A food and beverage company with an extensive chain of stores worldwide.
The company has many brick-and-mortar outlets across multiple time zones throughout North America. The company had difficulty tracking the publication status of store orders within North America, which led to problems with inventory management. These problems resulted in out-of-stock items, poor decision-making, and lost profits due to excess inventory.
Each store ideally posted daily inventory orders before they opened for business. Once the orders are posted, store partners need to review and approve the published orders. This review and approval process is important because it ensures that the orders are accurate and complete.
The company has set a cut-off time, which is the last time stores can make any changes or adjustments to their orders before they are sent to the distributors for processing. This cut-off time is critical for the company's operations, as it ensures that the store orders are accurate and inventory will be delivered on time. It also allows the company to avoid overstocking or stock-outs.
The lack of visibility into the store order status led to inventory management problems. Sales suffered because out-of-stock items led to customer dissatisfaction and lost conversion opportunities. The company also lost potential profits because the excess inventory generated higher carrying costs.
In brief, the company was looking for the following:
Our production support team integrated Slack with the company’s DB2 database to help them track the publication status of store orders. This integration allowed the support team to quickly alert the appropriate company resources if any discrepancies were observed in the ordering process. Our engineers then set up an automated process, called a cron job, that runs every hour from 12:30 to 3:30 AM Pacific time. This process is responsible for publishing the order publication status, which gives the company near-continuous monitoring of store order status. This time frame was chosen to validate the total number of orders that matched the forecast.
Our team implemented the solution in two stages. In the first stage, the system retrieves the total number of orders forecasted for the day. The new system also pulls the number of orders created and the pending forecast through the end of the hour. The system also checks for skipped orders that missed deadlines, invalid orders, and order delivery schedules. It also checks for duplicate orders and calculates how many orders remain unfulfilled.
In the second stage, the new solution posts order counts to the integrated Slack messaging service every hour. Image 3 shows a mockup of a posted Slack channel message.
As an example, let’s say that the order forecast is for 100 stores, one order each, distributed as shown in Image 3 above. In this example, an alert is triggered if the order cut-off is at 3:15 AM Pacific time and only 90 orders have been published. The new system sends out a message to all stakeholders regarding the missing ten orders via email and Slack. The replenishment team now has time to complete the missing orders manually and, therefore, can avoid any supply chain disruptions.
Key points in our solution include:
Key benefits the company enjoyed after project completion included:
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