Chief supply chain officers (CSCOs) have once-in-a-generation opportunity to pivot from cost-focused reactive operations to running a resilient and agile value chain. In the past three decades, supply chain operations have expanded across the globe, incorporating multiple partners, cultures and systems. As a result, CSCOs have long struggled to answer many questions in real time.
As explained in the recent IBM Consulting paper “Building intelligent, resilient and sustainable supply chains,” even a well-run supply chain has weaknesses that can leave companies asking: Where are my orders? What is currently in manufacturing? Do I have a supply shortage, and how do I mitigate it? When will the orders arrive at my distribution center or at my customer’s front door? What is in the container I’m tracking?
This lack of visibility means that most supply chain operations are fundamentally reactive—constantly catching up with events. Research from the IBM Institute for Business Value has shown that Fortune 500 companies lose anywhere from 2% to 5% of revenue due to misplacement of inventory or production of incorrect SKU and channel mix.
This isn’t tenable. The supply chains of the future will need intelligence, speed and agility to meet growing expectations of consumers and B2B partners. The next generation of supply chains embedded with exponential technologies will be able to predict, prepare and respond to rapidly evolving demand and a continually changing product and channel mix.
As companies mature their digital twin, prescriptive and cognitive capabilities, they have an opportunity to improve supply chain productivity by 10–15%, eliminate non-value-add work by 50–60% and improve their disruption response time from days to hours or minutes.
Cognitive supply chains harness data as fuel to build resilience and agility into their processes. A strong data foundation is an essential first step, but companies must further build capabilities in three areas to provide end-to-end visibility, optimization of upstream and downstream processes and simulations of scenarios and alternatives based on margin optimization or service level attainment.
When IBM focused on building these capabilities internally, it brought dramatic improvements. IBM employs supply chain staff in 40 countries, collaborating with hundreds of suppliers to make hundreds of thousands of customized customer deliveries and service calls in over 170 countries. In building the world’s first cognitive supply chain, IBM moved from inefficient, siloed, manual systems to one integrated system augmented by AI. For more detail, read the case study.
In the cognitive supply chain, rote work is reduced or eliminated, while an integrated supply chain picture emerges from multiple solutions, including a cognitive control tower, cognitive advisor and demand-supply planning and risk-resilience solutions. The end result is real-time, intelligent supply chain visibility and transparency.
The systems also sense and respond to changes in demand as they happen and simplify the automation of supplier management. On a minute-by-minute basis, employees have immediate access to the information they need to identify and mitigate disruptions.
Since its cognitive supply chain became operational globally, IBM has saved USD 160 million related to manufacturing optimization, reduced inventory costs, optimized shipping costs, better decision-making and time savings.
To follow this example and adopt proactive supply chain management, companies need to take three strategic actions:
1. Deploy a control tower and digital twin
AI has enabled the digital twin to provide visibility of events across customers, suppliers manufacturing locations and third-party logistics, and it has enhanced ability of companies to understand their operations real time. By adding a control tower—an AI-based simulation and recommendation engine—companies can more easily manage complex supply chain processes amid constantly changing market conditions, and they can prescribe recommended actions based on historical precedents or predetermined cross-functional business rules.
A capable digital twin solution should be paired with a control tower to:
Enable near-real-time visibility within the internal and external network
Ingest data across multiple supply chain functions such as planning, logistics, manufacturing and warehousing
Simulate and model network disruptions or the effect of near-term plans
Provide decision automation and recommendation engine capabilities
These capabilities transform business response during crucial moments. For example, during a supply disruption, an integrated system could detect the event, understand the timeline and impact of disruption, and recommend to either find alternative sources, put finished product on fair share allocation or increase order confirmation lead times.
2. Automate decision making and set up business value audit process
In previous work with leading companies, IBM consultants found that supply chain professionals make hundreds of decisions every day, ranging from inventory deployment, substitution, expediting and additional shifts to menial data cleansing ones. Even a capable control tower solution can’t address and automate all these value points individually. Companies should identify areas where decision automation and augmentation can bring bottom line improvements, add consistency and value quickly, and build momentum for further use cases. As an example, companies can deploy demand sensing and prediction algorithms to better match supply and demand if they have higher incidence of stockouts.
3. Build cross-functional workflows and avoid the functional excellence trap
As companies build digital capabilities, there is a temptation to focus on the most supportive functions to claim an early win. This may work in the short term, but it will ultimately reinforce the old supply chain model where functional excellence does not lead to a superior customer experience or reduced cost. Insist on re-imagining traditional processes and building cross-functional workflows where different functions and capabilities can improve business outcomes.
For example, don’t just focus on demand sensing capabilities; also train AI models for intelligent planning and risk mitigation. Insist on building automated sales and operation execution (S&OE) workflows wherein recent changes in demand patterns can be seamlessly propagated to inventory deployment and logistics. Use your capabilities to deliver superior customer service and more on-time in-full fulfillments.
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