Tag: Manufacturing

How to Improve Collaboration Between Your Developers and Testers

This proverb “a Tester & Developer are not two distinct entities but have adopted separate routes towards one common objective” is true to its words. While testers and developers think differently, their collaboration improves communication and mutual understanding. Only together will developers gain a deeper understanding of the benefits that thorough testing brings to the software development process. In contrast, developers can help to inform testers of any technical constraints and provide insights into potential implementation challenges. Through collaboration and sharing knowledge and perspectives, testers and developers stand to share much.   Here are some suggestions for promoting developer and tester cooperation: Early involvement of testers: Involve testers early in the development cycle, such as during requirement-gathering and design conversations. It allows testers to be able to give good feedback and identify potential test scenarios or problems to help them better understand the system and its intended purpose. Regular connects and communication channels: Set up ongoing meetings and communication pipelines among testers and developers to discuss requirements in detail, share updates, and address issues and concerns, if needed. This fosters transparency and ensures everyone is on the same page. Partnership in test planning: Promote collaboration between developers and testers during the process of test planning tasks. Testers will provide expertise in creating test scenarios and test case development, while developers will provide expertise in identifying risk areas and gaps in test coverage. Collaborative test case reviews: Run joint Test Case Reviews where developers and testers work together, reviewing test cases and providing comments. It helps align understanding, define specs, and establish any missing scenarios. Edge conditions or corner cases could be known to developers but might not have been considered by the testers. Continuous integration and automation testing: Use automated testing and continuous integration practices to have code integrated and tested throughout the day(s). Shared responsibility for the testing process allows developers to be part of the building/maintaining the automated tests, resulting in more time in the feedback loop and less burden on testers. Pair programming and coupling sessions: Promote tester and developer participation in Pair Program/pairing sessions — for working together on a particular feature or task. It promotes the sharing of know-how, and cross-training helps you learn more about what your peers do, as well as their perspectives and struggles. Continuous feedback and retrospectives: Collaboration needs to be evaluated through retrospectives as well as regular follow-up sessions. Encourage both testers and developers to provide constructive and open feedback to identify where improvements can be made and what has been done well. It provides an iterative feedback cycle that optimizes collaborating processes and fosters a culture of constant iteration. Knowledge-sharing sessions: Arrange lunch-n-learn sessions/Knowledge-sharing sessions where testers and developers can come together and speak about new topics they learned, share their experiences, or do some interactive workshop. Learning and sharing our experiences will create a fertile ground for sharing experience/knowledge transfer across borders.   By implementing the above mentioned points, testers, and developers can collaborate more successfully and help produce high-quality software.   Now, here are some insightful lessons that each group can pick up from the other: 1.Testers can learn from developers: Code quality with performance optimization: Writing clean, performant, and easy-to-maintain code is usually something developers are good at. From Developers — Testers can learn coding best practices to write better automation scripts and create reusable test cases, which will help improve test code quality. Developers can educate testers on optimizing the application, i.e., finding slow, high-resource locations (memory), detecting and fixing bottlenecks, and using profiling tools. Performance testing info can be used by testers to create performance tests or to identify performance issues. System architecture: Developers know very well how everything works and how the pieces fit together in the system architecture. Testers can use the architectural expertise inherent in development teams to identify potential hotspots and build tests aimed at core functionality. Technical skills: Programming languages, frameworks, and design patterns are valuable knowledge a developer can pass on to a tester based on their technical expertise. It can help testing teams better understand the implementation and write tests that are much better than before. Testability: By learning how developers write testable code, they can build better test cases, which leads to more reliable and sustainable test suites. Developers should advise regarding strategies such as dependencies injection, mocking, and modular design, which aid in testing the code.   2.Developers can learn from testers: Domain knowledge: Testers know the business domain and end-user requirements very clearly. They can share their domain knowledge with programmers who help them understand how their software will run within different environments. This data can give developers a leg up on identifying what users really need from a feature and how to design it accordingly. User perspective: During testing, testers often consider how end users use the application. Developers can learn from real-world user interaction, understand their pain points, detect usability issues, and make informed design decisions catering to the user’s needs if they work closely with testers. Test design and Test automation: Testers focus on designing testing processes where fallacies come to light and the system’s functionality gets validated. Testers can train developers on test design principles like boundary value analysis, equivalence partitioning, or ad hoc/exploratory testing. Developers can use these strategies as they develop to build better unit tests, which will find problems sooner rather than later. Testers know how to generate auto-tests. Testing folks can offer developers their insights on various test automation frameworks, tools, and practices. This insight allows developers to craft Unit tests, Integration Tests, and even Auto UI tests, leading to better Test coverage during the development process. Adaptability and resilience: Testers often face evolving requirements, tight deadlines, and changing priorities. They develop resilience and adaptability to deal with these challenges. Testers demonstrate skills in dealing with uncertainty, flexibility, and the ability to deliver value in an agile or iterative context — this is something developers can learn.   Tavant is actively exploring and integrating these

Field Service Technologies : Empowering mobility in a post-pandemic world

Explore how technicians can be empowered with mobility solutions to improve field productivity, reduce paperwork, and increase the accuracy of diagnostics.

Tavant Warranty Management

Tavant’s warranty software offers end-to-end warranty lifecycle management and helps organizations reduce warranty costs, increase supplier recovery, and improve aftermarket excellence.

Improved Dealer Vendor Experience Using Intelligent Aftermarket Platform

Tavant with EXCEL Industries co-presented a paper on Improved Dealer & Vendor Experience using an Intelligent Aftermarket Platform at Warranty Chain Management Conference (WCM) 2019.

How the right warranty management solution can help improve your organizationʼs bottom line

Executive-level Results from SFG℠’s 2017 Warranty Chain Management Benchmark Survey.

A Warranty System Upgrade for Robust Solution

Explore how manufacturers can effectively implement a robust warranty system to streamline their overall warranty process.

Field Service Optimization

Make your data work towards boosting productivity and increasing revenue

Digital Twins in Manufacturing 4.0

Digital twin enables you to create a virtual representation of a real-world product or asset that help businesses to make model-driven decisions. Explore more about Digital Twins.

Supercharging Service Contracts for Success: The Analytics Advantage

In today’s digital age, data is continuously generated from various sources, and businesses have access to vast amounts of valuable information. However, managing and extracting insights from this data can be a daunting task without the aid of advanced technology and analytics. This is particularly true for Service Contracts, where the success of these agreements depends on understanding customer behavior, equipment performance, market trends, and more. By leveraging advanced analytics, OEMs can effectively navigate through the sea of data, gaining actionable insights to make informed decisions. The true potential of advanced analytics lies in its ability to revolutionize service contract offerings, leading to improved operational efficiency and enhanced customer satisfaction. By embracing analytics-driven service contracts, OEMs can create a win-win situation, ensuring their consumers receive fair and transparent pricing, optimized contract options, and proactive support Let’s explore some of the key analytics options and understand how they drive business value for both OEMs and their customers: • Pricing Analytics Pricing Analytics empowers OEMs to understand price elasticity and set competitive contract prices that maximize profitability. By leveraging statistical modelling, machine learning algorithms, and market research, OEMs can analyze historical data, market trends, customer behavior, and contract performance. This analysis allows them to identify pricing patterns and optimize contract prices, ensuring both profitability and value for their customers. • Portfolio Optimization Portfolio Optimization involves tailoring service contract offerings to match customer needs while maximizing profitability. Through customer segmentation, contract performance analysis, and market demand evaluation, OEMs can identify the most valuable combinations of service contracts. This ensures customers get the precise coverage they require, leading to enhanced equipment performance and reduced downtime. • Profitability Analysis for Informed Decision Making By analyzing the financial performance of service contracts, OEMs can identify high-profit contracts and optimize low-profit ones, leading to overall enhanced profitability and sustainable growth. This analytics-driven approach enables OEMs to allocate resources effectively, prioritize contract management efforts, and make data-driven decisions that impact the bottom line positively. • Internet of Things (IoT) Analytics Utilizing IoT Analytics, OEMs can proactively address equipment maintenance needs, minimize downtime, and improve equipment reliability, ultimately resulting in higher customer satisfaction. IoT-connected devices provide real-time data on equipment health, usage patterns, and potential failures, enabling OEMs to take timely and informed actions. • Data Analytics for Enhanced Insights and Decision MakingBy applying machine learning, data mining, and predictive modelling, OEMs can gain deeper insights into contract performance, customer behavior, and market dynamics. This enables them to identify trends, predict service demand, anticipate customer needs, and optimize service contract offerings for greater customer value. • Remote Monitoring and Diagnostics Efficient Equipment SurveillanceRemote monitoring and diagnostics allow OEMs to keep track of equipment health, detect issues, and provide timely support without physical presence. This reduces response time, lowers service costs, and ensures efficient resource allocation, resulting in quick problem resolution and improved operational efficiency for customers. • Service Demand Forecasting for Effective Resource Planning By proactively aligning resources with anticipated service demand, OEMs can optimize service delivery, improve customer satisfaction, and reduce operational costs. Through historical data analysis, market trend evaluation, and predictive modelling, OEMs can accurately forecast service demand and plan their resources accordingly. Benefits of Service Contracts with Advanced Analytics Impact on Revenue Generation in Service Contracts: Optimized pricing, portfolio, and profitability analysis lead to increased revenue generation for OEMs, while customers benefit from fair and competitive pricing. Enhanced Equipment Performance: IoT Analytics and remote monitoring ensure better equipment reliability and performance, reducing downtime for customers and enhancing their operational efficiency. Data-Driven Decision-Making: Advanced analytics enables OEMs to make informed decisions based on data insights, resulting in better strategic planning and resource allocation. Cost Optimization: By identifying high-profit contracts and optimizing low-profit ones, OEMs can effectively manage costs and improve overall profitability. Improved Customer Satisfaction: With proactive support, personalized service contracts, and optimized offerings, customers experience higher satisfaction levels, fostering long-term relationships with OEMs. Final Thoughts Embracing advanced analytics in service contracts is the key to unlocking operational efficiency and profitability for OEMs while ensuring customers receive unparalleled value and support. By harnessing the power of data through analytics, businesses can stay ahead in today’s competitive landscape and offer their consumers a truly transformative service contract experience.

Driving Innovation in Warranty and After Sales: The Role of Generative AI in the Manufacturing Industry

Generative AI has gained significant prominence worldwide in 2023, transforming the way researchers, enthusiasts, and software developers tackle machine learning and artificial intelligence challenges. Generative AI is an artificial intelligence subfield that can create content in the form of text, images, music, and code. A massive amount of text data is used to train these models. Let us examine some use cases of these models in the manufacturing industry. Text Generation and Summarization: Large language models can generate text in a conversational and human-friendly manner. These models support several languages and aid in use cases such as producing content for marketing and sales departments, supporting developers with code documentation, and assisting developers in understanding the code written. Long-format papers can be summarized using Generative AI models to deliver precise, context-relevant information. Summarization can be tailored to the user’s preferences. Semantic Search Systems: These models can be used to build search and knowledge-based systems that can recognize the context in user queries and return relevant information, enhancing user acceptability and search experience over traditional keyword-based search systems. Question and Answering Systems: The generative models may also answer user queries by recognizing the context of the query and generating answers utilizing knowledge learned from massive amounts of data relevant to the user inquiry. Synthetic Data Generation: Generative models, with their vast knowledge base comprising massive amounts of data, may generate synthetic data for experiments and training machine learning models in situations where real-world data is unavailable. Image Generation: Generative models can create images with various artistic styles, settings, and colors. These are useful in generating synthetic images to aid users in machine learning modeling.   Applications in Manufacturing – Warranty and After Sales Claim Process Optimization: Warranty dealers and claim processors can use Generative AI models to revolutionize question-answering systems by answering queries with interpretable and appropriate reasoning by understanding the context and semantics of queries using a large number of documents. The systems shorten the procedure and optimize it. Customer service and support: Using generative language models such as GPT3.5 and GPT4, personal assistants and chatbots can be constructed to aid customer support teams in addressing client inquiries and issues relating to warranty, claim procedures, and troubleshooting steps. These models can also help with faster claim processing and provide a better client experience. Warranty Claim Validation: Claims processors can use Generative models to analyze and validate dealer claims. These models use warranty information, product specifications, and claim information to identify patterns of fraudulent claims and make decisions to automate the validation process, prevent fraud, and speed up claim settlement. Recommendations: Using usage patterns and historical data, large language models can provide individualized recommendations to clients and dealers regarding warranty coverage and upgrades. Text Sentiment Analytics: Customer evaluations and feedback can assist warranty providers and dealers in improving their service, identifying and resolving reoccurring issues, and enhancing the overall customer experience.  Without the need for training, generative models can assist in determining the sentiment of the text. These models extract textual patterns and provide reasoning for sentiment prediction. Intelligent Search System: Generative AI models can aid in the creation of a centralized knowledge base that dealers, technicians, claim processors, and warranty providers can use to find and obtain relevant information on claims, warranties, troubleshooting common issues, service manuals, and FAQs. It lets you quickly discover root causes, potential part replacements, SLAs, and applicable resolution actions. It can return relevant search results and citations, as well as supporting content related to the context of the query. Quality Control and Defect Detection: Generative AI algorithms can analyze a large amount of manufacturing data, including sensor readings and images, and process this information to detect defects and patterns identified in the data.   Tavant is actively exploring and integrating these cutting-edge features into the highly advanced Tavant Manufacturing Analytics Platform (TMAP). This strategic initiative aims to empower customers with a distinct competitive edge by utilizing advanced Generative AI models. In our initial forays into this dynamic field, we have successfully developed compelling POCs in the domains of chatbots, personalized assistants, and smart-search systems. Leveraging warranty after-sales data, these pioneering POCs deliver unparalleled value to dealers and claim processors. Some of the modules in TMAP where we are exploring Generative AI models are: Warranty – Automate claims processing, identify suspicious information, improve dealer performance, reduce warranty spend, enhance the quality of the claim, and identify anomalies in the image. Price – Recommend optimal parts price, completive pricing analysis, evaluate the performance of pricing strategies, monitor and alert price changes, and segment customers based on their price sensitivity. Quality – Identify product quality issues, failure rates, and areas for improvement by analyzing claims, returns, and repairs. Field – Optimize services using AI Smart search, service & parts demand to forecast, and real-time insights enabling you to improve service quality and enhance customer satisfaction. Contract – Enhance contract performance, improve profitability, mitigate risks, and strengthen customer relationships through personalized contract offerings and optimized prices.   Final Thoughts By utilizing the various text content available, such as installation and warranty manuals, service guides, and safety guidelines, Generative AI can transform the manufacturing industry by enabling technicians, dealers, and manufacturers with personalized assistants, chatbots, intelligent search systems, and recommendations. This can assist dealers in providing excellent customer care, as well as business users in identifying potential issues and improving the product and after-sales services.