Data is important to businesses in formulating strategies, streamlining operations, introducing new products and services, and ensuring customer satisfaction. But data alone isn’t much good unless it’s analyzed, understood and acted upon. Data analysis is benefiting from new technology tools by allowing analysts to dig more deeply into supply chains. At the same time, human judgment remains the most important element in analysis.
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.