The Covid-19 outbreak has shown that unforeseen events can wreak havoc on the global supply chain. Supply chains are also vulnerable to natural calamities, public strikes, temporary closure of routes and other local disruptions. This may result in halting of production, loss of business, and customer dissatisfaction. Therefore, supply chain managers need visibility to assess the impact of such disruptions and take immediate actions to arrange supplies from alternate sources.
Challenges in Supply Chain
The greatest challenge for supply chain managers is to balance demand and supply while optimizing inventory levels. Globalization has extended geographical scope of supply chain whilst increasing complexities. Outsourcing has taken supply chain beyond company’s manufacturing facilities. This demands the following:
- Effective collaboration and close monitoring
- Visibility to forecast and actual demand
- Monitoring and reacting to variations in demand
- Arranging supplies from alternate sources during disruptions
- Prediction of stockout and taking appropriate action to prevent it
- Rescheduling supplies during sudden drop in demand
- Avoiding overstock
Supply Chain Control Tower
Supply Chain Control Tower is a centralized ‘information hub’ that captures real-time data from internal and external supply chain systems to provide end-to-end visibility. It collects data from ERP, warehouse management system, transportation management system, supply chain planning, and supply chain partners like suppliers, customers, logistics service providers and 3PL/4PL providers. Supply chain data management can give a clear picture of strengths and weaknesses of supply chain.
“Control Tower capabilities as a concept, the result of people, process and organization facilitated by appropriately combined technology elements including Data-driven E2E supply chain insights and E2E decision making enabled by Visualization, Diagnostics, Predictive, Simulation, Responsive Collaboration, Learning and Automation”- Gartner
It works on following principles –
Supply Chain Control Tower uses predictive analytics to provide suggestions to avoid crises and increase efficiency in processes.
- Gather and analyze – Collect data from internal and external systems and relate it to provide end-to-end visibility. This includes master data like item codes, suppliers, customers and locations, and transactional data like customer orders, on-hand balances, purchase orders, in-transit material details and location of containers.
- Monitor – Predictive analytics provides exceptions like anticipated stockouts, low stock, future stocks, and criticalities due to delay in shipment. It also provides demand-supply balance of an item at a location.
- Interject – Action taken to overcome exceptions (stock transfer, expediate supply, defer shipments)
Supply Chain Control Tower has the following features –
- Provides real-time, on-hand data about the items at stocking location (factory, warehouse, DC, store, port). This data is collected from WMS or inventory systems.
- Provides visibility into incoming supplies for an item-location combination. Supplies from purchase orders, stock transfer order, shipment, and deliveries are collected from supply chain execution systems.
- Provides visibility into demand for an item at a particular location. Demand can be from forecast, sales order, transfer order, outbound shipment or delivery. This data is collected from Order Management, TMS and Planning systems.
- Calculates projected inventory based on above data.
- Creates exception alerts for current and future low stock or stockout by comparing calculated future inventory with minimum inventory levels.
- Creates exception alerts for expected over stock situation by comparing calculated future inventory with maximum inventory levels.
- Captures and displays location of shipments provided by shipment tracking systems. This also includes container number, content of container, expected date of arrival, mode of shipment, and vehicle/flight/vessel number.
- Calculates early or late arrivals based on above data.
- Early or late arrival data is used for calculation of projected inventory for a particular period.
- If early/late arrival impacts inventory in terms of over, low or out-of-stock, then the user is alerted by exception.
- Provides visibility into demand-supply of other locations which aids planners in mitigating critical situations.
- Provides tools like chats and emails to collaborate with stakeholders to reach a resolution.
- Color-coded and graphical representations help planners in monitoring and quickly draws attention to critical situations.
- Some control towers provide user interfaces for external entities like suppliers, customers and logistic service providers for them to send related data and monitor their part.
- Provides action like creation of transfer order, change date of delivery to expedite or defer supply and communicate actions to relevant internal and external stakeholders.
- Calculates financial impact of delay in supply.
- Centralized data management of entire supply chain.
- Provides APIs for integration with other systems like ERP, WMS, TMS, consignment tracking systems, and suppliers/customers/LSP systems.
- Real-time data gathering and information sharing.
- Provides analytical capability for drill down (example – purchase orders or sales orders for an item), calculate and display daily, end of month, end of week, end of quarter stock, color-coded and graphical display in dashboards.
- Real-time alerts for all supply chain disruptions and exception management.
- Predefined analytical dashboard. Capability to create new dashboards.
- Predictive data analysis and advanced analytics.
- Automated decision-making supported by machine learning to allow the supply chain to self-correct as needed.
- Role-based access control.
Control Tower Architecture
- KPI-based monitoring.
- Real-time visibility for end-to-end supply chain.
- Reduced time to identify exceptions and resolution.
- Reduced cost across all aspects of supply chain.
- Increased sharing and collaboration between internal and external stakeholders.
The following use case is an example of how a control tower helps to overcome global supply chain disruptions caused by unforeseen events. In this scenario, the shipment is delayed due to bad weather. Control tower senses this through carrier tracking system. It analyses downstream impact and predicts low stock at DC 1 and consequent stockouts at stores. The planner is alerted with this information. Control tower shows him the availability of these items in DC2. He collaborates with stakeholders at other DCs and arranges shipments of these items to stores.
Supply Chain Control Tower has emerged as a critical tool in managing local and global supply chains. With availability of big data and advanced analytics tools, it is capable of aggregating and analyzing data from various systems and provides single view of demand and supply. AI/ML is being built into control towers to help make entire supply chain autonomous and self-healing.