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In a research, IT teams reported spending 70% of their time on repetitive tasks and time-consuming activities.

Speed up production of information system

Reduce human intervention and operational costs

Increase customer satisfaction and operational efficiency. However, 88% of IT decision-makers and business leaders reported that they experience at least one challenge while scaling automation.

Therefore, this article explains IT automation use cases under three categories:

General

Industry-specific

Business function-specific

General applications Configuration and deployment automation

Configuration and deployment automation refers to automating the configuring and deploying of software applications, systems, or infrastructure. It involves activities such as:

Configuration management: It involves setting up and configuring systems, applications, and services.

Provisioning and deployment: It refers to the deployment of infrastructure, servers, and applications.

Continuous integration/continuous deployment (CI/CD): It is the build, testing, and deployment process for software development.

Resource management includes the provisioning, management, and optimization of physical and virtual resources in these environments:

Data center resource management

Cloud server management

On-premise resource management

Patch management: It includes the installation of software patches and updates across systems and applications.

Monitoring and alerting: It requires monitoring system performance, generating alerts, and taking corrective actions.

Backup and disaster recovery: It refers to the backup, replication, and recovery of critical data and systems.

User provisioning and access management: It is the creation, modification, and deletion of user accounts and access permissions.

Security and compliance: It contains security controls, vulnerability scanning, compliance audits, and threat response.

Incident response: Automating the detection, investigation, and remediation of security incidents and breaches.

Log management: Automating the collection, analysis, and archiving of system logs for troubleshooting and compliance.

181 zettabytes of data are expected to be generated by 2025, which requires effective data management strategies. Data management automation optimizes management strategies to improve efficiency, accuracy, and reliability in an organization by automating data-related tasks and activities, such as:

Data integration and ETL (Extract, Transform, Load): With these activities, IT teams extract data from different systems and databases to transform and load data.

Data backup and restore: It refers to the backup and restoration of data across different storage systems.

Data cleansing and transformation: IT teams are expected to clean and transform data to improve data quality and consistency in analysis.

Network and infrastructure automation allows organizations to manage and control networking and infrastructure components automatically. The way automation applies to these tasks and activities involve:

Network automation: It includes automating repetitive tasks like network configuration, monitoring, and troubleshooting tasks.

Data center automation: Data centers are servers, storage, and networking that can be automatically managed and provided by IT automation tools. Check out some of these data center automation tools.

DNS and DHCP management: IT automation can help with the management and configuration of DNS (Domain Name System) and DHCP (Dynamic Host Configuration Protocol) services.

Mobile device management: IT teams can automate mobile device management and security including applications and data. Review our comprehensive list of MDM software.

Server lifecycle management: It is the automation of servers management throughout their lifecycle, from provisioning to decommissioning.

According to McKinsey, 66% of businesses automated processes and workflows at least in one business function.

IT automation integrates systems and applications, orchestrates the information and action flow, and replaces routine tasks, approvals, and workflows to improve efficiency and reduce manual effort. Some of the examples include:

Service request management: IT teams can handle and fulfill user service requests automatically.

Change management: Automated change management allows changing approvals to IT systems and infrastructure.

Incident management: IT automation can track, escalate and resolve IT incidents with minimal human intervention. It can also automate the escalation and notification processes for critical incidents. Compare top incident management tools.

Workflow orchestration: It automates the coordination and sequencing of complex workflows involving multiple systems and processes.

Self-service IT: End-users can manage IT resources without manual intervention.

Asset management: IT teams can automatically track and manage IT assets throughout their lifecycle.

Service catalog management: It refers to the creation, management, and delivery of IT service catalogs.

Compliance auditing: Automating the assessment and reporting of compliance with regulatory and industry standards.

Service Level Agreement (SLA) management: Automating the monitoring and reporting of SLA compliance for IT services.

Workload automation: WLA is a type of IT automation that can automate the scheduling and execution of batch jobs, complex tasks and processes.

Service desk automation: It is another type of use case that can automate common service desk tasks, such as ticket routing, categorization, and resolution.

IT automation can help monitor, analyze, and optimize the performance of systems, apps, and infrastructures. Some examples are:

Capacity planning: It automates resource usage analysis and forecasting to optimize capacity requirements.

Performance monitoring and optimization: IT teams can leverage automation tools to monitor and tune system performance to ensure optimal operation.

Some IT automation techniques do not fall under specific categories. These tools may utilize software bots and machine learning technologies to automate an organization’s diverse business processes, functions, or activities.

Here are some examples of miscellaneous automation:

DevOps automation: It refers to automated process of collaboration and integration between development and operations teams for faster software delivery.

Configuration compliance: Automating the enforcement and validation of system configurations against established standards.

Quality assurance and testing automation refers to the use of automated tools and processes to enhance the efficiency and effectiveness of software testing and quality assurance activities. It involves automating tasks within the testing and quality assurance lifecycle, such as:

Test data generation: IT automation can automate the generation of test data sets to ensure sufficient and representative test coverage.

Test environment setup: In the testing cycle, test environment setup takes 17% of the time. IT automation can reduce this time by assisting with setting up and configuring test environments, including deploying required software and infrastructure.

Test result analysis: It refers to the automated analysis of test results, including the identification of failures, defects, and performance issues. Also, it can automate the generation of test reports and documentation to provide comprehensive test coverage and traceability.

Test script maintenance: It is the automated maintenance and version control of test scripts to ensure accuracy and consistency, which counts for 12% of time if done manually.

Test execution scheduling: IT automation enables the scheduling and coordination of test executions across different platforms and environments.

Test coverage analysis: IT automation tools can analyze test coverage to identify gaps and ensure adequate testing of software functionality.

Test Automation: In a research, manual testing is identified as the most time-consuming activity in the testing cycle by 35%. IT automation can tackle this challenge by automatically executing software tests to validate functionality and performance.

IT automation software can be deployed in different industries to reduce human error, manual processes, and operational costs. There are numerous different automation-related terms to describe automation technologies and these could be referred to as health tech or HR tech in different settings. However, they will involve automating tasks of IT processes.

These industries include:

According to McKinsey’s report, 43% of healthcare tasks can be automated to reduce processing costs and human error.

Patient Data Management: IT automation allows healthcare organizations to update and synchronize patient records across different systems and departments.

Appointment Scheduling: Automated systems can handle appointment booking, reminders, and rescheduling, improving efficiency and reducing errors.

Finance automation can save ~70% of finance operations costs by lowering mistakes and human intervention. IT automation in finance can streamline following activities:

44. Fraud Detection: IT automation can help detect suspicious activities and patterns in real-time, enabling financial institutions to prevent fraud and enhance security.

45. Compliance and Reporting: It refers to automating regulatory compliance processes, such as generating reports and ensuring data accuracy, can save time and reduce the risk of errors in financial services.

46. Risk Assessment: Finance teams can automatically analyze large volumes of financial data and identify potential risks, such as credit risks or market trends.

64% of manufacturing-related activities can leverage AI powered automation technologies, such as intelligent automation, robotic process automation, or IT automation tools.

47. Inventory management: Manufacturing firms can automate inventory tracking, ordering, and restocking processes to optimize stock levels, reduce costs, and prevent stock-outs.

48. Quality Control: Manufacturing companies play automated systems for real-time monitoring, data collection, and analysis to ensure product quality and identify defects early in the manufacturing process.

Retailers can lower IT inefficiencies and data inaccuracy by increasing automation use. The way an automated system can help retail and e-commerce platforms include:

49. Order Processing and Fulfillment: It automates order processing, inventory updates, and fulfillment workflows to accelerate order handling and reduce errors.

50. Customer Support: IT automation solutions can handle common customer inquiries, provide support, and improve response times.

IT automation types like workload automation and data warehouse automation can help logistics firms with:

51. Route Optimization: IT automation tools can automate the process of selecting optimal routes based on real-time data, such as traffic conditions, to improve efficiency and reduce delivery times.

52. Supply chain visibility: Logistics firms can use IT automation to track and monitor shipments, provide real-time customer updates, and enhance overall supply chain visibility.

Telecom companies can benefit from IT automation in several ways. Here are a few examples:

53. Network Management: Automation can assist in managing and monitoring telecom networks, improving efficiency, and reducing manual errors. It can involve provisioning, configuration management, and network troubleshooting. Automated network management systems can identify and resolve network issues in real-time, leading to faster problem resolution and improved network performance.

54. Service Provisioning: Automating service provisioning processes can streamline the delivery of telecom services to customers. This includes automated order management, new service activation, and provisioning resources like phone lines, broadband connections, or cloud services. By reducing manual intervention and streamlining processes, companies can accelerate service delivery, enhance customer satisfaction, and reduce errors.

55. Customer Support: Automation can enhance customer support services in the telecom industry by automatically handling basic customer inquiries, troubleshooting, and checking ticket status. Such automated ticketing systems can categorize and route customer issues to the appropriate support teams, ensuring faster response times and efficient issue resolution.

56. Billing and Revenue Assurance: IT automation can improve telecom companies’ billing accuracy and revenue assurance processes. Automated systems can gather customer data to generate accurate invoices and reconcile billing information with service provisioning and usage data. This helps reduce billing errors, identify revenue leakage, and improve financial processes.

57. Network Security: Automated systems can continuously monitor network traffic, detect anomalies, and trigger security alerts. Additionally, automated patch management and vulnerability scanning can help ensure that network infrastructure and systems are up-to-date and protected against potential security threats.

The major business functions that can leverage IT automation include:

HRIT automation can significantly streamline and enhance HR processes such as employee onboarding process, recruitment process, and performance management. According to Mckinsey, 56% of HR tasks can be automated to decrease processing costs and time.

58. Creating user Accounts: IT automation can create and provide new user account for a new employee, including:

59. Equipment and Software Setup: Automated workflows can assist and set up the necessary equipment for a higher employee experience, such as:

60. Onboarding workflows: IT automation can facilitate the creation and execution of standardized onboarding workflows, with onboarding tasks like digital forms completion (e.g., tax forms, emergency contacts), training module assignments, and task reminders for HR teams. Automated notifications can be sent to new employees to ensure a smooth employee onboarding process.

61. Self-Service Portals: Self-service portals or intranet platforms with automation capabilities can allow new employees to access essential information, resources, and training materials at their convenience. Automated workflows within this portal may guide employees through the process, provide relevant documentation, and facilitate communication with HR and IT departments.

62. IT Policy Compliance: Automation can help ensure that new employees are aware of and compliant with IT policies and security measures.

63. Recruitment process: By leveraging IT automation in recruitment processes, HR teams can automatically scan job boards, career websites, and social media platforms to source candidates, screen resumes based on predetermined rules to shortlist candidates, manage applicant information to track applicants and schedule interviews.

With IT automation, sales and marketing teams can:

Increase customer retention

Lead to higher an uplift by 10%

Improve customer experience by 43%

Handle customer onboarding process with minimal human intervention by automating.

66. Sales process and workflow automation: IT automation can streamline the sales process, other processes, and workflows, such as the procurement process or purchase orders process, by automating repetitive tasks such as sending follow-up emails, generating quotes, and managing contracts. Automated workflow can improve customer experience and reduce human errors.

67. Reporting and analytics: IT automation tools can collect and consolidate real-time and historical data from various sources, providing actionable insights into sales performance. As a result, a sales team can make data-driven decisions to enhance their customer retention rate.

Explore more on IT automation types by checking out:

Assess different vendors for each IT automation type by checking out our comprehensive and data-driven lists:

If you have more questions, we can always help:

Hazal Şimşek

Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

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Top 5 Use Cases Of Intelligent Automation In Hr In 2023

Human resource (HR) management is an important part of any business and involves many manual and repetitive processes. The adoption of AI and automation technologies in HR can bring significant benefits in terms of cost reductions and revenue increases. For instance, according to McKinsey, 40% of companies have been able to decrease their HR costs by more than 20% through the use of AI in 2023.

Intelligent automation, also known as cognitive automation or hyperautomation, which combines automation solutions such as RPA with AI techniques such as machine learning, NLP, computer vision, and conversational AI, can help companies automate end-to-end HR processes.

We list 5 use cases for intelligent automation in the human resources:

Screening resumes to eliminate unqualified candidates and creating a candidate shortlist can be a very time-consuming task if done manually. NLP-powered intelligent bots can:

Gather and screen resumes,

Compare applicant data with relevant job requirements and previously accepted applicant data,

Eliminate unqualified applicants,

Send emails to candidates according to their results.

Since it doesn’t work with predefined keywords and rules, AI-powered automation can reduce the number of false negatives and false positives, making the hiring process more efficient.

It is important to note that AI systems can be biased. Therefore, companies need to test and evaluate their automated recruitment tools to avoid biased hiring decisions.

Onboarding is an important process that impacts employee experience and retention. However, only 12% of employees believe their organization does a good job with onboarding. Intelligent automation can help companies streamline the onboarding process by:

Creating email addresses and user accounts for the tools used,

Granting access to required files or applications,

Sending relevant onboarding documents to the new employee,

Introducing them to the tools they will be using.

These can help companies reduce the time spent on manual onboarding tasks, quickly familiarize new employees with the company, and provide a more personalized onboarding experience.

You can also check our article on onboarding automation.

Travel and expense management is a repetitive and error-prone process that poses compliance risks due to potential issues such as missing receipts and non-compliant expenses. OCR-enabled intelligent automation can:

Collect employee travel expense receipts,

Extract the required data from receipts,

Cross-check individual expenditures against company and external expenditure regulations,

Complete payments or request approval of items that do not comply with expense policies.

Feel free to check our article on travel and expense automation for more.

Accurate payroll processing is critical for employee satisfaction, but the process involves error-prone tasks such as calculating commissions and overtime. Intelligent bots can reduce the time spent on processing payrolls and reduce errors by:

Collecting clock-in and clock-out data of employees from login platforms,

Recording overtime and adjusting the compensation accordingly,

Calculating commissions based on the company’s commission rules,

Generating reports on different payroll statistics for data-driven decision-making.

For more, feel free to check our article on payroll automation.

Similar to onboarding, offboarding an employee also involves tasks that can be automated with intelligent bots to improve efficiency and employee exit experience, such as:

Generation of exit documents,

Revoking access to company systems,

Conducting exit surveys,

Processing final payments and ensuring that departing employees no longer receive payment.

You can check our article on offboarding automation for more.

Feel free to check our other articles on:

Feel free to explore intelligent automation use cases for your business in our comprehensive article on intelligent automation examples.

You can check our data-driven list of intelligent automation solutions. If you need help in choosing a solution, we can help:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Top 30 Nlp Use Cases In 2023: Comprehensive Guide

Natural language processing (NLP) is a subfield of AI and linguistics which enables computers to understand, interpret and manipulate human language.

Although machines face challenges in understanding human language, the global NLP market was estimated at ~$5B in 2023 and is expected to reach ~$43B by 2025. And this exponential growth can mostly be attributed to the vast use cases of NLP in every industry.

You may be familiar with many day-to-day NLP applications such as autocorrection, translation, or chatbots. However, NLP has numerous impactful applications that business leaders are not aware of. Therefore, we compiled a comprehensive list of NLP use cases and applications and categorized them according to relevant industries and business functions:

General applications

One of the top use cases of natural language processing is translation. The first NLP-based translation machine was presented in the 1950s by Georgetown and IBM, which was able to automatically translate 60 Russian sentences to English. Today, translation applications leverage NLP and machine learning to understand and produce an accurate translation of global languages in both text and voice formats.

NLP is used to identify a misspelled word by cross-matching it to a set of relevant words in the language dictionary used as a training set. The misspelled word is then fed to a machine learning algorithm that calculates the word’s deviation from the correct one in the training set. It then adds, removes, or replaces letters from the word, and matches it to a word candidate which fits the overall meaning of a sentence.

Autocomplete, or sentence completion, combines NLP with certain machine learning algorithms (e.g. Supervised learning, Recurrent neural networks (RNN), or Latent semantic analysis (LSA)) in order to predict the likelihood of using a following word or sentence to complete the meaning.

Conversational AI is the technology that enables automatic conversation between computers and humans. It is the heart of chatbots and virtual assistants like Siri or Alexa. Conversational AI applications rely on NLP and intent recognition to understand user queries, dig in their training data, and generate a relevant response.

Chatbots have numerous applications in different industries as they facilitate conversations with customers and automate various rule-based tasks, such as answering FAQs or making hotel reservations.

For instance, Haptik produced a virtual assistant for Tata Mutual Fund to enhance customer retention and reduce call center workload. Initiative augmented the workforce of Tata by allowing employees to focus solely on urgent customer issues, by cutting call center enquiries by approximately 70%.

You can request a demo to see Haptik’s conversational AI solutions in action.

Voice recognition, also known as automatic speech recognition (ASR) and speech to text (STT), is a type of software that converts human speech from its analog form (acoustic sound waves) to a digital form that can be recognized by machines. ASR works by:

Splitting the audio of a speech recording into individual sounds (tokens),

Analyzing each sound,

Using algorithms (NLP, deep learning, Hidden Markov Model, N-grams) to find the most probable word fit in that language,

Converting the sounds into text.

Today, smartphones integrate speech recognition with their systems to conduct voice search (e.g. Siri) or provide more accessibility around texting.

Source: Lekta

Automatic text summarization is the process of shortening long texts or paragraphs and then generating a concise summary that passes the intended message. There are 2 main methods to summarize texts:

Cleaning the text from filling words

Sampling the text into shorter sentences (tokens)

Creating a similarity matrix that represents relations between different tokens

Calculating sentence ranks based on semantic similarity

Selecting sentences with top ranks in order to generate the summary (either extractive or abstractive)

Language models are AI models which rely on NLP and deep learning to generate human-like text and speech as an output. Language models are used for machine translation, part-of-speech (PoS) tagging, optical character recognition (OCR), handwriting recognition, etc.

Some of the famous language models are GPT transformers which were developed by OpenAI, and LaMDA by Google. These models were trained on large datasets crawled from the internet and web sources in order to automate tasks that require language understanding and technical sophistication. For instance, GPT-3 has been shown to produce lines of codes based on human instructions.

For more in-depth knowledge on sentiment analysis data collection, feel free to download our whitepaper:

Retail & e-commerce use cases

8. Customer service chatbot

A 2023 survey revealed that 65% of decision-makers in customer service believe that a chatbot can understand the customer’s context, and 52% said that chatbots can automate actions based on customer responses. Chatbots in customer service can:

For instance second hand car dealer Cars24, reduced its call center cost 75% by automating FAQs with a chatbot that deployed on WhatsApp and mobile app of the company.

To explore more use cases, feel free to read our in-depth article about chatbot use cases in customer service.

9. In-store bot

Several retail shops use NLP-based virtual assistants in their stores to guide customers in their shopping journey. A virtual assistant can be in the form of a mobile application which the customer uses to navigate the store or a touch screen in the store which can communicate with customers via voice or text. In-store bots act as shopping assistants, suggest products to customers, help customers locate the desired product, and provide information about upcoming sales or promotions.

10. Market intelligence

11. Semantic based search

Semantic search refers to a search method that aims to not only find keywords but understand the context of the search query and suggest fitting responses. Many online retail and e-commerce websites rely on NLP-powered semantic search engines to leverage long-tail search strings (e.g. women white pants size 38), understand the shopper’s intent, and improve the visibility of numerous products. Retailers claim that on average, e-commerce sites with a semantic search bar experience a mere 2% cart abandonment rate, compared to the 40% rate on sites with non-semantic search.

Read our article on the Top 10 eCommerce Technologies with Applications & Examples to find out more about the eCommerce technologies that can help your business to compete with industry giants.

Healthcare use cases

12. Dictation

To document clinical procedures and results, physicians dictate the processes to a voice recorder or a medical stenographer to be transcribed later to texts and input to the EMR and EHR systems. NLP can be used to analyze the voice records and convert them to text, in order to be fed to EMRs and patients’ records.

13. Clinical documentation

14. Clinical trial matching

NLP can be used to interpret the description of clinical trials, and check unstructured doctors’ notes and pathology reports, in order to recognize individuals who would be eligible to participate in a given clinical trial. The algorithm used to develop such an NLP model would use medical records and research papers as training data in order to be able to recognize medical terminology and synonyms, interpret the general context of a trial, generate a list of criteria for trial eligibility, and evaluate participants’ applications accordingly.

A team at Columbia University developed an open-source tool called DQueST which can read trials on chúng tôi and then generates plain-English questions such as “What is your BMI?” to assess users’ eligibility. An initial evaluation revealed that after 50 questions, the tool could filter out 60–80% of trials that the user was not eligible for, with an accuracy of a little more than 60%.

15. Computational phenotyping

Phenotyping is the process of analyzing a patient’s physical or biochemical characteristics (phenotype) by relying on only genetic data from DNA sequencing or genotyping. Computational phenotyping uses structured data (EHR, diagnoses, medication prescriptions) and unstructured data (physicians vocal records which summarize patients’ medical history, immunizations, allergies, radiology images, and laboratory test results, as well as progress notes and discharge reports). Computational phenotyping enables patient diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), etc.

In this case, NLP is used for keyword search in rule-based systems which search for specific keywords (e.g. pneumonia in the right lower lobe) through the unstructured data, filter the noise, check for abbreviations or synonyms, and match the keyword to an underlying event defined previously by rules.

16. Computer assisted coding (CAC)

Computer Assisted Coding (CAC) tools are a type of software that screens medical documentations and produces medical codes for specific phrases and terminologies within the document. NLP-based CACs screen can analyze and interpret unstructured healthcare data to extract features (e.g. medical facts) that support the codes assigned.

17. Clinical diagnosis

NLP is used to build medical models which can recognize disease criteria based on standard clinical terminology and medical word usage. IBM Waston, a cognitive NLP solution, has been used in MD Anderson Cancer Center to analyze patients’ EHR documents and suggest treatment recommendations, and had 90% accuracy. However, Watson faced a challenge when deciphering physicians’ handwriting, and generated incorrect responses due to shorthand misinterpretations. According to project leaders, Watson could not reliably distinguish the acronym for Acute Lymphoblastic Leukemia “ALL” from physician’s shorthand for allergy “ALL”.

18. Virtual therapists

Virtual therapists (therapist chatbots) are an application of conversational AI in healthcare. NLP is used to train the algorithm on mental health diseases and evidence-based guidelines, in order to deliver cognitive behavioral therapy (CBT) for patients with depression, post-traumatic stress disorder (PTSD), and anxiety. In addition, virtual therapists can be used to converse with autistic patients to improve their social skills and job interview skills. For example, Woebot, which we listed among successful chatbots, provides CBT, mindfulness, and Dialectical Behavior Therapy (CBT).

Banking use cases

19. Stock prices prediction

NLP is used in combination with KNN classification algorithms to assess real-time web-based financial news, in order to facilitate ‘news-based trading’, where analysts seek to isolate financial news that affects stock prices and market activity. To extract real-time web data, analysts can rely on:

To learn how web scraping is used in finance, read In-Depth Guide to Web Scraping for Finance.

Top 9 Real Estate Chatbot Use Cases & Best Practices In 2023

Real estate is the top industry benefiting from chatbots, followed by travel, education, and healthcare. Chatbots are available 24/7 for:

In this article we explore the top 9 use cases of chatbots in real estate to show their full potential for the real estate companies.

A survey showed that the first step for a home buyer is to search for properties online, and on average, it takes 10 weeks to settle on a property. 9 out of 10 respondents younger than 62 years old said that the most important feature of online search was the property photos.

From a customer point of view, chatbots are helpful in searching for homes to buy or rent, because they provide:

24/7 service: Most people search for properties outside working hours which makes it harder to reach out to a live agent.

Instant responses: Businesses that implement chatbots in their customer service department save up to 30% of costs by speeding up response times.

Multiple language options: This is especially important for foreigners moving to a new country where they do not speak the language.

Interaction capabilities beyond just text: For example, chatbots can help customers navigate pictures or the gallery of a property, especially for properties with numerous pictures. After viewing pictures, they could immediately ask more questions to the bot or skip the property completely.

Allowing chatbots to handle these queries frees up the real estate agents to focus on finding properties and optimizing their marketing strategy. Chatbots can lead to savings of up to $23 billion from annual salaries.

Top 8 Use Cases & Benefits Of Rpa In Manufacturing

Introduction

The global automation market size was expected to generate ±$214B by the end of 2023, of which ±$29B (14%) would have come from manufacturing and factory automation.

This is because numerous processes in manufacturing are repetitive, rule-based, and automatable by RPA bots. For instance, bill of materials (BOM), data migration and analytics, invoices, and inventory reporting are highly repetitive and time consuming tasks if done manually.

These are all processes that can be automated with RPA.

In this article, we are exploring the benefits of RPA and its top 8 use cases in manufacturing.

A typical rule-based process can be up to 70%-80% automated. RPA bots handle rule-based repetitive tasks and minimize the need for human interference.

In manufacturing, RPA bots can:

Decrease the labor time spent on routine tasks

Decrease the time-to-market (TTM)

Increase data quality and minimize process errors by minimizing human interference

Maintain an audit trail

Additionally, data collected by RPA bots can be used as input to manufacturing analytics tools and digital twin of an organization software to analyze the overall progress, identify gaps, and find improvement opportunities.

For more, see a comprehensive list of RPA benefits.

The following processes are highly automatable by RPA:

RPA bots can automate invoice processing by:

Extracting specific data from invoices using OCR

Converting the extracted data into a structured format

Compare invoices against purchase orders

Cross-check for duplicates

Updating invoice records in the ERP system

Learn more about invoice processing automation.

Global supply chains are complex since they involve:

Suppliers from variety of countries

Suppliers with different levels of technical sophistication

Smaller suppliers rely more on manual processes. Their customers, as a result, need to use paper or PDF-based documents to track their order shipments. RPA can automate repetitive aspects of such processes, helping humans focus on more complex tasks.

For instance, instead of the user manually copying and pasting a tracking number on the website to know the shipment’s latest location, RPA bots can be programmed to send him/her a push notification, or an email, any time there is a change in the shipping status.

Learn more about supply chain automation.

A bill of materials (BOM), also called product structure, is one of the most vital documents in manufacturing.

A BOM contains the list of raw materials, sub-assemblies, intermediate assemblies, sub-components, parts, and the quantities of each component required to manufacture an end product. RPA bots can be combined with OCR and be programmed to extract specific product or element data, and replicate human steps required to generate a bill of materials.

Learn more about RPA BOM.

Inventory management is the process of auditing and tracking the flow of assets and stock from the factory up until the point of sale. RPA bots can automate:

monitoring inventory levels

placing orders

generating receipts

tracking deliveries

replying to quotations and queries emails

RPA bots track and replicate GUI steps, therefore they can automate inventory reports by:

collecting and updating inventory data

generating inventory reports

sending reports to designated employees or personnel

For instance, a German, physical robot manufacturing company had to allocate labor to exchange data between its spare-parts inventory software and the ERP application. After leveraging RPA, they were able to have a real-time insight into their inventory of spare parts, thus automating 95% of reoders from different vendors. Moreover, manual errors from manual exchange of data — such as under/overestimating of inventory levels and wrongful reorders — were eliminated to increase reordering efficiency1.

Learn more about inventory management.

Statistics suggests that 37% of engineers’ time in the manufacturing is spent on collecting and analyzing data manually. On the other hand, manufacturing data requires continuous update and analysis. RPA bots can automate:

Cleansing inventory and financial data

Migrating data from older systems to newer systems

Processing unstructured data (e.g. invoices) via OCR

Scheduling data imports and conversions (i.e. converting data from one format to another)

Updating structured data in ERP systems

Managing data warehouses

Order fulfillment includes all the processes after receiving an order from a client, up until delivery. Bots can automate the end-to-end process of order fulfillment by:

Identifying order emails and notifications

Downloading order requirement files

Extracting relevant data

Updating the order fields and creating an order in the ERP system

Replying to clients with information about product availability and shipping options.

Proof of delivery (POD) documents are a type of receipt that proves that products or goods have been delivered to the client. RPA bots can be integrated with logistics ERP modules to track deliveries. If delivery data is detected, the bot will extract the data, update delivery fields in the ERP module, and generate a proof of delivery receipt.

Manufacturing can benefit from different emerging technologies to enhance performance, improve products, mitigate risks, and avoid human errors. Businesses should:

Use intelligent automation: Intelligent automation is the next generation of RPA tools with AI capabilities that can automate complex processes.

Leverage AI and Big data analytics: Manufacturing firms produce large amounts of operations and events data. Applying analytics to this data can uncover insights about production processes, analyze productivity, and identify recurring errors. In addition, analyzing manufacturing data will provide information about market trends and customer requirements in different periods, and enable predicting future customer needs.

For more on smart manufacturing, read:

AI in manufacturing

Manufacturing analytics use cases

IoT use cases in manufacturing

Embrace virtual and augmented reality: Manufacturing businesses can rely on virtual and augmented reality to design and create product prototypes, maintenance, repair, and training new employees on machines or software.

For example, some automobile manufacturers rely on virtual reality to ensure that vehicles are tested at early phases development, in order to optimize the design, tolerance and safety features.

However, there are some challenges that executives face in manufacturing digital transformation. A report in 2023 indicates that the following are the top barriers to the adoption of new tech:

Source: Future of jobs survey 2023

Nonetheless, statistics shows that ±93% of businesses agree that novel technologies are necessary to reach their digital transformation goals, and 70% of companies either have an on-going digital transformation strategy or are planning one.

To explore RPA applications in different industries, to check out these articles:

If you still have any questions about RPA, feel free to download our in-depth whitepaper on the topic:

And if you think your business will benefit from an RPA solution, don’t hesitate to check out our data-driven list of RPA platforms and vendors.

And we can guide you through the process as well:

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Physical robot manufacturing case study.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Test Automation Best Practices In 2023

Test automation has become essential to the software development life cycle (SDLC) in the fast-paced software development industry, providing faster and more efficient tests than manual testing.

It has become increasingly important to have a well-designed and well-executed test automation strategy to ensure that the software product meets the desired quality standards. In this article, we will discuss some of the best practices for test automation that can help you achieve optimal results and maximize the value of your testing efforts.

The first step in test automation planning is to define your objectives. Determine what you want to achieve with your automated tests. Ask your QA team the following questions:

Are you seeking to reduce manual testing efforts in your software development process?

Do you want to increase test coverage across your application’s features and functionalities?

Are you aiming to improve the overall quality of your software through more efficient testing methods?

Defining your objectives will help you determine what types of tests you need to automate and how to prioritize them.

Choosing the right test automation tool is the second step toward successful automation. Selecting a tool that fits the project’s specific needs and requirements is essential. The testing tool should preferably be:

Easy to use

Scalable

Codeless (depending on your team’s knowledge of coding)

Provide good documentation and support.

You can check our article “Top 10 Test Automation Tools for 2023: Detailed Benchmarking” for an in-depth guide to test automation tools.

Several Fortune 500 companies, including Nokia, Amazon, and BMW, rely on Testifi as a supplier of test automation solutions. Web & API testing capabilities are provided through their CAST solution, which includes tracking and a real-time performance dashboard.

See the demo below to understand how CAST works.

A test automation framework is a set of guidelines, rules, and coding standards you follow when developing automated tests. Defining your test automation framework will help ensure your tests are reliable, maintainable, and scalable. Some of the popular automation testing frameworks include:

Page Object Model (POM)

Behavior Driven Development (BDD)

and Data Driven Testing (DDT)

Implementing these test automation frameworks will improve the quality and efficiency of the testing process. See Figure 1 for an illustration of test automation frameworks.

It’s best to start with a small set of test cases and gradually increase the scope of automation. Starting with a small set of tests allows for better planning and implementation and helps identify potential issues before scaling up.

Test data should be kept separate from the automation code. This makes maintaining and updating test data easier without affecting the automation scripts. It also helps to ensure the consistency of test data across different test runs.

Test data management is creating, storing, and managing test data for your automated tests. Using test data management techniques is important to ensure your tests are accurate and repeatable; it will also promote data-driven tests. Some popular test data management techniques include

Data-driven testing

Random test data generation,

Test data masking

See Figure 2 to understand why test data management is crucial.

Source: Novature Tech

Figure 2: Why is test data management crucial?

Version control systems like Git allow for tracking changes to automation scripts and help manage collaboration between team members. It also enables the restoration of earlier versions of automation scripts in case of issues.

Using descriptive naming conventions for test cases and automation scripts can make understanding and maintaining the code easier. This helps improve team members’ collaboration and reduces maintenance time.

Test cases should be simple and easy to understand, with a clear purpose and expected outcome. This helps to avoid confusion and errors in test execution and results analysis.

Keeping test cases simple is essential for test automation because it improves your test suites:

Maintainability

Reusability

Reliability

Debugging,

Scalability

Simple test cases are easier to maintain, modify, and reuse in different contexts. They are also less prone to errors, more reliable, and easier to debug when things go wrong.

Furthermore, simple test cases are more scalable and can help you maintain a manageable and maintainable test suite over time. By keeping your test cases simple, you can improve the effectiveness and efficiency of your test automation efforts and ensure better quality software.

Regular maintenance of automation scripts is essential to ensure they work correctly as the application being tested evolves. Maintenance should include updating the automation scripts to reflect changes in the application and fixing any issues that arise.

Continuous integration (CI) and continuous deployment (CD) are best practices that help ensure the automation scripts are always up to date and integrated with the latest changes in the application. CI helps to automate the build process, while CD automates the deployment process.

Automated testing is a critical component of CI/CD because it enables developers to catch issues early in the development cycle and prevents errors from being introduced into the production environment.

According to McKinsey’s study on AI adoption, businesses that benefit the most from AI implement cutting-edge techniques like MLOps in their AI/ML initiatives.

Read our article to know the differences between continuous deployment and continuous delivery: “Continuous Deployment and Continuous Delivery”

Running tests on multiple platforms and browsers helps to identify any platform or browser-specific issues. One of the main aims of automated software testing is to improve the quality of software and applications; running tests on multiple browsers can improve the overall quality of the application.

Analyzing the test results regularly and taking corrective action to address any issues identified is essential. This helps to ensure the application’s quality and the automation scripts’ effectiveness.

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He received his bachelor’s degree in Political Science and Public Administration from Bilkent University and he received his master’s degree in International Politics from KU Leuven .

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