Supply Chain Data Analytics | 3 mins read

Supply Chain Data Analytics - 3 Strategies for Businesses

supply chain data analytics 3 strategies for businesses
Hanh Truong

By Hanh Truong

Supply chain data analytics is a critical component of supply chain management, in which the leveraging of metrics enables businesses to have a clearer insight into their movement of goods.

By extracting and translating large amounts of data into valuable information, organizations will be able to optimize all operational processes related to procurement, production, and distribution.

What is Supply Chain Data Analytics?

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Supply chain data analytics is the practice of examining large data sources and transforming them into valuable insights. Businesses will perform this to make informed decisions regarding the different processes within their supply chain, from the manufacturing of goods to its transportation.

Modern methods of analyzing supply chain data include the use of machine learning, artificial intelligence, statistical algorithms, and predictive analytics. These new forms of technology help business owners enhance their demand forecasting and their understanding of customer needs.

In order for supply chain analytics to be effective, administrators must collect data from their different systems and infrastructures. This helps guarantee that the supply chain planning will be synchronized and ensures that executives have accurate visibility into their processes and performance.

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3 Analysis Strategies

There are 3 key analysis strategies that will help management teams address specific parts of their supply chain.

1. Descriptive Analytics

This analysis method allows business owners to understand what is currently happening within their supply chain. To perform descriptive analytics, historical data must be examined so that patterns and insightful trends can be identified.

This form of analytics can help reveal-

  • Customer-Product Matrix - The relationship between specific customer demographics and product attributes.
  • Coefficient of Variation - The calculation of average demand for a product or service.

  • Customers per SKU - The number of shoppers that one item goes to.

  • Alert Reporting - Alerts and notifications that are sent to authorized users to inform them about an emergency, such as stock-outs.

  • Safety Stocks - The specific products that businesses need to have additional quantities for to avert instances of stock-outs.

2. Predictive Analytics

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Predictive analytics is the practice of forecasting future consumer demands and strategically preparing the inventory and operational processes.

Companies will also conduct this analysis to predict how customers may respond to a new product, service, or marketing campaign. This helps them decide whether it is profitable to allocate resources to the new project.

Predictive analytics is becoming increasingly popular for organizations to utilize, in which the number of companies that use this method has grown 76% since 2017, according to a recent study.

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3. Prescriptive Analytics

This process is used in conjunction with predictive analytics and helps companies determine what strategy needs to be put in place according to forecasts.

It compares historical data to real-time metrics to highlight potential opportunities in the future, as well as incoming threats. While this is similar to predictive analytics, prescriptive analytics will recommend what actions executives need to take to protect their supply chain.

How to Use Supply Chain Data Analytics

Supply chain managers can leverage their use of data analytics and optimize their operations in many ways, such as-

Forecast Demand

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Accurately using data to forecast demand gives businesses a great advantage in streamlining their supply chain and making sure that their customers' expectations are met.

Demand is consistently shifting and can be quickly impacted due to different factors, like new trends, weather, and global events. Although these factors are beyond a business's control, managers can act quickly and prepare themselves with precise and data-driven forecasts.

Strategize Predictive Pricing

Data can help businesses determine profitable pricing strategies for their products or services. With accurate demand forecasting, managers can effectively adjust the pricing of their goods to increase overall revenue, regardless of market conditions.

Manage Inventory

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Predictive analytics helps business teams identify what inventory products their company needs to stock, as well as the optimal quantities they must have on hand to fulfill demands.

This will help divert the possibility of ordering excess stock and overfilling storage facilities. It will also prevent understocking, which would have resulted in the loss of sales and increased costs in expediting more orders.

Data analytics also allows supply chain managers to plan their purchase orders and to organize their catalog of goods in the warehouse.

Streamline Logistics

Shipping and transporting goods to different destinations can be costly for a business and can increase a product's final price. With the use of data analytics, organizations can be cost-efficient.

For example, they can use predictive reports to determine how often they need to replenish inventory to minimize their order frequency.

Data can also be used to plan transportation routes to minimize delays due to traffic congestion, weather, and distance between delivery locations.

By taking advantage of the valuable information that comes from data analytics, businesses can streamline their supply chain and improve their efficiency.

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