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Case Study:

Demand Forecasting / Global Automotive Components Supplier

Inaccurate forecasting impacting

Driving Accurate Demand Projections for 1000s of SKUs in Parallel

Teranalytics Forecast.AI automated engine delivers best-in-class predictions

Challenge:

Inaccurate forecasting impacting profitability and inventory planning

Demand forecasting is often inaccurate because of the inherent complexity of the problem: first, it is not easy to directly forecast a metric that can be highly volatile; second, that metric is affected by multiple parameters whose influence itself varies over time. Producing a demand forecast that addresses either of these two challenges individually is bound to inaccuracies: a straight time series analysis would lose the causality/correlation effects of influential parameters, while a straight AI approach would lose the time dimensionality of the problem. Merging the two is therefore key, but is time consuming to implement ab initio. As a result, lots of the forecast produced by companies are currently created manually by a human analyst. This is both time consuming and prone to errors.

Solution:

Automated data-driven forecasting engine that automatically selects the best forecasting method for each SKU

1. Near just-in-time inventory control slashes carrying costs: Accurage forecast allows for leaner operation both at the inventory level as well as labor planning. Subsequent add-ons can penalize over- and under- inventory differently in order to deliver optimized tactical recommendations.

2. Teranalytics Forecast.AI hides the complex analytics in the backend and provides the user with an intuitive and simple to use interface, with cloud-based computation for speed efficiency.

3. The two-step approach ensures high accuracy at the desired aggregation level (at the SKU level or more broadly by geographies, by customer types, etc).

Step 1: Project. Important historical parameters are projected in time using advanced time series analysis.
Step 2: Combine. The projections are incorporated into machine learning models that are self-correcting and error bound.

Results:

Reduced Stock Inventory by 5% and Provided 95% Accurate Forecasts

  • Improved accuracy: study cases consistently show better matching to actual data in post-forecast analysis.
  • Drastic reduction in processing time: forecast that use to take hours can be produced in real time.
  • Significant cost reductions: savings in labor time and better utilization of industry experts.
  • Reduction of errors: manual processes are error prone and contribute so forecast inaccuracies.
  • More strategic discussions: run scenarios in real time and make informed decisions with executive team.
  • Increased competitive advantage: better forecast, better strategic decisions, cost reduction.
  • Competitive advantage: organizations become more agile and more responsive to customer demand.

Client Feedback:

“Thank you – this tool has quickly and significantly improved our operation.”

-Global Region Director-