Kinaxis Maestro Demand Planning Expert — Forecast Optimization

Altimetrik; Austin (TX); On-site; USD 120,000 - 190,000; Full time

Job summary

Altimetrik in Austin, TX seeks a hands-on Kinaxis Maestro Demand Planning expert to assess and improve forecasting in a high-mix, low-volume environment. You will investigate forecast performance, validate data quality, and tune configurations across PBOMs and SKU hierarchies to drive accuracy and service levels.

The role involves backtesting, pilot implementations, and collaboration with planners and IT to implement changes, monitor outcomes, and establish ongoing tuning processes.

Qualifications

• Hands-on experience configuring Kinaxis Maestro Demand Planning.
• Experience with high-mix, low-volume environments and make-to-stock.
• Ability to backtest forecasts and analyze bias.
• Understanding PBOM structures, attach rates, and forecast aggregation/disaggregation.

Responsibilities

• Assess forecasting process from data collection to forecast transfer to supply planning.
• Review Maestro configuration: product hierarchies, PBOM relationships, overrides.
• Validate historical data quality and identify data issues impacting accuracy.
• Investigate forecast errors by SKU, PBOM, product segment and horizon.
• Evaluate PBOM groups for forecasting suitability and consistency with demand behavior.
• Tune forecasting methods and parameters for intermittent demand and seasonality.
• Backtest forecasts and compare with alternatives; pilot changes.
• Implement approved changes and monitor performance; establish tuning process.
• Assess downstream effects on Oracle ASCP, inventory, shortages and service levels.

Skills

Kinaxis Maestro Demand Planning

Forecasting

Demand planning

Backtesting

Data analysis

Oracle ASCP

Tools

Oracle ASCP

Kinaxis Maestro

Job description

We are seeking a hands-on Kinaxis Maestro Demand Planning expert to assess and improve forecasting in a high-mix, low-volume, make-to-stock environment. The consultant will investigate current forecast performance, evaluate product and planning BOM structures, tune forecasting configurations, and recommend an alternative planning structure where supported by analysis.

The environment includes approximately 700+ finished-goods SKUs grouped into virtual planning BOMs (PBOMs), SKU-level sales history, history-derived attach rates, PBOM-level forecasting, and integration with Oracle ASCP. Reported forecast accuracy is approximately 65%, with a target of at least 75%, subject to validation of the metric and baseline.

Key responsibilities

• Assess the current forecasting process from historical data collection through forecast generation, review, approval and transfer to supply planning.
• Understand the actual Maestro configuration, including product hierarchies, PBOM relationships, aggregation and disaggregation logic, attach rates, forecasting parameters and overrides.
• Validate historical data quality, including missing periods, outliers, returns, stockout effects, discontinued products and new-product introductions.
• Investigate forecast errors and bias by SKU, PBOM, product segment and forecast horizon. Distinguish errors caused by data, model settings, product grouping, changing product mix and manual adjustments.
• Evaluate whether existing PBOM groups are appropriate for forecasting, considering demand behavior, volume, intermittency, lifecycle and component commonality.
• Assess attach-rate calculation methods, history windows, refresh frequency and stability.
• Tune supported forecasting methods and parameters for each demand segment, including treatment of intermittent demand, trend, seasonality and outliers.
• Compare the current approach with alternatives such as SKU-level forecasting, revised product families or a hybrid structure. Explain how each option affects demand accuracy and component planning.
• Conduct historical backtesting using consistent forecast horizons and data available at each forecast date.
• Recommend and implement approved changes, run a controlled pilot, and establish an ongoing monitoring and tuning process.
• Assess downstream effects on Oracle ASCP, inventory, shortages and service levels.

Required expertise

• Deep, hands-on experience configuring and troubleshooting Kinaxis Maestro Demand Planning.
• Strong understanding of product hierarchies, planning BOMs, attach rates and forecast aggregation/disaggregation.
• Experience diagnosing and improving forecasts in high-mix, low-volume or intermittent-demand environments.
• Strong knowledge of statistical forecasting, forecast bias, accuracy measures, segmentation and backtesting.
• Ability to inspect configuration and data directly, explain the underlying forecasting behavior, and implement changes.
• Experience with demand-to-supply planning integration; Oracle ASCP knowledge is preferred.
• Ability to translate findings into clear recommendations for planners, business leaders and technical teams.
• Current-process and configuration assessment.
• Validated forecast baseline and root-cause analysis.
• Recommended product/PBOM segmentation and attach-rate approach.
• Forecast-engine tuning recommendations and configuration changes.
• Comparison of current and alternative forecasting structures.
• Pilot results showing accuracy, bias and operational impact.
• Implementation roadmap, documentation and planner knowledge transfer.

Success criteria

Demonstrate measurable improvement against the agreed baseline using comparable forecast horizons and consistent metrics. Improvements should be sustainable across relevant product segments and support better inventory and service outcomes. The 75% target should be evaluated through backtesting and pilot results rather than assumed achievable in advance.