Why now, why the supply chain?
Enterprises have been focusing on trimming costs and enhancing productivity for years. Remaining relevant in today’s global economy has added new pressures on companies to focus on product design, innovation and operations, while cutting costs where possible. As a result, we’re seeing robotics and automation in plants and distribution centers, IoT and predictive analytics for live and predictive visibility, and artificial intelligence and machine learning to optimize decisions from planning to execution and delivery. The supply chain represents a digital catalyst to customer-centric business that supports waves of new opportunities while optimizing inventory, resources and costs.
In its Guide to Aligning Digital Business and the Digital Supply Chain, published January last year, Gartner discussed the findings of its 2017 digital supply chain survey. The survey revealed that 62% of supply chain practitioners anticipate digital business will drive more than half of their revenue within two years. But only 25% say digital projects across their companies are aligned under a single governance process. Substantial work needs to be done. Companies may recognize the value of a networked supply chain, but getting there is complex.
For example, the Natural Resources Defense Fund (NRDF) has estimated that the US food industry wastes up to 40% of the food produced in the US, costing the industry $218 billion per year. As TechTarget reports:
Much of this is due to challenges within the supply chain, where food industry leaders don’t have adequate data for effective decision making. As a result, about a third of fresh food spoils prematurely, driving up costs for growers, shippers, and retailers – and consumers of course.
A major barrier to effective decision making and digital innovation is the prevalence of silos that exist between internal departments, as well as external trading partners. Data and visibility are often locked away in pockets, prohibiting factories, logistics providers, carriers and other trading partners from planning and acting on accurate information. For example, a typical global manufacturer may operate five or even 10 ERP solutions, combined with different transportation management systems for each region. This fragmented tech landscape not only prevents departmental breakdowns of activity between transportation, sourcing and finance – it creates barriers within the transportation organization, which is forced to execute region-by-region versus operating on a global view. Patching together various systems or swivel-chairing is sometimes attempted to close the data gap between systems, but data latency and inaccuracy creep in here and cause havoc.
A foundational barrier
While AI, machine learning, IoT and robotics continue to gain interest and momentum, their impacts are limited by this fundamental breakdown in the underpinnings of the global supply chain. Today’s complex global supply chains are stretched thin and under pressure to make rapid decisions based on the movements and actions of trading partners and inventory often beyond their reach or control. What’s missing is the ability to connect trading partners into a common network that reaches across departments and trading partners and captures data for optimal decisions.
Enterprises need a well-planned strategy to fully optimize the potential of the supply chain and evolve toward a state of being intelligence-driven. True transformation goes beyond bolting on a warehouse solution or tracking shipping costs. Modernizing the supply chain has become a high-stakes necessity, deserving priority status and commitment from C-level executives. Shifts in mindset, as well as technology investments, are going to be required of companies, especially if they have not yet started on the journey to the connected supply chain. Consulting firm Accenture describes it this way in a 2018 report:
Supply chain organizations need to focus on reinventing their operating models to break silos, develop end-to-end visibility, and create an agile and seamless collaboration model ...
Leveraging artificial intelligence, companies can process massive and diverse data sets from across all functions to provide better visibility within the supply chain.
Ten characteristics of the modern supply chain
The supply chain of the future must be network connected, before it can be intelligent and self-driven. Here are ten characteristics of a modern supply chain.
- Dynamic – The modern supply chain is a global network keeping a real-time view of commerce, sensing the flow of resources and goods as they move through a series of live control points. The network captures and communicates observations from IoT- and network-based sensors and data collected from partners, suppliers, shipping agents and third-party logistics companies. Environmental conditions, such as extreme weather, or any influences on shipping lanes or port operations, such as worker strikes or civil unrest, are also monitored for impact.
- Smart – Leveraging Artificial Intelligence, the network knows the current state of transactions and whether processes are following ‘as expected’ norms. Machine Learning helps calculate ‘the state of normal’ and identify pattern shifts. Even before a definitive problem manifests itself, the system senses early warning signs, like a data point that falls beyond the acceptable parameters. Such anomalies automatically trigger further response.
- Collaborative – With direct connectivity to trading partners, the networked supply chain can take proactive steps to address any potential issues before a negative impact is unavoidable or severe. Early intervention is the key. The network uses collaboration tools to share live, accurate, and context-rich data to decision makers and stakeholders. This single view of information means there are no discrepancies or points for debate. Because the infrastructure, data, and network of partners are all in sync, advanced business intelligence tools can be applied without ‘translation’ issues.
- Graphical – Advanced solutions offer a graphical control tower view of the entire network, making it easier for stakeholders to view the end-to-end picture of outgoing shipments and incoming deliveries as they travel around the globe. This high-fidelity view brings complex synchronization to life, calling attention to potential issues or opportunities to realign plans, such as avoiding high-risk routes or eliminating redundancies that may not have been spotted before. A control center, supported by Artificial Intelligence, provides a continuous quick view of the big picture – with the ability to drill into underlying details and micro-influences.
- Predictive – Machine Learning elevates the efficiency of the networked system further, allowing it to automate reaction and apply predictive and prescriptive analytics. Augmented analytics apply algorithms and data science to anticipate the next likely data point, giving stakeholders the ability to predict the likely future or outcome. This can be used to model “what if” scenarios and to simulate the outcome of decisions, such as changing a supplier or shifting the destination to another port.
- Self-driving – The network becomes more accurate as it learns from patterns and draws from more and more data points and variables. Network intelligence empowers supply chain teams and end users to be more productive, able to make timely decisions based on real-time data and insights derived from the network. The supply chain evolves to a self-learning model, growing smarter, more efficient, and more reliable every day. Over time, specific tasks and decisions are placed on auto-pilot, driven by data and network-wide intelligence.
- Self-healing – Armed with advanced technology, the network can use automated workflows and event triggers to correct or “heal” issues that need prompt attention, such as weather-related issues or when ground transportation bottlenecks may jeopardize shipments with mission-critical contents or time-sensitive deliveries.
- Multi-dimensional – The networked supply chain can become a continually evolving and improving network, which analyzes and advises on several decision-points at once. When describing the benefits of an advanced supply chain network, Deloitte says organizations can “dramatically improve forecast accuracy at both the strategic and operational levels, enhance strategic pricing, improve forecasts for new products, converge multiple forecasting models into common insights platforms, shape demand to drive optimization…(and) automate response to market conditions.”
- Fiscally sound – Not only can predictive planning of the supply chain process reduce costs and improve efficiency, it can also support growth into new strategic markets and speed up the order-to-cash process. Increased visibility can also reduce the costs of inventory by controlling the need for safety stock and managing payments to suppliers to support cash flow strategies.
- Reliable – Continuous monitoring of the network helps to reduce surprises and costs. For example, IoT sensors on cargo containers, trucks, and pallets can identify when the materials have been exposed to out-of-tolerance temperatures, vibration, or shocks that may contaminate the contents or hinder performance. Adverse conditions can trigger deliveries to be rerouted and substitute material planned immediately – avoiding delays, downtime, and quality control issues, which alienate customers and threaten brand integrity.
The emergence of advanced supply chain solutions, with embedded analytics and Machine Learning, is transforming the supply chain from a pragmatic step to a strategic opportunity to impact the bottom line. Even tech-savvy companies have only started to understand the true potential of connected supply networks, which are self-driving, self-healing, and profit-driven. The one thing that is certain: Companies that put off modernizing their supply chain will fall behind and risk losing their competitive position. Now is the time for swift, bold action.