2024 Guide: Leverage Logistics Data Analytics to Reduce Freight Costs and Improve Supply Chain Efficiency

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Supply chain and logistics professionals today face immense pressure to simultaneously reduce costs, improve customer service levels, and innovate for the future.

Logistics data analytics empowers logistics teams to optimize carrier bid processes through data-driven volume and lane specificity. Further savings can be achieved by leveraging analytics to scan and audit large volumes of freight invoices to detect billing errors and anomalies to address.

Additionally, by leveraging logistics data analytics to assess carrier contract performance, shippers can determine optimal transportation network mixes balanced for cost and service.

While adoption barriers exist, the potential benefits of reduced freight spend and continuous supply chain improvements necessitate embracing data analytics. This guides provides an introduction to the benefits and capabilities of logistics data analytics when leveraged effectively as well as a step by step guide for getting started.

The Growing Role of Data Analytics in Supply Chain Visibility and Cost Reduction

Over the past decade, data analytics has become an invaluable tool for supply chain and logistics managers seeking to optimize complex distribution networks.

By collecting and analyzing information across the supply chain, companies can identify opportunities to streamline operations, reduce expenses, and improve services.

Data Analytics Enables Enhanced Visibility and Control

With data analytics, logistics managers can gain visibility into all aspects of their distribution operations, from supplier performance to carrier efficiency. This enhanced visibility enables them to pinpoint problem areas and make data-driven decisions to address issues quickly.

For example, analytics might show that a particular supplier has recently started experiencing delivery delays that are disrupting downstream supply chain processes.

Logistics managers can spot this trend through data analytics and proactively take corrective action like finding alternative suppliers or working with the incumbent to improve on-time performance.

Analytics Allows Logistics Teams to Uncover Cost Savings Opportunities

In particular, advanced analytics techniques help uncover significant freight expense reduction opportunities that have previously remained hidden.

By thoroughly analyzing past shipping data and identifying trends, logistics teams can right-size transportation budgets and avoid the 5-10% overspend that often occurs due to inefficient procurement practices, invoice errors, and unused contract discounts.

For instance, machine learning algorithms can process vast quantities of freight payment data, detecting unusual spikes in costs and alerting managers to quickly renegotiate rates if necessary.

Deep analysis of carrier performance data can also guide optimal network redesign decisions and help substitute lower-cost transportation partners for underperforming incumbents.

Forecasting and Budgeting

Analyzing past shipping volumes and spend enables logistics managers to create more accurate freight budgets. This minimizes waste from overestimating while also ensuring adequate transportation resources.

Additionally, data science techniques like predictive modeling and scenario analysis can simulate the impact of different freight volumes and spend assumptions during the budgeting process.

For example, algorithms can process data on past shipments and current macroeconomic conditions to forecast future demand within a defined confidence interval.

Logistic teams can use this demand projection as an input for transportation budgeting to arrive at a data-backed number, reducing the risk of misalignment with actual business needs.

This enables the creation of freight budgets with precise, data-backed inputs, minimizing mismatches between projected and actual freight spend.

Carrier Bid Optimization

Understanding current and anticipated shipping patterns allows logistics teams to better specify lanes, volumes, service requirements, and performance KPIs during the RFP process. This enables the procurement of better-matched carrier contracts that align with actual transportation needs.

Data analytics delivers the insights needed to provide carriers with realistic forecasts of volume flows and service expectations rather than vague estimates.

By assessing elements like seasonal sales changes, warehouse location expansion plans, and predicted customer demand, analytics can project volumes for major lanes with relevance to contract carriers.

Equipped with this granular intelligence, logistics managers can assemble RFPs that accurately reflect the company’s transportation needs across crucial parameters.

Contract carriers can then prepare tailored proposals meeting these data-driven shipping specifications. The end result is optimally configured carrier contracts secured through a tightly scoped RFP process.

Invoice Auditing and Anomaly Detection

Leveraging analytics techniques to audit every freight invoice rather than small sample sizes allows for the uncovering of billing errors and opportunities to capture missed discounts. Machine learning algorithms can automatically flag unusual charges for auditors to verify.

For example, an invoice audit algorithm can analyze thousands of historical invoices to determine expected cost ranges for standard shipping lanes. When a new carrier bill shows dramatically higher costs than the norm for the same origin and destination, the algorithm flags this for review.

Auditors can then examine the specifics of the shipment to determine if excessive charges were applied and request refunds or adjustments from the carrier as needed.

This creates hard dollar savings by ensuring every invoice aligns with contracted rates and established benchmarks.

Additionally, the same algorithms can detect sudden anomalies in overall freight spend – alerting logistics managers to quickly take corrective action like renegotiating rates if necessary.

For instance, a 20% increase in transport costs between two key distribution center locations might signify a carrier improperly updating a contracted rate. Analytics models catch this abnormal spend pattern, allowing logistics teams to address the root cause.

Network Optimization

Analyzing the performance of existing carrier contracts using KPIs like on-time pickup, on-time delivery, claims ratios and customer satisfaction helps determine the optimal transportation network. Consistently underperforming incumbents can be replaced with lower-cost carriers that demonstrate superior performance.

For example, analytics may reveal that a long-time national contract carrier is consistently delivering late to West Coast destinations, leading to customer complaints.

Data analysis shows regional carriers providing 60% better on-time delivery rates in Western regions at only a 10% higher cost. This significant service differential justifies non-renewing the national provider and shifting volumes to the regional carrier, despite slightly higher prices.

In this manner, data analytics makes determining optimal carrier networks possible by providing accurate views of carrier efficiency alongside logistics cost factors. Shippers can confidently realign transportation partner mixes to balance cost and service based on data.

How to Begin Leveraging Logistics Data Analytics Effectively in Your Company

Step 1:Data Collection and Integration

Start by identifying all potential data sources that impact your operations, such as inventory counts, shipment tracking, and customer feedback. This step is crucial, whether you’re using advanced software or managing data through more basic means.

Aim to centralize your data for easy access. Those with a TMS can leverage its integration capabilities, while others might use spreadsheets or database software to compile information from various sources into a single, manageable format.

Step 2: Predictive Analytics for Forecasting

Pinpoint the key variables that significantly impact your logistics operations, such as seasonal demand fluctuations, shipping volumes, or delivery timelines. This identification is essential for any forecasting effort, with or without advanced tools.

Apply predictive analytics to forecast future trends. For those with access to a TMS, take advantage of its built-in analytics capabilities. Alternatively, simple statistical tools or even Excel can be used to analyze historical data and make educated predictions about future needs.

Step 3: Carrier Selection and Bid Optimization

Gather and compare data on carrier performance to select the most cost-efficient and reliable options. Negotiate contracts with data-driven insights on performance metrics.

To further enhance decision-making, incorporate real-time data analytics to monitor ongoing carrier performance, ensuring flexibility and responsiveness to changes in service quality.

Step 4: Incorporating Freight Audit and Payment Analytics

Conduct regular freight audits to ensure billing accuracy. Analyze billing data for patterns to identify savings opportunities.

Use audit findings to negotiate better terms or correct billing practices with carriers. Leverage technology to automate the audit process, reducing manual effort and increasing the detection of discrepancies.

Step 5: Performance Analysis and Network Optimization

Collect data on carrier punctuality, freight condition, and customer satisfaction. Evaluate logistics network performance to identify improvement areas.

Apply insights to optimize operations, reallocating resources or adjusting routes for efficiency. Utilize advanced simulation tools to forecast the impact of changes before implementation, ensuring data-driven decision making.

Step 6: Accelerating Success with Expert Partnerships

Assess internal capabilities to identify areas needing external expertise. Research and select partners with a track record in logistics analytics and technology.

Integrate partner technologies and insights, regularly evaluating the partnership’s impact on operations. Foster a culture of continuous improvement by encouraging feedback and suggestions from your partners to refine strategies and operations further.

The Path to Ongoing Savings and Continuous Improvement

To fully leverage analytics and data science techniques for significant and sustained cost reduction, shippers should consider partnering with specialists in the transportation audit, payment, and analytics field. 

These firms provide advanced analytics platforms, experienced data scientists, and integrated invoice payment services proven to unlock 5-10% freight savings.

In addition to immediate cost reduction, these partnerships enable access to clean, normalized datasets that can fuel ongoing optimization efforts and efficiency gains across the supply chain. 

Specialists consolidate freight data from various transportation modes and sources into unified reporting dashboards. This provides unified visibility that shippers can use to guide decisions across the organization.

Leveraging Logistics Data Analytics with Nolan and Cunnings

For organizations aiming to streamline their supply chain and reduce freight costs, Nolan and Cunnings offer a strategic partnership grounded in advanced data analytics expertise from the first stop to the last mile.

Our approach integrates predictive analytics and sophisticated modeling techniques to optimize your logistics operations.

This integration not only aids in precise freight budgeting but also enhances carrier selection processes, ensuring that your transportation network is both cost-effective and reliable.

Unlocking Efficiency and Innovation in Your Supply Chain

By choosing Nolan and Cunnings as your logistics analytics partner, you gain access to a suite of tools and insights that drive operational excellence. We specialize in transforming complex data into actionable strategies, enabling you to navigate the dynamic landscape of supply chain management with confidence.

Our services are tailored to identify and address your unique logistics challenges, from optimizing route planning to streamlining carrier performance evaluation.

This targeted approach ensures that your logistics operations align with your business objectives, yielding tangible improvements in both efficiency and customer satisfaction.

Take the Next Step in Supply Chain Excellence

Ready to start leveraging your company’s logistics data analytics to reduce costs and improve overall supply chain efficiency?

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