for your assistance in this engagement. She was particularly impressed, when during a coffee-break the topic came up that you had generated a model to predict quarterly sales for Apple and how you had used your model to adjust your market position for Apple.

As part of your team's role in this audit engagement, you will be performing a large portion of the planning and testing of sales for the 2016 audit of Souper Bowl. AU-C Section 240.26 states that ''when identifying and assessing the risks of material misstatement due to fraud, the auditor should, based on a presumption that risks of fraud exist in revenue recognition, evaluate which types of revenue, revenue transactions, or assertions give rise to such risks.'' During planning for the audit, it was determined that the following three management assertions represent significant risks for revenues:

1. recorded sales occurred;
2. sales are accurately recorded; and
3. sales are recorded in the proper period.

In prior years, the audit approach relied on random sampling to test revenues. However, Nancy wanted your team to develop more focused procedures in the current year to hone in on potentially riskier sales transactions. As a result, the plan is to perform predictive sales analytics to identify unusual trends in the daily sales data with the goal of identifying sales on specific days at specific store locations that should be subjected to substantive testing due to heightened risks. The remainder of the population would then be sampled using a random sampling approach.

Based on your understanding of the Souper Bowl's business model, it seems clear that daily sales fluctuate with weather conditions. The client has provided the following files:

1. A file with daily store sales for fiscal years 2015-2016 (dt4TP_sales.csv).
2. A file that lists the stores (dt4TP_stores.csv). The file includes a field (nameWCL) that contains the name of the nearest NOAA - Weather Center Location.
3. A file that contains a list of store and regional managers (dt4TP_employee.csv).

Your team has extracted the following information from the NOAA (https://www.ncdc.noaa.gov/cdo-web/)

1. A list of the weather stations nearest to each store (dt4TP_WCL.csv)
2. The daily weather data from each one of these stations from January 1, 2015 to December 31st, 2016 (dt4TP_NOAA.csv)

Use the daily sales by location as provided by the client (2016 and 2015) and the weather data from NOAA to form expectations regarding reasonable level of revenues . Your goal is to develop a prediction model to identify potential outliers in the 2016 daily sales data.