Using Analytics for a Smarter Supply Chain in Manufacturing

Supply chain innovation can lead to incredible gains in business performance. Creating a sustainable, resilient supply chain is crucial for the long-term survival of any manufacturer, and smarter supply chains can serve as a competitive advantage. Today’s companies are using data analytics to create a smarter supply chain in manufacturing and experience the benefits that come as a result.

Benefits of a Smarter Supply Chain in Manufacturing

Investment in smarter supply chains pays long-term dividends. Manufacturers committed to optimizing their supply chains can expect to see the following benefits. 

Lower costs and improved profitability

Using supply chain analytics, manufacturers can uncover potential efficiency gains that would be difficult or impossible to see otherwise. From reconfiguring the assembly line layout to boost production volumes to more efficiently managing the movements of product in the warehouse, incremental improvements implemented over time can reduce costs and lead to significant gains in profitability. 

Reduced risk 

Advanced analytics can provide business decision-makers with sufficient warning about events likely to negatively impact the supply chain. Being able to plan for events such as the impending bankruptcy of a key supplier or port congestion can help manufacturers maintain production goals even during periods of significant disruption. 

Future-proofing

Modeling various scenarios to see their impact allows decision-makers to adjust their strategies accordingly and make contingency plans. Supply chain analytics pulls from a broad range of internal and external data sources, making it possible to optimize operations for the future before it arrives.

Using Data to Make Supply Chains Smarter

The amount of manufacturing supply chain data available today is staggering. Companies that take advantage of this data can create smarter, more-resilient supply chains that are capable of quickly adapting to unexpected aberrations. 

The role of supply chain analytics

Smarter supply chains are built on a foundation of data. Without an accurate, comprehensive view of the entire manufacturing operation, it’s impossible to make effective decisions. Supply chain analytics enables decision-makers at all levels of an organization to access the insights they need to maximize operational efficiency and productivity.

Types of supply chain analytics

There are four primary types of supply chain analytics. Each makes its own unique contribution to developing and maintaining a smarter supply chain.

Descriptive: Descriptive analytics deals with historical data. This rear view can identify important patterns by analyzing manufacturing data gathered from suppliers, sales channels, and customers to uncover important trends or patterns. For example, descriptive analytics can be used to examine historical purchasing patterns for certain products.

Predictive: Predictive analytics is used to model out a range of what-if scenarios. Predictive analytics analyzes a range of macro-level data including consumer demand, weather events, political unrest, and labor shortages to accurately predict how these and other factors may impact a manufacturer’s supply chain or production capabilities. Smarter supply chains must include robust contingency planning.

Prescriptive: Prescriptive analytics uses the results of predictive and descriptive analytics to suggest potential actions a manufacturer should take to achieve a set of predefined goals. One application of prescriptive analytics is identifying weak links in the supply chain. For example, analytics can flag a transportation vendor that consistently misses delivery deadlines and has recently cut back its workforce. A prescriptive analytics program may suggest this vendor is likely to go out of business within the next year, so it’s time to source a more reliable replacement. 

Augmented: Augmented analytics relies on artificial intelligence and machine learning techniques. This type of analytics involves massive, complex data sets from multiple sources to make highly accurate predictions. One exciting application of augmented analytics is the improvement of worker safety. Wearable sensors capable of collecting data on worker health, stamina, and exposure to occupational hazards can be used to monitor the stressors faced by employees, alerting management when interventions are needed.

Supply Chain Analytics Use Cases

A smart manufacturing supply chain is one that flexibly adapts and evolves over time, using all types of supply chain analytics to meet the changing needs of the manufacturer and its customers. We’ve looked at a few examples of how manufacturing data can be used to create a smarter supply chain in manufacturing. Now, let’s do a deeper dive into a few manufacturing use cases. 

Demand planning and forecasting

Traditional demand planning and product forecasting rely on historical data to estimate future demand. But using the past to inform the future is far from ideal. Getting this wrong can result in warehouses stocked with products that consumers have moved on from and back orders for in-demand items. Predictive analytics supplements historical data with additional data on current market trends and industry competition. This holistic approach to demand planning and forecasting more closely aligns production and customer demand.

Expense and overhead tracking

One of the most expensive parts of the manufacturing process is personnel costs. Accurately accounting for labor costs is essential for sustainable pricing. By installing sensors on the manufacturing line, the movements of individual workers can be tracked and analyzed to provide accurate estimates of how much time each person involved in the assembly process is investing in each unit produced. 

Proactive risk management

Complex supply chains pose a significant risk for manufacturers. Having a key supplier go offline due to a labor strike or adverse weather event disrupting transportation can cripple production, resulting in costly delays. Manufacturers and suppliers can opt to share data, allowing manufacturers to analyze supplier data to gain deeper insight into quality, on-time performance, and pricing for individual suppliers. This knowledge provides manufacturers with greater transparency into each link of their supply chain, allowing them to renegotiate pricing, address quality concerns, or switch to a more reliable supply partner. 

Snowflake for a Data-Driven Supply Chain in Manufacturing

Smart supply chains require both massive amounts of data and the powerful, dedicated computing resources needed to analyze it. Snowflake provides both. With near-limitless, scalable data storage for all data formats, manufacturers can consolidate data into a single source of truth. Additionally, Snowflake’s unique, high-performance architecture makes it possible to easily analyze your manufacturing data and uncover insights at any scale of data, applications, and users. Powerful built-in features and simple integration eliminate the interoperability issues that slow you down. You’ll get exponentially quicker insight from your data to drive critical business decisions.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.