EDITOR’S NOTE: This is part of a series of highly popular GxP Lifeline blog posts that we are republishing in order to share their important subject matter and insights with a wider audience and those who may be new to the blog.
As digital technologies continue to evolve, manufacturers are granted more tools to collect and translate the data they need for greater supply network visibility and optimized supply chain performance. These technological advances promise to usher in the next-generation digital supply chain, or Supply Chain 4.0.
Recent analysis suggests companies around the world will continue to make significant investments in technologies and services that enable digital transformation, driven largely by discrete and process manufacturing. Despite the complexity involved in technological disruption, supply chain digitization is at the top of many organizations’ list of strategic priorities, with half of recently surveyed organizations describing it as a top-three focus area.
This digital transformation has enormous implications across life science companies’ supply chain.
Supply Chain 4.0 has leading companies using new technologies to collect and process data to identify trends, potential issues and opportunities across many systems and functionalities at once. The resultant visibility helps provide a more complete understanding of every element of the supply chain, helping companies to improve decision-making, planning, and responding to issues.
When data is digitized and connected to other data points across the enterprise by the Internet of Things (IoT) and analyzed by artificial intelligence (AI) algorithms, this data becomes more usable and highly valuable. For forward-looking companies that are taking steps toward digital transformation, research is beginning to show a clearer business impact.
According to a study by McKinsey, organizations that digitize their supply chains aggressively “can expect to boost annual growth of earnings before interest and taxes by 3.2 percent. This growth proved “the largest increase from digitizing any business area” in McKinsey’s research. Moreover, the companies making use of supply chain 4.0 tactics were also forecast to raise annual revenue growth by 2.3 percent.
Leaders in digital supply chain management are gaining a variety of competitive advantages using Supply Chain 4.0 methods. According to Boston Consulting Group, key advantages include: increased product availability of up to 10 percentage points; more than 25 percent faster response times to changes in market demand; 30 percent better realization of working-capital reductions; 40-110 percent higher operating margins; and 17-64 percent fewer cash conversion days.
Several technologies have emerged to help life science companies across the globe excel in an increasingly digital economy. According to Capgemini, IoT and automation are the leading technologies deployed at one or more sites at scale in the supply chain, with blockchain, advanced analytics and artificial intelligence right around the corner when it comes to large-scale implementation. Here we’ll look at what these technologies mean for the life science supply chain.
The IoT holds real potential for optimizing supply chain operations, especially in companies’ need to collect data from across millions of devices and measure performance in real time. IoT devices provide real-time visibility of operations throughout the manufacturing process, from production through distribution. Manufacturers can embed IoT sensors in most items moving through their supply chain, gaining unprecedented visibility and traceability of parts for assembly, finished goods, and more.
“Other potentially impactful supply chain use cases are in preventative maintenance, sourcing, manufacturing, logistics, demand management, and services,” Gartner reported last year. “These include improved asset utilization, higher uptime through remote monitoring and maintenance, improved customer service by better understanding customer behavior and needs, and proactively responding to and shaping customer demand.”
Automating operations and systems can streamline work along the supply chain. For many life science companies, capturing and managing supplier data often entails dealing with data manually using a paper-based or partially electronic system, and then not updating the data regularly. Digital supplier and supply chain management solutions can be leveraged to collect and process real-time information automatically, thereby eliminating the slow, time-consuming effort of manually gathering, entering and updating data.
In operational processes, for instance, automated systems such as robotics and radio-frequency identification (RFID) can free up supply chain professionals from handling certain mundane processes to focus on more valuable tasks. This has the net effect of lowering operating costs and improving productivity. Manufacturers can expect the role of workers to be reimagined by machines and technology, not to be superseded by them.
Blockchain is a decentralized, shared, immutable distributed database of transactions, and it has the potential to be very disruptive. Smart contracts, traceability and authentication, and other highly decentralized supply chain management functions are considered key candidates for blockchain, although most supply chain blockchain projects are still pilot projects.
Citing serialization and track and trace as significant opportunities for blockchain application, Accenture research indicates that 64 percent of life sciences organizations are currently deploying blockchain, with another 30 percent planning to deploy it in the next few years. According to a recent PwC report, the pharmaceutical supply chain likely contains significant potential for near-term adoption. For example, a pharmaceutical company could check history and provenance of products through the immutable transaction history on the blockchain.
As IoT data continues growing at a rapid pace, the data is often unstructured, disorganized and incomplete. The massive amount of supply chain data collected is of little use if a company can’t quickly, intelligently analyze and leverage it. Advanced analytics can play a major role in making supply chain data usable and delivering significant benefits.
Advanced data analytics are providing greater insights into processes, products and people, and in turn, enabling supply chain leaders to make better decisions to improve operations and business. Promising use cases include demand/supply planning and predictive maintenance. For instance, Gartner says, “Prescriptive analytics can improve decision making in functional areas like supply chain planning, sourcing, and logistics and transportation, and can be deployed to improve end-to-end supply chain performance.”
AI and machine learning technologies, which learn over time as they are exposed to more data, have great potential to transform supply chain processes. They enable companies to collect data from a variety of areas and apply self-improving analysis, and as they are integrated throughout the supply chain, they will likely facilitate the automation of repetitive tasks and deliver intelligence throughout the supply chain systems.
Capgemini research shows that “AI delivers significant transformational benefits, from reducing churn to increasing regulatory compliance.” AI can be used throughout the supply chain to find patterns, forecast future scenarios, identify and correct data errors, surface risks, elevate IoT insights, and improve material planning, order scheduling and logistics.
The promise of digital transformation in the global supply chain is greater access to actionable data as a means of increasing operational effectiveness. In a recent Aberdeen study on manufacturing operations, approximately 47 percent of organizations said they believe they need to become more data-driven to remain competitive. Large-scale change is hard, but digital transformation presents the opportunity to revolutionize the supply chain and generate new business value throughout it.
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