Pepitone notes that his firm has collected data from multiple clients regarding the potential of AI for predictive maintenance. According to the firm’s analysis, companies employing AI to predict when devices will fail see an average savings of 25% to 40% on maintenance, as well as 35% to 45% on unplanned downtime.
While those numbers are partly due to the high overhead costs of highly manufacturing-centric organizations, they can apply to IT as well. Smart locker systems, for example, are increasingly popular as a flexible, automated solution for secure storage.
From the perspective of someone outside the locker itself, this seems simple enough for a person who accesses a specific compartment, logs in using a badge or other ID, and then closes up the locker door when they are finished. But to the IT professionals charged with managing these automated assets, each of those actions turns into a stream of data.
Operations managers have an easy job collecting the data, but a hard job with the analysis. This guide outlines how you can use smart locker analytics data to turn a digital padlock into a powerful strategic tool to maximise usage, forecast when maintenance is required and measure return on investment (ROI).
Setting the right metrics is the first step to good management. Raw transaction logs can be too much to handle. KPIs turn that data into clear signs of how well the system is working and how healthy it is.
First evaluate the overall locker network utilization rate (as a percentage of occupied lockers). This will help you determine if lockers have too much capacity (in excess of 40% unused) or are not fully utilized (below 40%). Additionally, certain lockers or locations may be approaching 100% capacity and could be impeding key business processes.
The number of check-ins and check-outs is what transaction volume keeps track of. Operations teams can figure out how much demand there is for the system by looking at this volume. A sudden rise in volume could be linked to a new hardware rollout or a change in remote work policies, which would mean that inventory levels need to be changed.
When planning for capacity and supporting staff, it’s helpful to understand peak usage times. The analytics for past IT peripheral requests by building shows that 70% of all requests for peripherals come in on Mondays between 8:00 AM and 9:30 AM. Since we are planning to restock the lockers on Friday afternoon, we need to make sure we stock enough that week so that we don’t run out by Monday morning.
This metric shows how long it takes for a user to get an asset after it has been deposited or a request has been approved. Long retrieval times for high-value assets mean that the workflow isn’t working well or that users aren’t in a hurry, which ties up capital in inventory that isn’t being used.
The turnover rate shows how often certain items, like loaner laptops or specialized tools, go through the locker system. High turnover means that there is a lot of demand and that shared resources are being used well. On the other hand, low turnover means that some assets are either not needed or are being hoarded by users.
Historical information is important to a retailer to plan on a daily, weekly, and monthly basis. However, even more important is real-time information, so those running the day-to-day operation of the store can see exactly what is going on in the business right now.
SignifiVISION™, a cloud-based solution, provides a graphical interface to provide management with real-time information by monitoring traffic flow, analyzing data from various sensors, and viewing multiple real-time information streams at once. Signifi’s real-time dashboards turn hours of raw sensor data into a living image of how your business is doing.
A live transaction log allows Security and IT administrators to monitor live transactions for system health verification. For example, if a user reports they cannot access a specific compartment, the administrator can query the live transaction log to determine the cause of the failure (e.g., incorrect PIN, expired credential, hardware failure, etc.) to quickly resolve the user’s issue.
Our service also provides real-time tracking of inventory located in facilities with lockers, such as headsets or keyboards. Our easy-to-use online interface provides real-time, up-to-the-minute information on the number of items of a certain type that are in stock. Helping you avoid running out of an item just in time, saving you time and increasing your productivity.
Smart lockers are complicated systems that use both electricity and mechanics. The dashboard keeps an eye on the health of the internal parts, such as the status of the network connection, the stability of the power supply, and the working condition of each electronic lock.
When used with automated alerts, real-time monitoring works best. You can set up the system to send email or ITSM notifications for important events, like a door left open, a network going down, or inventory falling below a certain level.
Our pre-built dashboards provide real-time information to aid your day-to-day activities and our Historical Reports provide trends over time to help anticipate future activity and make informed decisions.
Scheduled reports give you a regular way to review how things are going. Managers can find baseline usage levels and spot changes that need to be looked into by comparing the number of transactions that happen every day, week, and month.
While studying end-user behavior may uncover power users or individuals who could benefit from extra training, there is also an organizational benefit that can surface. This could extend to upgrading the permanent hardware in a department, based on the number of loans that department procures. The data would provide insight into the number of laptops needed to adequately support that department’s operations.
Many businesses see predictable changes in asset demand during certain times of the year. For example, they might hire a lot of new people in the summer or get a lot of equipment back at the end of a financial quarter. Historical reporting lets operations teams plan for these changes and change the layout of lockers as needed.
Long-term strategic planning needs year-over-year data. It shows how users are adopting the system and helps justify budget requests for system expansion or hardware upgrades based on proven, ongoing demand.
The main reason businesses buy smart lockers is to cut down on the loss of assets. Analytics give you the detailed tracking you need to hold people accountable.
The system keeps track of every asset’s movement through the locker network in full. If a valuable device goes missing, administrators can find out exactly where it went by looking at the last compartment it was in and the person who got it back.
Immutable audit trails take the place of sign-out sheets that are written by hand. The time and identity of the authenticated user are recorded for every transaction. This level of responsibility cuts down on the “shrinkage” that comes with shared equipment pools by a lot.
Companies can keep track of an asset’s whole life cycle by combining locker analytics with an ITAM database. The information shows how often a device is used, how often it needs repairs, and when it is no longer useful, which helps make decisions about future purchases.
When an asset is reported missing or damaged, the analytics platform gives you the paperwork you need for internal investigations or insurance claims. The system finds out who was responsible for the asset at the time of the damage.
The most advanced smart locker platforms use machine learning to go beyond just reporting what happened and start predicting what will happen next.
Historical usage data, along with knowledge of seasonal fluctuations and hiring forecasts, allows predictive models to determine future usage of an organization’s IT assets. As a result, IT professionals shift from a reactive model of constantly replenishing inventory to meet one day’s printer supplies needs, to a more proactive model of meeting future needs on a multi-week basis.
We monitor a number of key electromechanical parameters that can indicate hardware failures, such as increased solenoid drive current, extended lock open time for a solenoid lock, etc. These same parameters are used to generate predictive maintenance alerts during routine hours so the failed unit can be swapped out prior to a lockout event.
The locker network needs to grow along with the organization. Predictive analytics can tell when a certain location is getting close to its maximum capacity. This information can be used to justify adding more locker banks before the user experience gets worse.
Machine learning algorithms can spot small changes in how users act that point to bigger changes in how the system works. For instance, if people start using shared desktop peripherals less and less, it could mean that they will permanently switch to mobile-first work styles. This would give IT time to change how they buy things.
Even the most well-crafted report will never be 100% perfect. For this reason, having a strong analytics platform that offers users the flexibility to view and share on data in a format that is useful to them is key. For administrators, this means being able to generate custom reports that include the exact set of KPIs they need to see, filtered down to include data for a subset of users or organized by region.
It’s great that the raw data can be exported to add the locker analytics to other Business Intelligence (BI) solutions like Tableau or Power BI. It’s also available through an API for the extra advanced user.
Analytics give you the hard numbers you need to show that the smart locker investment was worth it.
Operations managers can turn operational metrics into financial savings by putting numbers on the decrease in lost assets, the decrease in IT service desk ticket volume, and the hours saved by automated distribution. This data-driven method turns the smart locker from a cost center into a proven way to improve efficiency.
The main goal of smart locker analytics is to keep getting better. Data should make things happen. If analytics show that large compartments are always empty and small compartments are always full, the locker bank’s physical layout should be changed.
If retrieval times are long, you should change how you talk to users. Organizations can keep improving their operations by treating the locker network as a dynamic, data-generating system. This way, the technology will always give them the most value.
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