Demand Management

Demand Management is a critical link in the entire Supply chain management essentially required to manage and forecast the demand of products to ensure high level of customer satisfaction and effective asset utilization.

It involves forecasting an unconstrained market demand for various products based on historical and dynamic real-time data of various causal factors. This forms a vital input to Supply Planning to effectively arrive at a feasible product distribution plan. Efficient Demand Management will save the business from supply imbalances such as stock dry-out, inventory overflow and uneconomical buy/sell scenarios.

Following are the key causal factors on which demand management in based on:

  • Market demand based on parameters such as sales levels, business cycles, response to promotions
  • Current inventory levels
  • Operational constraints
  • Prices
  • Environmental conditions

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Demand Planning Process

A wide range of advanced software solutions are available which provide a realistic and logical approach using sophisticated computing techniques such as Time series modelling (often based on historical sales and shipment data), seasonal linear regression, dynamic regression, moving average forecast, exponential smoothing, Holt-Winters additive and multiplicative modelling.

Demand planning usually spans over periods ranging as long as a year and is refreshed on a fortnightly or a weekly basis. A thorough mix of Manual inputs (judgmental forecasting) from sales, marketing, Finance and customer feedback groups coupled with automated outputs from statistical methods would yield a pragmatic forecasted demand.

Integration of production planning with demand planning is a key success solution to Supply Chain and to drive a positive business outcome. Production planning suggests ways and means as to how to profitably and feasibly meet the demand taking into account the available process unit and storage capacity along with the associated logistic issues. Production planning carried out on a holistic basis using a global model often suggests the most profitable location(s) to supply the demand.

Demand Forecast Engine

Challenges in Demand management

  • Identification of loop holes in historical data and forecasts
  • Choosing the right statistical model for forecasting
  • Identification of elements which can be forecasted and which cannot be
  • Over dependability on automatic forecasting
  • Validity of the data inputted to the forecast model
  • Closeness of forecast with actual results

What we can offer

  • Study the existing process “ As-is”
  • Perform Gap Analysis
  • Best practices and processes consulting and implementation to improve demand forecasting
  • Implementation of effective Sales and Operations Planning (S&OP) modules
  • Identification and resolution of pinching constraints through analytical tools
  • Establishing the business process of retro-analyzing the forecast to make the model more accurate
  • Integrating Demand planning process and tools with Production planning and Supply distribution tools