As part of my role at QAD I have the great honor of being part of the core advanced technology team. I am often engaged with discussions around emerging technologies and most recently I was asked if a particular technology “was in its infancy?” I have heard this question before but for some reason the literal analogy of an infant struck a nerve for the first time.
Would I equate the current state of the technology to that of an infant child? Does the comparison to childhood development hold up beyond infancy?
The charter of the QAD advanced technology team is to research new technologies across multiple industries but always with a focus on benefits to the manufacturing industry. There is a diverse set of technologies to consider and we regularly review topics such as IoT, artificial intelligence, machine learning, 3D-printing, cloud computing, data lakes, digital twins, RPA, blockchain, 5G and a host of others. The cross-functional team categorizes and prioritizes technology into maturity categories for the manufacturing industry. Some technologies such as 5G or blockchain are still very early in their maturity but are worth following. Others such as IoT and machine learning are further along but still finding their way. Still others like cloud computing for Demand & Supply Chain Planning have proven to have significant viability and are well on their way.
The purpose of this categorization is three-fold. First, QAD provides a real-world view of the importance of a given technology protecting our customers from the pre-viability hype that often arises. Secondly, QAD welcomes the opportunity to innovate and collaborate around emerging technologies. We have established QAD Labs as a formal process and are actively working with our customers on IoT, machine learning and RPA activities. Finally, as foundational providers of manufacturing solutions, we strive to incorporate technology into our offering as soon as viable. This provides QAD customers with the optimum balance of capability without unnecessary risk of too-early adoption.
I do want to return to the analogy of comparing certain technologies to an infant child. My children are all adults but my memory tells me that as much as my infants were loved and coddled, they were not particularly self-sustaining. A quick Google search tells me that infancy lasts from birth through about twelve months. Infants make remarkable progress every day but even at the end of infancy, they are still in need of significant feed and care. There are certainly technologies that do indeed feel that way. Each time you hear the expression, “this feels like a technical solution in search of a problem” then you are probably describing a technology in its infancy. QAD tracks infants but engages in a much deeper way with the technologies that have progressed a bit further in terms of maturity.
I believe that many of the technologies that QAD is following may better be described as in the toddler stage. Toddlers are up on their feet and have become walking and talking bipeds. There are still plenty of bumps and bruises from falls and a fair amount of nurturing involved. Google tells me that toddler lasts from twelve months until four years. That is quite a span of development but honestly some of the most enjoyable years of parenting.
QAD Labs is actively collaborating with our customers with technology that I would describe as late the toddler stage. Artificial Intelligence has certainly established itself within the consumer and social media world with firms like Amazon and Google and is probably well beyond the toddler stage. It’s not surprising that the application of AI in the form of machine learning for manufacturing has lagged that high-volume, high-revenue environment. However, machine learning is an example of a technology that I would describe as a late-stage toddler. The technology has advanced in terms of understanding, available tools, and skill development. There is certainly very fervent interest in machine learning for shop floor predictive maintenance and in Demand and Supply Chain Management. (DSCP) QAD Dynasys is at the leading edge of moving this toddler well into the next stage. Machine learning technology has found the perfect playground in the rich data sets of DSCP and the management of exception-based forecasting. Toddlers are the very essence of learning on the job and QAD customers are starting to see the impact from this applied technology.
QAD will continue to embrace advanced technology and systemically monitor its maturity. QAD is always looking for customer collaboration in this area. If you have a specific interest in nurturing a specific technology or perhaps babysitting some of the more advanced capability, QAD would love to hear from you. Please feel free to contact us at QAD_labs@qad.com .