At Widget Brain, we help customers daily with all sorts of questions around the use of algorithms in workforce. A major goal of our algorithms is to improve employee satisfaction and happiness by better predicting when an employee is required and how to make fair schedules that determine who works when.
In our daily discussions, we see a number of things that can lead to suboptimal or even bad results and therefore we decided to create a series of articles that each highlight one of the many best practices when it comes to the use of algorithms for labour demand forecasting and the creation of schedules.
Today, we will talk about Best Practices in Workforce Optimisation Part 2 – How to not estimate labour standards.
In my previous article, I wrote about heuristics in demand forecasting and scheduling. Now that you have thrown those heuristics out of the window, you can finally make highly accurate demand forecasts. But what now? Demand forecasts give you proper insights on what future demand will be, but it creates no value when not using that information correctly. Remember: knowing that you’ll sell 30 milkshakes between 18:30 and 18:45 in the middle of rush hour doesn’t tell you how many milkshake handlers and cashiers you need to perfectly cover your demand and not lose potential sales due to seemingly never-ending lines.
In order to translate forecasts into shifts that cover demand, you need labour standards. Labour standards within WFM appear in many different forms, and we utilise them to know how long work takes when a certain task needs to be executed. For example, for each milkshake you sell you need 2 minutes of a milkshake handler and 1 minute of a cashier.
One thing which pops up regularly when we work with customers is that we hear that they do not know what their labour standard is or should be. Then we are requested to help out setting the labour standards and there are several ways to do it, so let’s break them down.
There are different ways to define labour standards. Here are the most common approaches we follow ourselves or have seen customers following:
Let’s start with what the worst possible approach is: number 3. The third method will lead to a situation where current rosters with existing problems (like lots of over-rostering) will lead to a labour standard that will therefore uphold this bad practice. It also gives a false sense of security that the labour standard fits the rosters.
The other problem is that this approach will not take into consideration any productivity unless that is added as an additional percentage. To clarify, if the labour standard of making a milkshake is 2,5 minutes it would mean that if you expect to sell 10, you will need 25 minutes. However if you reverse engineer you might see that during one hour you sold 6 and another hour you sold 4. This would indicate that labour standard is 10 or 15 minutes, which is not true in both cases. This is because the unproductive time is already part of the calculation here, which you get no clear view on when you reverse engineer. In the original example we have 35 minutes of unproductive time, but that fact can be used in the rostering process to decide whether an hour of staff is required or not. The third approach is often taken because an organisation indicates they are not capable of providing a labour standard, but it’s not the best way to actually define it.
For organisations with more resources at hand, there’s always an option to go with approach 1. This approach is more time and money consuming, but you’re assured to get highly accurate labour standards defined specifically by your own business environment. Sometimes, however, organisations simply can’t or won’t wait or spend those resources. This is particularly the case when considering you could save more costs and lose less sales, and of course improve employee satisfaction and compliance, the sooner you start making optimised schedules. And that’s where approach 2 steps in.
Approach 2 is an iterative way to define labour standards by taking an existing labour standard and improving it as you make schedules with this standard. Let’s take a practical example: Say you processed 100 transactions on a day and you, based on experience, know that the average time per transaction is 10 minutes. Multiply this labour standard times the number of transactions to work out an expected number of labour hours. This could either lead to massive under or over rostering compared to actual rosters, which can then be used as a guidance to tweak the number up or down. When this is done, use an expected number of transactions for a new day or week to work out the required headcount and accompanying shifts. These results can be sanity checked by an expert to see if they are on the right track. This loop of improving the labour standard then goes on until the schedules using the latest version of the labour standard is up to, well, your own standards.
You might say that this sounds similar to approach number 3. There are similarities, but the big difference is that we start with an actual labour standard in approach 2. Reverse engineering based on rosters might sound like a good idea, but why not use the opportunity to work out the actual labour standard and avoid the under and overstaffing as shown in approach 3.
Option 3 is the typical approach because of time pressure or certainty to get results produced. It is always an option of course, but why risk it and take a bit more time to tackle the problem in a structured way. Approach 2 is the most accessible way to start using labour standards relatively quickly. Sometimes slow and steady does win the race and the business improvements end up being more realistic and better than trying to use a short term solution.
Labour standards play a crucial part in making sure a business knows how many minutes of work to schedule based on an expected demand. It is an important pillar of the roster next to having an accurate forecast. Labour standards allows you to automatically generate the optimal shifts with correct breaks and assigning them according to the preferences of staff. Don’t gamble on the labour standards, the turtle does win sometimes.
You offer a platform that integrates all the separate data points of your assets into a single, presentable UI. No longer do your clients need to separately monitor each of their assets, saving them a considerable amount of time. But what if there was a way to up the value of your IoT platform even more? That way is called AI. Read more.
Intelligent algorithms give a fresh perspective to supply chain players, allowing them to build on established rules to, run predictive analytics across scenarios, and facilitate decision-making in real time. Learn about some applications that enable logistics and distribution optimisation.
If you want to improve your machine's performance and output quality, Performance Analytics and Optimisation is a way to do that. You will not only achieve higher KPIs like units/hour and yield (%), but also get a better understanding of how your machines work and the ability to predict future performance. Read more.