Section 1: Executive Overview & Decision Framework

Industry data from the Warehousing Education and Research Council shows that facilities implementing engineered labor standards report average productivity gains of 22 to 28 percent within the first 12 months of deployment. Supply Chain Research emphasizes that these gains stem from time and motion studies applied directly to warehouse and production tasks rather than from historical averages that often embed inefficiencies.

Core Concepts and Definitions

Engineered labor standards establish measurable time expectations for repeatable tasks using predetermined motion time systems. The Maynard Operation Sequence Technique (MOST) breaks work into sequence models such as general move, controlled move, and tool use, assigning index values that convert to seconds. For example, a pallet pick operation coded as A1 B6 G3 A1 B0 P3 A0 totals 14 time measurement units or 0.084 minutes when the standard conversion factor is applied. Methods Time Measurement (MTM) provides finer granularity by analyzing basic motions including reach, grasp, move, and release at the therblig level, which suits high precision assembly lines where cycle times fall below 30 seconds. Hybrid approaches combine MOST for bulk handling tasks with MTM for value added packaging steps, delivering standards that reflect both speed and accuracy requirements.

Concrete application appears at Procter & Gamble plants where hybrid standards govern case packing operations. Operators follow a MOST sequence for pallet building followed by MTM derived elements for label application, resulting in a documented standard of 47 cases per labor hour. Real time shop floor data captured through IoT sensors refines these standards when volatility in production planning occurs, allowing adjustments for material variability without resetting entire benchmarks.

Actionable Implementation Steps

• Map all high volume tasks using process flow diagrams that capture start and end points, required equipment, and quality checkpoints.
• Select the appropriate predetermined motion system based on task complexity and cycle length.
• Conduct direct observation studies with trained analysts who record at least 30 cycles per task to validate index values.
• Integrate standards into the warehouse management system so that real time performance dashboards compare actual output against engineered targets.
• Establish feedback loops that route exceptions from production planning volatility back to industrial engineers for standard updates.

Decision Matrix for Approach Selection

Task Characteristics	Recommended Method	Application Conditions	Expected Accuracy Range	Company Example
Cycle time under 15 seconds, high repetition, fine motor elements	MTM	Assembly or kitting lines with strict quality tolerances	Plus or minus 3 percent	GEODIS electronics fulfillment centers
Cycle time 15 to 90 seconds, mixed manual and equipment moves	MOST	Receiving, putaway, and order picking in palletized environments	Plus or minus 5 percent	Walmart distribution centers targeting 380 cases per hour
Mixed task portfolio with both bulk handling and precision packaging	Hybrid MOST plus MTM	Facilities experiencing production planning volatility and variable order profiles	Plus or minus 4 percent	DHL e commerce hubs using RFID for real time validation
High equipment dependency and Overall Equipment Effectiveness tracking required	Hybrid with OEE integration	Production environments deploying IoT sensors on conveyors and lift trucks	Plus or minus 6 percent	Amazon robotic sortation sites measuring 450 units per hour

Why Engineered Standards Matter More Than Ever

Labor availability constraints and rapid shifts in demand patterns have elevated the need for objective productivity baselines. Supply Chain Research notes that organizations relying solely on historical data experience 15 to 20 percent lower labor utilization during periods of production planning volatility. Real time shop floor data from IoT and RFID deployments enables continuous calibration of standards, supporting through engineering practices that extend attention across the full product lifecycle rather than isolated manufacturing stages.

Amazon fulfillment operations illustrate the impact. Engineered standards tied to specific MOST sequences allow dynamic slotting adjustments when order profiles change, maintaining pick rates above 400 units per hour even during peak volatility. Walmart applies similar standards across its grocery distribution network to balance case handling with temperature controlled constraints, achieving documented labor cost reductions of 18 percent year over year. GEODIS and DHL leverage hybrid methods integrated with warehouse management systems to meet service level agreements that now include same hour fulfillment windows.

Human resources functions benefit directly because standards create transparent performance expectations that reduce disputes and support targeted training programs. When combined with value chain re engineering principles, these standards shift focus from final product output to end to end process efficiency, incorporating transportation data standardization and intelligent shop floor sensing for sustained gains.

Supply Chain Research recommends beginning with a pilot on the top 20 percent of tasks by volume, validating standards against actual output for 90 days before full rollout. This sequence ensures that metrics remain actionable and that adjustments for equipment effectiveness or material variability occur before scaling across multiple sites.

Section 2: Step-by-Step Implementation Playbook

This playbook from Supply Chain Research provides a phased approach to deploying Engineered Labor Standards in warehouse and production environments. The methodology relies on MOST, MTM, or hybrid time-and-motion techniques integrated with real-time shop-floor data. It draws on insights about production planning under volatility and the value of IoT and RFID deployments on intelligent shop floors to refine standard operating times. Each phase includes specific timelines, resource estimates, and tool requirements. Total program duration spans 22 to 28 weeks for a mid-sized facility with 150 to 300 associates.

Phase 1: Assessment and Baseline

Begin with a four to six week assessment to establish current performance levels and define targets. Focus on collecting human resources data on skills and labor alongside operational metrics. Use this phase to align standards with volatile production environments by capturing real-time shop-floor data that improves standard operating times.

Measure these specific KPIs: average picks per hour at 42, cases moved per labor hour at 28, travel time percentage at 35 percent, and Overall Equipment Effectiveness at 72 percent. Track error rates below 2 percent and schedule adherence above 88 percent. Compare against industry benchmarks such as 55 picks per hour after standards implementation.

Stakeholder alignment requires a formal checklist completed in week two. Confirm warehouse operations manager sign-off on scope. Secure human resources approval for labor data usage. Obtain IT confirmation on IoT and RFID integration readiness. Align finance on projected 12 to 18 percent productivity gains. Document union or works council notification if applicable.

Resource estimate includes two industrial engineers from Supply Chain Research, one WMS analyst, and three site supervisors for four hours daily. Tool requirements cover a tablet-based time study application from the MTM Association, SAP EWM version 9.5 or higher, and RFID readers from Impinj. Output a baseline report with current standard times versus observed times for 25 core tasks.

Phase 2: Design and Configuration

Phase 2 lasts five to seven weeks and focuses on building the standards library. Select MOST for repetitive high-volume tasks and MTM-1 for complex or variable tasks. Hybrid application covers 70 percent of tasks with MOST and 30 percent with MTM to balance speed and precision.

Key design decisions include defining elemental breakdowns for each task, such as reach, grasp, move, and position times measured in TMUs. Set allowances at 12 percent for personal time, 8 percent for fatigue, and 5 percent for delays. Configure system requirements to store standards in a central database accessible by the WMS for real-time comparison against actual performance.

Integration points include connection to shop-floor IoT sensors for automatic capture of machine cycle times and RFID tags on totes for location-based travel validation. Link to production planning systems to adjust standards dynamically under market volatility. Interface with UKG Dimensions for payroll and attendance data to calculate earned hours accurately.

Configuration steps require mapping 150 unique task codes in the first three weeks. Validate each standard against video analysis of 30 cycles. Build exception handling rules for tasks exceeding 20 percent variance from engineered time. Resource estimate covers three engineers, one IT integration specialist, and vendor support from Manhattan Associates for WMS configuration. Total effort equals 1,200 person-hours.

Phase 3: Pilot and Validation

Conduct the pilot over four weeks in a controlled area covering 20 percent of daily volume. Select a single shift and 40 associates performing receiving, putaway, picking, and packing. Limit scope to 45 task types initially to allow focused validation.

Daily monitoring checklist includes review of actual versus standard time variance at 8 a.m. and 2 p.m. each day. Confirm RFID data capture rate above 98 percent. Check OEE readings for equipment tied to labor tasks. Log any safety incidents or ergonomic concerns. Update the standards database with observed adjustments by end of shift.

Go or no-go criteria require average productivity improvement of at least 10 percent, standard compliance rate above 85 percent, and associate feedback score of 3.5 or higher on a five-point scale. Variance in standards must stay below 15 percent across all measured cycles. If criteria are not met by day 20, extend pilot by two weeks or return to Phase 2 for redesign.

Tool requirements include a real-time dashboard built on Microsoft Power BI connected to the WMS and IoT platform. Resource estimate includes one project manager, two analysts for daily audits, and site supervisors for coaching. Conduct 12 validation sessions with video review and MTM practitioner sign-off. Document all changes in a controlled revision log before advancing.

Phase 4: Full Rollout and Optimization

Execute full rollout in weeks 15 through 22 with a phased cutover across three waves. Wave one covers 40 percent of the facility in week 15. Wave two adds another 35 percent in week 18. Final wave completes the remaining 25 percent in week 21. Maintain parallel legacy reporting for the first 10 days of each wave.

Cutover plan requires freezing standards 48 hours before each wave. Load updated task codes into the live WMS environment during a four-hour maintenance window. Activate labor management reporting immediately after go-live. Provide on-site support from Supply Chain Research engineers for the first 72 hours of each wave.

Training consists of four-hour classroom sessions for supervisors followed by two-hour hands-on floor training for associates. Cover standard interpretation, performance feedback, and exception reporting. Use e-learning modules hosted on the company LMS for refresher access. Schedule 100 percent completion before wave start.

Hypercare lasts four weeks after final wave with daily stand-up meetings at 7 a.m. to review metrics. Target sustained productivity at 52 picks per hour and OEE above 80 percent. Continuous improvement process includes monthly standards audits using fresh time studies on 10 percent of tasks. Incorporate new IoT data feeds quarterly to adjust for process changes driven by production planning volatility.

Resource estimate for rollout totals six engineers, two trainers, and one change management specialist. Budget 2,800 person-hours across all waves. Post-implementation review at week 28 measures overall results against baseline, targeting 15 percent labor cost reduction and 95 percent standards accuracy. Establish a quarterly governance committee with operations, human resources, and IT to sustain gains through ongoing value chain re-engineering focus.

Section 3: Technology Landscape, Metrics & Pitfalls

Part A: Vendor and Technology Landscape

Supply Chain Research recommends evaluating technology platforms that integrate engineered labor standards with warehouse management systems through methods such as MOST and MTM. These platforms must support real-time shop-floor data capture to refine standard operating times under production planning volatility. Actionable steps begin with mapping current labor data flows, then issuing RFPs that require vendors to demonstrate integration with IoT and RFID for accurate time-and-motion tracking.

Manhattan Active WMS provides labor management modules that embed predetermined time standards and allow dynamic updates from shop-floor sensors. Its strength lies in scalability for high-volume distribution centers, with built-in analytics that align with Overall Equipment Effectiveness calculations. A documented gap is limited native support for hybrid MTM variants without custom extensions, which can increase implementation timelines by 8 to 12 weeks.

Blue Yonder WMS includes labor standards configuration tied to slotting and task interleaving. Real-world deployments show strength in forecasting labor requirements under variable demand, drawing on production planning data. The gap appears in slower refresh rates for real-time RFID feeds compared with specialized IoT platforms, requiring middleware in 60 percent of installations.

SAP EWM paired with IBP delivers robust labor standards through its Labor Management component, which accepts MOST-derived values and links them to manufacturing execution. Strengths include deep integration with ERP master data for lifecycle value chain visibility. Gaps surface in user interface complexity that extends training periods to 40 hours per planner.

Oracle Warehouse Management Cloud supports time standards via configurable work methods and mobile data collection. It excels when combined with IoT sensors for transportation data standardization. A recurring gap is weaker out-of-the-box reporting on employee-generated productivity metrics, often necessitating third-party dashboards.

Körber Supply Chain Software offers labor performance tools within its WMS suite that accommodate MTM standards and real-time adjustments. Strengths center on flexible rule engines for volatile environments. The primary gap involves higher licensing costs for advanced analytics modules that connect to shop-floor OEE data streams.

Kinaxis RapidResponse focuses on end-to-end planning with labor constraint modeling. It performs well when standards must adapt to market volatility but lacks native WMS execution depth, requiring interfaces to Manhattan or SAP systems.

RELEX Solutions emphasizes retail and warehouse labor forecasting. Its strength is probabilistic modeling of task times, yet it shows gaps in detailed MTM element libraries compared with dedicated industrial engineering tools.

RFP Evaluation Criteria

• Require vendors to demonstrate import of MOST or MTM elemental times with audit trails for updates based on real-time shop-floor data.
• Mandate proof of RFID and IoT integration that standardizes transportation and handling data within 5 seconds of task completion.
• Request case studies showing at least 15 percent improvement in labor utilization after 90 days in environments with production planning volatility.
• Specify support for Overall Equipment Effectiveness linkage to individual operator standards.
• Include requirements for mobile time-study capture that feeds directly into standards maintenance without manual re-entry.

Part B: Metrics That Matter

Metric Name	Definition	Benchmark Range	Measurement Frequency
Labor Utilization Rate	Percentage of available paid time spent on productive tasks measured via MOST standards	78 to 85 percent	Daily
Pick Rate Accuracy	Units picked per hour against engineered MTM standard for the task profile	52 to 68 units per hour	Per shift
Standard Time Compliance	Ratio of actual task time to predefined MOST or MTM standard time	92 to 105 percent	Weekly
Overall Equipment Effectiveness	Composite score of availability, performance, and quality linked to labor standards	72 to 82 percent	Real-time hourly
Indirect Labor Ratio	Hours spent on non-standard tasks divided by total labor hours	12 to 18 percent	Weekly
Standards Update Cycle Time	Days required to revise engineered standards after process change using shop-floor data	3 to 7 days	Per change event
Operator Performance Index	Individual output versus MTM-derived expectation adjusted for volatility factors	95 to 110 index points	Daily
Task Interleaving Efficiency	Percentage reduction in travel time achieved through standards-based interleaving	18 to 25 percent	Monthly

Part C: Top 10 Common Pitfalls

Pitfall 1: Standards remain static after initial implementation. This occurs because teams treat MOST or MTM values as fixed rather than feeding real-time shop-floor data back into revisions. Prevention requires scheduled quarterly audits that compare actual RFID-captured times against standards and trigger updates within 5 business days.

Pitfall 2: Ignoring production planning volatility when setting expectations. Planners apply average standards without buffers for demand swings. Prevention involves building volatility factors into the standards database and recalculating weekly using IBP or Kinaxis outputs.

Pitfall 3: Over-reliance on vendor default time libraries without site-specific MTM validation. This produces unrealistic targets that demotivate operators. Prevention mandates on-site time studies for at least 20 percent of task categories before go-live.

Pitfall 4: Failure to link labor standards to Overall Equipment Effectiveness dashboards. Data silos prevent root-cause analysis. Prevention requires configuring SAP EWM or Manhattan Active to push operator performance into the OEE calculation engine daily.

Pitfall 5: Inadequate training on mobile data capture tools. Operators bypass RFID scans, corrupting standards accuracy. Prevention includes 16-hour hands-on certification plus daily compliance audits for the first 30 days post-deployment.

Pitfall 6: Excluding employee-generated information from standards maintenance. Frontline feedback on method changes is ignored. Prevention establishes a formal suggestion workflow inside the WMS that routes observations to industrial engineers within 48 hours.

Pitfall 7: Selecting platforms without proven IoT integration for transportation data standardization. Manual entry reintroduces errors. Prevention demands RFP demonstrations of end-to-end RFID latency under 3 seconds using actual site hardware.

Pitfall 8: Setting benchmarks without reference to industry ranges for similar volatility environments. Targets become either too lenient or punitive. Prevention requires Supply Chain Research benchmark tables to be imported into the system and reviewed monthly against actual performance.

Pitfall 9: Neglecting lifecycle value chain considerations when engineering standards. Focus stays only on the warehouse segment. Prevention extends standards to include upstream manufacturing process times using Oracle or Körber cross-module linkages.

Pitfall 10: Skipping pilot validation of hybrid MOST and MTM methods. Full rollout reveals inconsistencies. Prevention limits initial deployment to one functional area for 60 days, measures compliance against the metrics table above, and adjusts elemental times before scaling.

SECTION 4: Building the Business Case & ROI Framework

Supply Chain Research recommends a structured approach to justify Engineered Labor Standards projects that relies on time-and-motion data from MOST or MTM methods integrated with real-time shop-floor information. This section provides the exact steps required to build credible financial models that connect productivity gains to overall equipment effectiveness improvements and reduced schedule volatility.

ROI Calculation Methodology and Cost Categories

Begin by collecting baseline data from the warehouse management system for a 90-day period. Use the following cost categories in every model. Direct labor savings are calculated from reduced hours at fully burdened rates. System integration costs cover connections between the labor management module and existing platforms such as Manhattan Associates WMS or Blue Yonder. Training and change management expenses include 40 hours per supervisor plus external MOST certification at 2500 USD per analyst. Ongoing maintenance covers annual software licensing at 18 percent of initial license fees plus quarterly audits that require two days of industrial engineering time. Technology hardware such as RFID readers and IoT sensors adds 45000 USD for a 200000 square foot facility. Model all categories in a five-year cash flow using a 10 percent discount rate.

• Step 1: Export pick, pack, and put-away transactions from the WMS and apply current MOST standards to calculate standard time per unit.
• Step 2: Multiply standard time by actual volume to determine required labor hours and compare against actual clock hours to quantify the gap.
• Step 3: Apply a 22 percent productivity lift target based on Supply Chain Research benchmarks from facilities that deployed real-time shop-floor data for production planning.
• Step 4: Convert hours saved into dollars using site-specific burdened rates that include benefits and overtime premiums.
• Step 5: Subtract all implementation and recurring costs listed above to arrive at net annual benefit.

Worked Example with Specific Before and After Metrics

The following table shows results from a 250000 square foot distribution center operated by a consumer goods manufacturer that implemented hybrid MOST and MTM standards alongside IoT-enabled task allocation. All figures reflect annual volumes of 4.2 million cases.

Metric	Before Implementation	After Implementation	Improvement
Average cases per labor hour	68	92	35 percent
Annual labor hours required	61765	45652	16113 hours
Fully burdened labor cost	1852950 USD	1369560 USD	483390 USD
Overtime percentage	14 percent	6 percent	8 points
Schedule adherence variance	22 percent	9 percent	13 points
Overall equipment effectiveness	71 percent	84 percent	13 points

Implementation costs totaled 312000 USD in year one, including 185000 USD for software and RFID hardware from Manhattan Associates, 72000 USD for training and certification, and 55000 USD for integration services. Net first-year benefit after costs reached 171390 USD. Subsequent years delivered 483390 USD in recurring savings with only 48000 USD in maintenance, producing cumulative five-year cash flow of 1857550 USD before discounting.

Presentation Approach for Leadership Versus Operations Teams

Prepare two distinct decks. For the executive leadership team, lead with a one-page summary that shows net present value, internal rate of return of 87 percent, and payback in 14 months. Include a sensitivity table that demonstrates how results hold even if productivity lift drops to 15 percent. Reference Supply Chain Research findings on real-time shop-floor data improving standard operating times to support the claim that standards remain dynamic rather than static. Limit the presentation to 12 minutes and end with a clear ask for capital approval.

For operations and industrial engineering teams, deliver a 45-minute working session that walks through the exact MOST analysis for the top five tasks, shows side-by-side time studies, and demonstrates the mobile dashboard used for daily variance tracking. Provide editable Excel models so supervisors can adjust volume forecasts. Emphasize that standards protect fair expectations and reduce schedule volatility described in Supply Chain Research papers on production planning under volatility.

Hidden Costs Most Teams Miss

Most implementations overlook supervisor time spent auditing standards after go-live. Plan for 0.5 full-time equivalent per 100 associates for the first six months. Data quality remediation frequently requires cleansing 18 percent of historical WMS transactions before accurate baselines can be set. Union or works council review cycles add eight to twelve weeks and external legal fees of 15000 USD. Software version upgrades every 18 months cost an additional 12 percent of the original license. Finally, temporary productivity dips of 8 to 12 percent occur during the first four weeks of rollout and must be modeled as a one-time expense.

Expected Payback Period Ranges

Facilities under 150000 square feet with annual labor spend above 1.2 million USD typically achieve payback in 9 to 14 months when RFID task allocation is included. Larger sites with complex multi-client operations see payback between 12 and 18 months because integration effort increases. Sites that already run Blue Yonder or SAP EWM experience the shortest timelines of 7 to 11 months due to lower interface costs. All ranges assume the 22 percent productivity target validated by Supply Chain Research case studies that combined engineered standards with real-time shop-floor visibility. Update the model quarterly using actual performance data to maintain accuracy throughout the project lifecycle.

SECTION 5: Advanced Patterns, Future Outlook & Methodology

Advanced and Hybrid Approaches

Engineered Labor Standards reach higher precision when practitioners combine traditional Methods Time Measurement (MTM) with Maynard Operation Sequence Technique (MOST) in hybrid models. Supply Chain Research recommends starting with a baseline MOST analysis on high volume tasks such as case picking and put away. Next, overlay MTM element times on variable elements such as travel paths that change with slotting. This hybrid method typically delivers standards accurate to within plus or minus 5 percent versus 12 percent for single method deployments.

Actionable steps include the following. First, map all warehouse tasks into 12 core categories using a cross functional team of industrial engineers and supervisors. Second, capture 200 cycles per task category with video at 30 frames per second. Third, apply MOST for fixed sequences and MTM for motion level detail on the same data set. Fourth, validate the hybrid standard on the floor for two full shifts and adjust any element exceeding a 3 percent variance. Real companies such as PepsiCo and DHL Supply Chain have reported 18 to 22 percent productivity gains after completing this sequence across 12 distribution centers each.

AI and ML Applications

Artificial intelligence and machine learning extend Engineered Labor Standards by ingesting real time shop floor data from IoT sensors and RFID tags. Supply Chain Research observes that facilities using Blue Yonder Labor Management integrated with Manhattan Associates WMS achieve dynamic standard updates every 15 minutes. The model ingests variables such as order mix, equipment status, and operator fatigue indicators to recalibrate expected times.

Implementation follows these steps. Install RFID readers at all pick faces and conveyor decision points to feed SAP Extended Warehouse Management. Deploy edge computing nodes from Siemens to process OEE data alongside labor transactions. Train a supervised regression model on 12 months of historical data to predict standard time adjustments under volatility. Monitor model drift weekly and retrain when mean absolute percentage error exceeds 4 percent. One automotive parts distributor using this approach raised Overall Equipment Effectiveness from 72 percent to 89 percent while improving labor utilization by 14 percent across 450,000 annual labor hours.

Emerging Best Practices

Leading operators now embed Engineered Labor Standards inside value chain re engineering programs rather than treating them as isolated warehouse tools. Through Engineering practices shift attention from final product output to full lifecycle performance including returns handling and reverse logistics. Supply Chain Research benchmark data across 200 plus facilities shows that organizations applying this lens reduce total supply chain labor cost per unit by 11 to 16 percent.

Follow these operational steps. Begin with a lifecycle value stream map that includes inbound, storage, fulfillment, and post delivery tasks. Identify 8 to 10 constraint points where real time shop floor data can improve standard operating times. Pilot hybrid standards at the two highest impact constraints for 90 days. Roll out successful pilots site wide while maintaining a central standards governance board that meets monthly. Include operator feedback loops through mobile apps to capture exceptions within 24 hours. Facilities that completed this sequence reported average incentive payout accuracy above 98 percent and grievance rates below 0.5 percent per 1,000 labor hours.

Future Outlook for 2026 to 2028

Between 2026 and 2028 Engineered Labor Standards will incorporate digital twin simulations and autonomous mobile robot path optimization. Vendors such as Symbotic and Locus Robotics already embed labor standards inside fleet management algorithms. Supply Chain Research projects that 65 percent of new warehouse management system implementations will include AI driven standards by 2028, up from 28 percent today. Volatility in production planning will drive demand for standards that update automatically when market conditions shift order profiles by more than 15 percent week over week.

Preparation steps include the following. Audit current data latency between shop floor sensors and the labor management system. Target sub 60 second refresh cycles. Establish a data science team of three to five analysts to maintain models. Negotiate contract clauses with WMS vendors that guarantee API access to real time element times. Run annual benchmark comparisons against the Supply Chain Research database of 200 plus facilities to maintain competitive positioning.

Supply Chain Research Methodology Note

Supply Chain Research evaluates Engineered Labor Standards through structured practitioner interviews with directors of operations and industrial engineering managers at 47 companies. These interviews are supplemented by vendor briefings from Manhattan Associates, Blue Yonder, and SAP plus direct implementation data collected during 38 site visits. Benchmark analysis draws on anonymized productivity and accuracy metrics from more than 200 facilities representing 185 million annual labor hours. All standards accuracy figures are validated against actual time studies conducted on site rather than self reported data.

Conclusion

Key decision points center on whether to pursue hybrid MTM MOST standards immediately, when to integrate AI driven real time updates, and how deeply to embed standards inside broader Through Engineering initiatives. Recommended next steps are to form a cross functional steering committee within 30 days, complete a 90 day pilot on two high volume tasks, and schedule a Supply Chain Research benchmark review at the six month mark. Organizations that follow this sequence consistently achieve 15 to 25 percent sustained productivity improvement while maintaining fairness and defensibility of the resulting standards.