Working with leading global innovators and an expert partner network, Intland Software is at the cutting edge of high-tech product development in various industries. Our close cooperation with these companies enables us to see patterns emerging in the world of software product development, compliance, and quality early on. In this blog post, we're giving a summary of lessons learnt over the course of 2018, providing insights into topics that we think will gain more importance next year.
Risk-based thinking
In the development of safety-critical devices, a risk-based approach has always been fundamental. Eliminating or minimizing potential hazards during development helps ensure the safety, reliability, and high quality of end products. Today, as value chains are getting more and more complex, Intland Software is experiencing the adaptation of this same risk-based thinking to questions of vendor selection.
Modern management standards necessitate a focus on overall risk assessment. ISO 9001:2015, for instance, very clearly requires a systematic analysis of risks throughout the company’s operations. All other regulations applicable to medical, pharmaceutical, and automotive product development tie in to these organization-wide governing standards, and define risks as a primary concern.
It’s no surprise then that we’re seeing the adaptation of the same risk-based thinking to processes of vendor selection. Vendor assessment is becoming more risk-driven as room for error is shrinking. Vendors (including parts suppliers, OEMs, and tools suppliers) are being subjected to more and more rigorous analysis not only from a product point of view, but also from a process perspective.
Importance of tool validation
Supplier relations isn’t the only process affected by growing scrutiny on quality. Regulations require that throughout the entire process of product delivery, any and all software tools that could potentially impact product quality have to be vetted and validated.
This qualification of development tools is a resource-intensive task. Analyzing the intended use of all the software platforms in use across the lifecycle, assessing their effects on quality, and verifying that they are fit for use is a lengthy and time-consuming process. Product developers operating in safety-critical sectors are facing the challenge of testing and validating their development tools as per the stipulations of various regulatory standards.
Intland Software sees a great opportunity in reducing the complexity of tool qualification in regulated environments beyond our general Validation Package. A member of our partner network has developed standard-specific validation templates to accelerate and simplify validation processes in medical and pharmaceutical development. Using these validation kits makes it easy to test and report on the suitability of codeBeamer ALM as per the guidelines of FDA Title 21 CFR Part 11, and relevant regulations in several industries.
Quality Management Systems
Because of growing scrutiny on process maturity, Quality Management Systems are becoming paramount. QMS provides a way to accurately describe processes, policies, and procedures, to assess risks, and to document actions – and they provide simple access to all this information.
We’ve already seen a convergence between Product Lifecycle Management and Quality Management Systems. Today, QMS and Application Lifecycle Management are also coming together to show evidence of software development process maturity across the lifecycle.
Validations and audits today also extend to processes, investigating procedures of production, governance, and documentation. QMS has two main pillars: document management and risk management capabilities. Together, these help establish a direct link from product control to the evidence level, showing a lifecycle-wide focus on quality.
In this respect, codeBeamer ALM is becoming a differentiator as it provides full traceability down to all product records. QMS helps define and manage processes, while ALM helps implement those processes and manage production. For quality-driven development, the two need to be connected, and we're seeing a growing need for integrated ALM+QMS on safety-critical markets.
Scaled Agile in safety-critical development
For years, developers of safety- or mission-critical products have been wary of adopting Agile strategies. Today, there is growing evidence on the maturity of iterative processes, resulting in increasing market pressure to make the Agile transition. Simply put, Agile enables developers to deliver quality faster, and is no longer a differentiator: Agile is in fact becoming the standard in many industries.
In safety-critical development, due to the quality concerns analyzed above, there is a growing need for the controlled adoption of the method. Frameworks to scale Agile are proving to be instrumental in enforcing process maturity while adopting the flexibility of traditional Agile.
Approaches to scaling Agile such as LeSS, DAD, and SAFe® are gaining traction in the enterprise world for their ability to enable the use of iterative development without jeopardizing process control. Meanwhile, we're experiencing a growing need for Application Lifecycle Management tooling that supports scaled Agile adoption via these frameworks, and facilitates compliance through automation and documentation. This trend is likely to ramp up in 2019, as late adopters jump on the Agile bandwagon while more experienced practitioners move on to embracing DevOps strategies.
Artificial Intelligence in ALM
AI is nothing short of a revolutionary technology. Possible use cases of Artificial Intelligence are popping up in various sectors every day, and we’re still in its days of relative infancy.
Today, AI is mostly used in applications where it can reduce manual work, or make more accurate predictions. It can reliably enhance the efficiency of processes with lots of repetitive, but relatively simple steps.
For instance, it is used with success in classifying loads of information, like categorizing digital data including invoices, contracts, and purchase orders. Another use case is in ERP systems, where AI can make predictions on consumption, greatly supporting inventory forecasting.
Predictive use is in fact one of the most promising applications of Artificial Intelligence in software development, too. But in order for deep learning algorithms to be accurate, you need vast amounts of structured, good quality, and easy to process data as an input in the learning phase.
Since ALM is a relatively new tool to support software development, the lack of reliable databases makes it difficult to take the first steps in harnessing the power of AI. We expect that Artificial Intelligence will first appear in use cases where it can reduce repetitive manual tasks, like similarity analysis or the classification of data such as requirements.
Find out more about what to expect in 2019
These are just a few of the major topics we've seen emerging or gaining importance over the course of 2018. Unfolding against the backdrop of digital transformation, there are certain trends and technologies in medical and automotive engineering that we think will define the direction of development in the digital health and mobility industries.
Sign up for our webinar on 16 January 2019 to understand the trends shaping the evolution of an increasingly digital landscape. Our analysis should help you prepare for both expected and unexpected challenges in 2019 and on:
