In today’s globalized and highly competitive business environment, supply chains play a critical role in determining the success or failure of an organization. A well-optimized supply chain can help companies reduce costs, improve customer satisfaction, and gain a competitive advantage. Supply chain analytics is a powerful tool that can help companies achieve these goals by providing insights into key supply chain performance metrics.
What is Supply Chain Analytics and why should you care?
Supply chain analytics is the process of collecting, analyzing, and interpreting data related to the supply chain to improve its performance. This data can be sourced from a variety of internal and external sources, including enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, point-of-sale (POS) systems, logistics data, and supplier data.
The goal of supply chain analytics is to gain insights into key supply chain metrics, such as inventory levels, delivery times, transportation costs, and supplier performance. By analyzing these metrics, companies can identify areas for improvement and take action to optimize their supply chain operations.
There are several benefits of using supply chain analytics in an organization. These include:
- Improved Forecasting: By analyzing historical sales data, predicted demand trends and other local/global factors that impact demand, supply chain analytics can help companies improve their forecasting accuracy. This can help reduce stock-outs and excess inventory, resulting in lower costs and improved customer satisfaction.
- Better Inventory Management: Supply chain analytics can help companies optimize their inventory levels by analyzing factors such as demand variability, lead times, and supplier performance. By ensuring that the right products are in the right place at the right time, companies can reduce inventory costs while improving service levels.
- Increased Operational Efficiency: By analyzing supply chain data, companies can identify inefficiencies in their supply chain network design. This includes transportation routes, scheduling, and warehouse strategy. This can result in lower costs, improved lead times, and improved end-to-end connectivity.
- Improved Supplier Performance: Supply chain analytics can help companies track and analyze supplier performance metrics such as on-time delivery, quality, and lead times. This can help companies identify underperforming suppliers and take action to improve their performance, resulting in better quality and lower costs.
- Enhanced Customer Service: By analyzing customer data, companies can gain insights into customer preferences and behavior. This can help companies improve their product offerings and service levels, resulting in increased customer satisfaction and loyalty.
Examples of Supply Chain Analytics:
There are several examples of how companies are using supply chain analytics to improve their supply chain operations. Some of these examples include:
- Amazon: Amazon is a pioneer in the use of supply chain analytics. The company uses predictive analytics to forecast demand, optimize inventory levels, and plan transportation routes. Amazon also uses machine learning to improve its delivery times by analyzing factors such as traffic patterns, weather, and road conditions.
- Walmart: Walmart uses supply chain analytics to optimize its inventory levels and reduce stock-outs. The company uses data from its point-of-sale systems to analyze customer demand and adjust its inventory levels accordingly. Walmart also uses analytics to optimize its transportation routes and reduce transportation costs.
- Procter & Gamble: Procter & Gamble uses supply chain analytics to optimize its production schedules and improve its product quality. The company uses data from its manufacturing systems to analyze production yields and identify opportunities for improvement. Procter & Gamble also uses analytics to track supplier performance and improve its sourcing strategies.
- General Electric: General Electric uses supply chain analytics to optimize its inventory levels and improve its manufacturing processes. The company uses data from its ERP systems to analyze demand patterns and adjust its inventory levels accordingly. General Electric also uses analytics to improve its production scheduling and reduce its lead times.
Key Supply Chain Metrics
Following are some of the key metrics and formulas commonly used in supply chain analytics:
1. Inventory Turnover Ratio:
Inventory Turnover Ratio measures the number of times inventory is sold and replaced in a given period. The formula for calculating inventory turnover ratio is:
Inventory Turnover Ratio = Cost of Goods Sold / Average Inventory
where Cost of Goods Sold refers to the total cost of goods sold during a period, and Average Inventory is the average value of inventory held during the same period.
2. Days Inventory Outstanding (DIO):
Days Inventory Outstanding measures the average number of days it takes for a company to turn its inventory into sales. The formula for calculating DIO is:
DIO = (Average Inventory / Cost of Goods Sold) * 365
where Average Inventory is the average value of inventory held during a period, and Cost of Goods Sold refers to the total cost of goods sold during the same period.
3. Warehouse capacity utilization:
Warehouse capacity utilization is a supply chain metric that measures the extent to which the available space in a warehouse is being effectively utilized. It indicates how efficiently the warehouse is being utilized to store and manage inventory.
To calculate warehouse capacity utilization, you need to determine the total warehouse capacity and the amount of space currently occupied by inventory. Here’s the formula:
Warehouse Capacity Utilization = (Occupied Warehouse Space / Total Warehouse Capacity) x 100
4. Fill rate
Fill rate measures the percentage of customer orders that are fulfilled in full and on time. The fill rate formula is:
Fill Rate = (Total Number of Items Fulfilled on Time) / (Total Number of Items Ordered) x 100%
The total number of items fulfilled on time represents the total number of items that were delivered to the customer on or before the requested delivery date. The total number of items ordered represents the total number of items that the customer ordered.
Some variations of fill rate are unit fill rate, line item fill rate, monetary value fill rate.
5. Perfect Order Fulfillment Rate:
The Perfect Order Fulfillment Rate is the percentage of orders that are fulfilled without any errors or defects. The formula for calculating Perfect Order Fulfillment Rate is:
Perfect Order Fulfillment Rate = (Number of Perfect Orders / Total Number of Orders) * 100
where Number of Perfect Orders refers to the total number of orders that are fulfilled without any errors or defects, and Total Number of Orders is the total number of orders received.
6. Total Cost of Ownership (TCO):
Total Cost of Ownership is the total cost of owning and operating a product over its entire life cycle. The formula for calculating TCO is:
TCO = Purchase Price + Maintenance Costs + Operating Costs + Disposal Costs
where Purchase Price is the cost of purchasing the product (also known as landing price which includes shipping and logistics cost as well)
7. Stockout frequency:
Stockout frequency measures how often a company runs out of stock or inventory. The formula for stockout frequency is:
Stockout Frequency = Number of Stockouts / Total Time Period
The number of stockouts represents the total number of times that the company ran out of stock or inventory during the time period being measured. The total time period is the length of time being measured, which could be a day, week, month, or any other time frame that is relevant to the business.
In conclusion, supply chain analytics is a critical aspect of supply chain management, and the formulas discussed above are just a few of the many tools used to optimize and improve supply chain performance. By using these formulas, companies can gain valuable insights into their supply chain operations and make informed decisions that can help them to reduce costs, improve efficiency, and increase customer satisfaction.