Supply Chain Analytics: What It Is & Why It Matters – Techbytes

What is supply chain analytics? How is it used in everyday business applications to improve the performance of enterprises in critical areas such as cost, inventory management, and customer satisfaction? This short guide provides information on these topics and insights into how supply chain analytics can help your company grow.

What is supply chain analytics?

Businesses have long relied on information to make critical decisions. Over the years, this process has evolved from using data volumes of 1's and 0's to using much larger sets involving terabytes (millions of rows) if not petabytes (billions or trillions of rows). The reason why supply chain analytics matter is because it provides companies with insights into – and the ability to manage - their supply chain.

What is a supply chain?

A supply chain consists of all activities, people, information, assets, and processes involved in getting a good or service from the raw materials stage through production and delivery to the end customer. In addition to suppliers and manufacturers all along this chain, logistics companies, warehousing companies, and others are increasingly part of the network.

How supply chain analytics works

Supply chain analytics software usually comes in two forms: embedded in supply chain software or in dedicated, separate business intelligence and analytics tool that has access to supply chain data. Supply chain managers use analytics software that offers predictive modeling to determine what factors most likely affect future demand for their products. These data-driven forecasts give them the ability to forecast accurately, which optimizes inventory levels and reduces risk.

Many supply chain managers implement "lean" principles in their operations, such as utilizing small lot sizes, generating small batches of products, and manufacturing those products as close to demand as possible. This allows them to reduce the amount of product held in inventory and their associated carrying costs.

Analytics can also be used before a product even hits stores. For example, it could be used by a consumer packaged goods (CPG) company to study which products are selling best in which locations and to identify ways to improve sales. For example, suppose one of the CPG company's food items is seeing higher sales in its Western stores that are located near ski resorts. In that case, they might launch other product advertising campaigns surrounding that theme. If the analytics reveal that their cereal boxes are not selling well because customers are struggling to open them, the CPG company might make modifications to their packaging for future production runs.

Companies can also use analytics to evaluate the pricing elasticity of new products. By assessing how price changes affect demand, they can optimize prices and fine-tune promotion campaigns accordingly.

There are many reasons why supply chain managers should care about big data and supply chain analytics. Improved forecasting gives them vital information about how best to plan their resources for future demand. Using this insight, they can make better decisions around inventory investment strategies. Supply chain analytics makes it easier to understand how changes in the market are impacting demand for products, allowing companies to stay ahead of the curve when it comes to meeting customer needs.

By connecting supply chain processes through a common platform, analytics also allows companies to track data from across the enterprise easily. This can help them identify ways to make operations more efficient and improve the customer experience.

Features of supply chain analytics

Supply chain analytics software includes typically most of the features below:

Data visualization:

This is the ability to dice and slice data from different angles to enhance insight and understanding.

Dashboards and reports:

Supply chain analytics software should provide ready-made data visualization tools that can be used for real-time reporting and dashboarding. They should also allow the users to create their reports or dashboards based on their preferences and needs—analytic tools such as predictive modeling, optimization, simulation, forecasting, statistical analysis, etc.

A digital twin of the supply chain:

The digitally simulated version of the supply chain within analytics software allows companies to make more informed decisions based on real-time data.

Connectedness:

This refers to how well an analytics software interacts with other operational technology (OT) systems in a company's IT environment, thus facilitating information sharing and collaboration among different departments.

Graph databases:

This is useful for scenarios where data is complex, interrelated, and has many dimensions. It allows users to explore the relationships within the exponentially-increasing data sets more easily.

Social media integration:

Suppliers, manufacturers, and other supply chain partners are now using social media platforms to share information about their products/services. Companies can use this information to enhance decision-making.

Natural language processing:

Most of the data in a supply chain is unstructured and stored in different formats. Natural language processing (NLP) makes sense of this unstructured data and converts it into structured data to enhance its utility.

Location intelligence:

This includes the ability to map and visualize data based on geographical locations.

Supply chain analytics is a powerful tool for all.

To keep up with today's market dynamics, many companies have realized that only those who use real-time data insights to make timely decisions will be able to differentiate themselves from competitors. Studies show that more than half of supply chains are using some form of supply chain analytics to gain insight into their business.

How is supply chain analytics used?

Supply chain analytics has become an integral component for managing the entire supply network in areas such as:

Demand planning and forecasting:

A key component in today's business environment, demand planning and forecasting to help supply the right goods to customers at the right time.

Inventory management:

An essential business function, inventory management involves identifying and maintaining the right amount of products as well as those that are always available. Once demand is forecasted, this information can then be used to manage purchasing and manufacturing processes for optimal results.

Transportation planning:

With this planning mode, real-time intelligence can be used to determine the best routes for trucks and other vehicles. This process often involves several layers of software applications according to different vehicle types, including the railroad.

Customer experience management:

Customer experience management (CEM) uses insights gained through supply chain analytics to provide improved customer service. The goal is to increase customer satisfaction and loyalty.

Global trade management:

Global trade management (GTM) allows companies to manage the process of trading across borders. This includes tasks such as exporting, importing, customs clearance, quota control, and much more.

Supply chain analytics plays a critical role today in many aspects of business operations. By using real-time data insights to improve the performance of their supply chain, companies can ultimately benefit in multiple ways, such as increasing profitability and gaining an edge over competitors.