Fashion Retail Analytics using PowerBI

The analytics module developed for the fashion industry incorporates the following needs for the end clients:

·         Consolidation of data from various sources such as spreadsheets and other relational databases.

·         Need of the end client to have a consolidated overview from various retail channels such as Retail sales, consignee sales, showroom sales.

·         Flexibility for analysis of the fashion data across various parameters: client, description of the module, style, size and color. The end clients are also looking at analysis across various date/time granularities such as year, year quarter and year month.

The module developed for the fashion industry enables the end clients to do the analysis across any levels of hierarchy. It enables the following:

·         Develop custom KPIs at any level of snapshot(date/time and garment granularity)

·         Enable data consolidation across multiple data sources

·         Flexibility in doing analysis across various views

The value proposition for the product comes as follows:

The benefit matrix of the product for the end client is as follows:

·  The product is looking at ease of deployment, ease of delivery, ease of maintenance.

The grid matrix for percentage calculation is as follows:

·    

                                                                                                                 

·        Based on the 2(two) measures quantity and value the end client should get at least 30 KPIs for percentages across various levels of date/time and product hierarchies.

      Attached are the first set of views for the end client:

    Consolidated dashboard: this view entails the comprehensive overview of metrics and KPIs after the consolidation has been completed. The following are the key tenets for the consolidated dashboard:

·          The view is independent of time

·         The  view is independent of date

Client view for value by year:

In the above view, we are looking at 2(two) separate measures quantity and sales.

In the attached dashboard,

The above is client analysis by fiscal years.

The  above module is the percentage client break up for a given fiscal year.

Thus the team has taken care of percentage snapshots at various levels of hierarchies.

  

Client View for Total Value By Year:

Client View for Total Quantity By Year:

Client View For Value By Quarter:

Client View For Total Quantity By Quarter:

Please contact the following for demo:
Apoorv Chaturvedi: support@mndatasolutions.com;support@turbodaatatool.com
Phone:+91-8802466356

Website; https://mn-business-intelligence-india.business.site/

Inventory optimization through Turbodata

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

Phone:+91-8802466356

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 FREE MICROSOFT POWERBI DASHBOARDS: please do one of the following:

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