Meaning of Demand

In managerial economics, demand means the quantity of a good or service that consumers are willing and able to purchase at various prices during a given period, other things being equal.
Three elements must be present:

• Desire for the product.
• Ability to pay (sufficient income).
• Willingness to spend at a given price.

If any one is missing (e.g., desire without money, or money without willingness), it is not economic demand.

Example (India):

• Many people may desire an iPhone 16, but only those who have sufficient income and are actually ready to spend at its price are counted in the demand for iPhone 16 in India.

Demand Theory and Objectives of Demand Analysis

Demand Theory – Core Idea : Demand theory studies how quantity demanded of a product changes when its determinants change.
Main determinants (demand function):

Where:

•  = Price of the good itself.
•  = Prices of related goods (substitutes, complements).
•  = Income of consumers.
•  = Tastes and preferences.
•  = Expectations about future prices/income.
•  = Number of consumers/market size.

Objectives of Demand Analysis in Managerial Economics : Managers study demand in order to:

• Estimate future sales – to plan production, capacity and staffing.
• Set prices – understand price sensitivity (elasticity) to choose profit‑maximising prices.
• Design product‑mix – choose which models, pack sizes or variants to offer.
• Plan marketing strategies – promotions, discounts, advertising budgets.
• Make investment decisions – capacity expansion, new plants, entry into new markets.

Example (Indian context):  Maruti Suzuki analyses demand by income segment and region before deciding how many units of Swift, Baleno or Brezza to produce and where to locate dealer stock.

Demand Schedule and Demand Curve

Demand Schedule

A demand schedule is a tabular representation showing different quantities demanded at different prices in a given time period, assuming other factors constant.

Example: Demand schedule for a prepaid data pack (per month):

Here, as price falls from ₹300 to ₹150, quantity demanded rises from 20 to 70 lakh packs.

Demand Curve

A demand curve is a graphical representation of the demand schedule, showing the relationship between price and quantity demanded.

• Price (P) on Y‑axis.
• Quantity demanded (Q) on X‑axis.
• It usually slopes downward from left to right, reflecting the inverse relation between price and quantity demanded.

Indian example:

• When Netflix India cut basic subscription prices from ₹499 to ₹199, subscriptions increased significantly, tracing a downward‑sloping demand curve.

Law of Demand

Statement:
Other things remaining the same (ceteris paribus), the quantity demanded of a good increases when its price falls and decreases when its price rises. Thus, there is an inverse relationship between price and quantity demanded.

4.1 Assumptions of Law of Demand

The law holds when the following remain constant:

• Consumer income.
• Tastes and preferences.
• Prices of related goods (substitutes/complements).
• Expectations about future prices or income.
• Population/market size.
• No major changes in fashion, technology, government policy, etc.

4.2 Reasons for Downward Slope

• Substitution Effect: When price of a good falls, it becomes cheaper relative to substitutes, so consumers substitute it for other goods.
• Income Effect: A fall in price raises real income (purchasing power); consumers can buy more.
• Law of Diminishing Marginal Utility: Each additional unit gives less satisfaction, so consumers buy more only at lower prices.

4.3 Indian Example (Jio Case)

• After Reliance Jio entered with very low data prices, the number of data users and usage per user increased sharply.
• This shows: lower price → much higher quantity demanded for mobile data in India (law of demand with high price elasticity).

5. Elasticity of Demand – Types & Measurement

5.1 Concept of Elasticity

Elasticity of demand measures the degree of responsiveness of quantity demanded to a change in one of its determinants (usually price).

5.2 Types of Elasticity of Demand

(i) Price Elasticity of Demand (PED)

Measures responsiveness of quantity demanded to a change in price:

• If : Elastic demand (quantity responds more than proportionately).
• If : Unitary elastic.
• If : Inelastic demand.

Indian example – Jio & Telecom:

• Sharp fall in data tariff led to disproportionately large rise in data consumption → highly elastic demand.

(ii) Income Elasticity of Demand (YED)

Measures responsiveness of demand to change in consumer income:

• Luxury hotels (Taj, Oberoi) and premium cars (BMW, Mercedes India) have high income elasticity – demand rises faster than income in boom periods.
• Basic food grains have low income elasticity.

(iii) Cross Elasticity of Demand (XED)

Measures responsiveness of demand for one good to price change of another:

• For substitutes (Jio vs Airtel): price of Airtel data up → demand for Jio increases → positive cross elasticity.
• For complements (cars and petrol): petrol price rises → demand for large SUVs may fall → negative cross elasticity.

(iv) Advertising (Promotional) Elasticity

Measures responsiveness of demand to changes in advertising expenditure.

 FMCG companies like HUL and ITC see higher sales of soaps, detergents, and biscuits when they increase ad spending on TV, digital and influencer campaigns.

5.3 Methods of Measuring Price Elasticity

1. Percentage / Proportionate Method

1. Total Expenditure (Outlay) Method – Marshall
   • If price falls and total expenditure (P×Q) rises → elastic demand.
   • If price falls and total expenditure unchanged → unit elastic.
   • If price falls and total expenditure falls → inelastic.
2. Point Elasticity Method – using calculus on a particular point of the demand curve.
3. Arc Elasticity Method – using average of two prices and quantities to measure elasticity between two points.

6. Supply Analysis (Link with Demand)

6.1 Meaning of Supply

Supply is the quantity of a good that producers are willing and able to offer for sale at different prices over a given period.

Law of Supply:
Other things being equal, higher price → higher quantity supplied; lower price → lower quantity supplied.

6.2 Factors Affecting Supply

• Price of the good.
• Prices of inputs (wages, raw materials).
• Technology.
• Government taxes and subsidies.
• Prices of related products.
• Expectations of future prices.

Indian example:

• If tomato prices rise sharply in local mandis, farmers may allocate more land to tomatoes in the next season, increasing supply.

Demand–supply together determine market equilibrium price and quantity.

7. Demand Forecasting

7.1 Meaning and Need

Demand forecasting is the process of estimating the future demand for a product for a specified period using past data and market information.

Importance for managers:

• Production planning (how much to produce and when).
• Capacity planning (plant size, machine purchase).
• Purchasing and inventory decisions.
• Manpower planning.
• Financial budgeting.
• Marketing strategy (ads, schemes, product launches).

7.2 Methods of Demand Forecasting

(A) Qualitative / Judgemental Methods

1. Survey of Buyers’ Intentions – ask customers directly how much they plan to buy.
2. Sales Force Composite – take forecasts from field sales staff and aggregate.
3. Expert Opinion / Delphi Technique – consult group of experts and iterate until convergence.

Example (India):

• A new EV start‑up surveys potential car buyers in Delhi, Mumbai, Bengaluru to estimate possible EV demand in the first three years.

(B) Quantitative / Statistical Methods

1. Trend Projection / Time Series Analysis
   • Use past sales data to fit a trend line and project future sales.
   • Suitable for established products with stable patterns.
2. Barometric / Indicator Method
   • Use leading indicators (GDP growth, IIP, income, interest rates) to predict demand.
3. Econometric / Regression Models
   • Establish statistical relationships between demand and determinants (price, income, advertising, competitors’ prices).
   • Example regression:

Where Q = quantity demanded, P = price, Y = income, A = ad expenditure.

Indian company examples:

• Maruti Suzuki, Hyundai, Tata Motors use past car registration data, interest rates, fuel prices, and income to forecast demand for each model by region.
• Flipkart and Amazon India use time‑series plus machine learning to forecast demand for mobiles, fashion, and electronics during Diwali sales and Big Billion Days.