Prophet 21 World Wide User Group Webinars. Barry Hallman. SQL Queries & Views. (Part 2)

Transcription

1 Prophet 21 World Wide User Group Webinars SQL Queries & Views (Part 2) Barry Hallman

2 Disclaimer This webinar is an attempt by P21WWUG members to assist each other by demonstrating ways that we utilize the Prophet21 system and other related products. The P21WWUG and the individuals conducting these webinars take no responsibility for potential issues that arise as a result of taking the advice given during the session. The P21WWUG does not recommend using any SQL statements to update your database without having those statements first reviewed by Activant or other experienced SQL professionals. Using SQL statements to update your database may result in corrupting your database 6

3 P21 WWUG Webinar Basic SQL Queries & Views Barry Hallman Purchasing agent ( ) Sales manager for wholesale distributor ( ) Regional manager for national computer retailer ( ) Director of computer consulting for an accounting firm ( ) Senior implementation consultant for local consulting firm ( ) Accounting software and network support (1986-present) Database manager (1992-present) Favorite color (blue) 7

4 P21 WWUG Webinar Basic SQL Queries & Views Overview Creating a weekly cash flow analysis can be a daunting and repetitive task, but in this webinar we will develop the tools necessary to project our income from receivables and our outflow from payables into weekly buckets to project our net change in cash. In order to do this, we will concentrate on developing well-targeted selections of data using SQL queries and views. We will use date functions to divide data into date ranges, we will use mathematical functions to find the maximum/latest and average values, and we will group the data into logical groups for analysis. These skills will be applied to both receivables and payables, and the results will be combined into one output result. We will also discuss ways to export this data to Excel for manipulation. 8

5 P21 WWUG Webinar Basic SQL Queries & Views OBJECTIVE Project Definition Management determined that they want to be able to look at a weekly cash flow report that reports expected weekly cash receipts from customers (A/R) and weekly disbursements to vendors (A/P). The data is to be divided into nine buckets one for each of the next eight weeks, and one for all transactions week 9 and beyond. ASSUMPTIONS Customers will likely pay according to their average fast slow days Vendors will be paid on the cash discount date. Cash discount would NOT be included in the transaction amount. ASSIGNMENT Create a series of queries that would collect the A/R and A/P activity, calculate the expected transaction date, and break the amounts into the 9 respective buckets. This data will then be pulled into Excel for further analysis. 9

6 P21 WWUG Webinar Basic SQL Queries & Views Agenda 1. Create a query to determine the customers normal pay schedules 2. Create a query to determine the amounts owed by customers 3. Break the A/R into weekly buckets 4. Create a query to determine the amounts owed to vendors 5. Break the A/P into weekly buckets 6. Join the A/R and A/P into one dataset 7. Discuss moving this data to Excel for analysis 10

7 P21 WWUG Webinar Basic SQL Queries & Views Understand how to: Objectives Compute values using SQL mathematical function Group data based on one or more columns Bucket data by dates (or any other) criteria Join disparate data into one dataset Prepare the data for export to Excel 11

8 P21 WWUG Webinar Basic SQL Queries & Views Step 1: Computing Customers Expected Payment Patterns What are P21 average fast slow days Where is the data found How is the data stored? How can data be grouped by customer? How is an average of the averages computed? 12

9 P21 WWUG Webinar Basic SQL Queries & Views Step 2: Retrieving the Open Receivables Computing the open amount of an invoice Restricting our data to open transactions Using the results from the view from Step 1 to calculate an expected cash receipt date Putting the data into weekly buckets by using date math and conditional processing programming constructs. 13

10 P21 WWUG Webinar Basic SQL Queries & Views Step 3: Retrieving the Open Payables Repeat Step 2 for accounts payable transactions using the discount date as the transaction due date 14

11 P21 WWUG Webinar Basic SQL Queries & Views Step 4: Combining the A/R and A/P Data Combine the data that is retrieved in Step 2 and Step 3 by using the SQL UNION join clause 15

12 P21 WWUG Webinar Basic SQL Queries & Views Step 5: Exporting the Data to Excel Prepare the data for export to Excel Using the QUERY RESULTS TO functionality Using the SAVE RESULTS AS functionality Special delimiters Opening a *.csv data file Parsing a text file Using MSQuery (Reference: WWUG webinar of 08/24/2010 by Don Williams) 16

13 P21 WWUG Webinar Basic SQL Queries & Views Live Presentation (Please refer to refer to separately published Supplement for printed details) 17

14 P21 WWUG Webinar Basic SQL Queries & Views Questions and Answers 18

15 Disclaimer This webinar is an attempt by P21WWUG members to assist each other by demonstrating ways that we utilize the Prophet21 system and other related products. The P21WWUG and the individuals conducting these webinars take no responsibility for potential issues that arise as a result of taking the advice given during the session. The P21WWUG does not recommend using any SQL statements to update your database without having those statements first reviewed by Activant or other experienced SQL professionals. Using SQL statements to update your database may result in corrupting your database 19

16 Contact Information Barry Hallman President Hallman Consulting & Services, Ltd. (513)

17 WWUG Webinar SQL Queries & Views (Part 2) Presentation Supplement September 7, 2010 Barry Hallman IT Specialist Big Chief, Inc. (513) DISCLAIMER o This webinar is an attempt by P21WWUG members to assist each other by demonstrating ways that we utilize the Prophet21 system and other related products. o The P21WWUG and the individuals conducting these webinars take no responsibility for potential issues that arise as a result of taking the advice given during the session. o The P21WWUG does not recommend using any SQL statements to update your database without having those statements first reviewed by Activant or other experienced SQL professionals. o Using SQL statements to update your database may result in corrupting your database

18 Supplement to SQL Queries - Part 2 Step 1a. Code Sample 1a. SELECT CCH.customer_id AS CustId,Cust.customer_name AS CustName,CCH.customer_credit_history_uid AS CCHUid,(year_invoiced * month_invoiced) AS MostCurrentYearMonth,avg_fast_slow_days AS Av_avg_fast_slow_days FROM customer_credit_history AS CCH (NOLOCK) INNER JOIN customer AS Cust (NOLOCK) ON CCH.customer_id = Cust.customer_id What this statement says is: Multiply the value in the column year_invoiced by 100, and then add to that product the value in the column month_invoiced. The result is a number that can be used for comparing the payment period with the current date in the next step. Examples: year_invoiced month_invoiced Result (2007 * 100) + 4 = (2009 * 100) + 6 = (2010 * 100) + 9 = Page 1 of 10

19 Supplement to SQL Queries - Part 2 Step 1b. Code Sample 1b. SELECT CCH.customer_id AS CustId,Cust.customer_name AS CustName,CCH.customer_credit_history_uid AS CCHUid,(year_invoiced * month_invoiced) AS MostCurrentYearMonth,avg_fast_slow_days AS Av_avg_fast_slow_days FROM customer_credit_history AS CCH (NOLOCK) INNER JOIN customer AS Cust (NOLOCK) ON CCH.customer_id = Cust.customer_id WHERE CCH.avg_fast_slow_days > -181 AND CCH.avg_fast_slow_days < 181 AND (CCH.year_invoiced * CCH.month_invoiced) < ((DATEPART(YEAR, GETDATE()))) * DATEPART(MONTH, GETDATE()) + 1 < > GETDATE() DATEPART These conditions serve to exclude any invoice that has been paid more than 180 days efore or after the due date. The assumption is that this was either a problem invoice or a data error that should be excluded, so as to not skew the computerd average. Returns today s date to SQL This SQL function extracts a specified portion of a date. The portions that can be specified are YEAR, MONTH, or DAY. Syntax: DATEPART({specifier}, {date}) Examples: (assumes the current date is Sept. 7, 2010) Formula Result DATEPART(YEAR, ) 2008 DATEPART(YEAR, GETDATE()) 2010 DATEPART(YEAR, GETDATE()) DATEPART(MONTH, ) 1 DATEPART(MONTH, GETDATE()) 9 DATEPART(MONTH, GETDATE()) DATEPART(DAY, ) 30 DATEPART(DAY, GETDATE()) 7 DATEPART(DAY, GETDATE()) ((DATEPART(YEAR, GETDATE()))) * DATEPART(MONTH, GETDATE()) What these statements say is: Include ONLY rows from the avg_fast_slow_days where the value in the column is greater than -181 (days) and less than +181 days, and include only rows from the avg_fast_slow_days table where the year and month of the stored fast/slow days are not later than this month of this year. Effectively, this screens out extraordinary collection periods or erroneous data. Page 2 of 10

20 Supplement to SQL Queries - Part 2 Step 1c. Code Sample 1c. SELECT CCH.customer_id as CustId,MAX(Cust.customer_name) AS CustName,MAX(CCH.customer_credit_history_uid) AS CCHUid,MAX(year_invoiced * month_invoiced) AS MostCurrentYearMonth,AVG(avg_fast_slow_days) AS Av_avg_fast_slow_days FROM customer_credit_history AS CCH (NOLOCK) INNER JOIN customer AS Cust (NOLOCK) ON CCH.customer_id = Cust.customer_id WHERE CCH.avg_fast_slow_days > -181 AND CCH.avg_fast_slow_days < 181 AND (CCH.year_invoiced * CCH.month_invoiced) < ((DATEPART(YEAR, GETDATE()))) * DATEPART(MONTH, GETDATE()) + 1 GROUP BY CCH.customer_id GROUP BY AVG MAX This SQL action causes data to be grouped or summarized by the column (or columns) specified. Whenever GROUP BY is used, ALL columns specified in the SELECT statement MUST be in an AGGREGATE function or listed in the GROUP BY clause. Returns the average value for this column for the group specified in the GROUP BY clause. Returns the greatest value for this column for the group specified in the GROUP BY clause. What these statements say is: After all the data has been read from the query, group the data by the unique values in the customer_id column, and for the columns with aliases CustName, CCHUid, and MostCurrentYearMonth select the maximum value for all rows for this customer. For CustName the maximum value is the customer name. For the CCHUid and the MostCurrentYearMonth columns this is the greatest value and is therefore the last recorded value for that column for that customer. The avg function computes the average of all the eligible values in the avg_fast_slow_days column. Page 3 of 10

21 Supplement to SQL Queries - Part 2 Step 2a. Code Sample 2a. SELECT 'AR' AS Source,InvH.company_no AS Company,AvgFSDays.CustId AS Id,AvgFSDays.CustName AS Name,InvH.invoice_no AS DocNo,InvH.invoice_date AS DocDate,InvH.total_amount AS DocTotal,(InvH.total_amount - InvH.amount_paid) AS BalDue,DATEADD(DAY, AvgFSDays. AV_avg_fast_slow_days, InvH.net_due_date) AS ExpPayDate FROM invoice_hdr AS InvH (NOLOCK) LEFT OUTER JOIN gbh_view_avg_fast_slow_days AS AvgFSDays (NOLOCK) ON InvH.customer_id = AvgFSDays.custid INNER JOIN customer_credit_history AS CCH (NOLOCK) ON CCH.customer_credit_history_uid = AvgFSDays.CCHUid WHERE InvH.paid_in_full_flag = 'N' AND InvH.total_amount - InvH.amount_paid <> 0 'AR' AS Source DATEADD This is a static designator that every row will have in the resultant recordset This computes the expected date of payment for each row as the net_due_date of the invoice plus the average avg_fast_slow_days for that customer. Syntax:DATEADD ({specifier}, {value}, {base date}) Examples: (assumes the current date is Sept. 7, 2010) Formula Result DATEADD (YEAR, 2, ) DATEADD(YEAR, 2, GETDATE()) DATEADD(YEAR, -1, GETDATE()) DATEADD(MONTH, 2, ) DATEADD(MONTH, 2, GETDATE()) DATEADD(MONTH, -1, GETDATE()) DATEADD(DAY, 2, ) DATEADD(DAY, 2, GETDATE()) DATEADD(DAY, -1, GETDATE()) What these statements say is: Create a column on every row and fill that column with AR. ( This will be used later to distinguish the accounts receivable records from the accounts payable records when the results are merged in step 4. ) Page 4 of 10

22 Supplement to SQL Queries - Part 2 Step 2b. Code Sample 2b. SELECT 'AR' AS Source,InvH.company_no AS Company,AvgFSDays.CustId AS Id,AvgFSDays.CustName AS Name,InvH.invoice_no AS DocNo,InvH.invoice_date AS DocDate,InvH.total_amount AS DocTotal,(InvH.total_amount - InvH.amount_paid) AS BalDue,DATEADD(DAY, AvgFSDays.AV_avg_fast_slow_days, InvH.net_due_date) AS ExpPayDate,Week1 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) < 8 THEN (InvH.total_amount - InvH.amount_paid),Week2 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) > 7 AND ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) < 15 THEN (InvH.total_amount - InvH.amount_paid),Week3 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) > 14 AND ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) < 22 THEN (InvH.total_amount - InvH.amount_paid),Week4 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) > 21 AND ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) < 29 THEN (InvH.total_amount - InvH.amount_paid),Week5 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) > 28 AND ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) < 36 THEN (InvH.total_amount - InvH.amount_paid),Week6 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) > 35 AND ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) < 43 THEN (InvH.total_amount - InvH.amount_paid),Week7 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) > 42 AND ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) < 50 THEN (InvH.total_amount - InvH.amount_paid) --(continued on next page ) Page 5 of 10

23 Supplement to SQL Queries - Part 2 Step 2b. Code Sample 2b. - continued,week8 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) > 49 AND ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) < 57 THEN (InvH.total_amount - InvH.amount_paid),Week9 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), net_due_date + AvgFSDays.Av_avg_fast_slow_days), 0) > 56 THEN (InvH.total_amount - InvH.amount_paid) FROM invoice_hdr AS InvH LEFT OUTER JOIN gbh_view_avg_fast_slow_days AS AvgFSDays (NOLOCK) ON InvH.customer_id = AvgFSDays.custid INNER JOIN customer_credit_history AS CCH (NOLOCK) ON CCH.customer_credit_history_uid = AvgFSDays.CCHUid WHERE InvH.paid_in_full_flag = 'N' AND InvH.total_amount - InvH.amount_paid <> 0 CASE WHEN THEN ELSE END ROUND DATEDIFF Following a {column name} = statement, this construct evaluates a true/false condition. When the condition is true, the value following the THEN keyword is assigned to the column. Otherwise, the value following the ELSE statement is assigned to the column. This construct is always finished with an END statement. Rounds a value to the number of decimal places specified. Syntax: ROUND({value or expression}, {decimal places}) This computes the difference between two dates Syntax: DATEDIFF({specifier},{base date},{comparison date}) Examples: (assumes the current date is Sept. 7, 2010) Formula Result ROUND( ,2) ROUND( ,0) 124 DATEDIFF(YEAR, , GETDATE()) DATEDIFF(MONTH, GETDATE(), ) DATEDIFF(DAY, , GETDATE()) 2 (years) -32 (months) 951 (days) What these statements say is: When the number of days between today s date and the expected date of the cash receipt (calculated as the net_due_date + the average fast/slow days for this customer) is less than 8 days, then assign the open amount of the invoice to the column Week1. Otherwise, set the value of Week1 to 0. If the difference in the number of days is more than 7, but less than 15, then assign the open amount of the invoice to the column Week2. Otherwise, set the value of Week2 to 0. Note: this logic gets repeated for weeks 3 through 9, where in week 9 there is no upper limit. Page 6 of 10

24 Supplement to SQL Queries - Part 2 Step 3a.. Code Sample 3a. SELECT 'AP' AS Source,InvH.company_no AS Company,InvH.vendor_id AS Id,Vend.vendor_name AS Name,InvH.voucher_no AS DocNo,InvH.invoice_date AS DocDate,-InvH.invoice_amount AS DocTotal,-(InvH.invoice_amount - InvH.amount_paid) AS BalDue,InvH.terms_due_date AS ExpPayDate FROM apinv_hdr AS InvH (NOLOCK) INNER JOIN vendor AS Vend (NOLOCK) ON Vend.vendor_id = InvH.vendor_id AND InvH.company_no = Vend.company_id WHERE InvH.paid_in_full <> 'Y' AND InvH.invoice_amount - InvH.amount_paid <> 0 What these statements say is: This code essentially mirrors the code developed in Step 2, but retrieves the A/P data instead of the A/R. One functional difference is that we are using the date that a voucher is due to be paid with discount (terms_due_date), rather than the value of the expected payment date based on the average fast/slow payment days from A/R as calculated in steps 1a 1c. Note: the voucher amount and balance due columns have both been preceded with the minus sign to make these negative amounts i.e. negative cash flows. Page 7 of 10

25 Supplement to SQL Queries - Part 2 Step 3b. Code Sample 3b. SELECT 'AP' AS Source,InvH.company_no AS Company,InvH.vendor_id AS Id,Vend.vendor_name AS Name,InvH.voucher_no AS DocNo,InvH.invoice_date AS DocDate,-InvH.invoice_amount AS DocTotal,-(InvH.invoice_amount - InvH.amount_paid) AS BalDue,InvH.terms_due_date AS ExpPayDate,Week1 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) < 8 THEN (InvH.invoice_amount - InvH.amount_paid),Week2 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) > 7 AND ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) < 15 THEN (InvH.invoice_amount - InvH.amount_paid),Week3 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) > 14 AND ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) < 22 THEN (InvH.invoice_amount - InvH.amount_paid),Week4 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) > 21 AND ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) < 29 THEN (InvH.invoice_amount - InvH.amount_paid),Week5 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) > 28 AND ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) < 36 THEN (InvH.invoice_amount - InvH.amount_paid),Week6 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) > 35 AND ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) < 43 THEN (InvH.invoice_amount - InvH.amount_paid),Week7 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) > 42 AND ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) < 50 THEN (InvH.invoice_amount - InvH.amount_paid),Week8 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) > 49 AND ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) < 57 THEN (InvH.invoice_amount - InvH.amount_paid),Week9 = (CASE WHEN ROUND(DATEDIFF(DAY, GETDATE(), InvH.terms_due_date), 0) > 56 THEN (InvH.invoice_amount - InvH.amount_paid) --(continued on next page ) Page 8 of 10

26 Supplement to SQL Queries - Part 2 Step 3b. Code Sample 3b - continued. FROM apinv_hdr AS InvH (NOLOCK) INNER JOIN vendor AS Vend (NOLOCK) ON InvH.vendor_id = Vend.vendor_id AND InvH.company_no = Vend.company_id WHERE InvH.paid_in_full <> 'Y' AND InvH.invoice_amount - InvH.amount_paid <> 0 What these statements say is: This code essentially mirrors the code developed in Step 2, but retrieves the A/P data instead of the A/R. One functional difference is that we are using the date that a voucher is due to be paid with discount (terms_due_date), rather than the value of the expected payment date based on the average fast/slow payment days from A/R as calculated in steps 1a 1c. Page 9 of 10

27 Supplement to SQL Queries - Part 2 Steps 4a. and 4b. SELECT * FROM dbo.gbh_view_open_ar UNION SELECT * FROM dbo.gbh_view_open_ap Code Sample 4a. Code Sample 4b. SELECT * FROM dbo.gbh_view_open_ar_by_week UNION SELECT * FROM dbo.gbh_view_open_ap_by_week UNION This type of JOIN function appends the results of two (or more) sets of data by taking all the rows of one dataset and adding all the rows from the other dataset. The only requirements of the datasets is: - Both/all datasets MUST have the same number of columns - The datatypes for each column must be the same in both/all datasets What these statements say is: These two code statements both work identically. Sample 4a retrieves the open invoice data from the view/query developed in Step 2a, and appends it to all the rows for the open voucher data from the view/query developed in Step 3a. Similarly, Sample 4b does the same thing, but uses the open invoice data in buckets from Step 2b, and the open voucher data in buckets from Step 3b. The data that is appended together is presented as a single dataset. Page 10 of 10