Friday, 21 September 2018

Automating balance sheet creation from multiple ERP systems


  • Turbodata has developed a solution for automated balance sheet developed as per schedule 6 of the Indian statutory reporting requirements.
  • Automation of the balance sheet development process thereby reducing the manual dependence on resources.
  • Balance sheet entries can be quickly done across multiple companies and entities based on the Tally logic. These entities could be on Tally or other systems such as SAP, Navision etc.
  • The balance sheet entries can be done across multiple time frames
  • The development is completely auditable.
  • Getting the format of the end client balance sheet reports
  • Extraction of the data from various systems for balance sheet development. after the data extraction, the data is cleansed and validated.
  • The granularity of the ledger balances is decided. the balance sheet entries can be developed on weekly, monthly, quarterly or yearly ledger balances. In case the end client desires to have the daily update of the balance sheets then it needs to indicate the same.
  • Mapping of the ledger names and the primary group names of the ledger balances with the balance sheet entries in the MS Excel.
  • The closing balances are matched with the ERP closing balances.
s.no
Primary group name
Scheduled 6 report
1.
Reserves
Reserves  & Surplus
2.
Capital Account
Equity Share Capital
3.
Secured
Long Term borrowing
4.
Unsecured
Other long term liabilities
5.
Bank Accounts

6.
Sundry Creditors
Short term borrowing
7.
Provision
Other current liabilities
8.
payroll
Other current liabilities
9.
Duties
Short term provision
10.
Deposits(group name)
Current Investment
11.
Sundry Debtors
Trade Receivables
12.
Cash
Cash and Cash Equivalents
13.
Loan & Advances: include secured loans, unsecured loans and bank OD account
Short term and loan advances
14.
Fixed assets
Fixed Assets
15.
Closing stock
Current Assets
16.
Deferred tax liabilities()
Current liabilities
17.
Profit and appropriation
Money received against share warrants
18.
Unadjusted forex gain/loss.
Gain/loss due to exchange rate variation
  •       Data consolidation takes times
  •       Manual process.
  •        Error prone: auditing of data is an issue.
  •        Inflexibility of  analysis: the end clients do not get graphs and dashboards
  •        The process is very time consuming
  •        The input spreadsheets could be imported in an xml format into a shared folder. The condition is that the input spreadsheet formats should not change for the end client.
  •        The xml inputs are then imported to the database from wherein the reporting takes place.
  •       Turbodata has a readymade datawarehouse for ledger and inventory with built in on fly calculations.  Turbodata inventory and Turbodata ledger have readymade data with perpetual valuations to re generate any type of MS Excel report.
  •        The data extraction shall take place in such a way that minimal RAM is used. This shall help the end client in the following ways:
  •         Multiple end users can update the spreadsheet data while data extraction is happening without any problems
  •           The source machine shall not be stuck.
  •        The solution shall be plug and play and very easy to use.
  •        The data audit step of matching the output with the source table entries shall be part of the solution
  •        The end client can get dashboards using a Business Intelligence tool of its choice. If the consolidated data is below 1 GB then Microsoft PowerBI can be used for getting best in class dashboards
  •        Data entry and data validation shall be designed as part of the solution.
  •     Lower cost of the solution through data compression and sql reduction.
  •        Reduced manual data entry time and quick turnaround times for development of the final reports.

Capturing Data Entry/Data Audit Errors
  •        Wrong tax filing specifically in online scenario.
  •         Wrong business picture
  •         Wrong predictive analytics.
  •       Duplicate payment entries
  •      Duplicate sales entries
  •        Receipt note entries but no purchase invoice entries
  •         Payments not having the required bill reference numbers.

Deployment of Turbodata for Retail company based out of Western India

The benefits of our solution are as follows:

The process of building the balance sheet entails the following steps:

The output of the balance sheet can be produced in MS Excel or any other BI tool, the sample is attached herewith.






Closing stock value: The end client shall have to pre decide the closing stock valuation method. The ETL team supports perpetual weighted average, periodic FIFO, periodic weighted average and perpetual FIFO(both at the order level and by the fiscal date)


Why use our system?
The solution conforms to the accounting standards for India and the respective countries.
The solution is automated(thereby reducing manpower costs). The ETL team has built in connectors for various ERPs such as SAP, Navision, Tally etc.
The process entails data cleansing, data auditing and data profiling before the balance sheet is developed.
Multiple locations data balance sheet is developed with ease and quickness.
The system is auto linked with the tax filings for the respective countries, such as GST in India.
The system seeks to ensure data consistency between the ERP system and the final spreadsheet output.

In balance sheet report developed as per scheduled 6 of the Indian statutory report requirement where current liabilities, non-current liabilities, current assets, non-current assets has been matched with our balance sheet shown in table below. The mapping has been done as per Tally ERP 9.0 specifications and the mappings can be appended.
                                                          





Video links:


Contact details of blog writer:



Name: Apoorv Chaturvedi
Email: apoorv@mnnbi.com
Phone: +91-8802466356
website: www.mnnbi.com
Blogs:  http://mndatasolutionsindia.blogspot.in/

Most of the businesses use MS Excel spreadsheets for data capture, data reporting. A common set of problems that arise during spreadsheet consolidation are as follows:

Turbodata helps resolve the problem by taking the following approach.
Turbodata has readymade datawarehouses associated with inventory, ledger where the on fly calculations are matching with a standard accounting package such as Tally ERP. The product has been designed using the philosophy of Goldratt, Deming and Toyota Production system. There are readymade extractors using C#,.Net for extracting data from ERPs such as Tally, SAP, Navision and even custom spreadsheets.
How does Turbodata help solve the problem?

Why use Turbodata?



Next steps:
The end client could send the following to the above email address:
·       Sample output spreadsheet to be automated
·       Sample input spreadsheets to be consolidated/The details of the source systems to be consolidated.
How Turbodata helped lower the costs of developing a datawarehouse and helped the end clients do predictive analytics with quickness and ease-applicable for retail sales and inventory(Website: www.mnnbi.com)

Purpose of the development of the product: The Turbodata team intends to reduce the costs of the analytic solutions by creating a single platform for ETL, Reporting, Reporting development and predictive analytics. The team also intends to provide the best in class analytics on the same machine on which the ERP is running or with the addition of minimum hardware requirements for the end client. This has been done to develop scalable systems that can be deployed over a large number of customers(with limited budgets) with ease(deployment, delivery and usage) and convenience(maintenance).
The end goal is to increase derisking and predictability for the end clients at lower costs.




Methodology for achieving the required ends for the end client:
·         Turbodata adopted the Inmon methodology for datawarehouse development so that multiple data sources could be added onto the same datawarehouse. That is the change from one data source to another was done with ease. More details on the attached web page link: http://mnnbi.com/mnnbi_tallydataconsolidation.html


o   The benefits of the normalization of data were as follows:
§  The incremental data load took minimum time and had minimum impact on the source system. The ETL team was able to commit the incremental data load to a maximum of 2GB RAM from multiple source systems. The source systems did not hang with the incremental data load working.
§  Error handling was done with ease at staging layer.
§  Massive data compression took place due to reduced data redundancies.
§  The business logic was coded between staging and the ODS layers thereby reducing the length of the final sql code.
The attached video shows a more detailed description of the benefits listed above:
https://www.youtube.com/watch?v=k9Oe9EDRfTM&feature=youtu.be
The joins were reduced in the data mart layer(over which a reporting layer was built).

The ETL team was able to develop extremely complex reports using the datawarehouse as in the attached sample:
https://www.youtube.com/watch?v=1ySNrgI4Na0&feature=youtu.be

Due to the data compression for most projects the ETL team are able to bring the data within 1 GB. Hence the desktop version of Microsoft Power BI could be used free of cost for the end client.

Reducing the cost of predictive analytics solutions
 Most of the end clients use high end predictive tools over the datawarehouse/ over the direct data extract from various source databases. With large datasets predictive analytics using in memory solutions entails high usage of RAM. The ETL team has gone around this issue in the following manner:
o   A seamless environment was created for ETL, reporting and thereafter predictive analytics on SQL/C# and .Net. The reasons for the same are attached herewith:
§  Maintenance becomes easier since there is a single platform for all aspects.
§  The cost comes down since the resources to be used for ETL can also be used for predictive analytics
§  Error handling becomes very easy since errors can be captured before in the


Hypothesis testing
Based on the hypothesis testing, the ETL team developed ARIMA analysis and Market Basket analysis in SQL using seamless integrated set of stored procedures. That is the ARIMA analysis flowed from the datawarehouse A,B,C categorization. The ETL team thus reduced the requirement for high end R and Python developers to code over the datawarehouse thereby presenting a seamless solution to the end client on a 8GB RAM machine.

Benefits to the end client:
·         The end client gets immediate and confirmed peace of mind and satisfaction through immediate deployment of predictive and forecasting analytics modules.
·         No additional hardware/software requirements need to be taken
·         The costs are way lower for the end client.
·         Large scale deployment is possible with the given set of solutions.
Please check the attached video for the same:
https://www.youtube.com/watch?v=PQfsfWLkaP8&feature=youtu.be
A more detailed video is attached herewith:
https://www.youtube.com/watch?v=7DoZDBay-bQ


Example of predictive analytics with Turbodata: Example of predictive analytics-Turbodata


Epilogue
The ETL team has been inspired by the following management books:
·         ‘Profit Beyond Measure’ by Thomas Johnson and Anders Brohms
·         McKinsey Mind by Ethan Rasiel and Paul Friga.
·         Blue Ocean Strategy by W. Chan Kim and Renee Mauborgne
·         Better India, Better World by Narayana Murthy

A number of times the end client types in wrong data into the source ERP system thereby resulting in wrong outputs and results. Junk inputs imply junk outputs.  The ETL team would recommend an auditable output from Turbodata to be used as part of the reporting purposes.  Wrong data inputs can impact the end client in one or more of the following ways:
As per the Toyota ProductionSystem, bad inputs should not be processed further as it adds to the final costs.
The ETL team(my firm) has found the following errors with regards to the data entry inputs specifically with Tally ERP 9.0.  

·         Stock input has been in one godown but stock outward movement has been from other godowns:





·         Missing purchase or sales order entries resulting in negative stocks at given points in time. One cannot have negative stock balances at any point in time.



Other data input errors that we have commonly seen are as follows:

How to resolve the errors:
·         In an object oriented program it is difficult to catch the errors on a real time basis. The ETL team recommends using the relational databases for catching the errors. The real time extraction module for Turbodata should be used for the same.
·         Transferring the data onto the third normal database is recommended. This helps catch data duplicity based on the composite keys.
For example if an end client has made the same amount payment for a given voucher on a given fiscal date, then the same should come as part of the discrepancy report. It is possible that the end client could be correct. There is also a possibility that the payment entries have been made by 2 different resources. Further handling of the given situation is as follows:
·         If the end client desires to catch the following error then the username by which the data entries have been done shall not be added to the composite key. In such a scenario there is a discrepancy between the Turbodata ledger balance output and the Tally report. The end client to approve the discrepant entry before the data is input into the system for auditing purposes.
Using perpetual valuations for ledger and inventory instead of periodic valuations. For example if an end client relies on periodic valuations for ledger balances then a duplicate payment entry then the periodic balances at the end of the fiscal month are difficult to catch. For example if an end client has a duplicate entry of Rs. 100k(One hundred thousand  only) over a balance of say Rs. 15000k(One fifty million only).
However using the perpetual system it is easy to catch the data entry errors.

Matching the consolidated trial balances and closing stock balances at the database level with the on fly calculations at the software level.

A small story for the end user: as Yuval Harari is Sapians says that mankind is primarily driven by myths. Hence many a managers are driven by myths regarding software or the consulting companies having the right audit numbers(with the managers inputting junk numbers).
A small story from one of my favourite books(Raag Darbari by Srilal Shukla) could best illustrate the point.
The protagonist Ranganath had gone from the city to visit his relative, an aunt’s husband , in the village. During the course of the village fair, it was suggested that the group goes and sees the village temple for the local goddess. At the temple Ranganath found that the statue instead of been of a goddess was of a soldier( for a goddess he was looking for two lumps  in front and two lumps in the back). The priest asked for donations for the goddess. To this request Ranganath refused saying that the statue was not of a goddess but of a man. There was an ensuing scuffle between the villagers and Ranganath. Ranganath was eventually rescued by his cousin. On going out and meeting other people, the cousin mentioned the following:
"My cousin has come from the city and is very well read. That is why he talks like a fool."
The author has always associated himself with Ranganath.


Source system: multiple installation of Tally ERP 9.1.
Problem : The end client desired to have a custom installation of Turbodata based on the unique requirements of its business. The product shall be used for designing a custom web interface for customer interaction. The key tenets of the solution that differed from the standard deployment of turbodata were as follows:
·         Standard price list instead of weighted average or FIFO pricelist.
·         Closing stock value was to be calculated at a godown and item level.
·         The solution was to work across multiple locations seamlessly with maximum RAM usage been 1 GB for both initial and incremental data loads.
·         Custom masters extraction for item, stock group, category .
·         GST classification to be extracted for the end client.
Time duration of the project: 2 weeks.
Approach of the ETL team:
·         Choosing the appropriate set of attributes to be loaded based on the modular approach. That is the required fields to be loaded for ledger and inventory were chosen.
·         Custom extraction for the tables: The process of normalization helped in the same since the attribute is to be loaded only once.
·         Testing of initial and incremental data loads in terms of time and load on the system. The incremental data load process helped at reducing the time of data load.
·         Data cleansing: special characters were removed from the item names. Also separation of the numeric values from the character fields
·         Data consolidation: multiple types of voucher types were loaded onto the datawarehouse.

Project has been done successfully. Hereafter the end client shall go for a MVC interface over the datawarehouse for reporting and customer interaction purposes.
Facebook post: https://www.facebook.com/Consolidation-in-MS-Excel-2007-524999194181956/







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Saturday, 8 September 2018

Single number for gross profit and closing stock valuations


Single number for gross profit for end clients: Many end clients use periodic method of inventory valuation. This could result in multiple valuations for closing stock and gross profit(yearly, quarterly, monthly, weekly).

For example we have been taught that if we sell a product at Rs. 10 and the cost is Rs. 5 then our gross profit is Rs. 5. But with periodic valuations(depending upon the time granularity), our cost could be 4.5, 5.3, 5 or 6. Hence our gross profit could vary depending upon the granularity of the time for closing stock calculation.

 For end clients desiring single number, we recommend perpetual FIFO and weighted average valuations(for a single number for gross profit calculations).

Why address this issue now?

  • The statutory authorities in India are doing invoice matching with GST implementation. Accordingly the statutory authorities could expect a single number for closing stock and gross profit from tax payers in India. Perpetual valuations could help the taxpayers secure their audit interests in a comprehensive manner.
  • The GST tax filing, balance sheet development and profit and loss statement development are all interlinked.
  • Turbodata provides profit and loss and balance sheet development services also along with perpetual valuations.


Attached are the other reasons for the perpetual valuations:

  • Consistent reporting for gross profit and closing stock across all items
  • Meeting the statutory requirements as per the government norms
  • Complete and thorough documentation for Profit and Loss and balance sheet statements
  • Orderline profitability: for those customers who desire to check the gross profit by customer, regions etc.


Contact details:
email: apoorv@mnnbi.com
website: www.mnnbi.com

Are you a customer having the following issues:

Having issues with large value of  slow moving inventory
Have issues with cash flow cycles
Do not have clarity regarding product profitability


Our product Turbodata can help your firm with resolving the above issues. The product is inspired by philosophy of The Goal by Eliyahu Goldratt and Profit Beyond Measure by Thomas Johnson and Ander Brohms(please see the appendix 1 for a summary of the philosophies)

Both the philosophies imply that the end client should use the order line profitability instead of using the periodic calculations. Only then would the end client get complete visibility into its operations and profitability by customer, region etc.

What is required for determining the orderline profitability?
For determining the same the end client needs to have valuations of inventory using perpetual method instead of the periodic method.
As a case to the point, consider the following:





In the attached scenario of an item, the valuation using weighted average/FIFO has been done on periodic basis. Hence the end client looses the orderline profitability details by using the same.

However in the snapshot below using Turbodata, the weighted average calculations are done on a daily basis(as in the attached snapshot)

 

This enables the end client to calculate orderline profitability.

Issues with calculating the orderline profitability:
v  In some of the software,  negative stock is allowed.  Because of the same orderline profitability calculations might be impacted. The sample below gives the first instance of negative stock for an item.
Sample attached below:






v  The physical stock entries valued at 0(zero) value can create discrepancies in the stock valuations.
v  Data consolidation from multiple systems could be required for calculating the same.
v  Data transformation in terms of business logic of the end client needs to be done so that the required calculations come into force.

By using Turbodata, the end clients shall be able to achieve the following:
v  Go towards orderline profitability by getting an estimate of cost of goods sold based on perpetual FIFO and weighted average calculations.
v  Achieve the following activities
o   Data consolidation
o   Data cleansing: clean the master data before reporting is done
o   Data profiling: find the first instance when the closing stock of an item turned negative at godown or consolidated level.
       Data analytics: have consolidated dashboards along with predictive analytics facilities at economical costs.
v  Better management of inventories: by finding the profitability of the sale of items at the orderline level for a given set of customers.
v  Prepare the data for predictive analytics and forecasting through data compression and sql reduction. The predictive analytics and forecasting is required to capture the variations from the standard values for sales. A significant variation is to be captured early so that the end client could take the corrective actions quickly.

Interested in moving towards orderline profitability:
v  Deployment of Turbodata solution(for testing sample data): USD 3000/-(USD Three thousand only)+taxes as applicable. Contact us for a sample demo
v  Buy our standard book based on Turbodata project experiences: USD 5/-(five) dollars
Please contact the following for the above for a demo
Name: Apoorv Chaturvedi
Email: apoorv@mnnbi.com
Website: www.mnnbi.com


Appendix 1

What do the above management philosophies say?
The Goal:
The Goal is inspired by the theory of constraints. This implies that there are 3 parameters that are critical for any firm:
v  Throughput: the rate at which the system generates the sales(our definition of cash sales)
v  Inventory: the input material required to convert the inputs material to final product for generating throughput.
v  Labor: The manpower required for converting inventory to throughput.
The protagonist Jonah in ‘Goal’ also insisted on standard deviations and variations to be part of the process. The variations to be detected on a close to real time basis so that any errors are caught beforehand.

Profit Beyond Measure:
Profit Beyond Measure  is inspired by the Toyota Production system. It emphasizes that the manufacturing company should function like a human body. The functional managers should account for self sustainability(standard cycle times), diversity and interdependence( the manufacturing managers need to look at the whole system like a human body and not just a single component).
The book emphasizes that there should be a reduction in inventory by reducing a changeover times at each of the working station. That is the manufacturing process should start once the customer order has come into the system. The book further looks at ‘Design to order’ by designing multiple configurable modules to offer the end clients multiple types of products.
The system emphasizes catching the errors in production cycle quickly so that there is reduced material wastage.

Sample example of inventory optimization:Inventory optimization of large trading company

For Inventory Optimisation, our top end functional consultants and partners
Mr. Sharad Maheshwari:  https://www.linkedin.com/in/sharad-maheshwari-688b733
 Mr. Puneet Khanna: https://www.linkedin.com/in/puneet2009/



For inventory optimization services, consider the following:

Automated GST Filing using Turbodata and GSP Partners

Are you facing the following issues with regards to GST filing?
  • ·         Delay in filing
  • ·         Concern regarding the changing regulations from the government
  • ·         Concern regarding reconciliation: specially for customers using MS Excel upload.
  • ·         Have a manual process for GSTR filing. The manual process is prone to error
  • ·         Have high manpower costs related with GST filing.

Turbodata shall help your firm with faster, easier and more convenient GST filing.
How is Turbodata different?
  • ·         All the reports for the end client shall be developed on the cloud installation. Only a minimal extract for all the vouchers and masters shall be done from the end client location. The ETL team shall commit to usage of maximum amount of RAM for the same(say 1 GB for incremental data extract)
  • ·         The end client can do the prior change of the data. The system shall automatically take care of the same. This is enabled through incremental data load process using data normalization.
  • ·         No reports shall be developed at the client location. All the reporting work shall be done at the server location.
  • ·         Initial and incremental transaction data extract shall be done from the end client location.
  • ·         The end client need not worry about re filing the GST reports since it shall be done by the GSP partner automatically.
  • ·         The package is very easy to deploy, deliver and maintain. No high end software are required. The system can extract data from SAP, Tally and other source systems with ease.
  • ·         Dependence on MS Excel for tax filing purposes is taken away since it could result in data errors and discrepancies.
  Current system:




Why is the Turbodata system better?

Turbodata system:


·         Turbodata system is inspired by ‘The Deming Way’, ‘The Goal’ and the Toyota production system and the Inmon methodology.  In a nutshell the following are the features copied from the above systems by Turbodata:
o   No error prone data should be passed for the reporting purposes. The data needs to be cleansed, audited and consolidated before report development.
o   The processing of the transaction should be done as soon as the transaction has been fed in the source system. That is the processing should take place on a real time basis and not specifically at the end of the month. Turbodata enables this feature in the following manner:
§  Each transaction fed into the end client source system is assumed to be an order from the end client.
§  The system offers the facility for real time extract and upload(current system is manual but the data can be loaded on a daily basis by the end client go the server)
o   Once the data has been loaded onto the server, it is transferred to a normalized database(insert, update and deletes). At the data warehouse level the data cleansing, data transformation, data consolidation activities are done
o   Once the data has been cleansed at the datawarehouse level then the reports for GST are developed. In one single lot, GSTR1, GSTR2 and GSTR3 reports can be developed.
o   Turbodata is integrated with at least one GSP partner. The end client could look at other GSP partner solutions if it desires the same.
o   The deployment of the solution is very easy and convenient. For any end client the deployment should take not more than 20(twenty) minutes. Minimum installation pre requisites are required.
o   The data for the end client is stored in a datawarehouse. The end client does not need to worry about changes in the statutory requirements. Other high end services like inventory optimization and predictive analytics are possible on the cloud.

To check why should the end client consider Turbodata GST, please check the following linkage:
http://mndatasolutionsindia.blogspot.in/2018/02/why-turbodata-gst.html


GST Reporting Issues:
Contact
Apoorv Chaturvedi
Phone: 8802466356
Email: apoorv@mnnbi.com
Website: www.mnnbi.com

Indicate the following:
·         ERP system/ERP systems
·         Turnover: frequency of load
·         Number of locations

 ·         Automation of GST filings(a sample with a GSP).
·         Attached is a blog for the same:
·         http://mndatasolutionsindia.blogspot.com/2018/07/spreadsheetconsolidationsample.html


·         Blog link: http://mnnbi.net/gst/gst.aspx


·         Preliminary video: https://www.youtube.com/watch?v=-Vp7JzHCYp0&feature=youtu.be

·         Detailed demo link: https://youtu.be/d-KD3mp1jic



Detailed demo link: https://youtu.be/d-KD3mp1jic

The demo was attended by a number of CFOs from industry.

Sample problems that a datawarehouse can solve:
·         Data audit and data profiling before GST is filed-samples given in the demo.
·         Single source of truth for all online GST reports. For example if GSTR1B2B is updated then HSN summary and document summary should also be updated.

Automation of spreadsheets: client reference(a major exporter based in Gurgaon). We are now able to automate the spreadsheets due to our technology of data compression and sql reduction at lower costs. The end client could send the sample profit and loss and balance sheets to be automated along with the source system details.
Attached is the blog link for the same:
http://mnnbi.net/spreadsheet/spreadsheet.aspx
Blog: http://mndatasolutionsindia.blogspot.com/2018/07/spreadsheetconsolidationsample.html


Capturing Data Entry Errors for Audit Purposes
A number of times the end client types in wrong data into the source ERP system thereby resulting in wrong outputs and results. Junk inputs imply junk outputs.  The ETL team would recommend an auditable output from Turbodata to be used as part of the reporting purposes.  Wrong data inputs can impact the end client in one or more of the following ways:
  •        Wrong tax filing specifically in online scenario.
  •         Wrong business picture
  •         Wrong predictive analytics.
As per the Toyota ProductionSystem, bad inputs should not be processed further as it adds to the final costs.
The ETL team(my firm) has found the following errors with regards to the data entry inputs specifically with Tally ERP 9.0.  

·         Stock input has been in one godown but stock outward movement has been from other godowns:





·         Missing purchase or sales order entries resulting in negative stocks at given points in time. One cannot have negative stock balances at any point in time.



Other data input errors that we have commonly seen are as follows:

  •       Duplicate payment entries
  •      Duplicate sales entries
  •        Receipt note entries but no purchase invoice entries
  •         Payments not having the required bill reference numbers.
How to resolve the errors:
·         In an object oriented program it is difficult to catch the errors on a real time basis. The ETL team recommends using the relational databases for catching the errors. The real time extraction module for Turbodata should be used for the same.
·         Transferring the data onto the third normal database is recommended. This helps catch data duplicity based on the composite keys.
For example if an end client has made the same amount payment for a given voucher on a given fiscal date, then the same should come as part of the discrepancy report. It is possible that the end client could be correct. There is also a possibility that the payment entries have been made by 2 different resources. Further handling of the given situation is as follows:
·         If the end client desires to catch the following error then the username by which the data entries have been done shall not be added to the composite key. In such a scenario there is a discrepancy between the Turbodata ledger balance output and the Tally report. The end client to approve the discrepant entry before the data is input into the system for auditing purposes.
Using perpetual valuations for ledger and inventory instead of periodic valuations. For example if an end client relies on periodic valuations for ledger balances then a duplicate payment entry then the periodic balances at the end of the fiscal month are difficult to catch. For example if an end client has a duplicate entry of Rs. 100k(One hundred thousand  only) over a balance of say Rs. 15000k(One fifty million only).
However using the perpetual system it is easy to catch the data entry errors.

Matching the consolidated trial balances and closing stock balances at the database level with the on fly calculations at the software level.

A small story for the end user: as Yuval Harari is Sapians says that mankind is primarily driven by myths. Hence many a managers are driven by myths regarding software or the consulting companies having the right audit numbers(with the managers inputting junk numbers).
A small story from one of my favourite books(Raag Darbari by Srilal Shukla) could best illustrate the point.
The protagonist Ranganath had gone from the city to visit his relative, an aunt’s husband , in the village. During the course of the village fair, it was suggested that the group goes and sees the village temple for the local goddess. At the temple Ranganath found that the statue instead of been of a goddess was of a soldier( for a goddess he was looking for two lumps  in front and two lumps in the back). The priest asked for donations for the goddess. To this request Ranganath refused saying that the statue was not of a goddess but of a man. There was an ensuing scuffle between the villagers and Ranganath. Ranganath was eventually rescued by his cousin. On going out and meeting other people, the cousin mentioned the following:
"My cousin has come from the city and is very well read. That is why he talks like a fool."
The author has always associated himself with Ranganath.


Apoorv Chaturvedi
Email: apoorv@mnnbi.com
Website: www.mnnbi.com

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