Learning Outcomes

After completing this lesson, learners will be able to:

• Explain customer and end-user demand forecasting.
• Describe how demand forecasts support supply chain planning.
• Explain how and why demand forecasts are updated.
• Describe the customer-order process.
• Distinguish between Available to Promise and Capable to Promise.
• Explain automated replenishment, online ordering and vendor-managed inventory.
• Identify controls required for accurate order processing.

1. Customer and End-User Demand Forecasting

A customer or end-user demand forecast estimates the products or services customers are likely to require during a future period.

Forecasts may estimate demand by:

• Product
• Customer
• Customer segment
• Geographic area
• Distribution channel
• Time period
• Order volume

Forecasting does not guarantee what customers will order. It provides a reasoned estimate that supports planning and decision-making.

2. Importance of Demand Forecasting

Demand forecasts support:

• Procurement planning
• Supplier scheduling
• Production planning
• Capacity planning
• Inventory management
• Warehouse-space planning
• Workforce planning
• Transport planning
• Budgeting
• Customer-service planning

Example

A beverage distributor expects demand to increase during South Africa’s summer and December holiday period.

The forecast allows the distributor to:

• Purchase additional stock
• Reserve warehouse capacity
• Schedule extra vehicles
• Plan employee shifts
• Coordinate deliveries with retailers

Without a forecast, the distributor may experience stockouts, delayed deliveries or emergency transport costs.

3. Information Used in a Demand Forecast

Forecasting may use:

• Historical orders
• Confirmed customer orders
• Sales trends
• Seasonal patterns
• Promotions
• Customer feedback
• Market research
• Economic conditions
• Competitor activity
• Product life-cycle information
• Weather patterns
• Planned customer projects
• Supplier and production capacity

Forecasts are more reliable when quantitative information is combined with relevant market and customer knowledge.

4. Preparing a Demand Forecast

A typical forecasting process follows these steps:

1. Define what must be forecast.
2. Select the forecast period.
3. Gather historical and current information.
4. Clean and verify the information.
5. Identify trends and seasonal patterns.
6. Select an appropriate forecasting method.
7. Prepare the forecast.
8. Test its accuracy.
9. Communicate the forecast to relevant departments.
10. Monitor actual demand and update the forecast.

5. Cleaning and Analysing Data

Before using historical information, the organisation should identify:

• Missing values
• Duplicate transactions
• Incorrect quantities
• Cancelled orders
• Unusual once-off orders
• Data-entry errors
• Exceptional events

An unusually large once-off order should not automatically be treated as normal future demand.

Analysis should identify:

Trend

A sustained increase or decrease in demand over time.

Seasonality

A repeating pattern linked to a particular period.

Examples include:

• Increased retail demand before holidays
• Agricultural demand during planting seasons
• School-supply demand before the start of a term

Variation

Short-term increases or decreases that may not follow a predictable pattern.

6. Forecasting Methods

Time-Series Methods

Time-series forecasting uses historical demand patterns to estimate future demand.

Examples include:

• Moving averages
• Exponential smoothing
• Trend analysis
• Seasonal analysis

Causal Methods

Causal forecasting considers factors that influence demand.

These may include:

• Price changes
• Promotions
• Economic activity
• Weather
• Infrastructure projects
• Customer growth

Qualitative Methods

Qualitative forecasts use professional judgement where sufficient historical information is unavailable.

Sources may include:

• Customer estimates
• Sales-team input
• Expert opinion
• Market research

Machine-Learning Methods

Machine-learning models analyse large datasets and complex demand patterns.

They still require:

• Accurate data
• Suitable model selection
• Human review
• Regular validation

7. Forecast Accuracy

Forecast accuracy measures how closely forecast demand matches actual demand.

Common measures include:

Forecast Error

The difference between actual and forecast demand.

Forecast error = Actual demand − Forecast demand

Mean Absolute Error

The average size of forecast errors without considering whether they are positive or negative.

Mean Squared Error

The average of the squared forecast errors. Larger errors receive greater weighting.

Root Mean Squared Error

The square root of the mean squared error, expressed in the same units as the original demand.

Forecast Bias

A pattern of consistently forecasting too high or too low.

A forecast should be sufficiently accurate for the decision it supports. It does not need to predict every order perfectly.

8. Updating the Demand Forecast

Forecasts should be reviewed regularly because demand conditions change.

Updates may be triggered by:

• New customer orders
• Order cancellations
• Promotions
• Price changes
• Supply disruptions
• Competitor activity
• Economic changes
• Weather events
• Customer feedback
• Changes in customer operations

Forecast-Update Process

1. Record actual demand.
2. Compare actual demand with the forecast.
3. Calculate and investigate variances.
4. Identify new information or changed assumptions.
5. Adjust the forecast.
6. Review the effect on inventory, capacity and transport.
7. Communicate the revised forecast.
8. Monitor the updated result.

An outdated forecast can cause incorrect procurement, production and inventory decisions.

9. Customer and End-User Order Process

An order is a confirmed request for specified goods or services under agreed conditions.

A typical order-management process is:

Product selection
       ↓
Order placement
       ↓
Order validation
       ↓
Credit/payment verification
       ↓
Availability check
       ↓
Order confirmation
       ↓
Picking or service preparation
       ↓
Packing and dispatch
       ↓
Delivery and receipt
       ↓
After-sales support or returns

10. Order Placement

Orders may be placed through:

• Sales representatives
• Email
• Telephone
• Customer portals
• Electronic data interchange
• E-commerce platforms
• Mobile applications
• Electronic procurement systems
• Physical service points

The organisation should provide customers with clear ordering instructions.

11. Order Validation

Before accepting an order, the organisation should verify:

• Customer identity
• Product or service code
• Description
• Quantity
• Price
• Delivery address
• Required delivery date
• Payment or credit status
• Contract terms
• Customer instructions
• Legal or regulatory requirements

Validation reduces incorrect, duplicate or unauthorised orders.

12. Availability Check

The organisation must determine whether it can meet the requested quantity and delivery date.

This may require checking:

• Available inventory
• Existing order commitments
• Expected receipts
• Production capacity
• Supplier lead times
• Warehouse capacity
• Transport capacity

The result informs the delivery promise made to the customer.

13. Available to Promise

Available to Promise, or ATP, is the quantity of inventory or planned production that has not yet been committed to another customer.

ATP answers:

How much can we promise from available or planned supply?

A simplified calculation is:

ATP = Available or planned supply − Existing customer commitments

Example

A distributor has 500 units available. Existing confirmed orders require 380 units.

ATP = 500 − 380 = 120 units

The distributor may promise up to 120 additional units, subject to other operational conditions.

14. Capable to Promise

Capable to Promise, or CTP, determines whether the organisation can meet an order by considering its wider operational capacity.

CTP may consider:

• Materials
• Supplier capacity
• Production capacity
• Labour
• Equipment
• Warehouse capacity
• Transport
• Lead times

CTP answers:

Can we obtain, produce and deliver the required quantity by the requested date?

An organisation should not confirm an unrealistic delivery date merely to secure an order.

15. Order Confirmation

Once the order has been validated and availability confirmed, the customer should receive an order confirmation.

It should include:

• Order number
• Products or services
• Quantities
• Prices
• Delivery location
• Expected delivery date
• Payment terms
• Relevant conditions
• Customer-service contact information

Any difference between the customer’s request and the accepted order must be communicated clearly.

16. Order Processing and Fulfilment

Order processing may include:

• Reserving inventory
• Creating picking instructions
• Picking the correct items
• Checking quantities
• Packing and labelling
• Preparing documents
• Scheduling transport
• Dispatching
• Updating the order status

Controls should prevent:

• Incorrect products
• Incorrect quantities
• Duplicate orders
• Unauthorised changes
• Incorrect addresses
• Missed delivery instructions
• Incomplete documentation

17. Delivery and Receipt

Delivery may require:

• Tracking information
• Proof of delivery
• Customer signature
• Electronic confirmation
• Quantity verification
• Condition inspection
• Recording shortages or damage

Treat the order as complete only when delivery and receipt have been properly recorded.

18. Returns, Feedback, and Follow-Up

After delivery, the customer may:

• Confirm satisfaction
• Report shortages
• Report damage
• Request a return
• Request an exchange
• Submit a complaint
• Provide feedback

This information should update customer records and support future improvement.

19. Types of Order Management

Automated Replenishment

Automated replenishment creates or recommends an order when inventory reaches a defined level.

It may use:

• Reorder points
• Minimum and maximum stock levels
• Actual consumption
• Forecast demand
• Lead times

Automated replenishment can reduce manual work and stockouts, but inaccurate system information may generate incorrect orders.

Online Ordering

Online ordering allows customers to place orders through an electronic platform.

Benefits include:

• Continuous access
• Faster processing
• Automated confirmation
• Fewer manual entries
• Order-status visibility
• Easier access to product information

Risks include:

• Cybersecurity threats
• Incorrect customer entries
• System downtime
• Payment failures
• Poor system integration

Vendor-Managed Inventory

Vendor-Managed Inventory, or VMI, is an arrangement in which the supplier monitors and replenishes inventory held by or for the customer.

The customer shares relevant inventory and demand information with the supplier.

Potential benefits include:

• Improved product availability
• Reduced ordering administration
• Better supplier planning
• Lower risk of stockouts
• Improved inventory coordination

VMI requires:

• Accurate information
• Agreed stock levels
• Defined responsibilities
• Reliable systems
• Trust between the parties
• Performance monitoring

20. Order-Management Performance

Order management may be measured using:

• Order accuracy
• Order cycle time
• On-time delivery
• Fill rate
• Perfect-order rate
• Cancellation rate
• Back-order rate
• Return rate
• Customer complaints
• Cost per order

Analyse performance results and use them to correct recurring failures.

21. Lesson Summary

• Demand forecasts estimate future customer requirements.
• Forecasts support procurement, inventory, production, warehouse and transport planning.
• Forecasts must be compared with actual demand and updated regularly.
• Orders must be validated before they are accepted and processed.
• ATP identifies uncommitted available or planned supply.
• CTP considers whether the organisation has the complete capacity to fulfil an order.
• Order management continues through confirmation, fulfilment, delivery, receipt and after-sales support.
• Automated replenishment, online ordering and VMI provide different approaches to managing orders and inventory.
• Order-management performance must be measured and improved.