If you want to understand the process of scheduling and to discern what is different between various system offerings, it is helpful to understand some of the basic terms that you are likely to encounter.
A good schedule starts with a forecast. Simply put, a forecast is a prediction of what the business will require. A sales forecast predicts the volume of sales your store will do during an upcoming specified period of time. A weekly sales forecast predicts your sales for an upcoming week. A daily sales forecast predicts sales for a given day.
Sales is not the only metric that is forecast. In fact, more advanced forms of forecasting are about forecasting workload volumes rather than just sales. Workload volumes are the volumes of things that create the work for your employees to do. When you buy groceries, your cashier’s workload comes from the items you buy and the tender transactions he/she must make to process your order. The sales are only a bi-product of those items that you choose to purchase. The items are what defines the work that needs to be performed to ring up your order.
Defining workload volumes
Here is an example that I think helps to understand volume drivers and illustrate how having the best approach to forecasting workload volumes and using time standards can optimize your results. Think about service work at your local grocery store’s Deli counter, and let’s compare three different scenarios:
- One person buys one package containing 10 pounds of roast beef.
- One person buys 10 packages each containing 1 pound of roast beef.
- Ten different people each buy 1 package containing 1 pound of roast beef.
If we only forecast the sales, and Roast Beef is $9.99 per pound this week, then each of these scenarios totals out 10 pounds of roast beef and sales of $99.99. But the work content between these scenarios is very different. Put yourself in the place of the Deli clerk who must meet and greet each customer and take their order, then slice the roast beef to order, and make the package(s) as requested. Each of these scenarios has the same sales forecast, but the workload volumes are really the customers, pounds, and packages that our Deli clerk must process. People might refer to these as workload volumes or as forecast elements, or volume drivers. But keep this example in mind because it’s a good one to remind you that all approaches to forecasting and defining workload volumes are not equal.
Understanding work content
When forecasts identify the true workload volume, then we can develop the clearest understanding of the work content that we are writing our schedule to satisfy. Work volume might be items, pounds, cases, shirts, etc. but work content is about time – hours, minutes or seconds. Once we forecast workload volumes we can calculate work content, or work demand, by using labor standards.
Labor standards can be very basic or very specific depending on how they are developed. Some organizations use reasonable expectations (REs) or rule of thumb standards because they do not have the systems or resources to create more specific or detailed labor standards. This is not a bad approach to get started. Our Deli clerk would much rather get time for the customers, pounds and packages she/he must produce rather than just an amount of time per dollar sold. But, as you have an opportunity to do so, it can help to have more detailed standards that take into account the type of equipment you have, the number of steps that need to be traveled, the packaging requirements, and the other specifics of what the work requires at this particular Deli to calculate the right amount of time fairly. Generally, standards of a more detailed nature are referred to as Engineered Labor Standards. An engineered labor standard defines how much time it takes to perform a specific method for a specific volume unit. Note again that just as we learned that just as all approaches to forecasting are not equal, neither are all approaches to building engineered labor standards!
Applying forecasted volume to time standards
When you have a forecast volume and a time standard for that volume you can multiply the two together to get what is known as raw work content time. This may also be referred to as engineered time or raw demand. The time is considered “raw” or “engineered” because while it tells you how much work is required, it is only a portion of the resource time you would need to schedule to have workers perform this work.
Transforming raw demand into scheduling demand
Several things are missing from raw demand to get to scheduling demand. Allowances for breaks or other personal fatigue and delay are not in the raw demand calculation. Also, let’s say the raw demand required 0.67 people to do the work. Can you schedule 67/100ths of a person? In most systems, scheduling is done on the quarter-hour, so you can round down your raw demand to .5 hours for this work, or you round up to 0.75. But what if we must always have one person scheduled for this work? Or what if we must have one but no more than 2? These are minimum and maximum constraints. All are examples of what we collectively refer to as staffing parameters.
Getting an understanding of your raw work requirements is critical, but additional processes occur to transform that raw demand into scheduling demand. This process of transforming raw demand to scheduling demand is usually referred to as staffing or demand planning or a part of developing a workplan.
We will cover all these staffing terms in greater detail as we discuss how automated systems can handle staffing to best meet your needs and optimize your scheduling. And, just like we learned for forecasting and for engineered standards – not all systems do it the same way, or nearly as well.
With these basic terms in mind you will be ready for our next edition of Scheduling Insights.
In his series, Scheduling Insights, Dan Bursik provides insights and strategies around effective retail labor scheduling, addressing a diverse array of challenges and topics. To read the previous edition, click here. To search for all editions of Scheduling Insights, click here.