NPD stands for "New Product Development." It refers to the process of creating and introducing new products or services to the market. This process involves various stages, from ideation and concept development to design, testing, and finally, the product's commercial launch. NPD is a critical aspect of business innovation and growth, as it allows companies to stay competitive, meet customer needs, and explore new market opportunities.
Today’s new product development process has increased in complexity because of electronic and mechatronic systems, systems engineering, product safety, and increased globalization of design and manufacturing both within an organization and its supply chain. Empirical evidence indicates that a substantial portion of warranty-related concerns in vehicles, ranging from 20% to 50%, stems from software-related issues. This finding may similarly apply to other intricate systems. In contemporary New Product Development (NPD) activities, digitalization initiatives frequently entail the integration of software, hardware, and globally dispersed product development teams in their implementations.
In recent times, New Product Development (NPD) has undergone significant changes and encountered various challenges; especially in industries Like Automotive, Aerospace, and Medical Devices, where advancements in hardware, electronics, and sophisticated software integration have been remarkable. The introduction of newer standards, such as cybersecurity, product standards, and Over the Air (OTA), has reshaped the landscape. Meeting demanding compliances and regulatory requirements has become essential, leading to a strong emphasis on careful adherence, and compliance. Moreover, NPD processes have become more intricate due to the increasing complexity of systems, necessitating a methodical approach to hardware, and software engineering architecture. NPD is further influenced by the emergence of a millennial workforce characterized by tech-savviness, mobility, and social networking skills. Furthermore, the industry has witnessed the formation of global alliances and the entry of new suppliers.
IoT (Internet of Things) is bringing fundamental changes to industrial and consumer products. The 22 Billion connected devices in 2018 are expected to rise to 50 Billion in 2030. An explosion of software and hardware increased the complexity of product design, giving rise to a distributed design network, or design supply chain. The coordination of design and manufacturing in the “Design Supply Chain” with the tremendous exchange of information, forms, and checklists is not possible without technology.
Digitalization is the use of digital technologies to convert a process into digital information. The process performances turn into data that can be used to manage the process. The goal of digitalization is to reduce the number of steps in a process, reduce lead time, and increase efficiency.
After winning the customer order (RFQ), an impact analysis has to be done and then onto program management, which includes a product safety plan, software project plan, cybersecurity plan and APQP plan. Then all this information comes into PPAP, then into the Manufacturing Execution System (MES), and the Total Product Maintenance (TPM) System.
NPD planning, requirements management, Design Failure Mode, and Effects Analysis (DFMEA) & Test plans, Process Failure Mode and Effects Analysis (PFMEA), & Control Plans and work instruction are the building blocks of NPD Digitalization. NPD Platform is a platform where the internal and external of the company come into one place. The Design Supply Chain can come into one location for all the design-related information including APQP Plans, Requirements, Configuration, Project Teams, and FMEAs and Controls.
Advanced product quality planning (APQP) Time plan, Project Safety Project plan, Software Project plan, and Cybersecurity Project plan came together to satisfy NPD planning, Product Safety, Software, and Cybersecurity Project plan.
Distributed projects are linked between Customer’s Design Team Globally and the Supplier’s Design Team – System, Sub System, Hardware, Software, and Testing all on the same page with linked dates. Supplier project plans are called Distributed Interface Agreements or Joint Agreements in some markets. This gives a better understanding of who is going to do what.
The process involves an automatic flow down of requirements, commencing from the system level, progressing through the subsystem level, and ultimately reaching the Design Failure Mode and Effects Analysis (DFMEA). The integration of requirements occurs within the Design FMEA flow down, wherein a test plan named Design Verification Plan & Report (DVP&R) becomes associated with the DMFEA. Subsequently, all these interconnected elements are incorporated into the Production Part Approval Process (PPAP).
The capability to classify requirements into distinct categories such as functional, safety, hardware, software, regulatory, and others is essential. In modern methodologies and requirement frameworks, both backwards and forward traceability are deemed necessary. At the very least, such traceability is indispensable for safety functions and their corresponding requirements.
The product itself encompasses various features, each of which is further subdivided into functions. These functions, in turn, are associated with specific requirements, and these requirements are accompanied by corresponding characteristics that undergo management throughout the manufacturing or assembling process.
Through the systematic flow-down process of requirements, originating from the Product and cascading down to subsystems and individual components, the DFMEA seamlessly integrates interconnected functions and requirements. This integration is facilitated by the incorporation of linked DVP&R, which, in turn, generates associated test plans that adhere to the V model approach.
New Product Development (NPD) represents a complex undertaking, involving several essential processes, including Design and Process Failure Mode and Effects Analysis (DFMEA and PFMEA), Design Verification Plan & Report (DVP&R), Process Flow, Control Plan, and Work Instruction.
Emphasizing design and process reuse can prove highly advantageous in this context, potentially leading to significant efficiency gains. Specifically, by leveraging existing designs and well-established processes, an approximate 70% reduction in documentation efforts can be achieved.
Inspections on the go with mobility and real-time data collection through sensors (IIOT – Industrial Internet of Things) will enable product development teams with newer insights on product failure, rejects, and equipment performance. Inspections linked to Control Plans, check sheets, and Work Instructions bridge the gap between product launch teams and the shop floor.
A predominant portion of the industry currently lacks an automated data collection process. However, advancements in technology have paved the way for integrating data from sensors, which can be efficiently transmitted through a router to a centralized big data platform. This setup allows for the real-time monitoring and analysis of inspection data.
Under the Product Development Management & Continual Improvement framework, several critical components play vital roles in ensuring the quality and efficacy of the development process. These components include Voice of the Customer (VOC) or Requirements gathering, Failure Mode and Effects Analysis (FMEAs), failure analysis, Audits, and Inspections.
To support and enhance these processes, advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and O-BOT are integrated throughout the digital platform. These intelligent technologies facilitate efficient data processing and analysis, enabling informed decision-making and problem-solving.
The data collected from various sources are channeled to a centralized big data platform. Here, the data undergoes thorough analysis, leveraging the power of AI, ML, and O-BOT, to derive valuable insights and patterns that can drive continual improvement and inform product development strategies
By integrating New Product Development (NPD) data, a remarkable reduction in the number of required forms can be achieved, as the same data can be efficiently reused throughout the product launch process. Specifically, this integration can lead to a reduction from approximately 413 distinct work products to only 200 different work products. Such streamlining of data usage translates to a significant reduction in engineering time, effectively halving the time required for product development through the adoption of digitalization.
The immediate Return on Investment (ROI) is readily evident from these efficiency gains. With accelerated product development timelines, companies can bring their products to market faster, thereby capturing market opportunities sooner. Additionally, the reduced engineering effort allows for better resource allocation and improved cost-effectiveness.
AI indeed plays a pivotal role in various aspects of business operations, including Validation systems, Recommendation systems, Predictive systems, and Robotic Automation. The incorporation of an O-BOT further enhances the capabilities of the organization by enabling seamless review of the Production Part Approval Process (PPAP) and other work products. It also facilitates the validation of PPAP and provides valuable recommendations for New Product Development (NPD), including the development of Failure Mode and Effects Analysis (FMEA) and control plans.
The Predictive system analyses inspection and NPD data, enabling the prediction and forecasting of project delivery timelines and associated risks. Robotic automation contributes to the digitalization of data that may not be readily available in a digital format, allowing it to be processed efficiently by automation tools and converted into a digital form.
The integration of AI will revolutionize the approach to NPD, particularly in a remote working environment. The traditional need for extensive "project reviews" will diminish as AI provides real-time monitoring and reporting capabilities. With alerts and escalation mechanisms in place, Project Managers can manage projects proactively and make timely decisions based on up-to-date information.
Emerging technologies have led to the integration of AI Recommendations within digital systems, offering valuable assistance in various domains. These AI Recommendations play an important role in addressing and mitigating risks in different areas. They include Hazard Analysis and Risk Assessment (HARA), which ensures the safety of products by identifying potential hazards and vulnerabilities. Additionally, Threat Analysis And Risk Assessment (TARA), that's performed to evaluate the risk associated with security incidents. Safety Analysis, encompassing DFMEA (Design Failure Mode and Effects Analysis), FMEDA (Failure Modes, Effects, and Diagnostic Analysis), and FTA (Fault Tree Analysis), further bolsters the system's resilience. Process Controls, encompassing Process Flows, PFMEA (Process Failure Mode and Effects Analysis), and Control Plan Work Instructions, streamline and optimize the manufacturing and operational processes. Moreover, the incorporation of Core Tools like APQP (Advanced Product Quality Planning), SPC (Statistical Process Control), MSA (Measurement System Analysis), and PPAP (Production Part Approval Process) ensures standardized and high-quality production practices.
O-BOT represents an innovative Artificial Intelligence solution designed to facilitate the review process of PPAP (Production Part Approval Process) or any other work product.
Leveraging advanced deep learning algorithms, particularly Natural Language Processing (NLP), O-BOT efficiently analyses and assesses the overall quality of PPAP submissions.
One of O-BOT's notable capabilities lies in its proficiency to discern failure mode characteristics upon reviewing the data provided. It effectively examines the causes attributed to certain failure modes, accurately distinguishing between causes and failures, thereby enhancing the accuracy of evaluations.
To ensure comprehensive and robust evaluations, O-BOT is equipped with a repertoire of 300 predefined business rules that are built into its framework. Additionally, O-BOT's adaptability is demonstrated through its capacity to be further refined and personalized over time. By leveraging machine learning techniques, it can be trained to internalize and accommodate an organization's unique rules and specific preferences, rendering the review process even more tailored and tailored to individual needs.
AI and Machine Learning are poised to revolutionize not just PPAP reviews but also to become the very foundation of ensuring quality in Engineering and Manufacturing.
The digitalization of New Product Development (NPD) offers a multitude of benefits that can revolutionize the way products are created and brought to market.
Firstly, it provides increased visibility of product development status across the entire enterprise, allowing stakeholders to stay informed and make data-driven decisions. By standardizing and reducing lead times for launching new products, digitalization enables faster time-to-market, ensuring companies can capitalize on market opportunities more efficiently. Seamless communication is facilitated through digital platforms, allowing internal product development teams, customers, and suppliers to collaborate effectively, exchange program timelines, work products, and reviews effortlessly.
With digitalization, all requirements are effectively managed, designed, and verified, eliminating potential oversights and ensuring that products meet their intended specifications. The integration of various aspects of the product lifecycle, such as requirements, design, manufacturing, inspections, and Corrective and Preventive Actions (CAPA), further enhances efficiency and quality.
A notable advantage of digitalization in NPD is the significant reduction in the effort required for developing work products. This enables teams to focus more on innovative ideas and creative solutions, optimizing the overall product development process. Drawing from the experiences of previous successful launches, digitalization allows companies to apply lessons learned from best-in-class programs to new initiatives, further improving the chances of success. Moreover, the utilization of AI and machine learning expert systems brings an added layer of defect prevention and avoidance. These technologies can analyze vast amounts of data, identify potential issues, and provide valuable insights to enhance product quality and reliability.
Another significant benefit of the digitalization of NPD is the ability to enable Business Intelligence (BI) to provide precise Key Performance Indicators (KPIs) and organizational performance metrics. With digital tools and platforms, companies can collect and analyze vast amounts of data related to their NPD processes. This data can then be transformed into actionable insights that drive informed decision-making.
In conclusion, the digitalization of New Product Development (NPD) presents a transformative shift that can revolutionize the way companies create, develop, and bring products to market. By embracing digital technologies, businesses can benefit from increased visibility, streamlined communication, and standardized processes, leading to faster time-to-market and improved product quality. The integration of AI and machine learning further enhances NPD efforts by providing valuable insights, defect prevention, and predictive capabilities. Business Intelligence tools enable precise KPIs and performance metrics, empowering informed decision-making throughout the NPD journey. Moreover, the seamless flow of data and real-time monitoring facilitate collaboration among global teams, enhancing efficiency, and ensuring compliance with industry standards and regulations. Overall, the digitalization of NPD offers immense potential for growth, innovation, and improved customer satisfaction in the dynamic and ever-evolving landscape of product development.