4 Ways Restaurants Are Maximizing Uptime with Unified IT Maintenance Models

With increasing use of on-site restaurant technologies, from inventory monitoring to tabletop ordering solutions, it’s challenging to ensure all systems and the underlying network continually operate at peak performance. And the more day-to-day functions and the quality of the customer experience depend on these IT solutions, the more harmful downtime becomes.

Unfortunately, restaurant groups typically face significant barriers to effective IT maintenance. A large number of geographically disparate locations, along with the usually tight margins and lean staffing ratios, make delivering high-quality support problematic. Nonetheless, some enterprises are outperforming the competition in this regard. Here’s how.

Unifying support arrangements

Restaurants understand the power of convenience. After all, it explains much of the popularity behind fast casual takeout and order-by-emoji pizza delivery. Hospitality operations leading in the technology realm are applying the same principle to IT support.

This means shifting from the status quo model, which relies on each original equipment manufacturer (OEM) to back their own products. Although this approach wasn’t an issue when most restaurants had a POS system and little else, today’s IT complexity means OEM support can involve a dozen or more providers. When on-site staff must first guess at the source of failure and then navigate a bulky instruction manual to identify the proper support contact, they lose valuable time.

The clear alternative is to unify most or all support under a multivendor provider or to establish an internal IT department to serve the same function. When there is a single contact, regardless of the issue on-site personnel encounter, getting assistance is quicker, easier, and less costly.

Eliminate the escalation

Another issue with OEM support is the escalation procedure. Many vendors want to limit customers’ access to their top-tier engineers, so the support process begins at a help desk and proceeds through several layers before field technicians are called out.

These delays can result in extended downtime. The model also increases the burden on local staff, who have to devote more time and attention to telephone-based troubleshooting. When outages occur during busy periods or when staff is already stretched thin, diverting a manager or other key team member to IT support can detract from customer service.

Using some form of centralized support, internal or outsourced, engages technical professionals almost immediately and accelerates time to resolution, often by as much as 250 percent.

Analyze outcomes

With separate support arrangements, there is little choice but to treat each IT failure de novo, as if nothing similar had ever happened before. Under a more unified model, it becomes possible to collect information about each support event, including the affected system, its age and service particulars, the root cause of failure, and repair or replacement outcomes.

IT professionals are well aware of the promise in business intelligence analytics. Leveraging such capabilities for the IT stack enables restaurant groups to realize insights about their technology systems and to make more informed decisions about which products to purchase, when to schedule maintenance, when to decommission and upgrade particular systems, how to secure the best resale price for older hardware, and more.

Anticipate the future

Whether in the manufacturing industry or the hospitality sector, maintenance tends to follow a similar course of development. In the early days, most companies inhabit a reactive stance, waiting for and responding to problems as they occur. Once more information is collected, preventive maintenance becomes possible but is usually limited to scheduled interventions. The ultimate goal, however, is to integrate predictive methods.

Fortunately, restaurant groups enjoy the scale to deliver substantial quantities of IT performance and maintenance data to business intelligence systems. When driven by machine learning, this data can, over time, fuel creation of highly accurate forecasts of future failures and contributing factors. Armed with such insights, enterprises can target appropriate action before downtime can even occur.

The Bottom Line

Optimizing IT support helps maximize uptime, but it can be a cost-saving exercise as well. Multivendor maintenance outsourcing, for example, is typically far less expensive than OEM support and can deliver immediate OpEx savings. When the selected provider or an internal team also turns to business intelligence analytics to target maintenance activity, slash the incidence of inefficient, reactive support events, and eliminate unnecessary repairs and upgrades, further budget advantages can be secured.