The foundation for retail and restaurant operations is built on customer satisfaction. But it’s hard to keep customers smiling if the payment terminal is out of order or the digital menu board is blank. Reacting to store maintenance issues diverts staff from serving customers and is often more expensive and time-consuming than expected. Adopting a predictive maintenance mindset can transform your operations.
Given the challenges of labor, rising costs, and increasingly complex technology, many retail operations – including quick-serve restaurants (QSRs) – react to maintenance problems only when they happen. In the restaurant segment, maintenance costs are approximately 1.5% of sales, according to the National Restaurant Association.
General maintenance expenses are typically 2 to 6% of the overall budget in the retail arena, QSRWeb reported. Overall, retailers spend an average of $5.02 per square foot on store maintenance, including HVAC, according to the Professional Retail Store Maintenance Association.
Physical assets represent a significant number on the balance sheet in low-margin businesses like retail and foodservice. To ensure those assets are earning the best possible return, it’s critical that equipment and services are supporting revenue and the customer experience.
A predictive maintenance strategy, enabled by edge computing and real-time information, can help enterprises manage operational costs and serve their customers in a competitive environment.
Predictive maintenance uses real-time data collected at the local level to predict when an asset will need intervention in advance of a failure. You can use on-premise and cloud computing to widen the window to schedule maintenance to avoid unscheduled downtime and manage the asset’s lifecycle. It also frees up store resources (labor etc.) that can then be deployed to serve customers better and improve the overall customer experience and realized revenue.
The High Cost of Reactive Maintenance
Store operators find they spend about one-third of the time creating and delivering products, whether stocking shelves or fulfilling food orders, and about one-third of the time providing customer service. The final segment is devoted to operational issues in the store, including repairs and maintenance.
Facilities management is often decentralized, so there may not be an enterprise-level maintenance strategy and management. Local management tends to react to outages and breakdowns rather than working to avoid them. Expedited customer support and repairs drive up costs and could leave the location without critical services or equipment.
Reactive maintenance is usually due to deferring expenditures on regularly scheduled service and ignoring minor issues in the hope they go away. Unfortunately, minor problems often turn into major system failures.
“Customer volume is often down during trying economic times, so owners and operators have a natural tendency to look for ways to make a little bit go a long way, Eric Lane, director of operations for the School of Hotel, Restaurant and Tourism Management at the University of Denver’s Daniels College of Business, said in QSR Magazine. “But this means they’re often too quick to cut back on preventative maintenance and safety concerns in their efforts for a better bottom line.”
Studies show that every dollar “saved” by deferred maintenance leads to sending $4 in direct costs on future repairs or replacement. Overall, the total costs might reach 15 times the amount that would have been spent on regular maintenance.
Adopt A Predictive Maintenance Strategy
Adopting a predictive maintenance strategy can change the equation of how much time and resources are spent on addressing avoidable operational problems. According to the Aberdeen Group, companies that implement a predictive maintenance model reduce unplanned downtime by 3.5%, reduce maintenance costs by 13%, and increase return on assets by 24%.
Given the level of technology available, gathering data on equipment and services is achievable with existing equipment. The challenge is harnessing the flood of data to generate actionable insights to formulate a response by the appropriate parties.
You can fundamentally shift maintenance procedures, eliminating inefficient reactive and scheduled preventive maintenance. Highly targeted predictive maintenance can reduce downtime, lower costs, and satisfy customers.
A comprehensive predictive maintenance solution can help prioritize response and integrate with a ticket system to facilitate service. Incoming alerts can be triaged in real-time with the appropriate service level. Some problems will be addressed remotely, while others may require a service call. Machine learning will refine the automated monitoring and alert process to optimize the system’s efficiency.
A predictive maintenance solution helps drive better use of resources by removing troubleshooting and repair from the on-site staff’s responsibility. With service attention at the right time, the equipment or service won’t have unscheduled downtime.
Edge-based solutions put the data collection and computing power in the store location, communicating through wired or wireless connectivity. The response can be governed by a service level agreement (SLA), ensuring the problem is addressed within a specified time frame.
Take control of your operating costs and customer service experience with a predictive maintenance strategy. Divert the time and resources spent solving operational problems to a more productive return on your assets.
Maven Wave and Atos work with Agile Retailers with cloud-based solutions that enable them to operate in open, sustainable ways. We have worked with retail, QSR and entertainment (theme parks) companies in reducing downtime, improving operating efficiencies, and reducing costs using edge-based solutions that operate with speed and precision.
Are you interested in learning more about these use cases and discussing how your organization can adopt a predictive maintenance solution to transform your operations? If so, contact us to schedule a time to meet with our experts.
*This post was co-authored by Manish Verma, Vice President and Client Executive Partner in Retail, Travel & Hospitality at Atos
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