As the digital manufacturing ecosystem evolves, it affords more opportunities to integrate increasingly more readily available transformative technologies into the manufacturing supply chain. This ever-changing landscape requires both continuous insight into the transformative technologies themselves as well as their application space. A fundamental characteristic of digitisation is the ability to leverage 1s and 0s to work exponentially. Digital assets enable you to communicate with exponentially more things, store exponentially more, analyse and inform exponentially quicker. The exponential factor, in this case, is our “X” factor in advancing the state of manufacturing. The manufacturing industry seems to be paying attention as a recent spending study report that over $1 trillion (USD) was to be spent on digital transformation; manufacturing was reported to be leading the way.
Given the information technology (IT) sector’s high-level of competence regarding digital assets, it’s reasonable that those ecosystems with depth and breadth in IT have a comparative advantage over those ecosystems with less. With a solid talent pool of IT-related technology and services, the Indian market is well-positioned to exploit this competency regarding the evolving state of manufacturing. Transformative technologies, such as, machine learning, augmented reality, generative design, etc are levelling the playing field for global competition and expanding the available opportunities to more competition; including outside of the traditional manufacturing space. There are Fortune 100 firms who are highly competent in the transformative technology realm beginning to exploit that knowledge in an apparent, even if unintended, foray into the traditional manufacturing supply chain.
The motivation for the 2019 presentation “Transformative Technologies: The impact to machining, services, and business models” was two-fold: 1) Raise awareness of the enabling technologies that are beginning to transform the manufacturing ecosystem 2) Pragmatically connect such technologies to one’s business strategy and model. For context, the vantage point employed refers to “advancing the state of manufacturing” rather than “advanced manufacturing.” Advanced manufacturing infers a relative operational comparison which could be within a single factory, between facilities, or even companies. By focusing on advancing the state of manufacturing, both operational and financial interest are kept high. This perspective allows one to vet technologies — not just for technology sake — that accelerate or better enable one’s business strategy (e.g. a tool and die company may use AM/3DP to more quickly, and iteratively, serve customers; not necessarily causing them to change business models). Such a perspective also allows leaders to reassess their business and determine if there is a technology that shifts how they profit in a marketplace (e.g. a builder who may no longer solely sell machines but in addition, or conversely, sells machine time). While the presentation required time invested to define and provide manufacturing context of transformative technologies, its primary purpose was to change the mindset of leaders to evaluate and investigate technology through a business strategy and business model filter.
Why transformative tech?
For most industrialised countries, the percentage of manufacturing value-add to their respective national Gross Domestic Products (GDP) is significant ranging from 10% to as high as 30%, where ten of those countries contribute 75% of total global manufacturing. India’s manufacturing value-add percentage to national GDP is approximately 15% (3% to global manufacturing) and has been trending at that mark for several decades. The nature of transformative technologies reduces many barriers to traditional competitors. For example, local ecosystems found it more affordable to procure & manage a local supply base. However, the digital transformation is enabling more non-local firms to participate, thus, compete within that local ecosystem for portions, if not all, of the available statements of work.
Keys to transformative technology
Transformative technologies are a more near-term reality than many may think. Given the enabling features of such technologies there is a new dynamic emerging in the global market of manufacturing from how things are made to creating new business models.
These technologies are better defined as “transformational” instead of “disruptive” as the inherent nature is benign regarding upsetting an industry’s status quo and the potential disruption rather lies in its end-use application. A few keys to many of these technologies are: 1) ability to augment existing processes and capabilities 2) the embedded features which reduce complexity 3) digital state which enables exponentiality.
Transformative technologies: A highlight
Artificial Intelligence (AI): Machine Learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. Most applications of ML are erroneously interchanged for AI and then, upon a deeper review, most find faults and voyage onward in a sidebar manner; discounting the benefit of ML. Notwithstanding the complexity of the subject, there is much practical merit in the appropriate application of the multiple disciplines of AI in manufacturing.
Additive Manufacturing: We are now five years later from Gartner’s Hype Cycle of 3D Printing 2014 where they mention that consumer 3DP was still five years away from mainstream adoption. But what is of interest is how much the AM/3DP market has accelerated in materials and applications in just the past few years.
Augmented Reality: It is the experience of augmenting a physical reality with perceptual information (usually computer generated or overlaid) and with over 76% of recently surveyed manufacturing firms realising a significant ROI, AR also seems the best-positioned near-term, most practical transformative technology for our industry. When researching business use-cases for AR suppliers it was found that there are four main categories in the AR hardware or software space:
head-mounted display (HMD)
heads-up display (HUD)
hand-held devices (e.g. smart phone, tablets)
Some of the business models supplied the hardware only, software only, or platforms that were hardware-agnostic and interoperable with other software packages, such as, a Learning Management System (LMS) or other training platforms. The most published use-case within manufacturing seems to be remote assistance or training. In either case, the limited resource — human expertise — was extended to more areas or people than physically possible.
Generative Design: Designers or engineers input design goals into generative design software, along with parameters, such as, materials, manufacturing methods, and cost constraints.” Generative design is different from pure topology optimisation which generally selects an optimal design based upon predetermined unit cell shapes (e.g. cylinder, triangle, square) and iterates attributes like size, thickness, and interface mating to find an optimal design. The emergence and acceleration of generative design seems to have its genesis in the confluence of technologies, such as, higher processing capabilities (e.g. graphics processing unit, GPU-based microprocessors), genetic algorithms (e.g. machine learning-based functions developed to solve a formulated problem), and the integration of finite element analysis (FEA) and computational fluid dynamics (CFD) and topology optimisation.
Advanced Robotics (Cognitive Automation): Cognition may be described as a spectrum from human to non-human, where the human end-point describes cognition in terms of perception, sympathy/empathy, and emotional awareness. In contrast, the non-human end-point describes cognition in terms of knowledge, experience, and situational awareness. When such a spectrum is applied to manufacturing there is evidence to apply “cognitive automation” to a couple of areas: robotics and actionable analysis.
Robotics, as a discipline, has undergone an evolution from passive, prescribed technologies, such as, pick-and-place, where the environment is relatively static and “if-then” conditions determine the work to semi-active, responsive technologies, such as, dynamic pick-and-place, where the environment is relatively dynamic (e.g. piece parts are mixed inventory in a bin). Each situation augments the system with technologies, such as, machine vision or lasers, but the robotic system’s response varies depending upon its inherent capability to relate and respond to the situation (i.e. its cognitive response). Cognitive automation, in this sense, describes a robotic system’s ability to improve its situational awareness and change its “machine state” to either participate and do a certain work or not. This same approach may also be applied to manufacturing data where actionable analysis leads to conclusions based upon a data system’s ability to improve its situational awareness and reach its objective function without the necessity of human intervention (i.e. cognitive input).
Macro tech trends
Electric Vehicles / Autonomous Vehicles are providing a most interesting backdrop for companies either producing transformative technologies or embedding those technologies into products. A few reasons seem to be emerging that suggest this: datacentric and data-dependent environments, new designs based on new functionalities (e.g. moving away from typical internal combustion engine-based drivetrains), new business models that subjugate typical consumption paradigms (e.g. real costs that include the purchase of capital equipment). How EV or AV products come to market are managed and sustained and ultimately used may have a significant affect on how augmenting tech-based business support and change buying or consumption habits; much remain unknown.
As new business platforms continue to emerge, such as, co-creating designs for products, manufacturing-as-a-service, and localised micro factories for production, increased market opportunities continue to become more dynamic. Again, this article is not to identify and prescribe discrete actions directed at market solutions but rather to highlight data points within the market and couple those data points with attributes regarding transformative technologies. Equipped with this type of information, a firm might be better aware to match needed attributes with their business to either accelerate or modify current business models.
The business case for developing a digital strategy lies primarily in three areas of data usage: 1) improving decision making 2) improving operations 3) monetisation of design. Ultimately, a digital strategy emphasises the value of data and utilises data-centric tools, resources, and functions to exploit the digital nature of your operations (front/back office, production, talent, supply chain, etc). A strategy would provide the North Star for your organisation that becomes a filter or prioritisation mechanism to map things like, technologies to functions to strategy (Table 1: Digital Technologies Affecting Strategic Playground).
Next steps – Take action
As Dr David Patterson, Professor Emeritus, University of California – Berkeley and Distinguished Engineer, at Google had coached AMT Board members back in the mid-2000s, “it is not the technology, it is the willingness to take action.”
I now leave my encouragement to do the same. Continued awareness and engagement are both necessary regarding transformative technologies and their potential impact to your business, market segment, and overall industry to stay relevant and compete. While there is great benefit to consider for optimising your own business it is just as important to realise how transformative technologies also reduce, if not remove, traditional barriers to entry into your market.