Operations in warehouses or distribution centers have many moving elements — from tracking and optimizing inventories, training staff and scheduling their workloads, managing shipping and logistics, planning workloads, and monitoring the movement of goods to ensuring equipment and machineries are up and running. Each of these elements has their own underlying data and systems that often overlap with each other and, depending on the business’s size, tied to multiple warehouses.
A warehouse dashboard addresses the need to aggregate and convert all these intricacies into meaningful information. It provides an overview of metrics and key performance indicators (KPIs) that can be measured and tracked against business strategy and goals. This is what makes dashboards still one of the top business intelligence technologies and capabilities that enterprises adopt and invest in.
Not all dashboards are created equal, however. Having too little data is a red flag, but having too much can be confusing and makes it impossibly difficult to derive actionable insights. Building an effective warehouse KPI dashboard involves knowing who and what it’s for, understanding what metrics and KPIs matter most, and having the right technologies and tools to develop one.
A warehouse dashboard pulls data from single or multiple sources — depending on the business’s size and technological infrastructure — and transforms it into a series of visualizations that can be updated in real time and allow its users to make informed decisions.
While it’s tempting to just build from an already existing warehouse KPI template, how you develop the dashboard depends on who will use it and what it will be for. For starters, you can narrow down your dashboard into four basic categories: analytical, operational, strategic, and tactical.
These categories are based on the business needs you’re trying to address:
Analytical warehouse dashboard: Pulls vast amounts of data to identify patterns or trends and compare them with historical data and other variables to derive predictions, adjust priorities, or recalibrate targets. Analytical dashboards are useful for organizations when there’s complex, massive, and dispersed data that need to be aggregated and condensed into insights.
Operational warehouse dashboard: More useful for managers in tracking activities in a given area and time. It has a shorter time horizon and spurs more direct action, but it needs more detailed datasets to uncover issues in operational processes.
Strategic warehouse dashboard: Might need data on critical success factors related to a more long-term organizational strategy. It might have high-level overviews, but it provides data that has a significant impact throughout the business.
Tactical warehouse dashboard: A combination of strategic and analytical dashboards and is designed to accommodate large amounts of data that its users can drill down into. It contains data measured against preset goals but has the functionality to delve into details beyond metrics and KPIs.
Here’s a gist of the different kinds of dashboard that you can consider building:
The benefits of having a warehouse KPI dashboard are clear: improved sales enablement, faster and more accurate decision-making, and greater collaboration, to name a few. Building one, however, is more than just picking the right visuals. There are nuances behind data that, when overlooked, can mislead lead managers or executives into drawing the wrong conclusions. Here are some general rules of thumb and best practices:
Design with the users in mind.
A dashboard is only as effective as how it can clearly convey information. As with any communication, the first step is to define the dashboard’s audience. Depending on who will use it, the dashboard might be viewed on browsers, as a desktop app, or on mobile devices. It might also need functionalities that visualize different kinds of data, and you need to design your dashboard around these elements.
The dashboard should provide the right data to the right person at the right time. It should show enough information to enable warehouse managers, for example, to assess the performance and efficiency of the warehouse’s operations. Executives and decision-makers, on the other hand, might want to look at KPIs and how they contribute to overall business goals.
Define key warehouse KPI metrics.
Your dashboard’s usefulness also depends on the metrics and KPIs that you want to track. Warehouse operations can be a sea of data. Managers or decision-makers could easily drown in it and find it difficult to know where to start and what to track.
Different users with different business goals will look at different data points. When building dashboards, start by identifying metrics that are the most relevant to the user. Understand the problems that the user is trying to solve, and how the dashboard can help them make the best decision. Start by adding key KPIs, then iterate and enrich the dashboard through feedback from users. Make sure that the dashboard is future-proof: There are metrics and data that can be integrated through APIs, for example, and there might be situations where you must pull them from multiple back-end systems.
Differentiate through context.
Make sure that you understand the context with which the dashboard will be viewed. Identifying the audience was the first step and defining the dashboard’s key metrics is the second. Now, you must flesh out the visualizations and see how (and if) they will be meaningful to the users.
For example, executive stakeholders might want a streamlined warehouse KPI dashboard with clear and simple visuals that can be read at a glance. An analyst or warehouse manager, on the other hand, might want to dig deeper into certain data points, so the dashboard would require further customizations.
How you present data matters, too: How information is formatted, for example, has been studied to have an apparent effect in decision-making. Project managers and warehouse managers might find interactive Gantt charts useful to track the team’s performance and schedules, but executives might prefer a leaner but simpler bar chart to see if the KPIs are being met. Context defines the visualization that would make your data more compelling and easier to understand.
Design the dashboard to be actionable.
Having more information is good, but what decision-makers need are insights they can act on. An interesting study showed that more data points don’t necessarily translate to better or more accurate decisions. An effective reporting dashboard helps separate signal from noise and enables them to quickly see opportunities or issues to be addressed.
Involve the users in the development process so you would have a better understanding of the metrics and KPIs that are tied to their responsibilities. It’s also useful to present data that can be digested by different users with varying levels of expertise and data literacy. If people don’t understand what the metric tracks or what the KPIs mean, the dashboard would less likely inspire action.
Develop and iterate through feedback.
As you, your team, or executive stakeholders use the dashboard, there will be feedback and additional requirements that you’ll have to incorporate to improve its effectiveness and value. User perceptions and expectations frequently change, and keeping a constructive, open dialogue is important to ensuring that the dashboard’s functionalities are always aligned with the users’ objectives.
Building a dashboard isn’t a one-off task either. It’s a long-term, ever-running project that requires resources to use, and you need the right technologies and tools to maintain, manage, and update it. Consider if the dashboard needs to be future-proof so that you can ensure that its underlying components can adapt to new technological developments or business priorities down the road.
Don’t just create a dashboard.
Traditional dashboards are typically limited to describing current or past events. Point-and-click dashboards and visualizations are falling out favor with businesses because they cannot account for outliers and unprecedented variables, like the COVID-19 pandemic and volatile customer demands and expectations.
Moving from description to automation and prediction requires knowing where and how data was generated, its context, and its impact to the business. Assess your organization’s resources, capacity, and readiness, particularly if the company has multiple sources of complex and dispersed data. For example, you need to gauge if you have the back-end infrastructures that can withstand gigabits or terabits of data that are being exchanged simultaneously as more users engage with the dashboard.
A dynamic warehouse KPI dashboard that can predict, prescribe, and explain a course of action incorporates technologies like AI, machine learning (ML), and anomaly detection that elevate dashboards beyond predefined conversations.
Lingaro Group’s supply chain analytics practice provides data engineering and advanced analytics services for logistics, warehousing, and transportation that enable enterprises to drive cost efficiencies and enhance service levels and operational efficiency. Lingaro helps organizations use data to gain end-to-end visibility in their supply chain that, in turn, enables them to accurately forecast demand, enhance resilience and responsiveness, maximize inventories, optimize resource allocation, streamline routes, minimize waste, and generate savings.