Dealing with the sheer volume of orders moving through your warehouse might require new hires, new equipment, new processes, new vendors, or some combination of the four. But how do you justify those new expenses in the warehouse budget? And how can you get the CFO on board?
Sometimes, dealing with the sheer volume of orders moving through your warehouse means investing in additional staff and equipment. But convincing those who make the decisions (and hold the purse strings) can be a challenge. How does one go about justifying additions to the warehouse budget?
An experienced manager might have a gut feeling about what’s needed to make things more efficient, but hard data is what’s going to sell it to the CFO. Efficiency, by itself, is an abstract concept, but it can be precisely measured using a number of different analytics. And when real numbers are backing up budget decisions, you can eliminate trial and error solutions or knee-jerk reactions to crises. Data analysis can also help managers look beyond their own blind spots and preconceptions by providing credible predictions instead of “what ifs”.
Witnessing day-to-day operations, a good manager will have ideas about where a company can do better. But no matter how well-regarded he or she may be as an employee, the CFO likely won’t simply take their word for it. There are budgets to maintain and higher-ups to sign off for big-ticket purchases or major procedural changes.
The CFO will want to know that managers have first done everything in their power for efficiency. Fixing existing problems and improving on what’s already in place needs to happen before a big outlay of cash. Data analytics can show exactly where efficiency is lacking. If it’s already maxed out, (kudos to you!) it will show that, too. Either way, it can provide the proof to the decision-makers when it’s time to hire, invest in equipment, or change directions.
Tracking data can show progress in four distinct ways:
When these metrics are reached or plateau, it can serve as either proof that a) the goals were achieved without the requested purchase and it wasn’t necessary after all, or b) the requested purchase is needed if the company wants to do even better. And let’s face it, what CFO is going to say no to increasing productivity, which in turn will increase profits?
As a first step, managers should run a warehouse health check, which we’ve covered previously. Tracking a strategic list of KPIs will help pinpoint and diagnose pain points that are standing in the way of productivity.
KPIs, or key performance indicators are just that—keys to unlocking the secret of how to make a warehouse more efficient. Warehouse management software can measure hundreds of different data points, offering a variety of metrics that show what’s going well and what isn’t. Managers can choose the ratios and benchmarks that mean the most for their business, track them, then work to improve them.
Machine learning software (such as Infoplus Insights) can come in handy as well. By looking for patterns in order data, machine learning software can group orders and determine the most efficient way to pick orders (for example) on any given day. Once it’s clear that processes are efficient, it’s easier to make the case that additional resources are needed.
It’s a common question in warehouses: Can your people work harder, or do you need more people? Some managers will always see adding to the crew as the answer to increase productivity, but without data to support the need for more people, the company will resist spending on another salary.
Tracking employees’ time on task and accuracy using barcode scanning can uncover ways to boost performance without adding staff. KPIs such as picking accuracy and cost per line item picked can help evaluate each worker’s efficiency. The answer to better performance may be something as simple as re-assigning tasks to better align with specific employee skills. Implementing a way to triage orders and rearranging work schedules can also improve workflow. For example, if quitting time means items don’t get shipped until the following day, perhaps staggered work shifts can help.
Once everyone is doing the job they are best at and eliminating all unnecessary steps, managers can be sure that they’ve done all they can for efficiency. They can then confidently request additional staff in order to scale or cut costs further.
Asking for capital investment in a big piece of machinery is a hard sell for managers, but sometimes, a warehouse needs to replace or add to their equipment. One way to convince higher-ups of the need is to track a machine’s out-of-service frequency. If production needs to stop often for repairs, the company could be losing money—especially if they’re paying for overtime to catch up once the equipment is up and running again.
Even if a new piece of equipment isn’t in the budget, analytics can help managers set up a preventive maintenance schedule when it will be least disruptive. This will cut down on downtime and possibly save money on repairs. It can also show ways to allocate equipment in the best possible way, for example, using an idle machine for a different purpose.
Managers must remember that more equipment won’t help if other aspects of the warehouse aren’t running efficiently and making the best use of automation and integration. Only by looking at the data will you determine if you really need another manual conveyor, for example, or if the money is better spent in automating the one you have.
Companies often remain loyal to a specific vendor or shipping partner over the years. They may fail to consider the supply chain’s responsibility for their own efficiency issues. KPIs for vendor management can supply the data that can indicate when it’s time to make a change.
Poor performance from vendors can have a domino effect throughout the businesses they service. Their inability to fulfill orders accurately and on time will hamper you meeting your order fulfillment benchmarks. Substandard products will affect your own quality control and rate of returns. Tracking all of these factors, along with pricing and terms information can give a clear picture of which companies earn a place on the supply chain and which should be let go.
Upper management may want to stick with the status quo for any number of reasons. Data can help a manager convince them when a change is in order for the sake of efficiency.
Our series Warehouse Set-Up 101 provides explicit instructions on how to optimize the layout of small, medium, and large warehouses for efficiency. The CFO may not be concerned with how a manager wants to organize the warehouse. But he or she will need convincing if the redesign plan includes expenses like slotting software or a rack labeling system to support a new, more efficient storage method.
Tracking KPIs in the warehouse can reveal the need for new storage options. To use an extreme example, if lightbulbs were stored in the same bin as bowling balls, breakage stats are sure to rise. New, separate storage containers would be a wise purchase. Monitoring space availability can improve inventory management by preventing over- or under-stocking, and make a case for auto-replenishment software.
Keeping tabs on all data points can illustrate where layout changes will help efficiency. Once those have been addressed, more expansive changes can be considered, such as adding another loading dock or even moving to a larger space. And those decisions can be backed up by data too.
A big part of efficient warehouse operations is the picking process. Fulfilling orders in a haphazard can have pickers taking more time than necessary to pick the items on their list. WMS software can run scenarios using current picking data to determine the best picking method. Looking at the models, managers may choose to use wave, batch, or zone picking, and have the software map out the most efficient pick paths.
Software that incorporates machine learning/AI can also select the best picking method. This type of technology (Infoplus Insights is a good example) looks for patterns and groups similar items together.
Analytics gathered from barcode scanning can also determine other operational decisions. For example, doing a quality control check as items are packed for shipping might be slowing things down. Plus, sending the order back to be corrected is happening late in the process—when it’s about ready to leave the building. It might make more sense to incorporate a quality control check at the time the item is picked from the shelf instead.
Operations data will show the nuts-and-bolts processes in every phase of order fulfillment. Managers can investigate and fix each problem area and then move on to the next. Sharing these stats with the CFO can make the case for tweaks throughout the warehouse. Perhaps more importantly, a manager’s use of data-driven decisions to improve efficiency wherever they can, demonstrates the credibility and proven good judgement of the manager him or herself.
Using data and analytics is the smart, strategic way to optimize efficiency. Without it, managers and CFOs are flying blind, relying on guesses and trial and error. These seven steps can launch data-driven efficiency:
The irony isn’t lost on us: To do all of this, managers might have to convince the CFO to invest in a robust WMS in the first place. But the knowledge that future decisions can be supported by facts and hard data is a strong incentive they’ll find hard to pass up.