Cloud, mobile, big data and analytics, and internet of things (IoT),robotics, cognitive computing/artificial intelligence (AI), 3D printing, augmented reality/virtual reality (AR/VR), and blockchain.
Prediction 1: By 2020, 60% of the top manufacturers will rely on digital platforms that enhance their investments in ecosystems and experiences and support as much as 30% of their overall revenue.
Manufacturers are looking to digital platforms as the underpinnings for collaboration and coordination processes, bringing together the essential technology components for the benefit of cloud-based ecosystems - including employees, customers, suppliers, and partners. Igital platforms allow manufacturers to more seamlessly and quickly apply new capabilities, leveraging technology for "experiences" and supporting revenue generation activities within an ecosystem.
Prediction 2: By 2021, 20% of the top manufacturers will depend on a secure backbone of embedded intelligence, using IoT, blockchain, and cognitive, to automate large-scale processes and speed execution times by up to 25%.
Most manufacturers will look for their major enterprise applications to be the means through which they automate and speed execution, using embedded intelligence. For many, this will happen through intelligent ERP systems, which integrate IoT for critical data input, cognitive to enhance the analytics, and blockchain to maintain the integrity of the data and decision making.These intelligent applications incorporate the four pillars of the 3rd Platform and increasingly embed and leverage the innovation accelerators - IoT, cognitive computing, next-generation security, 3D printing, robotics, and even AR/VR.
Prediction 3: By 2020, 75% of all manufacturers will participate in industry clouds, although only one-third of those manufacturers will be monetizing their data contributions.
Manufacturers are looking to industry clouds for sourcing and supplier management as well as working with customers. The cloud provides the mechanism for not only data sharing, analysis, and collaboration or joint ventures but also integration with even more data sources, such as environmental conditions (weather or traffic) or customer demand signals.In the most advanced stages, companies will also monetize the data through the clouds, for example, using aggregated performance data to create more automated replenishment of inventory or spare parts.
Prediction 4: By 2019, the need to integrate operational technology and information technology as a result of IoT will have led to more than 30% of all IT and OT technical staff having direct project experience in both fields.
The report shows that operational equipment has become widely instrumented, and increasingly interconnected, with IoT being a major contributor to connectivity. To leverage that connectivity, manufacturers are finding that the approach requires collaboration between information technology and operational technology and their respective organizations. OT includes the hardware and software that monitors and manages operational assets and processes on the plant floor and in the supply chain, for example, supervisory control and data acquisition (SCADA), meters, valves, sensors, and data historians. The fundamental ability to understand the business process, as well as work with the data that process generates, is leading to changes in how IT and OT work together on projects. Manufacturers will also look for new talent in both organizations to have a broader perspective that connects technology with business outcomes and requirements. Employees will increasingly take part in training programs that prepare them for the shift in roles.
Prediction 5: By 2019, 50% of manufacturers will be collaborating directly with customers and consumers regarding new and improved product designs through cloud-based crowdsourcing, virtual reality, and product virtualization, realizing up to a 25% improvement in product success rates.
Improving product innovation success rate (31%), better sensing and responding to customer needs (27%), and developing product-related services (30%) are all focus areas for manufacturers. And 39% of manufacturers are looking to apply analytics for improved ideation and innovation management - all indicators that the innovation management process (ideation, costing, product/formula modeling, and product portfolio management) needs to mature and extend beyond a small workgroup of marketing and design to include the extended internal, and external, team. This "team" should include tier 1 suppliers, partners, and at minimum a core group of strategic customers.
Prediction 6: In 2020, augmented reality and mobile devices will drive the transition to the gig economy in the service industry, with "experts for hire" replacing 20% of dedicated customer and field service workers, starting with consumer durables and electronics.
The gig economy has been defined to include part-time, temporary, and freelance jobs.Gig economy technology platforms have proliferated, including several that are geared specifically toward managing services, like HelloTech, which provides services for computer electronics, including computer repair, smart homes, networking, and internet.The increase of "experts for hire" in manufacturing service–related roles as customer demands for faster service intersect with digitally enabled service platforms. Now, customer service representatives have flexibility of both location and schedule (e.g., working from home Sunday evenings), and skilled field service workers can respond to more opportunities in the market, servicing an entire category of product (printers) rather than a specific brand.The benefits to manufacturers include cost savings through a variable workforce that can be more closely tied to customer demand, access to skilled experts who would not be traditional hires.
Prediction 7: By the end of 2020, one-third of all manufacturing supply chains will be using analytics-driven cognitive capabilities, thus increasing cost efficiency by 10% and service performance by 5%.
The Data from logistics operational systems, warehouse management systems, shipping manifests from OEMs, dealer management systems, and point-of-sale (POS) devices are collected then aid in creating supply chain models in the form of environmental, seasonal, and economic factors by creating cognitive models that can predict the inventory and logistics requirements with a high degree of accuracy.The concept of a cognitive supply chain also allows organizations to proactively manage inventory by moving it closer to customer demand, which ultimately can reduce the overall cost of supply chain operations and increase the service levels.
Prediction 8: By 2020, 80% of supply chain interactions will happen across cloud-based commerce networks, dramatically improving participants' resiliency and reducing the impact of supply disruptions by up to one-third.
Today, business networks are the essential enablers of digital transformation.Of the manufacturers that are participating in cloud-based commerce networks, 54% say they have seen tangible cost savings, and 44% indicate the networks allow easier access to suppliers and other types of providers. As such, companies are restructuring their supply chains to allow them to be quickly reconfigured depending on the order volumes and geographic source of demand. The best supply chains will be those that have the ability to quickly analyze large amounts of disparate data and disseminate business insights to decision makers in real time or close to real time.
Therefore, open and flexible cloud architectures will be an essential tool as they enable data generation from any source (both internal and external to the manufacturer), comprehensive and fast analysis, and then ubiquitous consumption (initially with on-premise access as significant but declining over time).
Prediction 9: By 2020, 25% of manufacturers in select subsectors will have balanced production with demand cadence and achieved greater customization through intelligent and flexible assets.
Manufacturers are now ready to launch digitally executed processes, thanks to the advancement in tools and machine technology. We see today the market availability of assets that are intelligent (i.e., able to take AI-powered decisions) and flexible (i.e., that can perform variable tasks without the need of human intervention, such as intelligent co-robots, 3D printers, and machines with retooling capabilities).
Different sectors will probably leverage technologies in different ways. In the fashion industry, we see already the availability of ready-made personalized clothing. Asset-intensive industries will probably able to reap the economic benefits of adapting production to demand requests and energy prices. In the engineering-oriented sectors, companies will establish autonomous end-to-end processes to deliver individualized and custom-based components and products. In the pharma industry, opportunities are around the delivery of mass-produced individualized drugs and treatment.
Prediction 10: By 2019, 15% of manufacturers that manage data-intensive production and supply chain processes will be leveraging cloud-based execution models that depend on edge analytics to enable real-time visibility and augment operational flexibility.
Factory execution processes have not yet been much impacted by cloud as much as other business domains, such as the supply chain. However, this is changing. The widespread availability of a reliable cloud infrastructure is making cloud a tool in the hand of process leaders. The opportunity of converting raw data from the machine level into enterprise-grade information can transform and elevate the role of shop floors in manufacturing organizations and make them central in the fulfillment process. To fulfill this promise, companies need to aggregate data from multiple sources and provide the right information, at the right time.