As retailers scrambled in 2020 to build new omnichannel capabilities, many new challenges surfaced. They had to quickly build software solutions and operational processes, change store layouts, and quickly train employees to play new roles. In the rush to handle all of those challenges, one thing that may have been missed was the ability to monitor the flow of orders, ensure that orders were being processed in a timely manner, and, where inevitable exceptions occurred, contact customers whose promised service timing would not be met.
This need to monitor, alert, and notify can be met via Control Tower functionality. An effective Control Tower solution allows a retailer to store all of the systemic events within the order lifecycle, compare the timing of those events to service timing promises to identify potential issues and execute the appropriate communications.
This requires the ability to:
- Ingest order and event information from order capture, order management, and fulfillment execution systems
- Build workflow definitions for each fulfillment type to be monitored
- Build rules for exception monitoring based on key workflow events and either elapsed time or time before a defined due time
- Monitor the data given the rule set
- Execute communications to internal stakeholders and/or customers based on the defined rules and current order states
In addition to the use of static rules, monitoring and notifications can be further enhanced by applying Machine Learning to vary the rules based on historical data and parameters such as Day of Week, Time of Day, special events and seasonality factors and comparisons of the current day’s progress to prior dates. That machine learning can be used to provide earlier alerts which can then be supplemented by user inputs. For example, Machine Learning may identify likely service issues base upon historic staffing levels but be corrected by a user that is aware that additional hours have been staffed for the day.
Depending upon the timing of a system generated alert, communications can be sent to internal users to drive corrective measures and/or to customers to notify them of delays.
While the primary goal will always be to meet promised service timing, a customer that is warned in advance that their pickup order will not be ready will be happy to avoid a wasted pickup trip and more likely to place future orders in spite of the delay. Also, an early notification to internal users may allow a service recovery that allows achievement of the original promise — an even better outcome.
In addition to the monitoring functionality that is core to a Control Tower solution, the data necessary for the Control Tower functionality can allow an extension of the Control Tower functionality to provide:
- Customer check-in capabilities
- Arrivals dashboards and/or associate notifications
- Wait time calculations (time from check-in to order completion)
- Additional operational reporting
Finally, where the Control Tower solution is able to ingest all order picking details, it can also be expanded to provide fill rate management alerts. If an item at Store X is being skipped or shorted, the Control Tower solution can look at open orders and determine the number of open orders for the item that will also have fill rate issues. This can be used to trigger cycle counting, back room replenishment, and/or updates to the available to sell balance for the item at that store.
Nextuple has had the opportunity to build this type of functionality for multiple retailers and can assist in assessing your needs and providing SaaS solutions to those needs. Contact us at firstname.lastname@example.org to learn more.