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How is logistics analytics driving business outcomes and growth?

Transportation and logistics companies generate and consume more data than almost every other industry. Despite this, they still find themselves lagging behind other B2B verticals in their ability to turn a profit from data.

With thinning profit margins and new contenders entering the logistics industry, the only way to outperform other companies is through brain, not brawn.

Logistics analytics offers the edge over the competition. Advanced data analytics doesn't just promise increased ROIs, but also gives logistics companies a way to streamline their business to cut costs, reach more customers, and discover new revenue streams to grow.

In this article, we’ll take a look at three areas of logistics analytics and how they help to drive business growth:

  1. The transformative potential of advanced analytics
  2. The challenges that need to be overcome in order to use these analytics
  3. The solutions to speed up analytic adoption within logistics firms

Curious about how Keboola can help you set up logistics analytics for success? Let’s jump on a call.

1. The transformative potential of advanced analytics

Advanced analytics has been shown to improve multiple facets of the logistics industry.

PERFORMANCE ANALYTICS

Peter Drucker, the founder of modern business management, famously said:

“If you can’t measure it, you can’t improve it.”

Logistics companies that strive to improve their business operations need to keep their finger on the pulse of their performance. 

Performance analytics is conceptually simple. The logistics company sets up metrics that measure KPIs (key performance indicators), which tell you how different aspects of the business are doing.

But the majority are still struggling to understand the basic unit economics of their operations. 

Who’s the culprit?

Working with multiple distributors within the supply chain raises issues of reporting variability and inconsistencies, making accurate data collection hard to achieve. With late-shipping and docking fees, freight prices vary dynamically. This makes it challenging to keep track of the cost of actual cargo or parcels to the business and whether there was a profit or loss on it. Seasonality effects also mean that it’s hard to do rota planning, i.e. how many people are needed on their shifts in the fulfillment center to satisfy demand when orders increase, while lowering idle time when there are no orders.

But we need those insights to improve business performance and remove troublesome bottlenecks that often spread inefficiencies throughout the system. 

A late delivery in the first mile acts as a domino effect to the last mile, and unless you have performance analytics set up to track those metrics, it’s hard to know which parts of your business need immediate attention to stop draining money.

For the majority of T&L companies, having accurate insights into their business operations would already put them ahead of the competition.

CUSTOMER ANALYTICS

Customer-facing analytics allow logistics companies to improve their user experience and delight their customers, thus increasing retention. Avoiding customer churn always helps the bottom line.

Sometimes, it’s as simple as increasing visibility through freight tracking. Set up sensors or GPS telemetry to allow customers to track the shipping and location of their packages and cargo, sending real-time data to their devices. 

But customer data analytics can also be more advanced. 

By joining data across CRMs, sales, and financials, logistics companies create customer profiles which are shared with sales and support teams. 

Customer profiles inform the sales and support operatives of each customer’s preferences. This helps them to personalize their sales pitches and support responses to better serve the individual, which in turn helps them to resolve tickets faster and hit those sales targets.

PREDICTIVE ANALYTICS

With the rise of big data analytics and machine learning, the logistics industry has tapped into the potential of predictive analytics. 

Predictive analytics looks at historical data (such as order ledgers, supply connections, pricing changes, etc.) and uses that data to build predictive models to forecast future trends.

The logistics industry is ripe with examples of how predictive analytics has given companies a massive advantage:

  1. Just-in-time inventory optimization. Lacking inventory when you receive an order leads to delays in order fulfillment. Sometimes, the inability to fulfill the order can even result in a lost business opportunity. On the other hand, having and managing a surplus of inventory means higher warehousing costs and pricey inventory operations. Just-in-time inventory optimization looks at past order trends to inform supply chain management of when a surge or dip in demand is likely to happen. They can then manage their inventory accordingly.
  2. Anticipatory shipping. Amazon uses past order behavior to anticipate when a customer will order an item before they actually do. How does this help them? They deliver the forecasted items to a fulfillment center closer to the customer, so that the item is nearer to the delivery address when the order is placed. This massively reduces delivery times and brings customers joy at the speed of their delivery.
  3. Route optimization. Route optimization algorithms look at the historical delivery routes, personnel schedules, fleet management data, and road maintenance to anticipate delays and suggest better, faster routes for deliveries. UPS uses ORION, a special algorithm to help them better plan their delivery routes. For UPS, using route optimization to eliminate one mile, per driver, per day over one year translated to savings of up to $50 million
  4. Predictive maintenance. Using sensors and the Internet of Things (IoT), logistics companies track the performance of their machinery and detect anomalies before they happen. Forecasts of when a machine will fail - before it actually does - give companies the chance to order spare parts in advance, replace the machine with a novel model, or pre-emptively service the machine. Predictive maintenance allows business continuity even in the face of rare events, such as malfunctioning machines. 

2. The challenges of implementing logistics analytics

Unlike other industries, the logistics industry does not suffer from a lack of data. They produce and consume some of the highest volumes of data assets across all B2B verticals. 

The challenges to logistics analytics lie elsewhere:

  1. Dispersed data. Data is scattered across many different systems. From CRMs, sales orders, and financial reports, to distributor pricing lists, warehousing reports, and more - there’s simply no consistent centralized storage where all of the data is available to the customer. Before any analytics can take place, data needs to be extracted from the various sources and connected between all of the varying systems. This can be labor-intensive and timely, which can delay time to insights and cause opportunity losses.
  2. Variable data quality. Sometimes, data is organized neatly in a relational database. Other times, the supply chain partners send screenshots of their reports. Variable data quality means additional labor times to clarify, validate, and clean data before it can be analyzed.
  3. Lack of integrated analytics. Logistics companies build reports as ad-hoc one-time analyses. Instead, they should be developing integrated systems, which streamline the process from data collection, cleaning, and analysis to deliver a constant stream of information to track performance.
  4. No breakdowns by business interest. Analytics reporting doesn’t support breakdowns into interesting business questions. For example, your company can tell whether you’ve made a profit or loss, but you’re unable to dig deeper and discover which business units performed better or worse, how the different countries of operations did, or - to go into even more detail - how profitable each parcel was. The challenge is not just collecting, cleaning, and analyzing data, but also building a data model that allows custom breakdowns by business interest.
  5. Underutilizing algorithmic advantages. From rota planning to predictive shipping, algorithmic advantages need two things: a stable data model and either in-house knowledge or outside tooling. These enable you to tap into the resources that modern predictive analytics has to offer.

3. How to get started with logistics analytics

So, what’s the solution to the challenges above? 

The simple answer is to collect, clean, integrate, and analyze data. Model the data to allow for breakdowns, and make data available to machine learning.

But how do you do it?

There are two possible ways:

  1. Either you build the entire engineering and analytic data process in-house, or
  2. You tap into software tools that build the process for you.

Keboola is a data platform that offers logistics companies end-to-end logistics analytics:

  1. Connect all of your varying sources of data with a couple of clicks. From CRMs and financial software to manual CSV file uploads, Keboola acts as a universal integrator for all incoming data.
  2. Clean your data and automate the process. Keboola offers easy-to-use tools to clean your data, as well as ones to automate the process. This means that you can set-it-and-forget-it. Once you establish the procedures necessary to output clean and validated data, you can let the machines repeat the process - no more manual cleaning every time.
  3. Model your data according to your business. By integrating into multiple databases, data lakes, and even just uploading your data to the destination of your choice, Keboola allows you to create the data model which best serves your company’s needs.
  4. Integrate your analytics with data visualization tools. Keboola connects to multiple dashboards and data visualization tools, such as Looker, Tableau, and others. This allows you to set up automated performance analytic tracking.
  5. Keboola comes with a data science suite of tools. From sandboxes to machine learning libraries, you can prototype and build your machine learning models within Keboola to unleash the potential of predictive analytics.

And that’s just the tip of the iceberg. Keboola is built with state-of-the-art scalability options, so it grows with your business. It also fortifies your operations with best-in-class security and allows collaboration across departments.

Curious about how Keboola can help you set up logistics analytics for success? Let’s jump on a call.


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