A decade ago, smartphones, social networking sites, cloud computing, and more were introduced. Such innovations compelled businesses across the globe to contemplate the effective adoption of technology and build higher engagement.
“The road to adoption of emerging technologies in an organization is a journey, not a race.”
Today, enterprises are building strategic approaches around technology adoption in their day-to-day business process to save time, money, and effort.
These approaches help companies to improve business processes such as sales, operations, supply chain, finance, human resources, IT, among others. These processes can be easily automated using technologies such as Big Data, IoT, Blockchain, and AI (ML, NLP/NLU/NLG) which is collectively termed as Business Process Automation.
Use cases of business process automation
For a better understanding of business process automation, let’s study the following use cases.
Customer Onboarding process: Technologies such as AI, RPA, and Blockchain can be used to automate repetitive customer onboarding process that requires manual intervention. Automating this process removes the delay for customers to use the product and enhances business growth.
Professionals can use AI-enabled BOTS to perform multiple tasks across different customer-facing applications without human intervention. OCR technique is used to scan customer documents and fetch relevant data to auto-fill and create the account.
For instance, once a lead is created in SAP CRM, a smart marketing module bot can:
- Connect to market data set
- Gather the available supply points against that lead
- Identify the type of services associated with them
- Generate a status report
- Showcase it to the account onboarding team
Once the account onboarding team verifies the report, a bot can open the account. In addition, customer contracts, NDA, etc., can be sent to customers using the blockchain-based digital signing solution – Cygnature
Inventory and Warehouse Management: Today’s business environment is dynamic and uncertain to effectively identify customer demands and patterns. This causes mismanagement in inventory and warehouse which in turn results in stockouts or overstocking.
With the combination of machine learning-based predictive algorithms and data streams, a forecasting engine can be developed that can perform inventory forecasting by analyzing demand and supply.
To implement a forecasting model, the primary requirements are the large datasets of,
- Products available each day in past
- Number of products sold per day
- Timeline when all the products were sold on sale
Once such historical datasets are available, the ML model can be trained to predict future stocks. Going ahead, evaluating the performance of predictions using the appropriate metric like root mean square can be done easily. A simple benchmarking model for each item/day to predict the sales can also be developed.
Customer Invoice Payment Prediction: Account receivable dept. maintains the cash flow in an organization and plans the collections process. A collections process depends on invoice due dates or when it shifts to a larger aging bucket. This reactive approach sometimes chokes the cash flow in the organization and causes monetary complications.
Machine learning-based proactive collection management process can be developed to enhance the output of the collections which will predict the invoice payment dates. Various factors of historical data such as past invoice count, past invoice count, due dates and months, etc., can be used to train a model which in turn will predict the invoice payment dates.
Machine learning methodology like binary classification model, multiclass classification model, random forest regression model, etc., can be used to identify the predictive payment date for each invoice.
Being Audited Indicator: One of the main aspects of the tax compliance process is how to identify the chances of being audited by auditors? By analyzing large internal company datasets, external audited and non-audited company’s datasets, it is easy to predict the chances of being audited and identify the tax risks.
A machine learning-based predictive statistical model, such as logistic regression can surely help.
Some of the factors to be considered to predict whether the return will be audited or not, are:
- Sales Liability has decreased highly than previously filed return.
- Input credit taken is higher than the previous month’s credit.
- Higher deduction in travel and restaurant categories i.e., proportionate ratio of travel/restaurant is higher than the total deduction
- Delayed return filing
- Ineligible Input Tax credit is taken but not reversed.
- Payment Date is higher than 180 days from the date of invoice but Input Tax Credit for the same has been taken until or unless reversal for the same is not done.
- Business is in loss for 2-3 consecutive years, etc. and more
Advanced algorithms like analysis of variance, polynomial regression, multivariate regression, etc., can be used to forecast the trends within the tax filing cycles, ETR (Effective Tax Rate) forecasting and predict the probability of any events.
The supervised learning technique like, classification algorithm (a decision tree) can be used to interpret the probability of future tax audit (basis the historical results) for a high/low sales liability, high/low input credit claim, high net worth, higher expenses on travel and restaurant, etc.
Supplier Selection Process: To make any procurement decision, selecting a potential supplier is the first step. This supplier can be an existing supplier or a new supplier. In a traditional process of supplier selection, human judgment plays the main role. A human can consider a few of the selection parameters which may help in making a qualitative decision. However, these parameters are not enough for making such judgments.
A Machine Learning model can be trained with historical data or organization decision-makers data to make predictions and recommendations. Basis the historical data, qualitative and quantitative features/variables can be identified and used to train the model. A few of the variables are as below:
- Product price
- Transportation and handling charges
- Product quality
- Defect percentage
- Delivery performance
- Customer response
- Geographical location
- After sale warranty
- Market reputation
Have a look at this workflow. Once an order is received in the organization –
- Machine Learning algorithm can filter the suppliers from supplier database basis the order specifications.
- RFQ can be rolled out to these selected suppliers.
- Now Ranking Neural Network (DeepRank) can be used to rank the supplier based on qualitative and quantitative variables.
Cygnet Infotech’s proprietary GRC (GST return compliance) score can also be used as an additional parameter to rank the suppliers.
Highest ranking supplier can be selected for the order fulfillment. This whole process is 100% dependent on the quality of data. The better-quality data used to train the engine, the better will be the predictions and recommendations.
Therefore it can be stated that technology can play a major role in automating business processes such as customer onboarding process, inventory and warehouse management, customer invoice payment prediction, supplier selection process, and more. In case, your business needs such technology implementations to improve your business process, reduce errors and enhance efficiency, you can connect with Cygnet Infotech: firstname.lastname@example.org.