What has understanding fan following for Katrina Kaif got to do with understanding GST filing behavior

This blog attempts to understand human behaviour is absence of standards or changing standards in our day to day lives.

According to the book ‘Seven Secrets of Pursuasion’ by James Crimmins, human beings, whose brain is governed to a large course by the reptilian instincts, follow a 'Law of Imitation'.

What is Law of Imitation: in hedgehogs the scientists found that females tended to get attracted to males already having prior female partners. The new prospect females would tend to ignore the eligible males but not having prior female partners. In human construct this implies fan following for super stars like Katrina Kaif and our belief in various social constructs(based on religious or societal norms).

‘Seven Secrets of Pursuasion’ also states that human beings look for immediate and confirmed emotional benefit rather than a long term rational benefit whose outcome could be uncertain. That explains millions of people smoking(the threat if cancer is in the future and uncertain).

‘Sapians’ by Harari refers to the same as ‘memetics’ or the propagation of cultural memes. The memes are self propagating and can be self destructing also. Memes do not necessarily benefit the person propagating the same.

According to 22 Immutable Laws of Marketing, human beings follow the ‘Law of Mind’ and the ‘Law of Focus’ wherein human beings use products that first come into the mind. The product should lead to a specific word in the prospect’s mind.

Where does it lead us? Based on the above, the following could be considered by Indian customers:

·         Tally is associated with matching balance sheet and profit and loss numbers in the Indian customer’s mind. Tally has the first mover advantage.

·         The Indian consumer could like the GSTR reports to be matching with the Tally GSTR reports on a plug and play basis. We are looking at immediate benefit for the consumer by matching numbers with an accounting software commonly used by the businesses in India.

·         The Indian consumer would like automated filing of taxes to reduce the prospect of manual errors.

In case your are looking at automated filing of taxes with numbers matching with Tally GSTR reports then please check the following link:

http://mndatasolutionsindia.blogspot.in/2018/02/why-turbodata-gst.html

Details of GST filing:http://mndatasolutionsindia.blogspot.in/2018/01/automated-gst-Turbodata.html

Apoorv Chaturvedi

Email: apoorv@mnnbi.com

Blog: http://mndatasolutionsindia.blogspot.in/

Phone: +91-8802466356

For GST services, consider the following:
email apoorv@mnnbi.com
Or fill up the contact form on the website  http://mnnbi.com/.

Optimize Construction Lending Costs using the principles of 'the Goal'

How to use ‘The Goal’ Philosophy to optimize resources for construction lending

The given note is based on the philosophy of ‘The Goal’ by Eliyahu Goldratt. ‘The Goal’ concentrates on the constraints faced by the firms in terms of resources.

Effect of delay between milestone achievements: The variance between the actual and the target dates in achievement of the milestones results in additional costs which result in increased cash outflows for the end client.

Hence it becomes important to predict the impact of variances between the actual and the target delivery dates and give the management multiple options in case of delays happening in achievement of milestones.

Predicting the costs of delays:

The following is the step by step guide for resolving the above issues:

·         Track the target versus actual milestones for the construction projects. In such a scenario the target dates need to be fixed. Accordingly the variances shall be calculated based on the achievements of the actual versus the target dates. If a software is not available for data capture then the software could be developed for the same.

·         The costs associated with the delays shall need to be tracked using dashboards. For the same data connectors are available with the ETL team for data consolidation, data transformation, data cleansing and data auditing.

·         Based on the cost of delays, hypothesis shall need to be developed for optimization purposes.

·         Best case and worst case scenario: based on the variances for the project implementation, the best case and the worst case scenario for each project stage shall be developed. The most probable cost shall be arrived based on the same.

·         Allocation of resources based on maximizing the NPV of the project: based on the expected variance in the cost of the project, the expected NPV is to be calculated. The projects having the maximum NPV shall be addressed first.

·         Crashing of the activities to meet the project deadlines: in case of delay and a set delivery date for the project, route optimization techniques to be used to meet the required deadlines at minimal costs.

·         Optimization of purchase costs based on delivery dates, delivery requirements of the end client.

    Other activities: purchase optimization through Turbodata
     Ledger optimization

Presented by:

Apoorv Chaturvedi

Email: apoorv@mnnbi.com
Blog: http://mndatasolutionsindia.blogspot.in/
Website: www.mnnbi.com

Other website links: http://www.mnnbi.com/mnnbi_constructionanalytics.html

Phone: +91-8802466356

Developing automated consolidated trial balance for 36 companies in the trading domain

Developing automated consolidated trial balance for 36 companies in the trading domain

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

·         Data transformation

·         Report generation using C#/.net interface.

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

Prepared by :

Apoorv Chaturvedi

Email: support@mndatasolutions.com;support@turbodatatool.com

Phone: +91-8802466356

Blogspot: http://mndatasolutionsindia.blogspot.in/

Optimizing Inventory for a large trading company

Optimizing Inventory for a large trading company

The end client is a large trading company based out of Delhi involved in trading of steel sheets/steel pipes. The steel sheets and steel pipes are used for casting designs in automotive sector.

Problem statement: The end client is looking to improve the inventory turnover ratios( as per the blog attached herewith) and the hypothesis of the end client is that it has a large amount  of slow moving stock.

Approach of the ETL team along with the management consulting firm(Govisory Services Private Limited):

• ·         Find the A,B,C category of stock for the items: the top 70% of the closing stock value as of the fiscal date was classified as category ‘A’, next 20% as category ‘B’ and last 10% as category ‘C’.
• ·         Inventory ageing analysis was done to calculate the age of the closing stock
• ·         Calculation of the over stock and under stock items: for the same the ETL team followed the following steps:

o   Calculation of the average sale value: this was done by dividing the total  of year to date sale of the item by the total number of active fiscal months in which the item was sold. Active fiscal months entail the date difference between the  minimum sale month for the item and the maximum sale date(31^st December 2017).

o   The Lower and upper control limits were set as 6 times the average sale value and 9 times the average sale value.

Key findings: most of the category ‘A’ stock items were found to be over stock by using the average method. The ETL team along with the consulting team found that it was the variance and not the averages that are a problem(The Goal Methodology). The end client had significant variations in sales between the fiscal months for the items.

Next step: in order to calculate the effect of variances on the sale, the ETL team decided to carry out the time series analysis over the last 2(two) fiscal years for the category ‘A’ items. This action was done through the proprietary code of ARIMA(Auto Regressive Integrated Moving Average) with the ETL team.

Based on the finding of the same for most of the items it has been found that level has been the most important indicator.

Other activities done by the ETL team for inputs from MS Excel spreadsheets:

• Data cleansing
• Data consolidation
• Data transformation

Presented by:

Ritu Lakhani

Email: ritu@mnnbi.com

Phone: 0124-4365845

Blog: http://mndatasolutionsindia.blogspot.in/

Website: www.mnnbi.com

Why Turbodata GST?

Inspired by 22 Immutable Laws of Marketing: AL Ries

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.

For other details regarding the GST filing, please consider the following page linkage:

http://mndatasolutionsindia.blogspot.in/2018/01/automated-gst-Turbodata.html

Contact:

Name: Apoorv Chaturvedi

Email: apoorv@mnnbi.com

Website: www.mnnbi.com

 For GST filing services, do the following:
email apoorv@mnnbi.com
Or fill up the contact form on the website  http://mnnbi.com/.

Turbodata Inventory: optimizing the contribution margin of a large robotics manufacturing firm

Inspired by : Manage It Like You Own It by Mike Hannan

Client problem: The manufacturing end client had a statutory requirement to report the inventory valuation numbers using weighted average valuations. This entailed working through the MRP(Material Requirement Planning) items and finding the closing stock valuations of work in progress items and the final items. Alternatively the margin of the final item needs to be also calculated. The end client looked to optimize the contribution margin of its processes by in depth analysis of the cost of materials leading to cost of sales analysis.

ETL team approach: The ETL team moved from periodic to perpetual inventory valuation after data cleansing, data profiling, data auditing, data consolidation and running the  perpetual inventory valuation based on the historical stock valuations after data transformation.

Steps followed:

·         Automated data capture on the client machine

·         Data cleansing of the end client master data

·         Data auditing by matching with the end client ERP audit reports. The perpetual valuations should match with the periodic valuations.

End client benefits:

·         The closing stock numbers cleared by a top 4 audit firms for statutory reports

·         Visibility into the contribution margin analysis for the end client.

·         Forecasting analysis possible that should help at increasing the inventory turnover ratios and improving the cash flow cycles.

Challenges:

·         Master data management

·         Data cleansing

·         Data profiling and data auditing

Contact: Apoorv Chaturvedi

Email: apoorv@mnnbi.com

Phone: +91-8802466356

Turbodata Inventory: optimizing the contribution margin of a large robotics manufacturing firm

Inspired by : Manage It Like You Own It by Mike Hannan

Client problem: The manufacturing end client had a statutory requirement to report the inventory valuation numbers using weighted average valuations. This entailed working through the MRP(Material Requirement Planning) items and finding the closing stock valuations of work in progress items and the final items. Alternatively the margin of the final item needs to be also calculated. The end client looked to optimize the contribution margin of its processes by in depth analysis of the cost of materials leading to cost of sales analysis.

ETL team approach: The ETL team moved from periodic to perpetual inventory valuation after data cleansing, data profiling, data auditing, data consolidation and running the  perpetual inventory valuation based on the historical stock valuations after data transformation.

Steps followed:

·         Automated data capture on the client machine

·         Data cleansing of the end client master data

·         Data auditing by matching with the end client ERP audit reports. The perpetual valuations should match with the periodic valuations.

End client benefits:

·         The closing stock numbers cleared by a top 4 audit firms for statutory reports

·         Visibility into the contribution margin analysis for the end client.

·         Forecasting analysis possible that should help at increasing the inventory turnover ratios and improving the cash flow cycles.

Challenges:

·         Master data management

·         Data cleansing

·         Data profiling and data auditing

Contact: Apoorv Chaturvedi

Email: apoorv@mnnbi.com

Phone: +91-8802466356

Website: www.mnnbi.com