The importance of KPIs
We live in a data-driven world where the range and volume of information is exponentially increasing year by year. From customers to operations to workforce, there is seemingly no end to sources of data, either. And consider that the proliferation of smart devices makes information more accessible to a lot more people, a lot more easily. But data alone is not useful, and can lead to information overload and burnout if not harnessed and interpreted correctly. In order to capture its full value, there needs to be solid guiding principles and techniques for transforming bytes of data into value-adding change.
Key performance indicators (KPI) can serve as that lens for navigating through the age of Big Data. The framework allows enterprises to set priorities, and then methodically implement ways to collect, track, and analyze information. It works as a channel for connecting metrics from the ground floor to corporate goals. The impetus for improvement can work both ways: unusual data patterns can trigger an action item for management, or often, strategic directives from higher-up can trickle down and incentivize tracking of certain data.
Important manufacturing KPIs such as overall equipment effectiveness (OEE) follow a very specific formula, where each criterion (availability, performance, quality) is then composed of various measures. This qualifies OEE as a compound metric, beyond a single dimension. This process can yield insights that connect the dots across multiple areas of an organization to give a more holistic view. With industry-standard KPIs like OEE, it also becomes possible to benchmark against best-practice competitors.
KPIs allow businesses to collect metrics that correspond to corporate priorities. Simply put, what gets measured gets done, and what gets rewarded gets done again.
Workforce analytics as the new frontier
As mentioned previously, most existing software in manufacturing such as ERP software is focused on equipment and other hard assets. But this presents a mathematical challenge. When calculating the effectiveness of machines, labor efficiency is not considered. This oversight can cause a discrepancy between expected output and real output, with no clear way of accounting for the difference. If this continues, business outlook will consistently be hampered by blind spots.
With labor, the reality is much more complicated. For example, fatigue is a common and well-documented phenomenon that can not only disrupt productivity, but also pose risks for the safety of everyone. In a more subtle manner, a sub-optimal delegation of shifts can stagnate productivity as well, such one employee being repeatedly scheduled for overtime instead of several employees sustaining a better balanced work pattern. In the language of lean manufacturing, these missed opportunities represent “wastes”.
Previously, these labor considerations were ignored due to the lack of a solution. When employees are being scheduled ad-hoc on pen and paper or on Excel, there was no easy way to either manage the workforce effectively or to capture relevant data. But the reason we are now able to characterize losses in labor productivity as “wastes” is precisely because there is a better alternative. With web-based scheduling software, there is a way to fix the primary pain of poor workforce schedules and in addition, gain an unprecedented access to a wealth of analytics.
While it was a previously neglected area, workforce analytics are gaining new momentum as a result of new advances in workforce management technology.
Workforce scheduling as the bedrock
So what is so special about workforce scheduling software? Surely, there are several components that make up a successful workforce management, and furthermore HR and ERP environments. But workforce scheduling is the first piece of the puzzle because it is where you start to do more with what you have. Even before discussion of hiring or firing employees, it is about finding and scheduling the best employee to work every shift, in every schedule you create. It is optimization in the truest sense, allocating resources to where they are most needed without causing disruption to workflows or requiring demographic changes.
Workforce scheduling software can accomplish this as a virtue of two things: how it is built and how it is delivered. While there are differences between software (read our Buyer’s Guide for a more in-depth discussion), customizability tends to allow for greater optimization. When a system is configured to a customer’s specific compliance needs and scheduling nuances (e.g. for assigning overtime), the accuracy potential for finding the best possible employee increases. The method of delivery, then, refers to the comparative advantages of using SaaS solutions versus on-premise software. The most cutting-edge features are being developed by SaaS vendors who emphasize frequent updates, scalability, and adaptability to changing needs. A good foundation, as explained above, serves as a good source of analytics.
In the big picture, having an optimized schedule already achieves the goal of increasing efficiency. In theory, it seems that a “perfect” schedule will yield a correspondingly perfect analytics report that shows all key metrics in balance. However, reality is rarely quite simple. Two scenarios that leave room for analytics to further investigate and optimize are first, a capacity problem and second, ad-hoc situations. The former refers to not having the right number of resources, thus requiring structural changes such as hiring and firing, or re-training employees for high-demand qualifications. The latter reflects the reality on the ground, where last-minute changes can occur anytime—someone calls in sick, equipment breaks down, and emergency situations may require additional staff. In this case, the goal is to recognize that buffer room is always needed and to allow for flexibility so that the repercussions are minimized.
Workforce scheduling provides a unique opportunity for channeling and sourcing key data about the workforce, while solving a critical business need.