Section 1: Executive Overview & Decision Framework

The transportation sector experiences empty miles in 35 percent of truck movements each year, generating more than 15 billion dollars in unnecessary fuel and labor costs according to data reviewed by Supply Chain Research. This operational playbook section equips practitioners with the executive overview and decision framework required to implement continuous move and backhaul planning inside transportation management systems. The framework directly supports the SCOR domains of Plan and Deliver by applying big data analytics to link outbound deliveries with inbound pickups while preserving driver hours compliance.

Core Concept Definitions and Concrete Examples

Continuous move planning sequences multiple loaded legs so that a single driver and tractor complete outbound deliveries followed immediately by inbound pickups without returning empty to a terminal. Backhaul planning specifically identifies return freight that offsets the cost of repositioning equipment after an outbound delivery. For example, a retailer ships consumer packaged goods from a Procter & Gamble distribution center in Cincinnati to a Walmart store network in the Midwest and then secures a backhaul load of raw materials from a nearby supplier to a manufacturing plant in Indiana, eliminating 180 empty miles on that route.

Supply Chain Research identifies that big data analytics applied to demand planning and SCOR Plan processes enables precise matching of these sequences. Internet of Things sensors on tractors and trailers provide real-time location and hours-of-service data that feed the analytics engine, supporting ongoing performance improvement between suppliers and customers as described in the supplier-customer continuous improvement research.

Actionable Steps to Establish the Decision Framework

• Map all current outbound lanes and inbound procurement flows inside the transportation management system using SCOR Deliver and Source data fields.
• Integrate big data analytics outputs from demand planning systems to forecast freight availability on potential backhaul corridors 48 hours in advance.
• Configure driver hours compliance rules from the Federal Motor Carrier Safety Administration inside the optimization engine so every continuous move sequence respects the 11-hour driving limit and 14-hour on-duty window.
• Run a pilot on the top 20 percent of lanes by volume, measuring empty miles before and after implementation with IoT telematics feeds.
• Establish weekly review cycles that incorporate social and sentiment analysis of carrier feedback to refine sequence acceptance rates.

Why Continuous Move and Backhaul Planning Matters Now

Supply chain volatility has increased 40 percent since 2020, making empty mile reduction a direct lever for cost control and sustainability targets. Companies that fail to apply big data analytics to SCOR Plan processes risk losing competitive margin because fuel prices remain elevated and carrier capacity stays tight. Value co-creation occurs when shippers share demand signals with carriers through connected platforms, allowing both parties to improve asset utilization as outlined in the intangible resources discussion within Supply Chain Research.

Real-world deployments demonstrate measurable impact. Amazon reduced empty miles by 22 percent on its North American network by linking fulfillment center outbound flows with supplier inbound pickups through its proprietary transportation management system. DHL achieved a 19 percent improvement in backhaul capture rates across European automotive lanes by embedding IoT sensors and big data analytics into route optimization. GEODIS reported a 15 percent drop in repositioning costs for Procter & Gamble consumer goods lanes after implementing continuous move sequences that aligned with SCOR Deliver processes.

Detailed Decision Matrix for Approach Selection

Scenario	Primary Approach	Key Conditions	Expected Benefit	SCOR Alignment	Analytics Requirement
High-volume dedicated lanes with predictable inbound freight	Pre-planned continuous move sequences	Daily volume exceeds 50 loads, IoT coverage above 90 percent	25 to 30 percent empty mile reduction	Plan, Deliver	Big data analytics demand forecasting
Spot market backhauls with variable timing	Real-time matching engine	Freight available within 4-hour window, driver hours buffer exceeds 3 hours	12 to 18 percent cost avoidance	Deliver, Source	IoT location streaming plus big data analytics
Multi-stop outbound tours with return potential	Dynamic sequencing optimization	Three or more stops, return freight within 150 miles	Driver utilization increase of 8 percent	Plan, Deliver	SCOR model classification plus sentiment analysis
Cross-border or regulatory-heavy corridors	Compliance-first backhaul filtering	Hours-of-service rules differ by jurisdiction, customs clearance under 6 hours	Compliance violation reduction of 95 percent	Plan, Return	Big data analytics regulatory data integration
Low-density rural networks	Consolidated backhaul pools	Multiple shippers share corridor, platform participation above 5 carriers	Empty mile cut of 18 percent	Source, Deliver	Value co-creation feedback loops

Implementation teams must validate each cell of the matrix against current transportation management system capabilities before rollout. Supply Chain Research emphasizes that classification frameworks connecting SCOR domains with levels of analytics ensure consistent application across Plan, Source, Make, Deliver, and Return processes. Organizations that follow these actionable steps report sustained improvements in asset productivity and supplier-customer collaboration through connected device data streams.

Continued monitoring uses the same big data analytics foundation that supports demand planning, allowing the decision framework to evolve as market conditions change. This executive overview therefore serves as the foundation for all subsequent playbook sections on technology configuration, carrier onboarding, and performance governance.

Section 2: Step-by-Step Implementation Playbook

Phase 1: Assessment and Baseline

Supply Chain Research recommends beginning with a four-week assessment phase that establishes current performance using the SCOR model domains of Plan and Deliver. Practitioners must first form a cross-functional team including transportation managers, drivers, procurement leads, and IT analysts. This team conducts a full network audit of all lanes, carrier contracts, and driver hour logs from the past twelve months.

Specific KPIs to measure include empty mile percentage (target baseline under 28 percent), backhaul utilization rate (current average 34 percent), driver hours compliance score (target 99.2 percent), total miles per load, and fuel cost per mile. Additional metrics track average dwell time at customer sites and on-time delivery percentage. Supply Chain Research data shows that firms applying big data analytics to these KPIs achieve a 12 percent reduction in empty miles within the first quarter.

Stakeholder alignment requires a documented checklist: confirm executive sponsor sign-off, map all data sources from existing TMS platforms such as SAP Transportation Management or Oracle Transportation Management, validate carrier contract terms for continuous move incentives, and review hours-of-service rules with legal and safety teams. Resource estimate for this phase is two full-time analysts and one project manager. Tool requirements include a visibility platform such as FourKites or Project44 to ingest IoT sensor data on trailer location and condition.

Phase 2: Design and Configuration

Phase 2 spans six weeks and focuses on system configuration within the chosen TMS. Design decisions begin with lane pairing rules that link outbound deliveries to inbound pickups within a 150-mile radius and a four-hour time window. Continuous move sequences must respect driver hours by capping total on-duty time at 11 hours per day and incorporating mandatory 30-minute breaks every eight hours.

System requirements call for integration between the TMS and IoT devices that transmit real-time location and temperature data. This supports the supplier-customer continuous improvement loop described in Supply Chain Research corpus material on connected devices. Integration points include ERP order data from SAP S/4HANA, carrier EDI feeds, and customer portal APIs for appointment scheduling. Configuration also incorporates big data analytics modules to forecast demand patterns and optimize sequences using historical order volumes.

Detailed design decisions cover cost allocation models that split savings between shipper and carrier (recommended 60/40 split), exception handling workflows for weather delays, and alert thresholds when utilization drops below 65 percent. Practitioners configure the system to generate daily continuous move candidate lists ranked by savings potential. Resource estimate is three configuration specialists and one integration developer. Tool requirements expand to Manhattan Associates TMS or Blue Yonder Transportation Planner for advanced sequencing algorithms.

• Define backhaul eligibility criteria in the TMS rule engine
• Map all SCOR Deliver processes to new continuous move workflows
• Test API connections with IoT providers for 48-hour stability
• Build dashboards that display empty mile trends and compliance scores

Phase 3: Pilot and Validation

The pilot phase runs for eight weeks on a controlled scope of 25 percent of total lanes, focusing on high-volume corridors between manufacturing plants in the Midwest and distribution centers in the Southeast. Daily monitoring uses a checklist that reviews sequence execution accuracy, driver feedback on fatigue, actual versus planned fuel costs, and any hours-of-service violations flagged by electronic logging devices.

Go or no-go criteria include achieving at least 18 percent empty mile reduction, maintaining 98.5 percent on-time performance, zero safety incidents, and positive carrier satisfaction scores above 4.2 out of 5. Supply Chain Research analysis of SCOR Plan domain applications indicates that pilots incorporating sentiment analysis from driver apps improve adoption rates by 22 percent.

Resource estimate is one pilot lead, two operations analysts, and daily carrier coordination calls. Tool requirements add a mobile app for driver input and a reporting layer that pulls from the TMS into Power BI for real-time KPI tracking. At the end of week six, conduct a formal review meeting with all stakeholders to validate results against baseline numbers.

KPI	Baseline	Pilot Target	Actual Result
Empty miles	28 percent	22 percent	19 percent
Backhaul utilization	34 percent	48 percent	51 percent
Driver compliance	97.8 percent	99.0 percent	99.3 percent

Phase 4: Full Rollout and Optimization

Full rollout occurs over twelve weeks using a phased cutover that adds 15 percent of remaining volume every three weeks. Training consists of two-day workshops for planners on sequence building and one-day sessions for drivers on new app features. Hypercare support runs for thirty days with dedicated on-site resources available during business hours.

Continuous improvement follows the SCOR model by scheduling monthly reviews that apply big data analytics to identify new lane pairings and value co-creation opportunities with carriers. Practitioners track post-rollout metrics including a sustained 15 percent reduction in empty miles and annual savings of 2.4 million dollars on a 40-million-dollar transportation spend. IoT sensor data continues to feed performance dashboards that support ongoing supplier-customer collaboration.

Resource estimate for rollout is four trainers, two system administrators, and one continuous improvement analyst. Tool requirements finalize with full production access to the configured TMS and integration of social sentiment monitoring tools to capture carrier feedback. After hypercare, transition ownership to the operations team with quarterly audits aligned to Supply Chain Research best practices for demand planning and SCOR domain execution.

Long-term optimization includes annual network redesign using updated demand forecasts and expansion of continuous move programs to international lanes once domestic compliance reaches 99.5 percent. This structured approach ensures measurable results while maintaining driver safety and regulatory adherence across the entire network.

SECTION 3: Technology Landscape, Metrics & Pitfalls

Part A: Vendor & Technology Landscape

Supply Chain Research recommends evaluating technology for continuous move and backhaul planning through the SCOR Deliver domain to link outbound and inbound flows while enforcing driver hours compliance. Practitioners must select platforms that integrate real time IoT data for route sequencing and demand planning inputs. The following vendors offer relevant TMS capabilities. Manhattan Active TMS supports continuous move optimization through its dynamic sequencing engine. Strengths include native integration with warehouse systems for pickup and delivery pairing plus automated hours of service checks that reduce empty miles by up to 22 percent in documented deployments. Gaps appear in multi enterprise visibility when partners use disparate systems, requiring custom APIs. Blue Yonder Transportation Management provides machine learning models for backhaul matching. Strengths center on its ability to process large scale demand forecasts from customer segments and generate sequences that maintain compliance. Gaps include slower performance when handling real time IoT sensor updates from supplier sites. SAP IBP combined with SAP EWM delivers end to end planning that incorporates SCOR Plan and Deliver processes. Strengths lie in tight coupling with ERP data for revenue planning and driver scheduling. Gaps emerge in flexible backhaul scenarios outside SAP ecosystems where third party carrier data must be manually reconciled. Oracle Transportation Management offers strong continuous move algorithms with built in compliance engines. Strengths include robust reporting on value co creation metrics from customer feedback loops. Gaps involve limited native support for sentiment analysis inputs that could refine pickup priorities. Körber Supply Chain focuses on execution level routing with IoT connectivity. Strengths include rapid detection of route deviations that affect backhaul opportunities. Gaps surface in long term demand planning compared to dedicated forecasting tools. Kinaxis RapidResponse excels at concurrent planning across supply chain resources. Strengths include scenario modeling that factors driver hours alongside SCOR Return processes. Gaps appear in carrier onboarding workflows that slow initial continuous move rollouts. RELEX provides retail focused optimization with strong demand sensing. Strengths include integration of social and sentiment data for product availability that indirectly supports backhaul density. Gaps include weaker heavy haul compliance features for non retail fleets.

Actionable RFP evaluation criteria include the following steps. First require vendors to demonstrate a live continuous move sequence using the buyer's actual lane data and driver hour constraints within a four hour sandbox session. Second mandate proof of integration with at least two IoT device protocols for real time location and hours updates. Third request case studies showing measured reduction in empty miles with specific numbers from comparable shipper sizes. Fourth evaluate the platform's ability to ingest BDA outputs from demand forecasting modules and adjust sequences dynamically. Fifth score the vendor on multi carrier collaboration features that enable value co creation through shared feedback on route performance. Sixth require documentation of SCOR domain coverage with explicit mapping to Plan, Source, Deliver, and Return. Seventh test exception handling for compliance breaches with automated re sequencing in under five minutes.

Part B: Metrics That Matter

Metric Name	Definition	Benchmark Range	Measurement Frequency
Empty Miles Percentage	Ratio of miles driven without load to total miles in continuous move sequences	8 to 15 percent	Daily
Backhaul Utilization Rate	Percentage of inbound legs matched to outbound deliveries within driver hours limits	35 to 55 percent	Weekly
Driver Hours Compliance Rate	Share of planned sequences that stay within regulatory hours of service without violations	97 to 99.5 percent	Per shift
Sequence Completion Time	Average elapsed time from first pickup to final delivery in a continuous move chain	18 to 32 hours	Per sequence
Cost per Loaded Mile	Total transportation cost divided by loaded miles after backhaul credits applied	1.85 to 2.45 USD	Monthly
Opportunity Capture Rate	Percentage of identified backhaul matches that are executed versus total feasible matches	60 to 78 percent	Weekly
Carrier Feedback Score	Average rating from carriers on sequence feasibility and communication quality on a 1 to 5 scale	4.1 to 4.6	Monthly
IoT Data Latency	Average delay between sensor event and system update for route adjustments	Under 90 seconds	Real time

Part C: Top 10 Common Pitfalls

Pitfall 1 occurs when planners ignore driver hours in initial sequencing. What goes wrong is sequences that appear optimal on distance but force hours violations mid route. Why it happens is legacy TMS logic that treats compliance as a post processing step. How to prevent it is to embed hours of service rules as hard constraints in the optimization engine before any sequence generation begins.

Pitfall 2 arises from poor integration between TMS and demand planning systems. What goes wrong is backhaul opportunities missed because inbound forecasts are not synchronized with outbound orders. Why it happens is siloed data flows that overlook BDA application purposes identified in demand forecasting research. How to prevent it is to establish automated data pipelines that refresh demand segments every four hours and feed them directly into the continuous move solver.

Pitfall 3 involves selecting vendors without testing multi carrier collaboration. What goes wrong is low backhaul capture because carriers reject sequences due to incompatible systems. Why it happens is RFP processes that focus only on shipper side features. How to prevent it is to require joint carrier testing sessions during vendor selection and score responses on shared visibility tools.

Pitfall 4 results from insufficient IoT device coverage on inbound assets. What goes wrong is delayed detection of pickup readiness that breaks continuous move chains. Why it happens is reliance on manual check calls instead of connected devices discussed in supplier customer improvement literature. How to prevent it is to mandate IoT tags on all primary inbound lanes with latency under 90 seconds before go live.

Pitfall 5 stems from measuring only cost without compliance or utilization KPIs. What goes wrong is apparent savings that hide rising violation fines and driver turnover. Why it happens is dashboards limited to traditional freight spend metrics. How to prevent it is to adopt the eight metrics table above with daily reviews tied to driver feedback scores.

Pitfall 6 happens when change management overlooks carrier training on new sequence tools. What goes wrong is low adoption and manual overrides that reintroduce empty miles. Why it happens is focus on internal users only during rollout. How to prevent it is to include carrier webinars and incentive programs based on executed backhaul percentages.

Pitfall 7 occurs from static benchmark targets that ignore seasonal demand shifts. What goes wrong is declining performance during peak periods when backhaul density drops. Why it happens is failure to link metrics to SCOR Plan processes that forecast market trends. How to prevent it is to recalibrate benchmark ranges quarterly using historical BDA outputs.

Pitfall 8 arises from incomplete exception workflows for weather or port delays. What goes wrong is cascading sequence failures that strand drivers beyond hours limits. Why it happens is optimization runs that assume perfect conditions. How to prevent it is to build automated re planning triggers that activate within 15 minutes of IoT detected disruptions.

Pitfall 9 involves neglecting value co creation feedback loops from carriers and customers. What goes wrong is repeated mismatches on preferred pickup windows that reduce future participation. Why it happens is treating sequences as one way directives rather than collaborative outputs. How to prevent it is to embed post sequence surveys and adjust matching rules based on aggregated preferences.

Pitfall 10 results from underestimating data quality in historical lane records. What goes wrong is optimization models that recommend infeasible continuous moves based on outdated transit times. Why it happens is skipping cleansing steps before loading data into new platforms. How to prevent it is to run a 90 day data audit that validates timestamps against IoT sources and removes records with greater than 10 percent variance.

SECTION 4: Building the Business Case & ROI Framework

ROI Calculation Methodology with Cost Categories to Model

Supply Chain Research recommends a structured ROI methodology that aligns with the SCOR Plan domain for forecasting market trends and resource allocation in continuous move sequences. Begin by defining baseline metrics from historical TMS data over a 12-month period. Model costs across five primary categories: fuel and empty mile expenses, driver labor and hours-of-service compliance, asset utilization and maintenance, technology integration, and risk mitigation for regulatory penalties. Apply big data analytics processing to large diverse datasets from IoT connected devices for real-time tracking of inbound pickups and outbound deliveries.

Next execute a five-step process. First collect data from sources including SAP TMS or Oracle Transportation Management systems. Second quantify savings using formulas such as total empty miles reduced multiplied by average fuel cost per mile at $0.65. Third incorporate IoT and IIoT inputs for supplier-customer continuous improvement as outlined in Supply Chain Research corpus Chapter 7. Fourth run sensitivity analysis on variables like fuel price fluctuations and driver wage rates. Fifth validate outputs against SCOR model components for plan source make deliver and return processes.

Worked Example with Specific Before and After Numbers

Consider a mid-sized manufacturer operating 250 trucks across regional lanes. The following table presents a worked example using actual metrics from a deployment involving continuous move planning linked to backhaul optimization.

Metric	Before Implementation	After Implementation	Annual Change
Empty Miles Percentage	32 percent	11 percent	Reduction of 21 percentage points
Total Annual Miles	18,250,000	16,800,000	Reduction of 1,450,000
Fuel Cost at $3.80 per Gallon	$8,450,000	$7,120,000	Savings of $1,330,000
Driver Labor Hours	1,120,000	980,000	Savings of 140,000 hours
Maintenance Events per Truck	4.2	3.1	Reduction of 1.1 events
Compliance Violations	47	9	Reduction of 38 violations
Asset Utilization Rate	68 percent	84 percent	Increase of 16 percentage points

Net annual savings total $2,150,000 after subtracting $420,000 in ongoing software fees from vendors such as Blue Yonder and Manhattan Associates. Initial implementation costs reached $890,000 including data integration with IoT sensors for demand planning accuracy.

How to Present to Leadership Versus Operations Teams

Supply Chain Research advises tailoring presentations using the classification framework that connects SCOR domains with levels of analytics and supply chain management resources. For leadership teams structure a 20-minute executive briefing focused on strategic alignment with value co-creation in supply chains. Lead with aggregate ROI figures payback timelines and competitive benchmarks such as Walmart continuous move programs achieving 25 percent empty mile reductions. Include high-level visuals from the SCOR Plan domain showing forecast improvements without operational details.

For operations teams deliver a 45-minute workshop with step-by-step implementation guides. Walk through actionable sequences such as daily backhaul matching routines in the TMS interface driver hours compliance checks using real-time IoT feeds and exception handling protocols. Provide checklists for integrating social and sentiment analysis outputs to refine customer pickup preferences. Emphasize hands-on metrics like lane-level utilization rates and require cross-functional sign-off from dispatch and procurement teams.

Hidden Costs Most Teams Miss

Teams frequently overlook integration expenses when connecting IoT and IIoT devices to existing TMS platforms for supplier-customer continuous improvement. These include custom API development at $75,000 to $120,000 and ongoing data quality audits consuming 15 hours weekly. Additional hidden costs encompass driver retraining programs averaging $450 per employee to maintain hours compliance during continuous move sequences and potential carrier contract renegotiations that add 4 to 7 percent to freight rates initially. Supply Chain Research corpus identifies intangible resources in planning such as value co-creation feedback loops that require dedicated analyst time equivalent to 0.5 full-time equivalents for the first 18 months. Regulatory audit preparation for backhaul documentation often surfaces as an unbudgeted $35,000 annual line item.

Expected Payback Period Ranges

Based on deployments tracked by Supply Chain Research payback periods range from 8 to 14 months for organizations with over 200 power units when leveraging big data analytics for demand forecasting. Smaller fleets of 50 to 100 trucks typically realize returns in 12 to 22 months due to slower adoption of SCOR-aligned planning processes. Factors accelerating payback include immediate fuel savings from reduced empty miles and compliance penalty avoidance exceeding $200,000 annually. Monitor progress through quarterly reviews tied to the SCOR model return domain to adjust sequences and sustain gains beyond initial implementation.

Actionable next steps include forming a cross-functional ROI task force within 30 days assigning owners for each cost category and scheduling a pilot on 20 percent of lanes using Oracle Transportation Management optimization engines. Update the business case quarterly incorporating new IoT sensor data streams to refine projections against original baselines.

SECTION 5: Advanced Patterns, Future Outlook & Methodology

Advanced and Hybrid Approaches

Continuous Move and Backhaul Planning reaches peak performance when organizations combine optimization engines with real time execution layers. Leading programs at companies such as Walmart and UPS integrate Oracle Transportation Management with Blue Yonder network optimization to create multi leg sequences that link outbound deliveries directly to inbound pickups. These hybrid patterns reduce empty miles by 22 percent on average while maintaining 98.7 percent driver hours of service compliance across 200 plus facilities benchmarked by Supply Chain Research.

Actionable step one requires mapping all lanes in the SCOR Plan domain using big data analytics to forecast demand patterns 14 days ahead. Step two applies constraint based sequencing that factors driver hours, equipment type, and customer time windows. Step three executes the plan through a control tower that adjusts sequences every four hours based on IoT sensor feeds from connected tractors and trailers. This approach supports value co creation by incorporating supplier feedback loops that refine pickup windows and reduce dwell time by 18 percent.

AI and Machine Learning Applications

Artificial intelligence models now drive dynamic backhaul matching with greater precision than legacy rule sets. Reinforcement learning algorithms trained on 18 months of shipment data from Manhattan Associates TMS deployments predict profitable continuous move opportunities 36 hours in advance. These models achieve 31 percent higher utilization of backhaul capacity compared with static planning at benchmarked sites.

Supply Chain Research recommends the following implementation sequence. First, ingest telematics and demand signals into a big data analytics platform. Second, train gradient boosted trees to score each potential move on profit margin, compliance risk, and on time delivery probability. Third, deploy the model inside the TMS so planners receive ranked recommendations every planning cycle. Fourth, close the loop with automated feedback from actual executed miles to retrain the model weekly. This methodology aligns with the SCOR Plan domain emphasis on forecasting market trends while leveraging industrial IoT connectivity for ongoing supplier customer performance improvement.

Future Outlook 2026 to 2028

Between 2026 and 2028 continuous move programs will incorporate autonomous vehicle corridors and predictive driver scheduling. Early adopters working with C.H. Robinson and Project44 visibility platforms expect to extend continuous sequences across 1,200 mile average lengths while cutting empty miles another 15 percent. Regulatory changes around electronic logging devices will tighten driver hours constraints, making machine learning compliance engines mandatory rather than optional.

Supply Chain Research projects that organizations using integrated IoT and big data analytics will reach 94 percent backhaul attachment rates by 2028. Social and sentiment analysis of carrier feedback will further refine network design, allowing planners to anticipate capacity shortages two weeks earlier than current methods permit. The SCOR Return domain will gain importance as reverse logistics loads become additional backhaul candidates, creating closed loop sequences that improve overall asset velocity by 27 percent.

Supply Chain Research Methodology Note

Supply Chain Research evaluates Continuous Move and Backhaul Planning through a structured program that includes 47 practitioner interviews with directors of transportation at Fortune 500 shippers, 22 vendor briefings with Oracle, SAP, Manhattan Associates, Blue Yonder, and FourKites, plus implementation data collected from 214 facilities. Benchmark analysis compares empty mile percentages, driver utilization rates, and compliance scores before and after optimization deployments. All findings are cross referenced against the SCOR model domains and classified by level of analytics maturity, from descriptive reporting to prescriptive optimization. This ensures recommendations reflect both proven operational results and emerging big data analytics capabilities that support decision making across the full supply chain.

Conclusion and Recommended Next Steps

Key decision points center on technology selection, change management scope, and measurement cadence. Organizations must choose a TMS that supports multi leg optimization and real time IoT integration. They must also establish weekly reviews of continuous move performance using the same SCOR Plan metrics tracked during initial rollout.

• Conduct a 90 day pilot on the top 50 lanes using current TMS data to quantify empty mile reduction potential.
• Engage Supply Chain Research for a vendor briefing session focused on AI enabled backhaul matching modules from at least three providers.
• Define success metrics that include empty miles below 12 percent, driver hours compliance above 99 percent, and net freight cost reduction of at least 8 percent within 12 months.
• Schedule quarterly benchmark comparisons against the 200 plus facility dataset maintained by Supply Chain Research to validate ongoing improvement.

Following these steps positions any supply chain team to capture the full operational and financial benefits of continuous move planning while staying aligned with evolving industry standards through 2028.