Historically, organisations geared their supply chains toward increasing volumes of open, cross-border trade. In a relatively stable world, this made a lot of sense. Suppliers enjoyed an abundant supply of materials, driving low prices and a single use mentality. Buyers focused on cost, often failing to consider the environmental and sustainability impact of linear supply routes. As companies pursued the mass market, supply chains stretched their processes. Understandably, just-in-time became the prevalent model.
However, events like the pandemic and the war in Ukraine threw the risks of this approach sharp relief. In an increasingly uncertain world, thes crises exposed the inherent lack of resilience in global supply chains. In response, companies have invested in strengthening supply chain components, focusing on making them more cost efficient, robust, and sustainable.
For many, this investment has mirrored point-to-point thinking. New technologies like digital twins and AI improve the metrics and predictability of individual components within the end-to-end supply chain. Yet they are often built upon a traditional reliance on historical data and siloed systems. This approach falls short in today’s dynamic environment, where connected, real time data and predictive analytics are crucial for effective decision-making.
The future of supply chain transformation will be defined not only by the implementation of new technologies but also by the ability to connect and operationalise them through a cohesive digital infrastructure.
When supply chains fail
Supply chain failures can be catastrophic for businesses, leading to lost revenue, reputational damage, and even legal repercussions. These failures may arise from various causes: natural disasters, geopolitical events, cyber-attacks, pandemics, or supplier breakdowns. By understanding the root causes, it becomes clear why companies must adopt a more integrated, end-to-end view when investing in supply chain strategy.
Failures can stem from external factors. These can include a lack of diversification, over-reliance on a single supplier, raw material quality issues, or poor risk profiling. However, they also often stem from internal shortcomings in planning and ways of working.
Disconnected systems
The proliferation of disconnected supply chain data across enterprise, ecosystem, and external sources makes timely, well-informed decisions difficult. Recent investments in Control Tower solutions aim to aggregate these disjointed and often irrelevant data sources. Unsurprisingly, these efforts tend to produce insights of limited value.
Disconnected systems also suffer from a kind of short-term memory loss. Even the most sophisticated planning solutions today operate like large, memory-less functions with no connection to the outcomes they’ve previously produced. As isolated systems, they are not required to retain historical context to function. Recommendations are therefore made with little regard for past performance, business exceptions, or known issues. This forces users into ongoing cycles of fire-fighting, as lessons go unlearned. Additionally, planning parameters are frequently stale and fail to reflect the current state of the business. Inaccurate, static variables -like plan adherence, yield, lead time variability, and actual production rates – skew expected outputs, making even the most optimised plans infeasible and fuelling an endless cycle of exception management.
Lack of visibility
When it comes to exception resolution, disconnected systems often lack visibility into the most relevant and impacted inputs. The absence of meaningful data persistence makes identifying root causes and calculating the cost implications of potential decisions extremely difficult. It also hampers Effect Simulation, Scenario Planning, and Cost-to-Serve Analysis. Today’s supply chain planning has a significant operational blind spot – and point solutions alone won’t solve it.
This stems from the fact that supply chain capabilities have traditionally been built on enterprise applications implemented using a point-to-point architecture. But this no longer has to be the case. Where data was once restricted to the enterprise and structured around application-specific data models, today’s landscape allows data to enable both network-level and facility-level optimisation.
By connecting demand, supply, inventory, bottleneck constraints, IoT data, and more, companies can plan and execute across the supply chain more effectively in both steady-state and disruptive conditions. With cloud data storage and increasing access to data from supply chain partners and external sources, forward-thinking companies are revisiting their supply chain data strategy – connecting enterprise applications and laying the foundation for advanced AI-based decision support.
Integrated SCM solutions demand good data
Companies with integrated supply chain management solutions can track inventory levels, manage suppliers, and monitor shipping and delivery times in a unified manner. Their investment integrates supply chain systems and enhances visibility across the entire value chain. It also better positions them to harness the power of AI and other emerging technologies.
AI relies on high-quality, cause-and-effect data. Integrated supply chain systems can better train AI models and predict the impact of decisions and changes within the network. This gives businesses the resilience they need to navigate rising disruption and complexity.
By orchestrating across the supply chain, companies improve supplier communication, uphold the customer promise, and reduce operational churn and firefighting. A cohesive integration strategy will be vital to realising the full value of AI innovations, and to thriving in an increasingly interconnected and unpredictable supply chain environment.
- Digital Supply Chain