Wednesday, 21 March 2018

Resolving Data reconciliation and tax filing problems for GST


Contact details of blog writer:


Name: Apoorv Chaturvedi
Phone: +91-8802466356
website: www.mnnbi.com


Problem:

The ETL team intends to solve the following problems regarding GST filing:
·        Wrong data entries
Solve the problems of under filings for the government: As per the Times of India article, the government is dealing with under recovery for GST collections in India.

o   Help the end clients meet the statutory requirements
·        Manual processes in filing taxes
·        Reconciliation of taxes: the ETL team believes that tax refund should be the ultimate target: As per the changed government norms, the buyers and suppliers could have  to reconcile the entries before filing the GST Returns.

http://www.business-standard.com/article/economy-policy/infosys-chief-nandan-nilekani-plan-on-gst-invoice-matching-may-be-tweaked-118022300073_1.html
·        Work across multiple systems: plug and play module

Solution: 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 cleansingdata transformationdata 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


  Sample video link: https://www.youtube.com/watch?v=sYbeBfc3ozo&feature=youtu.be

       The product uses optimum RAM so that the source system does not hang during extraction as given in the following video:
       https://youtu.be/7CULkzc5h2g



Why Turbodata GST?
Based on the above philosophy, the following are the reasons one should look at Turbodata GST as a GST filing solution.
·         Law of Focus: The end client should have one word embedded into customer’s mind. In such a scenario most of the end clients have Tally embedded into their minds for GST filing. Turbodata GST matches the numbers with Tally GST reports to give customers peace of mind and satisfaction.
·         Law of opposites:
o   Turbodata offers cloud based GST filing system while Tally offers desktop/server based GST filing system
o   Turbodata enables faster, easier and more convenient data upload facilities than Tally ERP 9.0.
o   Turbodata offers historical data correction automatically for GSTR reports while in case of Tally the end client shall have to re file the offering.
o   Turbodata GST can work with multiple ERP systems and is extremely scalable.
·         Law of Ladder: Tally has top of mind recall for the customers for accounting accuracy. Turbodata GST team recognizes the same. It matches the GSTR reports with those of Tally GSTR while making it easier and more convenient than Tally to file GST taxes.
·         Law of Mind: Tally stands for accuracy while Turbodata stands for speed matching with Tally numbers. Turbodata GST offers GST filing services matching with Tally ERP 9.0 for any ERP.


      Capturing data entry errors:

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.




    Reconciliation for GST filing: 


   Understanding the behavior of average Indian customer.

This blog attempts to understand human behaviour is absence of standards or changing standards in our day to day lives.
Based on the above philosophy, the following are the reasons one should look at Turbodata GST as a GST filing solution.
·         Law of Focus: The end client should have one word embedded into customer’s mind. In such a scenario most of the end clients have Tally embedded into their minds for GST filing. Turbodata GST matches the numbers with Tally GST reports to give customers peace of mind and satisfaction.
·         Law of opposites:
o   Turbodata offers cloud based GST filing system while Tally offers desktop/server based GST filing system
o   Turbodata enables faster, easier and more convenient data upload facilities than Tally ERP 9.0.
o   Turbodata offers historical data correction automatically for GSTR reports while in case of Tally the end client shall have to re file the offering.
o   Turbodata GST can work with multiple ERP systems and is extremely scalable.
·         Law of Ladder: Tally has top of mind recall for the customers for accounting accuracy. Turbodata GST team recognizes the same. It matches the GSTR reports with those of Tally GSTR while making it easier and more convenient than Tally to file GST taxes.
·         Law of Mind: Tally stands for accuracy while Turbodata stands for speed matching with Tally numbers. Turbodata GST offers GST filing services matching with Tally ERP 9.0 for any ERP.
·         The Indian consumer would like automated filing of taxes to reduce the prospect of manual errors.


     Data reconciliation for GSTR reports: 

   

        Attached are the videos regarding the working of the connector:


      Attached video explains the benefits of Turbodata-GST  for the end client:

        Reports that have been developed and validated with large Tally data by the ETL team:
·         Nil rated invoice analysis; this includes the logic for zero rated, exempt, export(LUT Bond), nil rated invoices. The logic shall include the specs for those cases where a ledger could be declared in multiple ways by the end client.
·         Document summary: this gives the consolidated document statement for outward supply, debit notes and credit notes. These details shall include the non-GST ledgers, items that are non-GST, registered sales, unregistered sales.
·         HSN summary: this report includes only the GST ledger and GST applicable items. The logic for the same shall include the logic for reporting for nil rated items, nil rated ledgers(no tax on the given ledgers), handling of debit notes/credit notes for HSN reporting.
what is not included?
HSN reporting for those items whose HSN number is not given in the HSN master as given by Tally ERP 9.0.
·         GSTR1CDNR: this gives the details regarding the complete debit and credit notes for the end client.
·         GSTR1B2B: this report gives the consolidated details for the B2B transactions for the end client.
    Why should the end client choose the solution based on Turbodata:
·         Better data auditing enabled by incremental data load. Say a company has 10 branches. For each branch there are average 5 relevant reports(GSTR1B2B etc). It is difficult for the manager to ensure that all the required reports will be loaded onto the system on a daily basis(using Excel). The following are the advantages of Turbodata:
o   Usage of datawarehouse to capture history where not possible in the source system like Tally(Nil rated, GSTR1B2CS etc)
o   Lower upload time and single upload facility: by uploading the raw data. It should take less than 2 minute to upload the entire data on a daily basis.
o   Better data auditing and validation for cases entailing credit notes/debit notes/nil rated invoices etc than with ERPs such as Tally/SAP etc.
o   No prior knowledge of complex taxation laws required(hence no need to know the date and time when particular taxes are to be filed): the etl team begs to differ with the same. The end client shall need to know about the basic details for correct filing.
S  Sample spec for GSTR1B2BH1: Attached is a sample specification for GSTR1B2BH1 for the end client. The ETL team shall follow the set process flow for capturing the GSTR1B2B details for the end client for any ERP.

Process flow chart for GSTR1B2B for GSP partner/End Client
B  Based on the sample data for various companies given by the GSP partner, attached is the specification for the GSTR1B2B invoices.

             Step 1: Check for any ledger where GST is not applicable.
             Step2 : check for any non –GSTN items in the extracted data. All the invoices associated with the non-GST items are to be excluded.
             Step3 : check for GSTN number(registered and unregistered). This shall be done from the daybook only and not from the masters.  The GSTN data checking shall need to be done as per daybook entry.
      Note: the error in the GSTN number shall be demarcated by the ETL team.
       Pick up all the sales entries: this shall be done based on the union of the following 2 rules:
             The following primary group names shall be extracted for sales vouchers: The following shall be the order of checking these details:
o             Find if the partyledgername is sundry debtor
o             In second step find if the ledgername belongs to ‘sales account’
o             In third step exclude the credit notes and debit notes from the transactions above.
             Pick up all the entries from the sales table(with voucher type as ‘sales’) where the GSTNTRANSACTIONTYPE  belongs to ‘sales’
       Note: these details shall also pick up the sales exempt entries. Also the credit notes/debit notes and the nil rated invoices that have the GSTNTRANSACTIONTYPE as ‘sales’ shall be picked up. The latter two types shall thereafter be reported under GSTR1B2B.




             Extraction of ‘ nil rated’
o             The Nil rated transactions shall be extracted using the column GSTNATUREOFTRANSACTION based on the following logic from the data extract above:
             Exempt: sales exempt from the GSTNATUREOFTRANSACTION.
             Export: the voucher type should be export or GSTtransactionname should have export. However the export parties do not have GSTN number hence this step shall not be done as part of GSTR1B2B and related extractions.
             Nil rated: the GSTOVRDNNATURE should have ‘sales%nil%’ entry.
             NONGST: ISNONGST flag has to be not null in the stg_ledger.
             REVERSECHARGEFLAG: THE REVERSECHARGEFLAG is not null
      No other GSTNATUREOFTRANSACTION entries shall be considered as of now. The remaining vouchers from above should go into GSTR1B2B.
     Inclusion of credit notes/debit notes : the credit notes with the following logic shall be added to GSTR1B2B.
o             The partyledgername has a GSTIN number
o             The vouchertypename is a credit note/debit note
o             The reason code is blank. That is the end user has not filled any of the Tally related options.
I     In the above scenario the behaviour of the credit note/debit note shall be governed by the nature of the invoice the note number is referencing. For example if the credit note/debit note references nil rated invoice then the required credit note/debit note shall be marked as ‘nil’ rated and so on.

     
E    Extraction of credit notes data: CDNR and CDNUR is self explanatory.
       The credit notes shall be marked as registered or unregistered based on the GSTN number in the daybook.

      CDNR notes: The CDNR entries shall be based on the following logic.

o             The partyledgername is ‘sundry debtors’
o             The ledgername is ‘sales account’
o             The voucher typename is credit note or debit note.
o             The partyledgername has a verified  GSTN number.
o             The credit note has a ‘note no’ attached to the same.
o             The reason code for the credit is one of the 7(seven) reason codes given by Tally ERP.
    Union the output of the following logic.
o             The partyledgername is ‘sundry debtors’
o             The ledgername is ‘sales account’
o             The voucher typename is credit note or debit note.
o             The GSTNTRANSACTIONTYPE of the ledger entry is not related with ‘sales’
o             The credit note has a ‘note no’ attached to the same.
o             The partyledgername has a verified GSTN number
o             The reason code for the credit is one of the 7(seven) reason codes given by Tally ERP.

   After this step has been completed the nil rated credit notes and debit notes shall be excluded. The following is the suggested logic:
    From the given output above the ETL team shall select those notes that are nil rated/exempted/non-GSTtransactiontypes/ REVERSECHARGEENTRIES as per Tally. For the same the GSTN team should maintain the entire history into its target tables. The following is the definition of the Nil rated/exempted/Non-GSTTRANSACTIONTYPE/REVERSECHARGEENTRIES:
             Extraction of ‘ nil rated’
o             The Nil rated transactions shall be extracted using the column GSTNATUREOFTRANSACTION based on the following logic from the data extract above:
             Exempt: sales exempt from the GSTNATUREOFTRANSACTION.
             Export: the voucher type should be export or GSTtransactionname should have export. However the export parties do not have GSTN number hence this step shall not be done as part of GSTR1B2B and related extractions.
             Nil rated: the GSTOVRDNNATURE should have ‘sales%nil%’ entry.
             NONGST: ISNONGST flag has to be not null in the stg_ledger.
             REVERSECHARGEFLAG: THE REVERSECHARGEFLAG is not null

    For now the ETL team shall exclude those credit notes that do not have any tax component associated with the same. These notes shall be classified as ‘nil rated’.

    Note regarding filing:
o             In case the debit note/credit note has a wrong note number then the etl process shall still file the same. The gstn portal shall mark the entry as wrong
o             If the debit note/credit note has a note number with a note date that is prior to the minimum date of the Tally instance then Tally ignores the same. However the etl team shall extract the same and file GST with it
o             If a debit note/credit note has a missing note number  then Tally excludes the same, but the ETL team shall include the same in nil rated . It shall be caught as an error by the GSTN portal.











Monday, 19 March 2018

Developing an optimization module for inventory



The given blog has been developed for helping the end clients get rid of slow moving stock at healthy margins. The solution from Turbodata entails selling the stock items through the secondary sales channel involving sites such as indiamart, tradeindia etc.


Contact details of blog writer:

Name: Apoorv Chaturvedi
Email: support@mndatasolutions.com;support@turbodatatool.com
Phone: +91-8802466356



Problem that we see to resolve

·        Gauge the value of the closing stock using ABC analysis.
·        Find the slow moving stock within category A
·        Sell the items that are slow moving and have high stock value.
·        Find the buyers for the given stocks with the following parameters:
o   Buyers with good payment history to get additional discounts
o   Buyers with poor payment history to be blocked or not get high discounts


Solution

Attached is the flow of the information for solving the required problem:



Data auditing:


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.


Data consolidation example:


Deployment of Turbodata for Retail company based out of Western India


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.

Data consolidation for Trial Balance example:
Problem: the end client required consolidated ledger balances and balance sheet details across 36 companies. With the given software that the end client had the process was taking a lot of time. The system would hang during the process of consolidation and generation of the required reports.

Methodology of the ETL team: the ETL team consolidated data ledger data from all the 36 companies. In order for the end client to generate balance sheet/trial balance details on any fiscal date the ETL team did the following activities:
·         Perpetual ledger balance details were stored by partyledgername and ledgername.
·         The associated cost center details for the ledger were also stored. The Profit and Loss statements could be generated according to the cost center details.
·         The ETL team was able to generate the balance sheet details, trial balance details across all the companies.
·         The end client could get the access to the balance sheet details across multiple companies.

The following system was used to match the trial balance details:
·         Data audit: the ETL team used the perpetual ledger balance details to arrive at the closing ledger balance details on the given fiscal date. The closing ledger balance on the given fiscal date was matched with the trial balance details from the software. The software was able to handle the cases where opening ledger balance was non zero.

Final result:
·         The audit numbers of the resulting output were matching with the software output.
·         The report refresh times was crashed by more than 90%(ninety) percent
·         The software did not hang during the process of initial and incremental data load and during the process of report generation.
Other benefits to the end client:
·         Better scope of cash flow availability: since the end client is having the cash flow balances on each fiscal date, hence the end client is able to capture the variances in payments across all ledgers. This helps the end client at better planning of the cash flows.
For the process of data consolidation, the following actions were done:
·         Data cleansing
·         Data consolidation
·         Report generation using C#/.net interface.

       Further ledger analysis was done as given in the following link:
       Ledger analysis link



      Dashboards for hypothesis testing:

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 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
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


Predictive analytics for sales forecasting

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:
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:

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:
A more detailed video is attached herewith:


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

Understanding the buyer profile in detail

Problem statement: a number of firms use periodic statement for ledger analysis(monthly, quarterly and yearly). In such a scenario these firms loose the day by day and transaction by transaction history of ledger balances. This information is required for the ageing analysis, in depth accounts receivable analysis per ledger. As an example, consider the following:


The above snap shot indicates the ledger balance on any fiscal date by partyledgername and ledger name(the group name is a roll up). From the daily ledger balance, the end client should be able to extract the trial balance, balance sheet and even profit and loss statements.
As an example consider the following snap shot:


Because of keeping the ledger balance history, the end client is able to find the cash balance as of the given fiscal date. Thereafter it has been able to capture the cash balances on a monthly, yearly, quarterly basis as given below:
Monthly report:

Daily Report:


Pre requisites for achieving the same:
The historical ledger balances need to be calculated and the closing ledger balance on the current fiscal date shall need to be matched with the ledger balance on the last day of the ledger balance history table as given below:



Alternatively the debit and credit balances need to be matched as given below:


The process replicates the ledger balancing related with bitcoins.

For achieving the same the end client needs to do the following:
The ETL team would be also able to offer Business Intelligence and predictive analytics services along with ledger analytics.

Ledger analytics is also related with GST filing.

Further case studies for consolidation of data can be seen from the following link.


Initial and Incremental data Load Template by M&N Business Intelligence-SAP Data Services

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