Data is defined as facts, figures or information that are stored in a variety of places such as invoices, contracts, and bills of lading. By collecting data, a business can improve shipment transparency and visibility, operational efficiency, and products and services. All of which attracts more users.
Transparency and visibility are crucial, particularly if something goes wrong while shipments are in transit. By utilizing data, a split-second decision doesn’t have to be made without adequate support.
Transparency and visibility are also important when reviewing invoices and contracts with supply-chain partners. Despite good intentions, hidden costs can occur. They usually come in the form of surcharges such as extra delivery-area fees, additional handling, and fuel. Often they make the difference in a retailer’s ability to offer “free” or one-day delivery.
Access to data derived from goods fulfillment is central to the achievement of both visibility and speed. In today’s retail environment, speed to market, accurate order fulfillment and efficient last-mile delivery are keys to success. In addition, data plays a major role in forecasting and optimizing inventory. Consumption rates and inventory levels are among the data points critical to proper inventory planning and development.
However, data is just data unless it’s analyzed and acted upon.
Business consulting firm McKinsey describes supply-chain analytics as the ability to use data and quantitative methods to improve decision making for all activities across the supply chain. While data analysis has been utilized for years, the introduction of new technologies such as artificial intelligence, machine learning and more have led to the ability to uncover additional data elements that were never used before, and are now contributing to forecasting in today’s supply chains. For example, traditional data monitoring, which would involve sales and order tracking along with point-of-sale data, is now being supplemented with weather, events and news.
Nevertheless, the human element in data analysis must not be forgotten. Data needs context and interpretation. Often there are often variables at play that only humans can understand. In addition, data analytics needs people who have an understanding of how the models work, so that they can establish which information is useful. They’re also needed to provide an ethical and moral dimension to decision-making, which data alone can’t do.
Data analytics continues to grow in importance, along with new technology tools to support it. However, it’s important that companies recognize the importance of human judgment in having the last word when it comes to decision-making. The human element is needed throughout the chain, both in understanding when data analytics is needed, but also when it’s not.
The evolution of data analytics is underway. It will speed up processes by offering real-time analysis for solutions such as dynamic pricing, routing and inventory replenishment, aided by technology tools such as artificial intelligence and the internet of things. While the technologies hold great promise in providing more insights and deeper analyses of supply chains, the need for human judgment will continue to be equally important.
Rich Rosario is Director of Database Modeling & Data Analytics with Spend Management Experts.
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