"Manufacturers of every size and shape are changing rapidly because of new digital technologies, new competitors, new ecosystems, and new ways of doing business,” says Kimberly Knickle, research vice president, IT Priorities and Strategies with analyst firm IDC Manufacturing Insights. “Manufacturers that can speed their adoption of digital capabilities in order to create business value will be the leaders of their industry."
Technologies that will have the greatest impact include cloud, mobile, Big Data and analytics, and the Internet of Things (IoT). Manufacturers also have high expectations for the business value of technologies that are in earlier stages of adoption, such as robotics, cognitive computing/artificial intelligence (AI), 3-D printing, augmented reality/virtual reality (AR/VR), and blockchain.
IDC recently surveyed the global manufacturing landscape to compile a reporting profiling coming trends in manufacturing supply chains. When creating its predictions the firm examined ecosystems and experiences, greater intelligence in operational assets and processes, data capitalization, the convergence of information technology and operations. Most of the group’s predictions refer to a continuum of change and digital transformation (DX) within the wider ecosystem of the manufacturing industry and the global economy.
Here are some predictions from the report that apply to the supply chain:
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. The platform facilitates information exchange and processes, at scale, simplifying connectivity and ensuring a level of security and trusted business interactions. The platform will be anchored by an open architecture and open access and, in many cases, by an open marketplace to support monetizable information flows and new revenue opportunities. As a result, digital platforms allow manufacturers to more seamlessly and quickly apply new capabilities, leveraging technology for "experiences" and supporting revenue generation activities within an ecosystem. While platforms may support traditional revenue streams online, some new opportunities will also develop explicitly because of the ecosystem effect.
By 2021, 20% of the top manufacturers will depend on a secure backbone of embedded intelligence, using IoT, blockchain, and cognitive systems, 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. We're in a transition stage where systems of record are being replaced by new systems of intelligence, which retain the core "systems of record" capabilities while layering in new technologies and capabilities. 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, 3-D printing, robotics, and augmented reality/virtual reality.
These systems leverage cloud and machine learning but more generally analytics of all types to manage data coming from new and existing sources. Some of the outcomes are:
- IoT: Actual product/asset performance data that can initiate preventative maintenance activities and increase customer satisfaction; inventory tracking to facilitate higher levels of accuracy in the supply chain, minimizing order delays resulting from inaccuracies
- Cognitive: Advanced analytics to complement existing analysis, focusing more on identifying patterns and prerequisites for workflows and processes, such as preventative maintenance and customer sentiment to direct sales, identifying customer preferences for more efficient product innovation
- Blockchain: Data to ensure the authenticity and quality of goods in transit, increasing product and service quality; speeding processing from order to cash and traceability for data and contracts
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.
Product failure rate is high across industry, in some cases, up to 80%, in large part because manufacturers don't consistently take the time to understand customer needs at the front end of innovation. Or they presumed what the market wanted. This is a lesson the consumer goods industry learned decades ago because of a highly competitive market and varied product portfolio. These same "fast-moving," dynamic characteristics are making their way into other industries that traditionally have had a longer product life cycle, such as automotive, heavy equipment, and industrial machinery. Companies in asset-intensive industries like chemicals also recognize the need for a proactive, flexible approach to product and process innovation. With the growth and maturation of cloud-based platforms, the integration of social media–like capabilities within collaborative innovation systems, and the broader use of simulation and virtualization of product models or digital twins, the tools are available now for manufacturers in all industries to progress and modernize their approach to ideation, innovation, and new product development.
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, according to IDC Manufacturing Insights' 2017 Product and Service Innovation Survey. 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. Automotive manufacturers such as Ford and Daimler have emerging initiatives around design thinking and customer experience design. Crowdsourcing with a broader audience of customers, prospects, and domain experts should also be a part of this growing open innovation paradigm.
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%.
Most of the larger organizations have been investing in supply chain technologies that can enable the data capture and analysis functions. IDC defines the concept of digitally enhanced supply chain as something that would leverage internet of things and sensor data to provide real-time data insights that can essentially serve as inputs for building a cognitive model. Further, deep learning modules can aid in the creation of cognitive models, which in turn would be the core of a highly automated supply chain. This will drive cost efficiencies in labor expenditures, waste reduction, and better utilization of assets. Also, improvements in service performance will extend to delivery times, allocating inventory to high-priority orders, and faster new product introductions.
The key sources of this data would be logistics operational systems, warehouse management systems, shipping manifests from OEMs, dealer management systems, and point-of-sale (POS) devices. The data thus collected will aid in creating supply chain models that account for the unstructured data 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. Organizations have been investing in applications with an aim to disrupt their existing supply chains and create a competitive differentiation through increased customer satisfaction levels.
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. The challenges for digitally transforming the existing supply chains are equally daunting and would require the complete ecosystem to be at the same level of maturity coupled in terms of both technology and business processes.
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. In fact, recent IDC research highlights how the majority of companies realize the tremendous potential of expanding their focus beyond the four walls of their enterprises to collaborate with their business partners. 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 (IDC's 2016 Supply Chain Survey). This requires a completely different management approach and use of tools than traditional, linear supply chains. 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. At the same time, operators try to take fixed costs out of the network so that the supply chain profitably operates regardless of demand level. This cost focus is particularly high when serving emerging economies where demand is much less predictable.
Since business success will be centered around the timely and effective analysis of the large data sets generated by business and sensors, it is the view of IDC that 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).
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.
So far, companies' decisions are mostly torn between two main options: from one side, an on-premise execution system directly linked to machine data that guaranteed reliability, and latency, but lacked flexibility and accessibility; from the other side, a cloud-based system that ensures easy deployability and collaboration while sacrificing data availability and granularity.
To overcome this, companies will need to reconcile data in the production process that is execution relevant — that needs very little latency and cannot be transferred easily via cloud — with data that is visibility relevant for which cloud could be the best alternative. Interesting enough, IDC Manufacturing Insights' recent survey highlighted how cloud investment and OT/IT integration will take very high priority among operational technologies investments, with more than 40% of companies prioritizing investments in cloud software and platforms to support their OT processes.