Start by checking flexpos hourly sales against forecasts to set the sales schedule

What is the initial step to find the sales schedule?

• A. Review the sales from the last year
• B. Access the flexpos hourly sales and compare it to forecasts
• C. Contact the regional manager for updates
• D. Check the sales reports posted in the break room

Answer: (B)

The initial step to find the sales schedule involves accessing the flexpos hourly sales and comparing it to forecasts. This approach provides a data-driven foundation for understanding current performance trends relative to expected outcomes. By analyzing the hourly sales data, it becomes possible to assess fluctuations in customer demand throughout the day, which can inform scheduling decisions. Additionally, comparing this data to forecasts allows for a more strategic evaluation; it helps identify discrepancies between projected sales and actual sales patterns. This analysis is crucial because it enables decision-makers to adjust staffing levels based on actual performance, ensuring that the operation runs efficiently and can meet customer needs effectively. The other options, while they may provide relevant context or background information, do not directly focus on the immediate actions needed to establish the sales schedule. For instance, reviewing last year's sales offers historical data but may not accurately reflect current trends. Contacting the regional manager for updates or checking posted sales reports may provide additional insights, but these steps are secondary to directly analyzing the most current sales data for effective scheduling.

Title: Mastering the Sales Schedule: Start with FlexPOS for Real Clarity

If you’ve ever stood in line at Jersey Mike’s and wondered how the crew always seems to glide through the lunch rush, you’re not alone. The secret isn’t luck or magic; it’s a dependable, data-driven approach to staffing and scheduling. And the very first move in that approach is simple, practical, and surprisingly powerful: pull the FlexPOS hourly sales data and compare it to forecasts. Let me explain why this single step sets the tone for an efficient, responsive store operation.

The core idea: data beats guesswork every time

When you’re staring at a busy lunch service or a bustling dinner rush, it’s easy to rely on hunches about when customers will come in or how many crew members you’ll need. But the moment you pull the FlexPOS hourly sales data, you’re grounding your decisions in actual numbers. Hour by hour, you can see demand patterns—when crowds swell, when quiet spells drag on, and where your staffing is likely to be a touch light or heavy.

Now, pairing that data with forecasts takes it to a new level. Forecasts are the map—how many sandwiches you expected to sell, on which hours, given promotions, weather, or school schedules. Compare forecasted sales to what actually happened in those same hours. If the two lines don’t align, you’ve found a real opportunity to tighten up scheduling and operations.

Let’s break down what this looks like in practice.

Step one: access the FlexPOS hourly sales

Think of FlexPOS as your real-time (or near real-time) lens into customer flow. The hourly sales view shows you every hour’s performance, not just daily totals. Here’s how to approach it without getting lost in the numbers:

• Open the FlexPOS dashboard and navigate to hourly sales. If you’re new to the layout, there should be a quick guide or a legend explaining what each column means (orders, revenue, average ticket, etc.).

• Focus on the hours that matter most. For many Jersey Mike’s locations, lunch and dinner windows—roughly 11 a.m.–2 p.m. and 5 p.m.–8 p.m.—are the sticky periods. Don’t drown in every tiny detail; pull the data for those peak windows first.

• Note variability. Do some days show spikes at certain hours while others stay flat? Make a mental bookmark of these patterns. The goal is to surface the hours that demand more hands on deck.

Step two: bring forecasts into the picture

Forecasts are the yardstick you’ll compare against. They’re not perfect, but they’re incredibly useful for spotting trends and gaps. Here’s how to use them effectively:

• Pull the forecast for the same time window. You want apples-to-apples comparisons: hour to hour, same day part, same promotions if possible.

• Look for gaps. Where actual sales are higher than forecast, you might need more staff or faster handoffs to keep service smooth. Where actual sales lag forecast, you might reallocate labor or adjust prep levels.

• Don’t chase a single hour. It’s tempting to react to one anomaly, but you’ll sleep easier if you view a small set of adjacent hours to see whether a trend holds.

Why this pairing matters: the data tells a story

Here’s the thing: data on its own can feel cold and abstract. It’s easier to react when you see a story unfold. When actuals consistently beat forecasts by a healthy margin in a lunch window, you’ve got permission to adjust up staffing to prevent wait times from creeping. Conversely, if a forecast predicts a surge that the actual data never materializes, you can scale back slightly or re-allocate seats to other tasks (food prep, line management, cleanup), keeping the team lean without compromising customer experience.

This approach isn’t about chasing perfection. It’s about responsiveness. It’s about reading the rhythm of the day and letting the numbers echo what customers are actually doing. It’s a balance of science and a touch of street-smart intuition.

A real-world flavor: what successful shifts look like

Imagine a Jersey Mike’s location near a business district, with a predictable lunch crowd and a variable after-work rush. The FlexPOS hourly data shows that:

• 11:30 a.m. to 1:00 p.m. often hits a peak, sometimes stretching a bit longer than forecast.

• 1:00 p.m. to 2:30 p.m. tends to dip, with a smaller but steady stream of orders.

• Weekdays see a mild after-work uptick around 5:30 p.m., while weekends are more spread out.

The forecast for the same hours aligns reasonably well, but there are a few gaps. For instance, actual lunch-hour demand runs 15–20% higher on Wednesdays than forecast. The team responds by scheduling additional front-line staff just before the rush, ensuring a smoother flow from order to pickup. That small adjustment can shave minutes off wait times, which in turn boosts customer satisfaction and encourages a return visit.

On those quieter hours, the forecast might exceed actual sales by a similar margin. What happens then? You shift resources away from noncritical tasks during those times—perhaps delay a cleaning task to a slightly later window, reassign a runner to help in the front line, or push back some prep until the post-lunch lull passes. It’s about using energy where it matters most and staying flexible.

Common missteps to avoid

• Relying only on last year’s numbers: History is helpful, but it can mislead you if patterns shift due to promotions, seasonality, or neighborhood changes. Use last year as a baseline, not a blueprint.

• Waiting for someone else to surface the data: The data is there for you to own. If you’re not sure how to pull FlexPOS hourly sales, ask for a quick walkthrough or request a short training—this is an investment in smoother operations.

• Ignoring forecasts entirely: Forecasts aren’t perfect, but they’re not decoration either. They give you a direction. Compare, question discrepancies, and adjust accordingly.

• Overcorrecting based on a single hour: It’s tempting to swing for the fences after a surprising spike. Instead, look for a consistent pattern across a few hours or days before changing schedules.

A practical, repeatable routine

To make this approach a everyday habit, you can adopt a simple routine. It won’t take long, and the payoff adds up over weeks:

• Each morning, pull FlexPOS hourly sales for the target day. Note the actuals against the forecast for the main lunch and dinner windows.

• Mark any hours where the delta exceeds a sensible threshold (say, ±10–15%). Those are your focus hours for staffing decisions.

• Check promotions, local events, or weather notes that could sway demand. If you’re aware of a nearby school event or a sports game, factor that into your interpretation of the data.

• Adjust staffing levels accordingly for the next shift. A few extra hands in peak hours can prevent bottlenecks; a lighter schedule during slower hours helps with cost control.

• Close the loop: at the end of the day, capture what worked and what didn’t. Jot down any insights for the next cycle—the goal is to iterate, not to reinvent the wheel each day.

A few quick tips to smooth the process

• Keep it visual: use a simple chart that maps actuals vs forecast across hours. A quick glance should reveal where the gaps live.

• Tag the context: add a short note about promotions, weather, or local events on days with big deltas. Those notes help future decisions.

• Involve the crew: share high-level insights with the team. People perform best when they understand the “why” behind a schedule change.

• Test small, learn fast: try a modest adjustment for a couple of days, monitor the effect, then scale up or revert as needed.

What this means for customer experience

A well-tuned schedule isn’t just about saving money or keeping labor happy. It directly influences the customer’s experience. Shorter wait times, accurate order fulfillment, and a calm front line all add up to a better visit. Customers notice the difference. They’re likelier to come back, tell a friend, and maybe grab an extra drink or side while they’re at it. In the end, the data-driven approach to scheduling is a service upgrade—quiet, effective, and incredibly practical.

Phase 3-ready mindset, with a human touch

For teams moving through the later stages of training, this approach emphasizes two core ideas: be data-informed and stay adaptable. You’re not locked into a rigid timetable; you’re building a responsive system that respects the customer flow and the reality of daily operations. It’s a good blend of soft skills—clear communication, teamwork, and situational awareness—and hard skills—reading a dashboard, interpreting forecasts, and translating numbers into action.

If you ever feel the scene get a little intense, remember this: the first step is the simplest one. Access the FlexPOS hourly sales and compare them to forecasts. From there, you’re armed with a clear picture of when to staff up, when to scale back, and how to keep the service steady even as the crowd ebbs and flows. It’s a practical approach that pays off in smoother shifts, happier crew members, and customers who leave with a couple of extra smiles tucked into their takeout bags.

Bringing it all together

• Start with FlexPOS hourly sales to see the real clockwork of your day.

• Compare those hours to forecasts to identify gaps and opportunities.

• Use the insights to adjust staffing, prep, and pace to match demand.

• Keep the loop going with quick notes and continual minor tweaks.

• Tie it back to the customer experience: faster service, consistent quality, and happy visitors.

That’s the rhythm of effective scheduling in a fast-paced environment like Jersey Mike’s. It’s not glamorous, but it’s remarkably reliable. And when you see a shift in the numbers aligning with a smoother service, you’ll know you’ve hit on something genuinely practical.

If you’re exploring this topic, you’ll find that the most satisfying part isn’t the numbers themselves but the way they translate into calmer shifts and better guest moments. So the next time you’re tasked with setting the schedule, remember the first step: check the FlexPOS hourly sales and see how they line up with forecasts. The rest follows—one hour at a time.