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Fabric and Home Décor Manufacturing Company

Module: Production Planning and SchedulingIndustry: Textiles
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Client Overview:  ​The Client is the world’s largest maker of soft furnishing fabrics with daily production of over 120,000 sq mtrs of high-quality fabrics for homes across 5 manufacturing plants in India. With team of 250 people in product development, company produces 20,000 SKUs/year.

Key Features

  1. Simultaneous Capacity / Material Planning
  2. Variant Management Logic  
  3. Sequence Dependency  
  4. Scenario Planning
  5. Custom Logic for Weaving & Fabric Dyeing

Problem Method and Result

The Puzzle

  • The client has a strong orientation towards product development and thus has a highly complex product mix problem. 
  • The client was running SAP and was in search of a planning and scheduling solution.
  • The objective was to improve capacity utilization. And visibility on the estimated delivery date to improve customer service level and improve the throughput..
The Solution
  • The current ERP was designed to handle the different variants (design and matching) of the same SKU.
  • PPS had to customize the SCM tool to incorporate this variant feature.
  • Custom logic was built to take care of the logic requirements of each of the different work centers in production, such as Weaving and Fabric Dyeing.
  • APS looked at both Material and Capacity Planning to arrive the final schedule.
  • Fabric Dyeing incorporated concepts of Sequence Dependent Set Up times. Whereas Weaving incorporated concepts of balance to weave.
  • APS also helped them generate custom reports for capacity, inventory and work order planning.
The Result
  • ​Better visibility of the Production.
  • Better Productivity and Capacity Utilization.
  • Based on the output of the tool, a single plan is created taking into account real time inventory and material constraints.
  • Using this tool, the client was able to analyze and simulate various scenarios.
  • The client could commit delivery dates to their clients thus improving customer service levels.

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Fabric & Domestic Cleaning Products Company

SOLUTIONS DESIGNED TO SYSTEMATICALLY ESTIMATE MARKET DEMAND 

Be it New Product Development, Planning, or Promotions, implementing collaborative Demand Forecasting provides you with statistical / ML / AI data to support the course of action in the medium term. 

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the puzzle

Demand Planning comprises key step of arriving at a baseline number. This is done by using Statistical and ML/AI methods. The Module applies various methods of forecasting on the historical Sales data to arrive at the forecast in the coming months. The forecasting can be done at various levels such as SKU-Depot / Brand-Region / SKU Region 

the solution

Statistical Methods employing different forecasting techniques provides an effective baseline

Classification and Grouping of Products, Outlier Detection and Correction to improve Forecast Accuracy

Choice of Statistical and ML / AI methods depending on nature of dataset for improved forecasting

Comparison between actual v/s planned forecasts to detail the variability of products

the result

Multiple Heirarchy

Forecasting at multiple Hierarchies e.g. SKU-Location, SKU-Sales Office, Brand-Channel etc.

Supersession

Supersession that allows linking of old to new items to provide meaningful continuity in history for effective forecasting new product

Statistical Methods

Variety of Forecasting Techniques using efficient Python to take care of different distributions such as seasonality, promotions etc. Also allows for Outlier Detection

Classification of Products

Dynamic Classification of Products across Runner / Repeater / Stranger using different parameters of frequency, variability etc.

ML / AI Methods

Selection of different AI / ML Methods for cases where ML can be implemented 

Choice of Error Methods

Selection of Techniques based on different Error Measures with ability to add custom Statistical Techniques