Dashboards as an integral part of supply chain visibility

A logistics dashboard is the perfect playground for data exploration, as it lets visualize almost any kind of data depending on your needs, issues, and control points. For instance, say a logistics company faces frequent delivery delays. This issue definitely has hidden root causes. The company’s executives along with data analysts consider all the metrics that influence deliveries, including:

• warehouse operators’ performance
• average order fulfillment time
• average vehicle loading time
• drivers’ performance
• vehicle idling time

Then, data analysts retrieve data on these metrics from corporate databases to include them on the supply chain analytics dashboard. The dashboard, in turn, can highlight the things that impede timely deliveries. Thus, supply chain data visualization can be helpful in identifying the root causes of bottlenecks that decrease your company’s profits, lower your competitiveness, or increase customer churn. Dashboards can also help you measure your achievements, encouraging you to set ambitious new goals.

Supply chain visualization contributes to the supply chain control tower, which allows company executives to efficiently monitor all business processes and make timely decisions.

In this post, we discuss common types of logistics dashboards, analyze popular supply chain visibility tools, and help you prepare your company to develop efficient dashboards. But to make the most of logistics dashboards on your way to digital transformation, you should first ensure that your data analysis results in valuable information. Let’s have a look at a few methods that can help you define the value of your data.

The methods we discuss in this section help you answer two major questions:

• What data is valuable enough to visualize and analyze?
• What data can you discard?

Efficient evaluation of your organization’s datasets requires fruitful collaboration within and across various business levels. If only C-level executives evaluate your organization’s data, they can come to subjective conclusions and miss important insights. While C-levels can have their own understanding of the business and in some cases rely on gut feelings, it’s wise to justify any beliefs and feelings with reliable data and facts.

Douglas Laney, an advisor on data analytics strategies, has developed data evaluation methods that he discusses in his book Infonomics. Among the many methods he proposes, we’ve chosen the two we consider the most important for logistics data visualization.

Method 1. Business value of information (BVI)

This evaluation model allows you to determine which business processes can benefit from which data by assigning a relevance score to each data asset with respect to a particular business process.

The BVI formula includes the following variables:

• Relevance — indicates whether a certain dataset is useful for a business process (it’s a subjective measure from 0 to 4)
• Validity — the percentage of data records that have correct values and
• Completeness — the total number of data records in a particular dataset
• Timeliness — a score showing whether data is up-to-date and how quickly data is collected

Once you know which data is especially important for each business process, you’ll be able to visualize and analyze only those datasets that bring the best outcomes. This can also help you prioritize your business processes to prevent you from randomly rushing into the analysis of each and every one.

Method 2. Performance value of information (PVI) 

Another method shows how data assets impact key performance indicators (KPIs). Douglas Laney suggests that this method answers the question: How much does having this information improve business performance? The PVI approach is preferred for measuring business benefits from certain business metrics.

The PVI formula contains the following variables:

KPIi = business processes functioning with information (informed group)
KPIc = business processes functioning without information (control group)
T = average lifespan of the data instance
t = duration of the KPI measurement

A positive PVI means that a dataset is valuable for a certain business process, whereas a negative PVI indicates that the dataset is not valuable for the process.

Performance value of information can help you match the right datasets with the right business metrics to derive the most value for your business.

These two foundational data evaluation techniques tell you which data is relevant to your core business processes and performance metrics. Thus, you’ll know which data would be beneficial to include on your dashboards so they generate the most insightful information. Next, let’s discuss different types of logistics dashboards.

In this section, we take a look at three types of logistics dashboards, each corresponding to a particular level of supply chain management: strategic, tactical, and operational. You can also explore in detail which supply chain technologies to choose for different levels of management. When employees at all levels are on the same page, the company has a greater chance of achieving that alluring supply chain visibility everyone is aspiring for.

Of course, it’s not only horizontal supply chain management levels that influence your choice of logistics dashboard; you should also consider the vertical organizational levels. A logistics manager at the tactical level from the transportation department measures different KPIs than a manager at the same level but from the order fulfillment department. Further, we give examples of supply chain dashboards taking into account a horizontal and vertical organizational structure.

Read also: SaaS transportation management system

Level 1: Strategic

Strategic dashboards are built for the organization’s top management. C-suite executives need a helicopter view of the business to measure KPIs based on accurate and relevant data.

Dashboards help executives make long-term decisions (e.g. how to improve company services to meet volatile market demands, how to decrease operating costs and increase revenue) and analyze business performance on a large scale.

Strategic dashboards must be based on business needs and goals, as they set the agenda for lower types of dashboards. For example, Canadian natural health products distributor Purity Life experienced a $500,000 drop in inventory reserves thanks to strategic dashboards that highlighted important inventory management issues which, in turn, prompted efficient actions at lower levels. Another example is from one of our clients, a US 3PL company. As part of our big data analytics solution, we implemented a scalable business intelligence solution for data visualization to help company executives make more informed decisions, predict stock planning, optimize route and load planning.

Strategic dashboards will be different for different C-level executives. CEOs, CTOs, CFOs, CIOs, and CMOs each need a different supply chain metrics dashboard, and it’s impossible to build a custom dashboard that satisfies them all. Some of their needs and interests, of course, intersect, but many KPIs are different. Let’s have a look at a dashboard that could interest a supply chain COO.

Level 2: Tactical

Mid-management teams work with tactical dashboards that show monthly, quarterly, and annual business performance. With the help of tactical dashboards, middle managers can set mid-term company goals which, roughly speaking, are steps towards meeting full-scale strategic goals. Such dashboards can also reflect whether a business is actually moving towards strategic goals, as C-level executives can sometimes set goals that are too ambitious or unrealistic.

Mid-level managers may want to see, for example, a carrier’s performance over a month to decide whether it’s worth continuing cooperation. Wilko, a UK retailer of household goods, decided to build full-fledged carrier software to manage multiple carriers and efficiently track their performance. This solution allowed Wilko to achieve cost savings of around £250,000 during the first year after implementation.

Let’s imagine how a tactical dashboard for tracking monthly carrier performance can look:

Level 3: Operational

The third dashboard type is operational. Such dashboards help logistics managers monitor day-to-day supply chain operations like order fulfillment, production, load planning and space calculations, and delivery to make timely changes to the workflow when necessary. These dashboards facilitate even smaller steps towards meeting general strategic goals.

Operational dashboards help logistics managers tackle troublesome logistics areas in a timely manner before they impact higher levels. For instance, they can do this by ensuring preventive maintenance for equipment with decreasing performance metrics. Swedish seal and bearing manufacturer SKF has long been using data visualization techniques to monitor machine efficiency. This allows the company to grow year over year. SKF even offers its own technical solutions to help other manufacturing companies maintain a high level of machinery performance.

Let’s have a look at an example of an operational manufacturing dashboard.

It’s essential for a company to develop all three types of logistics dashboards, as visibility at the lower operational and tactical levels helps the company to achieve strategic long-term goals. There is also a certain domino effect, as if anything goes wrong on the operational level this may affect higher levels. And vice versa: vague and unrealistic goals at the strategic level will make dashboards useless and lead to chaotic management at the tactical and operational levels. Strategic logistics dashboards can have a drop-down feature that allows for clicking on a particular indicator or figure to see how it was formed (e.g. to trace transaction chains) at lower levels.

Let’s now find out how valuable data visualization is in the context of the control tower framework for supply chain management levels.

Read also: Our expertise in developing data science solutions

How a control tower impacts each level of the supply chain

The consulting firm Kearney provides an overview of a supply chain visibility control tower and discusses how valuable it is for each supply chain management level in the illustration below. Kearney analysts have come to the following conclusion: it’s the strategic level that defines whether a control tower and, as a consequence, data visualization will bring value to other supply chain management levels. Thus, it’s beneficial to start with elaborate strategic dashboards first and then move to develop dashboards for lower levels to analyze the necessary supply chain control tower metrics.

Now we come to the practical part of our article: analysis of common business intelligence tools.

Next, we’ll compare common BI tools that help us build different data dashboards. There are many BI tools on the market, and you can consider your goals and needs to choose the most appropriate one. An important reminder: to implement any BI tool in your organization, you’ll need a trustworthy team of specialists to configure everything correctly and build the desired dashboards the way you want.

We’ll take a look at a few common tools and offer you an easy-to-follow comparison table. You can use it as a guide to making your own decision, as we aren’t trying to convince you to choose one option over another. Also, make time to read our article on the difference between BI and advanced analytics solutions. That article addresses many supply chain use cases for both business intelligence and advanced analytics.

Tableau

Main characteristics:

• Vast data science and data analytics opportunities thanks to a wide range of Tableau products
• Industry-specific solutions for retail, healthcare, manufacturing, banking, and insurance
• Choice of a comfortable environment: on-premises, cloud, or hybrid
• Own ETL tool, Tableau Prep, to extract and transform data directly from any database or even spreadsheets
• Mobile access to Tableau Mobile to track crucial KPIs on the go
• Vast integration capabilities with data warehousing tools like Amazon Redshift or data lakehouse solutions like Databricks
• Quarterly software updates

Power BI

Main characteristics:

• Open and clear pricing policy, splitting all products into two packages: Power BI Pro and Power BI Premium
• Power BI Premium allows you to go beyond BI dashboards and work with AI-driven analytics
• Data modeling tools available before loading data
• Intuitive and familiar software navigation for Microsoft Office users
• Simple integration with Microsoft Azure solutions and more difficult integration with other solutions compared to Tableau
• Monthly software updates

Qlik Sense

Main characteristics:

• Data warehouse and data lake formation tools
• Integration with a wide range of services, databases, and data sources
• Historical and advanced analytical processing available in the cloud or on-premises
• Interactive dashboards that users can modify in real time
• Alert system to get timely notifications about any data changes
• Analytics based on the associative engine, which goes beyond SQL queries and quickly provides associations between datasets
• Full SaaS deployment option for enterprises

Read also: How we implemented a data lake for a manufacturing company

Amazon QuickSight

Main characteristics:

• Cloud implementation with a pay-per-session plan
• Native and easy integration with other AWS services
• Serverless architecture with robust auto-scaling possibilities
• SPICE (super-fast, parallel, in-memory calculation engine) allows a large number of users to simultaneously access analytics results
• Requires a separate ETL tool, AWS Glue
• QuickSight pre-built ML algorithms enable predictive analytics with little additional effort

ThoughtSpot

Main characteristics:

• Open cloud analytics platform with flexible APIs for developers
• Simple integration with modern data warehouse solutions
• Developer-friendly environment that gives lots of room for customization
• Subscription-based alert system for employees
• Built-in presentation mode to introduce insights right within the application
• Data modeling and filtering techniques

All of the above tools have many features in common, so it might be difficult to make the right choice. We suggest you consider the other services and tools you use at your organization. For instance, if you’ve recently deployed an enterprise data warehouse on Amazon Redshift, you can automatically connect all of your data to Amazon QuickSight by simply approving it in your account settings.

If Microsoft Azure services are more common at your company, you can consider Power BI. If you don’t depend that much on any particular vendor, you can consider Tableau, Qlik Sense, or ThoughtSpot. And, of course, you should compare each vendor’s prices to ensure you get a balanced and cost-effective solution.

The list of BI tools we’ve discussed in this section isn’t exhaustive. To navigate all the choices, make a list of priorities and non-negotiables.

You can build an efficient supply chain performance dashboard only if you thoroughly prepare. Below are things to consider before implementing data visualization technologies in your organization.

• Robust data fabric. Under the hood, you’ll need to ensure a modern data stack responsible for data ingestion and data storage. The optimal solution could be implementing a data repository as a single source of truth for all your corporate data. Check out our extensive guide on data repositories to ensure you implement the right one. In case you’ve already made up your mind in favor of building an enterprise data warehouse, you can read another blog post to find out what you need for this process to go smoothly. Once you’ve consolidated the data necessary for analysis in one place, providing data analysts with a single point of entry, you can move to implement business intelligence.
• BI implementation. Another important step is thinking through how to integrate business intelligence into your logistics workflow, taking into account all of your business needs, requirements, and goals. You can use the following schema to make sure you don’t miss anything. It contains seven important steps you should take before launching your first data visualization projects.

• Training on data literacy.  All of your attempts to derive value from BI tools will fail if you don’t ensure your employees understand how to manage, share, and treat your corporate datasets. It’s also important to keep up with modern data trends to become a leading supply chain company. Additionally, you should evaluate to what extent your employees are flexible in adopting new ideas and changes. In our article on how to prepare for building a digital transformation roadmap for a supply chain company, we offer clear steps to change the mindset in your organization.

Only with a balanced blend of the right vision, data technologies, and people can you begin developing insightful logistics dashboards. But what can possibly await us in the future? Let’s elaborate.

Read also: Our software reengineering and support expertise

The last section of this article we devote to forecasts. Gartner has pulled together data analytics predictions up to 2025. We would like to share a few of them with relevance to our topic:

• By 2023, it will be crucial to involve cloud architects when purchasing analytics and BI software.
• By 2025, 80 percent of organizations that plan to scale their digitization initiatives will fail unless they establish a modern data and analytics environment.
• By 2023, 30 percent of organizations will foster entire data analytics communities to use collective efforts for generating business insights and value.

We’ve also done our own research to find a few more insights on the future of logistics dashboards:

• Building dashboards will become a common practice, but only for specific requests, without propagating redundant dashboards that only confuse rather than inform.
• In the nearest future, the lines between business intelligence and advanced analytics will be almost completely blurred, mostly thanks to the integration of AI/ML capabilities in BI reports and dashboard generation.
• Big data analytics will go hand in hand with BI dashboards to provide even more business insights and open new horizons of supply chain visibility.

To begin with data visualization today, managers along with their data teams should:

• specify which issues in the organization hinder proper business performance or which areas need significant improvement
• define which data and data sources are valuable and relevant for analysis
• build an efficient data fabric to ensure secure data storage and easy data retrieval
• think through how to implement BI reporting and dashboards in the organization’s workflow
• make a clear request for building data dashboards depending on the manager’s position in the horizontal and vertical organizational structure

These five steps seem straightforward, but each requires hours of diligent work. At Yalantis, we strive to make the process of data visualization as seamless as possible for our clients no matter how difficult each client’s case may seem at first sight. We’re open to tackling any of your logistics data challenges and helping you reap the benefits of building informative logistics dashboards.