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Optimizing Supply Chains and Logistics Management with Data Analytics

Analytics has always focused on gaining insight into processes that enable its adherents to make data-driven decisions and to understand complex systems. One example of a complex system that produces big data sets is the supply chain. However, many companies struggle to harness data from their supply chain to successfully transform and optimize how they function for two major reasons – a lack of technical capability and the technology to capture and evaluate data.

A lack of technical capabilities refers to the limited experiences the average supply chain manager has with data management while the second involves difficulties with collecting data from data-producing sources. Despite these challenges, handling supply chain and logistics requirements in today’s dynamic world without digital assistance is similar to driving a car with no speedometer and side-view mirror or wing mirror. The supply chain manager may successfully use past experiences to navigate recurring supply chain challenges but as more complex problems arise, the more inaccurate applying rule of thumb becomes.

Leveraging Digital Transformation Solutions to Capture Supply Chain Data

Managing an enterprise’s supply chain and logistics operations involves keeping tabs on multiple interrelated activities. These interrelated activities or areas include:

  • Customer Demand – Insight into the demand for products or raw materials is necessary for devising a functional supply plan to meet customer requests. Digital transformative tools such as demand forecasting software take in historical demand data, matches them with date-specific fluctuations to produce accurate demand forecasts. In this example, the forecasting tool applies predictive analysis to determine the demand for accurate supply chain planning.
  • Sourcing Inventory – The recent global events such as the pandemic and traffic at the Suez Canal highlighted the importance of developing an agile sourcing process to optimize supply chain performance. These aforementioned developments also highlight the importance of developing a more robust process to determine sourcing requirements other than relying on the traditional procurement volumes and supplier performance review data. For an optimized supply chain, enterprises must develop predictive risk management capabilities that deal with disruptive forces alongside the ability to evaluate cost models and warehouse capacity to ensure sourced items are properly stored.
  • Warehousing Capacity – Handling inventory and transporting materials or finished items from warehouses or storage areas to the manufacturing floor or retailers requires proper management. Successfully storing goods and minimizing travel distances improves supply chain performance for goods with short-shelf life and heavy-duty equipment. Thus, gaining insight from warehousing capacity and the logistics required to transport items is crucial to developing optimized supply chain plans. Digital transformation solutions such as inventory management software and Enterprise Resource Planning (ERP) software can be used to capture and manage warehouse-related data.
  • Transportation – Finally, the transportation of items is an interrelated area that plays an important role in determining the effectiveness of a supply chain plan. Today, trucking and courier companies utilize fleet management solutions to track trucking data such as fuel consumption rate, distance traveled, geo-locating trucks, and the environment within trucks. Analyzing the data captured from the courier process assists with reducing wait times when delivering materials or finished products across the supply chain. Here, IoT devices, RFID tags, and integrated optimization and navigation platforms help with capturing and analyzing transportation data.

Relying on Software for Supply Chain Analytics

The interrelated areas associated with supply chain management provide some insight into why analytics has become a complicated process. Relying on paper calculations and Excel records no longer provide the dynamism required to optimize the complex supply chains of the modern era – this is where digital transformation software comes in.

Simulation modeling software, ERP, and the Digital Twin provide manufacturers, suppliers, and warehousing managers with powerful tools to evaluate supply chain data with optimization as the goal. The example of Cosan, a world leader in the agricultural manufacturing sector, highlights the importance of supply chain planning with analytical software. Cosan’s complex supply chain consists of 18 production plants, 2 refineries, two poet terminals, and a supply network across Brazil.  The company struggled with reducing the capital expended to deliver sugarcane residues to its production plants.

To reduce its supply chain operational costs, Cosan developed a discrete event simulation model to analyze the dynamics and bottlenecks involved with transporting raw materials and increasing the capacity of raw materials delivered to its production plants. The developed simulation model helped the enterprise conduct an accurate predictive risk analysis over a 32 week-season. The results helped management discover bottlenecks with its queuing process and to develop optimized plans for its fleet management and resource allocation processes.

As stated earlier, capacity planning in warehouses or distribution centers is an interrelated area that significantly affects the performance of an enterprise’s supply chain operations. Simulation modeling software analyzes capacity and workforce-related data to improve warehouse operations and to optimize supply chain performance.

One example is the use of discrete event simulation modeling by a beverage distribution center to improve its warehouse operations. The enterprise successfully modeled the complex parameters within its warehouse such as its wide array of materials consisting of 324 SKUs, fluctuating demand, shift hours per worker, storage space etc. The analysis of its warehousing process showed that proper staffing and an improved capacity will reduce its load preparation time by approximately 15%.

These examples highlight the importance of capturing interrelated data sets from an organization’s supply chain and the application of analytical technologies to develop optimized management plans. Leveraging simulation modeling technologies provide the technological solution to analyze supply chain data and optimize performances. Thus, companies must develop the capabilities to capture and analyze supply chain data to remain competitive and navigate through real-time fluctuations that lead to downtime and waste.

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