There will have to be increased attempts to view supply chain strategies as a way of generating the revenues of the firm, much in the same way product mix, advertising, and price are viewed as elements of its marketing strategy. Order management, shipping execution, requirement planning, creation of purchase orders, receivables, cash management, supplier performance, and reporting are some of the many areas that supply chain analytics taps into. As well as a full roster of services that range from specialty repair and fabrication to inventory, supply chain management.
Content Analytics
An important goal of supply chain analytics is to improve forecasting and efficiency and be more responsive to customer needs. By digitizing your supply chain management, you free up resources for more value-added tasks – and gain access to new tools that will give you a real-time view of your supply chain resilience. Organizations also use content analytics software to provide visibility into the amount of content that is being created, the nature of that content and how it is used.
Within the business sector, logistics can be applied to information, transportation, inventory, warehousing, material handling, and packaging, disposal, and security. Optimization balances supply to meet demand at the lowest possible cost (investment in inventory), using the least company resources, for a given service level, for each item at each site within the entire supply chain. Plus, if the production quality or transportation link gets broken at the end of the chain, you end up with a poor product and a dissatisfied customer.
Financial Data
While there are several options available, business intelligence tools (BI) and business analytics tools (BA) are arguably the most widely implemented data management solutions. End-to-end supply chain risk management can be improved by evaluating current conditions with existing data pools. Also, from a functional standpoint, key performance indicators encompass a wide variety of financial, marketing, sales, customer service, manufacturing, and supply chain metrics.
Efficient Operations
Operational analytics is a more specific term for a type of business analytics which focuses on improving existing operations. Supply chain digitization presents the ability for professionals to rely on real-time data to make decisions about shipping, inventory, invoicing and more. To begin with, creating a more efficient, effective supply chain means your business spends less time thinking about “how” and more time on delivering now.
Many organizations are also using BI tools to highlight patterns found in historical data that may yield clues to future risks and opportunities in supply chain or transportation networks. The capability to collect, distribute, share, and analyze information to make decisions based on real-time data and predictive analytics, and create new business value, has improved considerably. Above all, forecasting demand is essential to supply chain management, and businesses can best forecast product demands through the timely synthesis of information.
Instead, a casual observer might interpret the activities at the factory as evidence of an intensive effort to improve supplier management, at the same time. And also, the networked operating model of supply-chain capabilities offers a solution to make decisions with near real-time reports from current data, and drive continuous improvements throughout the whole supply chain.
Real Delivery
When implementing sales analytics at your organization, you will want to start by taking stock of your sales metrics. The real-time supply chain operates on a more granular timescale than ever before, requiring tight alignment between planning and execution processes as well as real-time planning and execution capability. Hence, real-time delivery of analytics speeds up the execution velocity and improves the service quality of your organization.
Want to check how your Supply Chain Analytics Processes are performing? You don’t know what you don’t know. Find out with our Supply Chain Analytics Self Assessment Toolkit:
