MEMORANDUM

MEMORANDUM
PRIVILEGED AND CONFIDENTIAL — ATTORNEY WORK PRODUCT
FROM: James Underwood, Lead Associate Calderwood & Harkness LLP 2200 Ross Avenue, Suite 3600 Dallas, TX 75201
TO: Dr. Priya Ramanathan, Lead Consultant Corbin Risk Advisors LLC 1901 N. Moore Street, Suite 1020 Arlington, VA 22209
DATE: May 14, 2025
RE: Expert Work Plan — Delano v. Stonefield Logistics Group Inc., Case No. 3:25-cv-01847-RBC, U.S. District Court for the Northern District of Texas, Dallas Division
CC: Bernard Crawford (Responsible Partner, Calderwood & Harkness LLP); Devon Archer (Working Associate, Calderwood & Harkness LLP); Lisa Cheng (Paralegal, Calderwood & Harkness LLP)
CONFIDENTIALITY LEGEND: This communication is protected by the attorney-client privilege, the work product doctrine, and the protections afforded to expert consulting materials under Fed. R. Civ. P. 26(b)(3) and 26(b)(4). It has been prepared in anticipation of litigation and at the direction of counsel. This memorandum and all attachments hereto are intended solely for the use of the addressee and those individuals identified in the "CC" line above. Any dissemination, distribution, or copying of this communication by any person other than the intended recipients is strictly prohibited. If you have received this communication in error, please notify the sender immediately and destroy all copies.
I. Purpose and Engagement Overview
This memorandum sets forth the analytical framework, data requirements, and deliverable timeline for the engagement of Corbin Risk Advisors LLC ("Corbin Risk") as the expert consulting and testifying firm retained by Calderwood & Harkness LLP ("C&H") on behalf of its client, Stonefield Logistics Group Inc. ("SLG" or "Stonefield"), in connection with the above-captioned litigation. This memorandum is issued pursuant to, and should be read in conjunction with, the Expert Retention Agreement dated May 8, 2025, executed between Calderwood & Harkness LLP (on behalf of SLG) and Corbin Risk Advisors LLC, which governs the terms and conditions of the engagement, including fee arrangements, confidentiality obligations, and scope of work.
Dr. Priya Ramanathan has been designated as the Lead Consultant and Expert Economist for this engagement. Dr. Ramanathan's qualifications — including her Ph.D. in Applied Economics from the University of Chicago, her prior work as a consulting and testifying expert in more than forty wage-and-hour class and collective actions, and her extensive experience with statistical analysis of worker classification disputes — make her ideally situated to provide the analytical rigor this matter requires.
The primary objective of the engagement is to provide statistical and economic analysis supporting Stonefield's opposition to plaintiff Marcus Delano's anticipated motion for conditional certification under 29 U.S.C. § 216(b). Specifically, Dr. Ramanathan's analysis should demonstrate that the approximately 1,187 drivers who signed Independent Contractor Operating Agreements ("ICOAs") during the relevant period (April 30, 2022 to present) are not "similarly situated" for purposes of collective treatment under the Fair Labor Standards Act. The analysis will focus on quantifying material variance in working conditions, hours, pay structures, route acceptance behavior, and contractual arrangements across SLG's fourteen distribution hubs and three ICOA versions, thereby undermining the premise that a single representative proceeding can fairly adjudicate the claims of the entire putative collective.
As a secondary objective, should the matter proceed beyond conditional certification to the more rigorous decertification stage, the analytical framework established during this initial phase will lay the groundwork for a comprehensive decertification analysis. The data infrastructure, statistical models, and preliminary findings developed during Phase 1 through Phase 4 (described below) will be designed with this contingency in mind, such that the transition to a full decertification analysis can occur efficiently and without the need to reconstruct the foundational analytical work.
Additionally, the work performed under this engagement may support SLG's anticipated motion to compel individual arbitration for approximately 640 drivers who signed ICOA v3.0 (the January 2023 revision containing an arbitration clause with a collective action waiver). Dr. Ramanathan may be asked to provide supplemental analysis demonstrating the distinct characteristics of the v3.0 signatory population and the segmentation implications for collective treatment. The timing and scope of any such supplemental analysis will be determined based on strategic decisions made by Bernard Crawford and the litigation team.
II. Case Background and Litigation Context
A. Parties and Claims
The named plaintiff in this action is Marcus Delano, a former delivery driver who performed last-mile residential delivery services for Stonefield Logistics Group Inc. from approximately March 2021 through September 2024. Mr. Delano was assigned to and operated out of the Fort Worth distribution hub, designated internally by SLG as HUB-01. During his tenure, Mr. Delano was classified by SLG as an independent contractor and signed successive versions of the ICOA, most recently ICOA v2.0 (August 2020 revision).
Mr. Delano alleges that he was misclassified as an independent contractor and should have been treated as an employee under both the Fair Labor Standards Act, 29 U.S.C. § 201 et seq., and the Texas Payday Law, Tex. Lab. Code §§ 61.001–61.095. The gravamen of the complaint is that SLG exercised sufficient control over its delivery drivers — and that the economic reality of the working relationship was such — that the drivers were employees entitled to overtime compensation under the FLSA. Mr. Delano alleges that he regularly worked between 55 and 65 hours per week (averaging approximately 58 hours per week) and was compensated on a per-delivery basis without receiving overtime compensation for hours worked in excess of 40 per week.
Plaintiff is represented by Claudia Hartwell and Jordan Brashear of Hartwell & Brashear PLLC, a Dallas-based plaintiffs' employment law firm with significant experience in FLSA collective actions. Their participation is notable in that Hartwell & Brashear has successfully obtained conditional certification in at least three prior misclassification cases in the Northern District of Texas within the past five years.
The defendant, Stonefield Logistics Group Inc., is a Delaware-incorporated, Texas-headquartered transportation and logistics company specializing in last-mile residential delivery services. SLG is a portfolio company of Ridgepoint Capital Partners Fund III, LP, which holds approximately 72% of SLG's equity following its acquisition of the company in July 2019 for approximately $285 million. SLG currently operates 14 distribution hubs across four states — Texas, Oklahoma, Louisiana, and Arkansas — engaging approximately 1,450 current delivery drivers and a total of 1,187 unique drivers who performed services at any point during the relevant period of April 30, 2022 through the present.
B. Proposed Collective
Plaintiff seeks to represent a collective consisting of all individuals who performed delivery driving services for SLG pursuant to an Independent Contractor Operating Agreement at any of SLG's 14 distribution hubs from April 30, 2022 (three years prior to the filing of the complaint) through the date of final judgment. Based on SLG's internal records, the estimated size of the proposed collective is 1,187 unique drivers.
A critical feature of this case is that three distinct versions of the ICOA are in play across the proposed collective, each containing materially different contractual terms:
•  ICOA v1.0 (2018): The original form agreement, drafted by Pennfield & Daye LLP, which established the baseline independent contractor relationship but did not contain an arbitration clause or collective action waiver.
•  ICOA v2.0 (August 2020 revision): An updated agreement that modified certain performance metrics, adjusted delivery rate schedules, and revised the termination provisions, but likewise did not include an arbitration clause or collective action waiver.
•  ICOA v3.0 (January 2023 revision): A substantially revised agreement that, among other changes, added a mandatory individual arbitration clause and a collective action waiver, materially altering the dispute resolution framework applicable to drivers who executed this version.
Of the 1,187 drivers in the proposed collective, approximately 640 drivers signed ICOA v3.0, while the remaining approximately 547 drivers remain on earlier versions (v1.0 or v2.0). This segmentation is of critical importance both to the conditional certification analysis and to the anticipated motion to compel individual arbitration.
C. Plaintiff's Control Allegations
Plaintiff's complaint and the anticipated conditional certification motion are expected to assert that SLG exercised pervasive control over its delivery drivers, negating the independent contractor classification. The specific control allegations set forth in the complaint, which Dr. Ramanathan's analysis should directly address where possible, include the following:
1.  Route dictation: SLG dictated daily delivery routes to drivers via its proprietary RouteSync dispatch platform, with limited or no driver discretion in determining the sequence, timing, or geographic scope of deliveries.
2.  Mandatory branding: Drivers were required to wear Stonefield-branded uniforms, display SLG identification badges, and affix SLG vehicle placards to their personal vehicles at all times while performing deliveries, creating the outward appearance of an employment relationship.
3.  Mandatory training: Drivers were required to complete 16 hours of initial safety and customer-service training upon onboarding and 4 hours of quarterly refresher training thereafter, with attendance tracked and enforced by SLG.
4.  Prohibition on subcontracting: The ICOA nominally permitted drivers to subcontract delivery obligations, but only with SLG's prior written approval. Plaintiff alleges that zero subcontracting requests were approved during his tenure, rendering the provision illusory.
5.  Customer-facing delivery windows and performance penalties: SLG set customer-facing delivery windows and imposed penalties on drivers for late deliveries and customer complaints, further constraining driver autonomy.
6.  SLG-provided equipment and technology: SLG provided drivers with handheld scanners, access to the RouteSync routing software platform, and telematics hardware (GPS tracking devices), creating a technological infrastructure of control.
7.  Required daily delivery windows: Drivers were required to be available for deliveries during the hours of 7:00 AM to 8:00 PM, Monday through Saturday, with limited ability to deviate from this schedule.
8.  Mandatory GPS tracking: All drivers were required to have SLG-provided telematics devices installed and operational on their vehicles at all times while performing deliveries, enabling continuous real-time monitoring of driver location, speed, and route compliance.
9.  Minimum acceptance rate: Drivers were required to accept a minimum of 85% of delivery assignments offered to them within any rolling 30-day period. Falling below this threshold triggered a mandatory performance review and could result in contract termination.
10.  No rate negotiation: Drivers had no ability to negotiate per-delivery rates, which were set unilaterally by SLG at $14.50 to $38.00 per delivery depending on package weight category, distance category, and delivery type (standard versus white-glove).
Dr. Ramanathan's analysis should specifically address items 1, 4, 7, 8, 9, and 10 above through quantitative analysis of the data sets described in Section III, with the goal of demonstrating that the degree of control exercised by SLG — and the extent to which drivers exercised autonomous judgment — varied materially across hubs, ICOA versions, and individual drivers.
D. Damages Exposure
Plaintiff's damages theory, as articulated in the complaint, proceeds as follows:
•  Average weekly unpaid overtime per driver: $193.50, calculated as 18 overtime hours per week (58 average hours minus 40 regular hours) multiplied by the difference between the claimed overtime rate of $32.25 per hour (1.5 times the effective hourly rate) and the effective hourly rate of $21.50 per hour ($1,247 average weekly gross pay divided by 58 average weekly hours).
•  Per-driver damages over the relevant period: Approximately $31,153.50, calculated as $193.50 per week multiplied by approximately 161 weeks (April 30, 2022 through an assumed judgment date of approximately August 2025).
•  Full collective single damages: Approximately $36,979,204.50, calculated as $31,153.50 multiplied by 1,187 drivers.
•  Full collective damages with FLSA liquidated damages (willful violation): Approximately $73,958,409.00, representing double the single damages figure under the FLSA's liquidated damages provision for willful violations, 29 U.S.C. § 216(b).
This damages exposure underscores the critical importance of defeating or narrowing conditional certification. Even if the collective is reduced through the arbitration motion (removing the approximately 640 v3.0 signatories), the remaining 547-driver collective would still present single damages exposure of approximately $17,040,964.50 and liquidated damages exposure of approximately $34,081,929.00.
E. Procedural Timeline Relevant to Expert Work
The following procedural milestones are relevant to the expert work plan and deliverable timeline:
As discussed in Section V below, the expert work plan is structured around the critical path of the August 13, 2025 deadline for Defendant's Response in Opposition to Plaintiff's Motion for Conditional Certification.
III. Data Requirements
This section identifies the data sets that Corbin Risk Advisors will require from SLG (through C&H) in order to perform the analyses described in Section IV. The data requirements are organized by source and type. Unless otherwise indicated, all data requests cover the relevant period of April 30, 2022 through the present. C&H will coordinate data collection with SLG's internal team, with Lisa Cheng serving as the primary data liaison. For each data category, we identify the specific fields needed and the analytical purpose the data will serve.
A. RouteSync Dispatch Logs
We require complete dispatch records from the RouteSync platform for all delivery assignments made to drivers across all 14 SLG distribution hubs during the relevant period (April 30, 2022 through the present). RouteSync is SLG's proprietary dispatch and route management platform through which delivery assignments are communicated to drivers and through which drivers accept or decline individual assignments.
The following data fields are needed for each dispatch record:
•  Driver identification number (unique SLG-assigned driver ID)
•  Hub assignment (hub code, e.g., HUB-01 through HUB-14)
•  Date of dispatch
•  Delivery assignment timestamp (date and time the assignment was pushed to the driver)
•  Delivery acceptance or decline indicator (whether the driver accepted or declined the assignment)
•  Delivery acceptance or decline timestamp
•  Delivery completion timestamp (if accepted and completed)
•  Delivery type classification (standard delivery vs. white-glove delivery)
•  Package weight category (as classified by SLG's rate schedule)
•  Distance category (as classified by SLG's rate schedule)
•  Per-delivery rate assigned to the delivery
•  Customer delivery window (the customer-facing time window for the delivery)
•  Delivery status (completed, failed attempt, returned, cancelled)
•  Customer complaint indicator (if any complaint was logged for the delivery)
Analytical purpose: The RouteSync dispatch data is foundational to several of the analyses described in Section IV. It will be used to analyze route acceptance rates relative to the 85% minimum acceptance rate requirement, to measure the frequency with which individual drivers declined delivery assignments, to assess the extent of driver discretion in accepting or declining assignments, and to quantify the variability in these behaviors across hubs and ICOA versions. Additionally, the dispatch data will provide the basis for analyzing delivery mix (standard vs. white-glove), rate structures, and the relationship between delivery characteristics and driver compensation.
Data production coordination: SLG's IT department will need to prepare and produce a data extract from the RouteSync platform. The primary contacts for this data extraction are Margaret Hollis (Vice President of Operations) and Aaron Turcotte (Director of Information Technology). Lisa Cheng is coordinating the data request with these individuals and will ensure that the extract is provided in a machine-readable format (preferably CSV or structured database export) suitable for statistical analysis.
B. GPS Telematics Data
We require GPS tracking data from the SLG-provided telematics devices installed on all driver vehicles during the relevant period. These telematics devices were mandatory for all drivers performing deliveries under the ICOA and recorded continuous location and vehicle operational data.
The following data fields are needed:
•  Driver identification number
•  Hub identification code
•  Date
•  Ignition on time (timestamp of first vehicle start each day)
•  Ignition off time (timestamp of final vehicle shutdown each day)
•  Route coordinates and waypoints (GPS latitude/longitude at regular intervals, ideally every 30 seconds or at each delivery stop)
•  Deviations from RouteSync-assigned routes (if flagged by the telematics system)
•  Idle times (periods where the vehicle was stationary with the ignition on)
•  Total daily mileage
•  Speed data (if available, for route reconstruction purposes)
Analytical purpose: The GPS telematics data is critical to reconstructing actual hours worked by each driver on each day during the relevant period. By analyzing ignition on/off times and route activity, Dr. Ramanathan can calculate total daily and weekly hours for each driver with a high degree of precision. This data will also be used to assess route deviation frequency — i.e., the extent to which drivers departed from RouteSync-assigned routes — which serves as a quantitative indicator of driver autonomy and discretion. Patterns of route deviation, start/stop time variability, and daily mileage will be compared across hubs and over time to assess whether drivers exercised meaningfully different levels of discretion depending on their hub assignment, ICOA version, or other factors.
C. Pay Records
We require complete payment records for all 1,187 drivers in the proposed collective during the relevant period.
The following data fields are needed:
•  Driver identification number
•  Hub identification code
•  Pay period (start and end dates)
•  Number of deliveries completed during the pay period
•  Per-delivery rate applied (broken out by delivery type and category if available)
•  Gross payment amount for the pay period
•  Any bonuses, incentive payments, or other positive adjustments
•  Any penalties, deductions, or chargebacks
•  Net payment amount
Analytical purpose: The pay records are essential to calculating effective hourly rates for each driver. By dividing gross pay by hours worked (as derived from the GPS telematics data), Dr. Ramanathan can determine the effective hourly rate for each driver across all pay periods. This analysis will reveal the variance in earnings across drivers and hubs, assess whether drivers had a genuine opportunity for profit or loss (a key factor under the economic reality test), and evaluate the representativeness of plaintiff's claimed effective hourly rate.
With specific reference to the named plaintiff, Marcus Delano's pay records indicate that his average weekly gross pay in 2023 was $1,247, yielding an effective hourly rate of approximately $21.50 ($1,247 ÷ 58 hours). A critical question for Dr. Ramanathan's analysis is whether this figure is representative of the broader driver population or whether it constitutes an outlier. Preliminary indications suggest significant variance across hubs and delivery mixes, which would undermine the premise that a single damages calculation can be applied uniformly to the proposed collective.
D. ICOA Execution Records
We require complete records of which ICOA version each driver signed, including execution dates, hub assignments at the time of signing, and any subsequent amendments or modifications.
The following data fields are needed:
•  Driver identification number
•  ICOA version executed (v1.0, v2.0, or v3.0)
•  Execution date
•  Hub assignment at the time of execution
•  Any amendments or addenda executed subsequent to the original ICOA
•  Any records of drivers transitioning between ICOA versions (e.g., drivers who originally signed v1.0 or v2.0 and subsequently signed v3.0)
The critical breakdown is as follows: of the 1,187 drivers in the proposed collective, approximately 640 drivers signed ICOA v3.0 (the January 2023 revision containing the arbitration clause and collective action waiver), while the remaining approximately 547 drivers remain on ICOA v1.0 or v2.0.
Analytical purpose: The ICOA execution records are essential to segmenting all analyses by ICOA version, thereby demonstrating that the three ICOA versions create materially different contractual relationships between SLG and its drivers. The presence of the arbitration clause and collective action waiver in v3.0 — absent from v1.0 and v2.0 — is a critical differentiator for both the conditional certification analysis and the anticipated motion to compel individual arbitration. Beyond the arbitration clause itself, the three ICOA versions contain different provisions regarding performance metrics, rate structures, termination procedures, and other terms that may affect the classification inquiry, further supporting the argument that drivers on different ICOA versions cannot be treated as a single homogeneous collective.
E. Hub-Level Operational Data
We require hub-specific operational policies, procedures, and staffing records for all 14 SLG distribution hubs. The following reference table provides the hub designations, locations, and approximate driver counts during the relevant period:
The following data fields and documents are needed for each hub:
•  Identity of hub manager(s) during the relevant period
•  Local delivery mix (percentage of standard deliveries vs. white-glove deliveries)
•  Local subcontracting approval rates (number of subcontracting requests received and approved/denied)
•  Any hub-specific supplemental policies or procedures (e.g., local operating guidelines, hub-specific performance standards, or local addenda to the ICOA)
•  Geographic service area size (square miles covered by the hub's delivery territory)
•  Customer base composition (residential vs. commercial, major retail partners served)
•  Staffing levels over time (monthly driver counts)
•  Any hub-specific operational changes implemented during the relevant period
Analytical purpose: Hub-level operational data is essential to demonstrating that drivers at different hubs operated under materially different working conditions, levels of control, and economic arrangements. Differences in delivery mix (e.g., a hub that primarily handles standard deliveries versus one with a high proportion of white-glove deliveries), geographic service area size (e.g., a dense urban hub versus a sprawling rural service territory), customer base composition, and local management practices all bear directly on the classification inquiry and undermine the premise that a single set of "similarly situated" findings can be applied across the entire collective. If, for example, the Houston hub (HUB-03) has a subcontracting approval rate of 15% while the Fort Worth hub (HUB-01) has a rate of 0%, that variance is directly relevant to the degree of entrepreneurial opportunity available to drivers at different hubs.
F. Subcontracting and Multi-Platform Engagement Records
We require records relating to drivers' subcontracting activity and engagement with competing delivery platforms or other delivery services, to the extent such records exist within SLG's systems. Specifically:
•  Records of all subcontracting requests submitted by drivers during the relevant period, including the requesting driver's identification number, hub assignment, date of request, reason for request, and SLG's approval or denial decision with date and rationale.
•  Any records or knowledge of drivers' engagement with competing delivery platforms (e.g., Ashbury Corporation, Aldermere Enterprises, Cobalt Ridge Corporation, Strathmore Holdings) or other delivery or transportation services during the relevant period. This may include information obtained during onboarding, exit interviews, performance reviews, or other interactions.
•  Tax records or other indicia of multi-platform work, if available from SLG's records. For example, if SLG maintains copies of drivers' W-9 forms or has any information regarding drivers who reported income from multiple sources, such records would be relevant.
Analytical purpose: The subcontracting and multi-platform engagement data bears directly on the entrepreneurial opportunity factor of the economic reality test. If a meaningful proportion of drivers performed work for competing platforms or other employers during the relevant period, this would tend to demonstrate that the drivers operated as independent businesses rather than as employees of SLG. Conversely, if subcontracting was nominally available but never approved (as plaintiff alleges), this would support the control argument. The analysis of these records will assess the variance in driver behavior regarding outside work and subcontracting activity, both across hubs and across ICOA versions.
G. Training and Compliance Records
We require records relating to mandatory training and performance compliance for all drivers during the relevant period:
•  Records of mandatory safety and customer-service training, including initial 16-hour onboarding training and quarterly 4-hour refresher training sessions, with attendance records for each driver.
•  Performance review records triggered by drivers falling below the 85% minimum delivery acceptance rate within any rolling 30-day period, including the driver's identification number, hub assignment, date of the triggering event, acceptance rate at the time, and outcome of the performance review.
•  Records of all disciplinary actions, warnings, or contract terminations related to performance metrics, customer complaints, or other operational standards, including the nature of the infraction, the hub at which it occurred, and the disposition.
Analytical purpose: These records will allow Dr. Ramanathan to assess whether training requirements and performance standards were uniformly applied and enforced across all 14 hubs, or whether there was meaningful variance in enforcement practices. If, for example, certain hubs rarely conducted performance reviews for drivers below the 85% threshold while others strictly enforced it, this variance would undercut the premise that all drivers were subject to the same degree of control. The training and compliance data also provides insight into the permanence and exclusivity of the working relationship and the degree to which SLG supervised and directed driver conduct.
H. Driver Demographic and Tenure Data
We require the following information for each of the 1,187 drivers in the proposed collective:
•  Driver start date (date of initial ICOA execution and commencement of deliveries)
•  Driver end date (if applicable — date of contract termination, non-renewal, or voluntary departure)
•  Hub assignment(s) during the relevant period, including any transfers between hubs
•  Vehicle specifications (make, model, year, and type of personal vehicle used for deliveries, which was required to be a cargo van or box truck, model year 2015 or newer, meeting SLG specifications)
•  Any records of driver-owned equipment or tools beyond the vehicle itself
Analytical purpose: This data will support analysis of the permanence of the working relationship — a key factor under the economic reality test. Drivers with short tenures may have treated the SLG engagement as one of several gig-economy relationships, while drivers with multi-year tenures may exhibit characteristics more consistent with employment. The vehicle specification data is relevant to the driver's investment in equipment and tools, another factor in the classification analysis. Tenure variance and hub transfer patterns will also inform the cluster analysis described in Section IV.H.
IV. Analytical Framework
A. Primary Analytical Objective: Demonstrating Lack of Commonality for Conditional Certification
At the conditional certification stage under FLSA § 216(b), courts in the Fifth Circuit apply the two-step certification process established in Lusardi v. Xeroxx Corp., 118 F.R.D. 351 (D.N.J. 1987), and adopted by the Fifth Circuit in Mooney v. Aramco Services Co., 54 F.3d 1207 (5th Cir. 1995). At the first step — conditional certification — the plaintiff bears the burden of demonstrating that the putative collective members are "similarly situated" within the meaning of § 216(b). While this standard is acknowledged to be lenient, requiring only a "modest factual showing" that the plaintiff and the putative collective members were subject to a common policy or plan that violated the FLSA, courts have increasingly recognized the value of quantitative evidence at this stage, particularly where the defendant can demonstrate through expert analysis that the putative collective members' experiences were too varied to support collective treatment.
Dr. Ramanathan's analysis should focus on demonstrating statistically significant variance among the 1,187 drivers in the proposed collective across multiple dimensions — including hours worked, pay, route acceptance behavior, route deviation patterns, multi-platform engagement, and contractual terms — that undercuts the premise of collective treatment. The goal is not merely to identify some variance (which will inevitably exist in any large dataset) but to demonstrate that the variance is of such magnitude and character that individualized inquiries would necessarily predominate over common questions, rendering collective treatment unmanageable and inappropriate.
While we acknowledge that the conditional certification standard is lenient and that courts at this stage do not require the defendant to prove that collective treatment is inappropriate, quantitative evidence of dissimilarity can be highly persuasive — particularly in the Northern District of Texas, where judges have shown receptivity to expert submissions at the conditional certification stage. Moreover, the analytical work performed at this stage will position the defense for a robust decertification motion at the second stage, where the standard is significantly more rigorous and the court engages in a closer examination of whether the collective members are truly similarly situated based on the evidence developed through discovery.
B. Analysis 1: Hours-Worked Variance
Using GPS telematics data (Section III.B) and RouteSync dispatch logs (Section III.A), Dr. Ramanathan should reconstruct weekly hours worked for all drivers across all 14 hubs during the relevant period. Hours worked should be defined based on ignition on/off times from the telematics data, cross-referenced with active delivery assignments from RouteSync, to capture the period during which each driver was actively engaged in delivery work on each day.
The following descriptive statistics should be calculated:
•  Mean, median, standard deviation, and interquartile range of weekly hours worked, calculated (i) for the overall collective, (ii) by hub, (iii) by ICOA version, and (iv) by time period (quarterly or semi-annually, to identify trends over time).
•  Frequency distribution of weekly hours worked across the entire collective, with particular attention to the percentage of drivers whose weekly hours fall within, above, and below the range alleged by plaintiff (55–65 hours per week, with an average of 58 hours for Delano).
A central question for this analysis is whether plaintiff's alleged 55–65 hours per week (averaging 58 hours) is representative of the broader driver population or whether it constitutes an outlier. If, for example, the median weekly hours across the collective is 42 hours with a standard deviation of 12 hours, and Delano's 58 hours falls more than one standard deviation above the mean, this would strongly support the argument that Delano's experience cannot be generalized to the collective.
To test whether hours-worked distributions differ significantly across hubs, Dr. Ramanathan should employ analysis of variance (ANOVA) or, if the data does not satisfy the normality assumptions required for ANOVA, the non-parametric Kruskal-Wallis test. These tests will determine whether the observed differences in hours-worked distributions across hubs are statistically significant or attributable to chance.
Dr. Ramanathan should produce the following visualizations to accompany the analysis:
•  box plots of weekly hours worked by hub (14 boxes, one per hub), showing the median, interquartile range, and outliers for each hub.
•  Histograms of the weekly hours-worked distribution for the overall collective, with Delano's average hours marked for comparison.
•  Time-series plots showing trends in mean weekly hours by hub over the relevant period.
C. Analysis 2: Pay and Effective Hourly Rate Variance
Using pay records (Section III.C) and hours-worked reconstructions from the telematics analysis, Dr. Ramanathan should calculate the effective hourly rate for each driver across all pay periods in the relevant period. The effective hourly rate is defined as gross pay divided by hours worked (as reconstructed from telematics data) for each pay period.
The following analyses should be performed:
•  Calculation of mean, median, standard deviation, and interquartile range of effective hourly rates (i) for the overall collective, (ii) by hub, (iii) by ICOA version, (iv) by delivery type mix (proportion of standard vs. white-glove deliveries), and (v) by driver tenure.
•  Comparison of the effective hourly rate distribution to plaintiff's claimed rate of $21.50 per hour, including determination of what percentile Delano's rate falls within the overall distribution.
•  Analysis of whether per-delivery rate structures, when combined with differences in delivery mix and geographic service area characteristics across hubs, produce meaningfully different economic outcomes for drivers at different hubs.
•  Assessment of earnings volatility for individual drivers over time (month-to-month or quarter-to-quarter variance in effective hourly rates) to evaluate whether drivers bore genuine economic risk.
This analysis directly addresses the "opportunity for profit or loss" factor of the economic reality test and, critically, demonstrates whether the single damages calculation proposed by plaintiff ($193.50 per week per driver) can be applied uniformly without individualized inquiry into each driver's actual hours, pay, and effective rate.
D. Analysis 3: Route Acceptance and Delivery Refusal Rates
Using RouteSync dispatch logs, Dr. Ramanathan should analyze delivery acceptance and decline rates across the proposed collective, with specific reference to the 85% minimum acceptance rate imposed by SLG.
The following analyses should be performed:
•  Calculation of mean, median, and distribution of delivery acceptance rates by hub and by individual driver over the relevant period.
•  Identification and quantification of drivers who regularly declined deliveries (i.e., maintained acceptance rates at or near the 85% minimum) versus those with near-100% acceptance rates. This distribution is significant: drivers who routinely exercised their contractual right to decline 15% of offered deliveries exhibited a different degree of autonomy than drivers who accepted virtually all assignments.
•  Assessment of whether the 85% minimum acceptance rate was uniformly enforced across all 14 hubs, or whether enforcement varied by hub. Cross-referencing acceptance rates with performance review triggering data (Section III.G) will reveal whether hubs with similar acceptance rate distributions had different enforcement outcomes, indicating hub-level managerial discretion.
•  Analysis of the temporal patterns of delivery declines — e.g., whether declines were concentrated on certain days of the week, during certain hours, or during certain seasons — which may reveal that drivers exercised strategic judgment in selecting deliveries rather than simply accepting all assignments.
E. Analysis 4: Route Deviation and Scheduling Flexibility
Using GPS telematics data, Dr. Ramanathan should measure the frequency and magnitude of deviations from RouteSync-assigned routes, as well as the variance in daily start and stop times, to assess the degree of scheduling and routing flexibility exercised by drivers in practice.
The following analyses should be performed:
•  Quantification of route deviation frequency: for each delivery day, measure whether the driver followed the RouteSync-assigned route or deviated (by resequencing deliveries, taking alternative routes, or making non-delivery stops). Calculate the proportion of delivery days on which meaningful deviations occurred, by hub and by driver.
•  Measurement of route deviation magnitude: for days on which deviations occurred, quantify the extent of the deviation (e.g., additional mileage, departure from assigned delivery sequence, time impact).
•  Analysis of daily start and stop time variability: within the required 7:00 AM–8:00 PM delivery window, determine the actual range of start and stop times for each driver and each hub. If drivers at some hubs consistently started at 7:00 AM and ended at 7:00 PM while drivers at other hubs exhibited wide variance in start and stop times, this differential is relevant to the degree of scheduling control.
•  Comparison of route deviation and scheduling flexibility patterns across hubs, ICOA versions, and over time.
This analysis addresses the "degree of control" factor of the economic reality test. If a meaningful proportion of drivers regularly deviated from assigned routes and exercised discretion in scheduling their work within the delivery window, this would tend to support the independent contractor classification. Conversely, if route compliance was near-universal, this would tend to support the employment argument. Critically, if the degree of route deviation and scheduling flexibility varied significantly across hubs, this would undermine the "similarly situated" premise.
F. Analysis 5: Multi-Platform Engagement and Subcontracting
To the extent data is available (Section III.F), Dr. Ramanathan should analyze the proportion of drivers who performed work for competing delivery platforms or other employers during the relevant period, as well as the frequency and outcomes of subcontracting requests.
The following analyses should be performed:
•  Quantification of the proportion of drivers known to have performed work for competing delivery platforms (e.g., Ashbury Corporation, Aldermere Enterprises, Strathmore Holdings, or similar services) during the relevant period.
•  Analysis of subcontracting request volume and approval/denial rates by hub. If certain hubs approved subcontracting requests while others uniformly denied them, this variance is directly relevant to the degree of entrepreneurial opportunity available to drivers and undercuts the premise of collective treatment.
•  Assessment of whether multi-platform engagement and subcontracting behavior differed across ICOA versions, which may indicate that different contractual frameworks produced different levels of entrepreneurial activity.
We recognize that data availability for this analysis may be limited, as SLG may not have comprehensive records of drivers' outside engagements. Dr. Ramanathan should identify any data gaps and recommend supplemental data sources or collection methods (e.g., targeted discovery requests or driver questionnaires) that might address those gaps in later phases of the litigation.
G. Analysis 6: ICOA Version Segmentation
Dr. Ramanathan should segment all of the analyses described in Sections IV.B through IV.F above by ICOA version (v1.0, v2.0, and v3.0). This segmentation analysis serves dual purposes.
First, it demonstrates that the three ICOA versions create materially different contractual relationships between SLG and its drivers. The differences among the three versions extend beyond the arbitration clause and collective action waiver added in v3.0 to include differences in performance metrics, rate structures, termination provisions, and other terms that may affect the degree of control exercised by SLG and the economic reality of the working relationship. Dr. Ramanathan should identify and quantify any behavioral differences between drivers on different ICOA versions — for example, whether v3.0 drivers exhibit different acceptance rates, hours-worked patterns, or multi-platform engagement levels than v1.0 or v2.0 drivers.
Second, the ICOA version segmentation directly supports the anticipated motion to compel individual arbitration for the approximately 640 ICOA v3.0 signatories. By demonstrating that v3.0 drivers constitute a distinct subpopulation with different contractual terms and different behavioral characteristics, the analysis reinforces the argument that these drivers should be severed from the collective and compelled to arbitrate their claims individually pursuant to the arbitration clause and collective action waiver in their contracts.
H. Analysis 7: Hub-Level Cluster Analysis
Dr. Ramanathan should perform a multivariate cluster analysis (or similar technique, such as latent class analysis or principal component analysis with clustering) to identify natural groupings among the 1,187 drivers based on multiple dimensions simultaneously, including:
•  Weekly hours worked (mean and variance)
•  Effective hourly rate (mean and variance)
•  Delivery acceptance rate
•  Route deviation frequency
•  Tenure with SLG
•  Delivery type mix (proportion of standard vs. white-glove)
•  Multi-platform engagement (to the extent data is available)
The purpose of this analysis is to determine whether the 1,187 drivers form natural clusters — and, critically, whether those clusters align with hub assignments, geographic regions, or ICOA versions rather than forming a single homogeneous population. If the cluster analysis reveals, for example, that drivers at the Houston hub (HUB-03) cluster together and are distinct from drivers at the Lubbock hub (HUB-13), which in turn are distinct from drivers at the Oklahoma City hub (HUB-06), this would directly support the argument that individualized inquiries would predominate over common questions and that collective treatment is inappropriate.
The cluster analysis should be presented with clear visualizations (e.g., dendrograms, scatter plots of principal components with cluster assignments, heat maps of cluster characteristics) that can be effectively communicated to the court in the expert declaration.
I. Statistical Methodology Notes
The following methodological guidelines apply to all analyses described in this section:
•  All statistical tests should employ a significance level of α = 0.05, consistent with standard practice in the social sciences and with the level typically accepted by courts evaluating expert statistical evidence.
•  All analyses should be fully replicable and documented in sufficient detail to permit verification by opposing experts and to withstand cross-examination at deposition and trial. Dr. Ramanathan should maintain a complete analytical record, including all data processing steps, code (in Stata, R, Python, or similar statistical software), intermediate outputs, and assumptions.
•  All methodologies employed should be consistent with the standards established in Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993), and the standards applicable in the Northern District of Texas, including that the methodology has been tested, subjected to peer review and publication, has a known error rate, and is generally accepted within the relevant scientific community.
•  While courts at the conditional certification stage do not typically subject expert declarations to full Daubert scrutiny, the expert declaration filed in support of Defendant's Response in Opposition should be rigorous enough to withstand any challenge to its admissibility and should be prepared with the understanding that it may be subjected to more rigorous scrutiny at the decertification stage.
•  Dr. Ramanathan should be prepared to explain all methodological choices in plain language suitable for a judicial audience and should ensure that the expert declaration is accessible to a generalist judge without specialized statistical training.
V. Deliverable Timeline
A. Phase 1: Data Collection and Intake (May 14 – June 1, 2025)
During Phase 1, Calderwood & Harkness will coordinate with SLG to compile and produce all data sets identified in Section III above. The primary SLG contacts for data collection are Margaret Hollis (Vice President of Operations), Aaron Turcotte (Director of Information Technology), and Trevor Sandoval (General Counsel). Lisa Cheng, C&H paralegal, will serve as the primary data liaison between SLG's internal team and Corbin Risk, ensuring that data extracts are produced in the required formats and contain all specified fields.
Upon receipt of each data set, Dr. Ramanathan and the Corbin Risk associate analyst team will conduct an initial review of the data for completeness, quality, and usability. This review will include verification that all required fields are populated, identification of any missing data or formatting issues, preliminary assessment of sample sizes by hub and ICOA version, and identification of any additional data needs not anticipated in this memorandum.
An initial data quality assessment memorandum from Corbin Risk to C&H is due by June 1, 2025. This memorandum should identify any data gaps, quality concerns, or additional data requests, and should confirm that the data received is sufficient to proceed with the analyses described in Section IV. If material data gaps are identified, C&H will coordinate supplemental data requests with SLG on an expedited basis.
B. Phase 2: Preliminary Analysis (June 1 – June 20, 2025)
During Phase 2, Corbin Risk will conduct preliminary statistical analysis across all seven analytical dimensions described in Section IV (hours-worked variance, pay and effective hourly rate variance, route acceptance and delivery refusal rates, route deviation and scheduling flexibility, multi-platform engagement and subcontracting, ICOA version segmentation, and hub-level cluster analysis).
A preliminary analysis report is due to C&H by June 20, 2025. This preliminary report is a work-product communication to counsel, not intended for filing with the court, and should be clearly marked as "PRIVILEGED AND CONFIDENTIAL — ATTORNEY WORK PRODUCT — NOT FOR FILING." The purpose of the preliminary report is to allow the C&H litigation team to assess the strength of the statistical arguments, identify the most compelling findings, refine the analytical approach in light of the preliminary results, and identify any additional data needs or methodological adjustments before plaintiff's conditional certification motion is filed (anticipated July 14, 2025).
The preliminary report should include, for each analytical dimension, a summary of key findings, initial statistical results (including significance tests), preliminary visualizations, and Dr. Ramanathan's assessment of the strength of the evidence supporting the "not similarly situated" argument. The report should also identify any areas where the data is inconclusive or where additional analysis may be needed.
C. Phase 3: Refined Analysis and Draft Declaration (June 20 – July 15, 2025)
Following receipt of plaintiff's motion for conditional certification (expected to be filed on or about July 14, 2025), Dr. Ramanathan should refine the analysis in light of plaintiff's specific arguments, supporting declarations, and any expert submissions filed by plaintiff. The refined analysis should directly address the factual assertions and legal arguments made in plaintiff's motion, ensuring that the expert declaration is responsive and targeted.
C&H will provide Dr. Ramanathan with plaintiff's motion for conditional certification, all supporting declarations, and any plaintiff expert submissions promptly upon filing. C&H expects to have these materials available to Corbin Risk within 24 hours of filing.
A draft expert declaration for C&H review is due by July 15, 2025. The draft declaration should be a substantially complete document suitable for filing, including all analytical results, statistical tables, charts, and visualizations, along with Dr. Ramanathan's opinions and the bases therefor. The declaration should be structured to address each of the analytical dimensions described in Section IV in a logical sequence, with clear headings and cross-references to supporting exhibits.
We note that the draft declaration due date of July 15, 2025, falls only one day after the expected filing of plaintiff's motion. To the extent plaintiff's motion raises arguments or presents evidence not anticipated in the preliminary analysis, Dr. Ramanathan should flag any areas requiring additional analysis or modification in the draft declaration, and the C&H team will work with Dr. Ramanathan to address those issues during Phase 4.
D. Phase 4: Finalization of Expert Declaration (July 15 – July 25, 2025)
During Phase 4, C&H will provide detailed comments on the draft declaration, and Dr. Ramanathan and the C&H legal team will engage in iterative revisions to finalize the declaration. The primary C&H reviewers will be James Underwood (Lead Associate) and Devon Archer (Working Associate), with oversight and final approval from Bernard Crawford (Responsible Partner).
The revision process should include review and refinement of all statistical analyses and conclusions, integration of legal arguments and standards into the analytical framework (to be supplied by C&H counsel), editing for clarity, precision, and persuasiveness, and formatting of all exhibits, tables, and charts for court submission.
The final expert declaration is due July 25, 2025. This date provides approximately 19 days of buffer before the August 13, 2025 deadline for filing Defendant's Response in Opposition to Plaintiff's Motion for Conditional Certification, allowing time for final formatting, exhibit preparation, and any last-minute adjustments.
E. Phase 5 (Contingent): Supplemental Analysis for Arbitration Motion
If, based on the litigation team's strategic assessment, C&H determines to file a motion to compel individual arbitration for the approximately 640 drivers who signed ICOA v3.0, Dr. Ramanathan may be asked to provide supplemental analysis regarding the segmentation of the collective. Such supplemental analysis would draw upon the ICOA version segmentation analysis described in Section IV.G and would focus on demonstrating that the v3.0 signatory population constitutes a distinct subgroup with different contractual terms and, potentially, different behavioral characteristics.
The timeline for any such supplemental analysis is to be determined based on strategic decisions regarding the timing of the arbitration motion, which may be filed concurrently with the response to the conditional certification motion, as a separate motion, or at a later stage of the litigation. C&H will communicate the scope and timeline for any supplemental analysis to Dr. Ramanathan as soon as those strategic decisions are finalized.
VI. Budget Parameters and Fee Arrangements
The fee arrangements for this engagement are governed by the Expert Retention Agreement dated May 8, 2025, between Calderwood & Harkness LLP (on behalf of SLG) and Corbin Risk Advisors LLC. The following summary is provided for reference and to establish the budget parameters within which the work described in this memorandum will be performed.
Fee Rates:
•  Dr. Priya Ramanathan (Lead Consultant / Expert Economist): $550 per hour
•  Corbin Risk associate analysts: $375 per hour
Budget for Conditional Certification Phase (Phases 1–4):
The total budget for the conditional certification phase of the engagement is $175,000. This budget is allocated as follows:
•  $90,000 for the preliminary analysis component (Phases 1 and 2), which is included within the firm's Phase 1 billing cap of $475,000. To clarify, the firm's Phase 1 billing cap of $475,000 encompasses $385,000 in legal fees and $90,000 in expert preliminary analysis costs. This $90,000 component will be billed through C&H and included in C&H's Phase 1 invoices to SLG.
•  $85,000 for the refined analysis and expert declaration component (Phases 3 and 4), which falls outside the firm's Phase 1 billing cap and will be billed directly by Corbin Risk to C&H for submission to SLG.
Total Estimated Expert Budget Through Trial:
If the matter does not settle and proceeds through all phases of litigation including trial, the total estimated expert budget is $450,000. This estimate is subject to revision based on the scope and complexity of the work required at the decertification stage, the extent of discovery, and whether the matter proceeds to trial. Any material revisions to the budget will be communicated to C&H and SLG in advance.
Invoicing:
Monthly invoices from Corbin Risk are to be submitted to Calderwood & Harkness LLP, attention: Renata Vasquez (Billing Partner), for review and submission to SLG for payment. Invoices should contain sufficient detail to permit review of time entries and expenses, consistent with the requirements of the Expert Retention Agreement and SLG's outside counsel billing guidelines.
Budget Overages:
Any expenditure anticipated to exceed the Phase budget established above requires prior written approval from Bernard Crawford (Responsible Partner). Dr. Ramanathan should notify James Underwood promptly if the pace of work or scope of analysis suggests that a budget overage may occur, so that approval can be obtained before additional work is performed.
VII. Communication and Confidentiality Protocols
The following protocols govern communications between Corbin Risk Advisors and Calderwood & Harkness LLP throughout the engagement:
Privilege and Work Product Protection:
All communications between Corbin Risk and C&H are to be treated as privileged attorney work product, protected under Fed. R. Civ. P. 26(b)(3) (trial preparation materials) and Fed. R. Civ. P. 26(b)(4) (expert trial preparation materials). All draft analyses, preliminary reports, internal memoranda, notes, emails, and other communications between Corbin Risk and C&H constitute consulting work product and should be clearly marked as "PRIVILEGED AND CONFIDENTIAL — ATTORNEY WORK PRODUCT" until and unless specifically designated as testifying expert materials.
Consulting vs. Testifying Expert Designation:
Dr. Ramanathan's engagement is initially in the capacity of a consulting expert under Fed. R. Civ. P. 26(b)(4)(D). Designation as a testifying expert under Fed. R. Civ. P. 26(a)(2) will occur when the expert declaration is finalized for filing with the court. Upon designation as a testifying expert, the final expert declaration and the materials considered in forming the opinions expressed therein will be subject to disclosure under Rule 26(a)(2)(B). However, draft reports and communications between Dr. Ramanathan and C&H counsel are protected from disclosure under Fed. R. Civ. P. 26(b)(4)(B) and (C), regardless of the designation.
Confidentiality of SLG Data:
All data received from SLG through C&H is subject to the confidentiality provisions of the Expert Retention Agreement and any protective order entered in the litigation. Corbin Risk personnel who access SLG data must be identified to C&H and must execute any required confidentiality acknowledgments. SLG data should be stored on secure, encrypted systems and should not be transmitted to any person or entity not authorized under the Expert Retention Agreement.
Primary Contacts:
•  James Underwood, Lead Associate — primary day-to-day contact for all substantive matters
•  Devon Archer, Working Associate — secondary contact for substantive matters; available for detailed data and analytical discussions
•  Bernard Crawford, Responsible Partner — escalation contact for strategic decisions, budget approvals, and senior-level review
•  Lisa Cheng, Paralegal — primary data liaison for coordination of data production from SLG
All substantive communications from Dr. Ramanathan should be directed to James Underwood and Devon Archer. Communications regarding data production logistics should be directed to Lisa Cheng. Budget-related communications should be directed to James Underwood with a copy to Renata Vasquez.
VIII. Next Steps
The following action items should be initiated immediately upon receipt of this memorandum:
1.  C&H to initiate data collection from SLG: Beginning the week of May 14, 2025, Lisa Cheng will coordinate with Margaret Hollis, Aaron Turcotte, and Trevor Sandoval at SLG to compile and produce all data sets identified in Section III. James Underwood will provide an introductory communication to SLG's team outlining the data requirements and the timeline for production.
2.  Dr. Ramanathan to designate Corbin Risk associate team: Dr. Ramanathan should designate the Corbin Risk associate analysts who will be assigned to this engagement and confirm their availability through at least August 2025 (covering Phases 1 through 4). Please provide the names, qualifications, and hourly rates of the designated associates to James Underwood by May 16, 2025.
3.  Initial call to discuss data specifications: An initial working call between James Underwood, Devon Archer, and Dr. Ramanathan is proposed for May 16, 2025, to discuss the data specifications in detail, refine the analytical framework, and address any preliminary questions regarding the scope of the engagement. Lisa Cheng will send a calendar invitation with dial-in details.
4.  Preliminary data intake checklist: Dr. Ramanathan should provide a preliminary data intake checklist to C&H by May 19, 2025, specifying the preferred data formats, file naming conventions, and any additional data fields or documentation needed beyond those identified in Section III.
5.  ICOA versions and comparison chart: C&H will provide Dr. Ramanathan with copies of all three ICOA versions (v1.0, v2.0, and v3.0) and the ICOA Version Comparison Chart prepared by Devon Archer on May 12, 2025, by May 15, 2025. These documents will enable Dr. Ramanathan to understand the contractual differences among the three versions and to design the ICOA version segmentation analysis accordingly.
Please do not hesitate to contact me with any questions regarding this memorandum or the scope of the engagement. We look forward to working with you and the Corbin Risk team on this matter and are confident that a rigorous, data-driven analytical approach will serve Stonefield's interest