When world events disrupt business supply chains, smart companies weather the storm with flexibility, contingency planning, and dual sourcing. Even smarter companies adopt data analytics in their best practices to spot opportunities amid simultaneous disruptions in the supply network, spikes in consumer demand, and interruptions in transportation.
Supply chain disruption in the market is not new. Silk and spice traders in the 17th Century faced extreme challenges in their efforts to maintain a consistent supply chain. Treacherous weather, greedy pirates, regional conflicts, trade disputes, and the like continually disrupted trade.
In the modern era, maintaining an optimal supply chain requires preparing for even more unknown risks and the trade-offs of using data from multiple sources to manage your plans. Unfortunately, fewer than half (47 percent) of companies surveyed in a Ventana Research Next-Generation Business Planning Benchmark survey said their supply chain plans are accurate. Even more concerning is that a majority (53 percent) of respondents say they have either a limited or no ability at all to understand the trade-offs.
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Analytics can dramatically improve visibility across the end-to-end supply chain, increase understanding of inventory status and location, and provide predictive analysis that enables companies to withstand future shock events. By drawing in data from multiple sources across the supply chain network, companies enable their decision-makers with timely and complete insight. They can analyze supplier status, manufacturing capacity, inventory, supply/demand balance, and logistics datasets. The idea is to cover the entire supply chain, providing insightful analytics for quick visibility into changes in demand so that you and your network partners can adjust production and fulfillment.
Analytics helps supply chain managers evaluate the challenges and monitor the benefits of real-time insights to optimize actions such as:
Modern procurement starts with standardizing, streamlining, and automating the overall procurement process. Analytics provides visibility into the source-to-pay process. For example, you might need visibility into the supply of direct and indirect material and the resulting impact on order fulfillment, revenue, and cash flow. That requires cross-functionality, or the ability to share data seamlessly between departments. Your finance division should be able to assess the cost of reducing contractual procurement due to a rapid decrease in orders or in demand. And when supply drops and orders cannot be filled, analytics can minimize the effect of disrupted supply on financials.
Maintaining inventories is critical and involves comprehensive visibility and management of material, inventory status and turns, and product costs across the extended supply chain. By using analytics alerts to rapidly identify tasks requiring immediate attention, managers can rebalance inventory by location due to sudden demand spikes and stockouts. Improving stocking and handling of different types of materials is enabled by using analytics to monitor and manage material status and trends. Managers who need to quickly understand the cost impact of expediting shipments to meet demand or the costs of holding unexpectedly slow demand can use analytics to combine material status with finance.
Of course, products don't just materialize from one place to another. Shippers and buyers need immediate visibility into available shipment options and associated costs. Given industry volatility, analytics solutions combine data sources to enable determining the best way to fulfill transportation needs, from simple point-to-point to complex multimodal, multileg, and cross-dock operations. And if air freight is too costly, or unavailable, analytics helps managers quickly analyze scenarios to evaluate the impact of alternative shipment options.
Perhaps the biggest value of analytics is to create cross-functional departments—those that integrate the view of multiple supply chain functions into a single perspective to enable quick insight and decisions in a volatile market environment. Modern supply chains are a network of suppliers, logistics firms, warehouses, and customers. Sudden disruptions at any stage in the chain affect all parts, but getting visibility on any one part can be difficult.
Let's say there is a sudden spike in demand for things like food, or medicine, or laptops. Supply chain managers will need to work with finance, sales, and others to determine the costs of overtime production, expedited material supply, and shipping.
In another scenario, the demand for a product stops abruptly. Managers will need to gain valuable insight into the cost of shutting down production and the cost of delaying supply (think purchase contracts). Then there is the cost of terminating marketing campaigns, the cost of carrying inventory, and the impact on cash flow.
One of the hardest situations for supply chain managers is to balance and rebalance demand. In this case, the product might be flying off shelves in hard-hit areas one quarter only to be overstocked in the next quarter. This volatile demand can generate extraordinary measures to secure supplies on time. And if data analytics is not part of the process, companies may spend too much time assigning costs to different parts of the business. Often, no one asks: Why was expediting or paying a premium necessary in the first place?
What's needed in each of these scenarios is the capability to ingest data from multiple sources, create a cross-functional view among supply chain, finance, and executive divisions, and conduct scenario and predictive analysis. This is where the combination of Oracle Analytics aligned with Oracle Autonomous Data Warehouse can come in handy, as they are tailor-made for tackling these kinds of challenges.