Global supply chains have undergone several hiccups over the course of the COVID-19 pandemic and continue to face disruptions due to lack of adequate planning against the uncertainties on the scale of a global pandemic. Infusing intelligence into enterprise data has somewhat bolstered business operations to continue in the face of growing demands and the current supply chain crisis. The question to ask is “Can data alone help sustain any future calamity?” and “Can it substitute human spontaneity with intelligent decisions to evade any crisis?” The answer to both is a reassuring YES.
For example, using data, shippers can create an evolving sales forecast to predict transportation procurement needs, plan inventory replenishment and adjust the allocation of inventory across their full networks. The historical data insights from transportation analytics can further improve forecast accuracy, reduce costs, and automate planning in inventory management and transportation capacity procurement. These tools are invaluable in retail supply chain analytics and allow for a more complete view of the network size and volume.
The challenge today is how businesses can make best use of the data residing within their supply chain management software. Data science technologies can help process this data and extract actionable insights to provide new capabilities and features to a supply chain management system.
Data-driven, smart supply chain management network can help businesses in:
- Predicting risks and taking proactive measures to minimize losses
- Optimizing inventory
- Making supply chain intelligent and self-learning
- Driving real-time coordination between various components of the supply chain network
- Making data-driven decisions
- Driving innovation
In a recent survey conducted by SAP, 1000 American adults were asked to share their sentiments about the supply chain disruption during the pandemic. The findings reveal the growing frustration of consumers as the holiday supply chain challenges loom. 83% of consumers are reported to have experienced out-of-stock items during the pandemic and gave in to panic-buying. 65% of the same consumers also strongly feel that companies should have found solutions to supply chain challenges by now.
Bringing your data deluge to your competitive advantage
The end-to-end supply chain process needs to be fed with the right insights to identify trends and risks involved in timely shipment of goods and services. Amidst a constant flow of data from the ERP system, accounts payable and receivable, B2B SaaS applications, warehouse management software, transportation and logistics platforms etc., it gets trickier to maintain product quality, save costs, and optimize inventory.
The key to managing the constant data inflow lies in applying intelligence to the supply chain analytics. Businesses are increasingly relying on machine-learning-backed supply chain solutions to accurately forecast consumer demand amidst a crazy sales history.
The drivers of sales often depend on the industry. Be it consumer goods, automotive, life science or chemicals — machine learning models need to be trained with additional data, such as marketing budgets, product prices, holiday and calendar events. For predictive analytics to identify business opportunities and risks, historical data of key business drivers, such as sales and marketing budgets, act as a backbone that need a robust business intelligence solution to extract insights from the pool of unstructured data. Machine learning identifies the importance of these business drivers and gives weight to each one, based on their direct impact on consumer demand. This helps the system generate forecast results with greater precision.
As companies gear up for the future, they must evolve their supply chain planning and processes to minimize operational silos and risks associated with market disruptions, and maintain business continuity through timely response to external changes. This readiness is possible by tapping into data beyond the usual procurement and ERP systems and incorporating inputs from marketing budget plans for the next quarter, proposed product pricing, and pending calendar events along with changing demographic trends.
The SAP ML model applies these learnings and generates demand forecast, helping business operations adapt to changes automatically.
Improving profitability with a digital-first supply chain system
An intelligent supply chain system does more than applying learnings from the database and making decisions autonomously. In fact, it’s one of the first steps towards improving profitability by:
- Optimizing inventory
- Automating processes like supply and pay agreements, order placements etc.
- Controlling operating expenses by preventing distribution errors and incorrect orders
- Settling outstanding payments and missed invoices without human intervention
Responding to the customer churn as your key priority
With a growing number of options, consumers are barely reluctant about changing their vendors and suppliers. Lowering switching costs, offering competitive products and services, and improving customer experience are the top three reasons why businesses need to constantly analyze their customer churn rates. Additionally, it’s more expensive to acquire a new customer than to sell more to an existing client. So organizations need to deploy an ERP system that can evaluate and measure customer churn on a recurring basis.
Optimizing inventory levels with real-time insights
Companies following lean inventory practices ensure customers get what they want when they want, and for a reasonable price. However, in the recent rounds of lockdowns, we all witnessed concerns about supply chains and shortages, which led companies to rethink their strategy of eliminating excess inventory. Most inventory planning software is not designed to optimize inventory levels by learning continuously from data. That’s where a modern platform such as SAP S/4HANA is essential for infusing intelligence to the steady stream of data and generating actionable insights on SKUs, raw material supplies and demand for goods. Backed by powerful analytics, it’s able to monitor inventory metrics in real time across all devices, identifies any anticipated issues with inventory levels and notifies when certain KPIs exceed the desired thresholds.
Fecha de publicación: 23/12/2021