7. Master Production Scheduling

Introduction and Context

Production planning and control translates market demand into coordinated decisions about what will be produced, in what quantities, and when. Within this system, the Master Production Schedule (MPS) occupies a central position. It converts the broad intentions of the aggregate production plan into a time-phased statement of end-item production. The MPS is therefore neither a simple sales forecast nor a detailed shop-floor dispatch list. It is a managerial commitment that links demand management, material planning, capacity planning, purchasing, and manufacturing execution.

The importance of the MPS becomes clear when an organisation produces many products using shared resources. A sales department may promise rapid delivery, while production faces limited machine hours, labour constraints, supplier lead times, and changeover costs. The MPS provides a common plan against which these competing requirements can be evaluated. It also establishes the basis for the Material Requirements Planning (MRP) system. Once end-item quantities and due dates are established, the MRP system can calculate requirements for components, subassemblies, and purchased materials.

An effective MPS must be sufficiently stable to support execution, yet sufficiently responsive to changes in demand and supply. This balance is managed through planning time fences, lot-sizing rules, order-promising techniques, and priority policies. The chapter develops these ideas and shows how managers can construct and evaluate an MPS under realistic constraints.

Learning Objectives

After studying this chapter, the learner should be able to:

1. Explain the purpose, structure, and strategic role of the Master Production Schedule.

2. Distinguish among aggregate planning, MPS, MRP, and detailed shop-floor scheduling.

3. Construct a time-phased MPS using forecasts, customer orders, inventory records, safety stock, and lot-sizing rules.

4. Apply demand time fences and planning time fences to control schedule changes.

5. Calculate projected available balance, available-to-promise, and capable-to-promise.

6. Evaluate demand-priority policies and their effects on customers, inventory, capacity, and profitability.

7. Identify operational, technological, ethical, and managerial risks in MPS implementation.

8. Recommend improvements to an MPS in a practical production environment.

The Role of the MPS in the Planning Hierarchy

The MPS is a statement of planned production for selected finished products or major product families, usually expressed in weekly or daily time buckets. Its planning horizon must cover at least the cumulative lead time of the relevant product and materials, and often extends beyond it to provide visibility for capacity and procurement decisions.

Aggregate production planning operates at a higher level. It balances total expected demand with broad capacity, workforce, inventory, subcontracting, and overtime decisions. The MPS disaggregates this plan into specific end items. MRP then explodes the MPS through the bill of material and calculates time-phased component requirements. Finally, detailed scheduling sequences operations on individual machines and work centres.

These levels are interdependent. A mathematically feasible MPS may still be operationally poor if it creates excessive setup activity or violates labour availability. Conversely, a detailed schedule cannot compensate for an unrealistic MPS. The planning system must therefore provide feedback upward: capacity problems discovered at the MPS or MRP level may require changes to the aggregate plan, product mix, delivery promises, or sourcing strategy.

The MPS serves several purposes:

• It translates strategic and aggregate plans into executable product quantities.
• It provides a stable input to MRP and purchasing.
• It supports customer order promising.
• It reveals capacity and material constraints early.
• It coordinates sales, operations, procurement, finance, and logistics.
• It establishes accountability for production commitments.

The MPS should not be interpreted as an unchangeable forecast. It is a controlled plan that is revised through agreed rules and decision rights.

Essential Concepts and Terminology

A forecast is an estimate of future demand, normally derived from historical data, market intelligence, customer information, or statistical models. A customer order is committed demand with a specified quantity and due date. Independent demand arises from external customers, whereas dependent demand is derived from the requirements of another item, as in components needed for a finished product.

The planning horizon is the future period covered by the schedule. A time bucket is the unit of time used in planning, such as a day, week, or month. A planned order receipt is the quantity scheduled to become available in a specified period. A projected available balance (PAB) is the expected inventory position after considering beginning inventory, receipts, and demand.

A lot-sizing rule determines the quantity produced or purchased when replenishment is required. A fixed lot size uses a predetermined quantity; lot-for-lot produces exactly the net requirement; economic order quantity balances ordering or setup cost against holding cost; and period-order quantity covers requirements over a selected number of periods.

A planning time fence is a boundary beyond which schedule changes are relatively easy and within which changes require progressively greater management approval. A demand time fence is the period in which actual customer orders, rather than forecasts, dominate the demand calculation. The terms are related but not identical: the demand fence concerns the source of demand, while the planning fence concerns the freedom to change the plan.

Available-to-promise (ATP) is the uncommitted quantity of a product that can be promised to customers based on the MPS and existing orders. Capable-to-promise (CTP) extends this logic by checking whether production capacity, materials, and critical resources can support a requested quantity and date, even when it is not already represented in the MPS.

Constructing the Master Production Schedule

MPS construction begins with the definition of the planning environment. The planner identifies the end items to be scheduled, the time buckets, the planning horizon, opening inventory, safety-stock requirements, confirmed orders, forecasts, scheduled receipts, lead times, lot-sizing rules, and available capacity. Data quality is crucial. An inaccurate inventory record, obsolete bill of material, or missing customer order can make an apparently precise schedule unreliable.

A basic PAB calculation for period t is:

PABt = PABt−1 + MPS receiptst − demandt

where demand may be forecast, actual orders, or a policy-based combination of the two. If safety stock is required, the proposed MPS must prevent the PAB from falling below that level unless an authorised exception exists.

For example, suppose a product begins with 300 units, has a safety stock of 100 units, and faces the following demand over four weeks: 180, 250, 150, and 300 units. If the lot size is 400 units and no scheduled receipts exist, the planner may schedule 400 units in week 1, 400 in week 3, and 400 in week 4. The resulting PAB is 520, 270, 520, and 620 units respectively. Although inventory is higher than the minimum in the final periods, the plan satisfies the lot-size rule and protects against stockouts. A lot-for-lot policy would reduce inventory but might create more frequent production starts.

In practice, the demand figure used in each period is not always simply the larger of forecast and orders. Organisations adopt policies such as:

• Orders-only within the demand fence: actual orders replace the forecast inside the fence.
• Maximum of forecast and orders: protects against a forecast being understated when orders exceed it.
• Forecast consumption: customer orders consume an equivalent quantity of forecast; additional orders above forecast increase demand.
• Priority-based allocation: scarce supply is assigned according to contractual, strategic, profitability, or service priorities.

The choice must be explicit. Otherwise, the same demand may be counted twice or customer orders may be ignored.

A Worked MPS Illustration

Assume the following simplified conditions for a finished product:

• Beginning inventory: 250 units
• Safety stock: 100 units
• Fixed production lot: 300 units
• Existing scheduled receipt: 200 units in week 2
• Forecast demand: 120, 180, 220, and 200 units in weeks 1–4
• Confirmed orders: 150, 100, 260, and 180 units in weeks 1–4

Under a maximum-of-forecast-and-orders policy, demand becomes 150, 180, 260, and 200 units. The planner first considers the opening stock. After week 1, inventory would be 100 units, exactly equal to safety stock. In week 2, the scheduled receipt raises available inventory before demand, resulting in 120 units after demand. In week 3, inventory would fall below safety stock, so a 300-unit MPS receipt is needed. In week 4, another 300-unit receipt is required to maintain the safety-stock requirement. The schedule is therefore shaped not only by demand but also by the timing of receipts and the lot-sizing rule.

This plan must then be checked against the product's lead time. If production requires two weeks, a receipt in week 3 requires release in week 1. If week 1 lies inside a frozen fence, the planner may need to use overtime, subcontracting, inventory, or a customer-delivery adjustment rather than simply moving the production order.

Planning Time Fences and Schedule Stability

A production schedule becomes ineffective if every new order causes immediate replanning. Frequent changes create material shortages, excess work-in-process, setup losses, expediting, and distrust between departments. Time fences introduce discipline by distinguishing periods with different rules.

A common arrangement includes three zones. In the frozen zone, usually the immediate future, MPS quantities and dates cannot be changed without senior approval. The slushy zone permits limited changes if capacity and material conditions remain acceptable. The liquid zone permits routine adjustment as forecasts, orders, and supply information improve. The actual lengths depend on product lead time, manufacturing flexibility, supplier responsiveness, and customer expectations.

Time fences should not be treated as purely administrative boundaries. They represent economic consequences. A change inside a frozen zone may require cancellation costs, premium freight, overtime, setup loss, or renegotiation with customers. A company with short changeover times and modular resources may use a shorter frozen zone than a company dependent on long-lead imported materials.

Effective governance defines who may authorise changes, what evidence is required, and how exceptions are recorded. A useful exception process identifies the reason for change, customer or financial impact, affected resources, substitute actions, and responsibility for approval. Performance measures should distinguish justified responsiveness from uncontrolled instability.

Lot-Sizing Decisions and Their Consequences

Lot sizing connects the MPS to inventory and production economics. Large lots reduce the frequency of setups and may improve machine utilisation, but they increase finished-goods inventory, obsolescence risk, and exposure to demand uncertainty. Small lots improve responsiveness and reduce inventory, but may increase setup time, scheduling complexity, and unit cost.

The principal approaches include:

Lot-for-lot. The planned receipt equals the net requirement. This is suitable where holding costs are high, setup costs are low, and production can be changed frequently.

Fixed-order quantity. Every replenishment uses a predetermined quantity. This may reflect a standard batch, container size, minimum supplier quantity, or process economics.

Economic order quantity. The quantity is based on a trade-off between setup or ordering cost and inventory holding cost. Its classical assumptions are often unrealistic in complex production environments, so it should be treated as a decision aid rather than an automatic answer.

Period-order quantity. A single receipt covers requirements for a specified number of periods. It can reduce administrative effort but may create uneven inventory.

Part-period balancing and least-unit-cost methods. These heuristics compare incremental setup savings with additional holding cost. They are useful when demand is uneven and the planning environment does not justify a full optimisation model.

Lot-sizing should also consider setup sequence, product families, shelf life, quality risk, minimum run lengths, campaign production, and capacity bottlenecks. A financially attractive batch size may be operationally infeasible if it exceeds available storage or consumes a bottleneck resource at the wrong time.

Order Promising: ATP and CTP

Customer service requires more than reporting the next open production slot. It requires a credible promise based on supply and demand commitments.

ATP is normally calculated at the first MPS receipt and at subsequent MPS receipts. A simplified formula is:

ATP at the first receipt = beginning inventory + first MPS receipt − customer orders until the next MPS receipt

For a later receipt, ATP equals that receipt less customer orders from that receipt period until the next MPS receipt. ATP is allocated to new orders, and the remaining balance represents uncommitted supply. Forecast demand is generally not treated as a firm customer commitment in the ATP calculation, although organisational policies vary.

For example, if beginning inventory is 500 units, the first MPS receipt is 400 units, and confirmed orders before the next MPS receipt total 650 units, ATP is 250 units. A new order of 300 units cannot be promised from existing supply without changing the schedule or accepting a shortage.

CTP is more advanced. It asks whether the organisation can produce and deliver a requested order by evaluating available capacity, material availability, labour, tooling, engineering constraints, and possibly supplier response. CTP is especially valuable in engineer-to-order, configure-to-order, and high-variety environments. ATP answers, “What uncommitted supply exists in the current plan?” CTP answers, “Can the system create the required supply by the requested date?”

An ATP or CTP system should provide a promise date, quantity, and relevant assumptions. A promise that ignores a constrained bottleneck is not customer service; it is deferred failure.

Demand-Priority Policies

When supply is scarce, the organisation must decide which demand receives priority. A first-come, first-served policy is simple and may appear fair, but it may not maximise strategic value. Other policies prioritise contractual customers, critical spare parts, high-margin products, key accounts, urgent medical or safety-related requirements, or orders with severe lateness penalties.

Priority rules should be transparent and consistent. If sales personnel privately override the sequence, the MPS loses credibility and internal conflict increases. A robust policy may combine several criteria: contractual obligation first, safety-critical demand next, then customer value, lateness, and contribution margin. However, purely financial prioritisation can damage long-term relationships or neglect social and regulatory responsibilities.

Allocation decisions also depend on product interchangeability. If two products use the same constrained resource but serve different markets, the opportunity cost of allocating capacity to one product must be considered. Optimisation models can support this analysis, but managerial judgement remains necessary when information is incomplete or strategic factors are difficult to quantify.

Capacity Evaluation and Schedule Quality

An MPS is valid only if it is feasible or if the organisation has a defined recovery plan. Rough-cut capacity planning compares MPS requirements with available capacity at critical work centres. It need not model every operation in detail; its purpose is to detect major overloads before orders are released to MRP and the shop floor.

Capacity evaluation should consider regular time, overtime, subcontracting, labour skills, machine availability, maintenance, setup time, yield losses, and bottleneck interactions. Nominal capacity is not the same as practical capacity. A machine theoretically available for 40 hours may provide substantially less effective capacity after breaks, maintenance, changeovers, and quality losses.

Typical responses to an overload include moving production, changing lot sizes, using overtime, subcontracting, cross-training employees, substituting materials, revising the product mix, or negotiating delivery dates. Each response has cost and risk. The preferred solution is not necessarily the one with the lowest immediate cost; it should also consider reliability, quality, strategic capability, and future flexibility.

Useful MPS performance measures include schedule adherence, forecast error, customer-request-date attainment, promised-date reliability, inventory turns, finished-goods obsolescence, expedite frequency, capacity utilisation at bottlenecks, and the number and magnitude of schedule changes within the frozen zone. Measures should be interpreted together. High utilisation with poor delivery performance may indicate that the system lacks protective capacity.

Digital Systems, Analytics, and Governance

Enterprise resource planning systems typically maintain MPS records, inventory balances, bills of material, routings, orders, receipts, and time-phased requirements. Advanced planning and scheduling tools can evaluate alternative production sequences, capacity constraints, and promise dates. Dashboards may display exceptions such as projected stockouts, late orders, overloads, and demand changes.

Analytics can improve forecast integration, identify systematic bias, estimate the probability of demand scenarios, and simulate the effects of alternative lot sizes. Artificial intelligence may assist with demand sensing or anomaly detection, but it does not remove the need for sound master data and managerial accountability. A machine-learning model trained on biased order history may reproduce allocation inequities or systematically under-serve smaller customers.

Governance is therefore essential. Organisations should define data ownership, approval rights, audit trails, override procedures, model validation, and cybersecurity controls. Automated rescheduling should be bounded by time fences and exception thresholds. Human planners must be able to understand why a recommendation was made and must remain responsible for decisions with significant financial, contractual, safety, or social consequences.

Illustrative Case: Rebuilding the MPS at Meridian Appliances

Background

Meridian Appliances, a fictional manufacturer, produces three refrigerator models on shared assembly resources. Demand has become more volatile because retailers place promotional orders with short notice. The sales team frequently promises delivery dates without consulting production, while planners respond by changing the MPS several times each week.

Situation and Problem

The company has acceptable average inventory but poor delivery reliability. Expedites are common, premium freight costs are increasing, and production supervisors report that schedules are never stable. A review shows that the MPS combines forecasts and customer orders inconsistently. Promotional orders are added to the forecast even when they have already replaced it, causing apparent demand inflation. In addition, the schedule uses a common two-week frozen period even though one model requires imported compressors with an eight-week lead time.

Analysis

The cross-functional team maps the planning process and finds four central weaknesses. First, there is no formal demand time fence, so planners do not know when orders should replace forecasts. Second, ATP is calculated from total inventory rather than from specific MPS receipts. Third, rough-cut capacity checks ignore setup time at the shared assembly line. Fourth, sales overrides are not recorded, making it impossible to distinguish legitimate exceptions from uncontrolled changes.

The team analyses twelve weeks of demand and identifies the assembly line as the bottleneck. It also finds that large batches of one model reduce setups but leave other models late. A simulation comparing fixed large lots with smaller family-based campaigns shows that modestly smaller lots, combined with protected capacity for priority orders, improve delivery reliability without materially increasing total production cost.

Approach and Solution

Meridian introduces a three-zone planning policy. The first two weeks are frozen, weeks three to five are slushy, and later weeks are liquid. The demand fence is set at one week for standard models and three weeks for promotional models. Within the demand fence, confirmed orders replace the relevant forecast rather than being added to it.

The company adopts a fixed weekly capacity check that includes setup hours. ATP is calculated by MPS receipt, and CTP review is required for orders that exceed ATP or request delivery inside the frozen zone. A priority policy gives precedence to contractual retailer commitments, then safety-related replacement units, followed by standard orders ranked by promised-date risk and contribution margin.

Sales and operations jointly approve exceptions. Every change records its reason, affected customer, capacity effect, and recovery action. Planners also create a small protected capacity reserve for urgent orders instead of scheduling the line continuously at nominal capacity.

Outcome

After implementation, the schedule contains fewer last-minute changes and production supervisors report improved sequence stability. The company reduces emergency expedites and improves the reliability of promised dates. Inventory does not fall immediately because management deliberately uses some stock as a buffer while data quality improves. The most important improvement is not a particular algorithm; it is the creation of shared rules connecting demand, capacity, promising, and accountability.

Learning Points

The case demonstrates that MPS performance depends on governance as much as calculation. Time fences are effective only when exceptions are controlled. ATP is meaningful only when receipts and commitments are accurately represented. Lot sizes must reflect setup economics and customer-service requirements. Finally, a cross-functional process is essential because no single department possesses all information needed to make a credible production commitment.

Challenges, Risks, and Common Mistakes

A frequent mistake is treating the forecast as an order or treating an order as additional forecast. This creates double counting and inflated production plans. Another error is ignoring opening inventory accuracy. A schedule built on records that do not match physical stock can produce either unnecessary production or unexpected shortages.

Planners may also use lot sizes mechanically without considering capacity, shelf life, obsolescence, or changeover sequence. Freezing too much of the schedule creates inflexibility, while freezing too little creates nervousness. A further risk is promising from aggregate inventory rather than from time-phased, product-specific supply.

Other risks include:

• obsolete or inaccurate bills of material and routings;
• failure to include yield loss, scrap, setup, or maintenance time;
• excessive dependence on nominal capacity;
• unauthorised sales overrides;
• poor communication of schedule changes;
• excessive use of safety stock to conceal planning weaknesses;
• algorithmic allocation that disadvantages particular customer groups;
• cyber or system failures that disrupt planning data.

Managers should avoid optimising one measure in isolation. Maximising machine utilisation may increase inventory and reduce delivery performance. Minimising inventory may increase changeovers and shortages. The appropriate objective is a balanced service, cost, capacity, and risk outcome.

Professional and Ethical Considerations

Production promises affect customers, employees, suppliers, and investors. It is professionally irresponsible to promise dates that are known to be infeasible merely to secure an order. When shortages arise, allocation should follow documented principles rather than personal influence or discriminatory practices. Safety-critical products require special controls, and regulatory requirements may override commercial priority.

Transparency is also important when data or models are uncertain. Planners should communicate assumptions, confidence, and constraints rather than present a precise date as a guarantee. Supplier capacity, labour availability, and environmental restrictions should not be hidden in order to make the MPS appear feasible. Ethical planning protects organisational credibility and supports durable customer relationships.

Emerging Directions

MPS practice is moving toward shorter planning cycles, integrated business planning, scenario analysis, and constraint-aware order promising. Cloud systems increasingly connect customer orders, supplier information, production status, and logistics data. Digital twins can test alternative schedules before implementation, while probabilistic planning can represent demand and supply uncertainty more realistically than a single deterministic forecast.

The emerging emphasis is not complete automation but augmented decision-making. Systems can identify trade-offs and recommend alternatives, while managers determine acceptable risk, strategic priority, and customer policy. The organisations most likely to benefit are those that combine reliable master data, disciplined governance, capable planners, and technologies that make constraints visible.

Best Practices for MPS Management

A mature MPS process generally follows these practices:

1. Maintain one agreed set of demand, inventory, capacity, and order data.

2. Define planning and demand time fences according to actual lead times and flexibility.

3. Use explicit rules for combining forecasts and customer orders.

4. Check rough-cut capacity before releasing the schedule to MRP.

5. Select lot-sizing rules according to total cost, setup conditions, service needs, and risk.

6. Calculate ATP from time-phased receipts and committed orders.

7. Use CTP when requested supply is not available in the existing MPS.

8. Establish transparent demand-priority rules.

9. Record and review every significant schedule exception.

10. Measure both schedule stability and customer-service performance.

11. Keep human accountability for automated recommendations.

12. Review the MPS regularly through a cross-functional sales and operations process.

Chapter Summary

The Master Production Schedule is the central time-phased plan connecting aggregate planning with material planning, capacity management, customer promising, and shop-floor execution. It specifies planned production of end items while balancing demand, inventory, lot sizes, lead times, and resource constraints. Projected available balance calculations help determine when receipts are required, while planning and demand time fences control the stability and responsiveness of the schedule.

Lot-sizing decisions influence setup cost, inventory, flexibility, and service. ATP identifies uncommitted supply within the current MPS, whereas CTP evaluates whether new supply can be created using available materials and capacity. Demand-priority policies are necessary when supply is scarce, but they must be transparent and ethically defensible. Digital systems and analytics improve visibility and scenario analysis, yet they depend on accurate data and sound governance. A successful MPS is therefore both a calculation and a cross-functional managerial commitment.

Key Terms

Master Production Schedule: A time-phased plan specifying quantities and timing of finished-product production.

Planning Horizon: The future period covered by a production plan.

Time Bucket: The unit of time used to organise planning data.

Projected Available Balance: Expected inventory after planned receipts and demand are considered.

Safety Stock: Inventory held to protect against uncertainty in demand or supply.

Planning Time Fence: A boundary defining the degree of freedom to change the schedule.

Demand Time Fence: A period in which actual orders receive priority over forecasts in demand calculation.

Lot-for-Lot: A lot-sizing method producing exactly the net requirement.

Fixed-Order Quantity: A policy using a predetermined replenishment quantity.

Economic Order Quantity: A quantity balancing ordering or setup cost with holding cost.

Available-to-Promise: Uncommitted product supply that can be promised from the existing plan.

Capable-to-Promise: A promise based on the ability to create supply using capacity and materials.

Independent Demand: Demand originating from external customers.

Dependent Demand: Demand derived from the requirement for another item.

Rough-Cut Capacity Planning: A high-level check of capacity against MPS requirements.

Frozen Zone: The near-term period in which schedule changes require formal approval.

Slushy Zone: A period in which limited, controlled schedule changes are permitted.

Liquid Zone: A later planning period open to routine adjustment.

Demand Priority: A rule for allocating scarce supply among competing requirements.

Schedule Nervousness: Excessive changes in planned quantities, dates, or sequences.

Review Questions

Short Answer Questions

1. What is the role of the MPS in relation to aggregate planning and MRP?

2. Distinguish between a planning time fence and a demand time fence.

3. Define projected available balance and state its basic calculation.

4. What is the difference between ATP and CTP?

5. Why can a large production lot increase operational risk?

Long Answer Questions

1. Explain the process of constructing an MPS, including demand, inventory, lot-sizing, lead-time, and capacity considerations.

2. Discuss how frozen, slushy, and liquid zones contribute to schedule stability.

3. Compare lot-for-lot, fixed-order quantity, economic order quantity, and period-order quantity methods.

4. Explain how demand-priority policies should be designed and governed in a capacity-constrained environment.

5. Evaluate the role of ERP, analytics, and AI in MPS management, including data and governance risks.

Analytical / Application Questions

1. A product has beginning inventory of 250 units, safety stock of 100 units, and weekly demand of 180, 250, 150, and 300 units. Using a fixed lot size of 400 units and no scheduled receipts, construct a feasible four-week MPS and calculate the resulting projected available balances.

2. A customer requests 300 units in week 3, but ATP is only 180 units. Explain how a planner should evaluate the request using CTP, including capacity, materials, time fences, and priority policy.

3. A factory experiences frequent schedule changes because sales orders are added to forecasts inside the demand fence. Diagnose the likely consequences and recommend a revised demand, promising, and governance process.

22. Multiple Choice Questions with Answers

Question 1

What is the primary role of the Master Production Schedule?

A. To sequence every individual job on every machine

B. To specify time-phased production quantities for selected finished products

C. To calculate only the annual sales forecast

D. To replace all purchasing decisions

Correct Answer: B. To specify time-phased production quantities for selected finished products

Question 2

If beginning inventory is 500 units, the first MPS receipt is 400 units, and committed orders before the next MPS receipt total 650 units, what is the simplified ATP?

A. 150 units

B. 250 units

C. 400 units

D. 900 units

Correct Answer: B. 250 units

Question 3

Which statement best distinguishes CTP from ATP?

A. CTP ignores capacity while ATP includes capacity

B. CTP evaluates whether new supply can be created using capacity and materials

C. CTP applies only to forecasts, while ATP applies only to orders

D. CTP is used only after production is completed

Correct Answer: B. CTP evaluates whether new supply can be created using capacity and materials

Question 4

What is the main purpose of a frozen zone?

A. To permit unlimited schedule changes

B. To eliminate the need for safety stock

C. To protect near-term production from uncontrolled changes

D. To postpone all customer orders

Correct Answer: C. To protect near-term production from uncontrolled changes

Question 5

Under a lot-for-lot rule, the planned receipt generally equals:

A. The economic order quantity

B. The net requirement for the period

C. The largest possible batch

D. The previous period's production

Correct Answer: B. The net requirement for the period

Question 6

A company adds confirmed customer orders to the forecast even when the orders have already replaced forecast demand. What is the most likely result?

A. Demand double counting

B. Lower projected demand

C. Automatic capacity expansion

D. Reduced inventory exposure

Correct Answer: A. Demand double counting

Question 7

Which factor is most important when evaluating whether an MPS is feasible?

A. Nominal machine nameplate speed only

B. Available capacity including setup, maintenance, labour, and bottleneck constraints

C. The number of sales employees

D. The age of the product catalogue

Correct Answer: B. Available capacity including setup, maintenance, labour, and bottleneck constraints

Question 8

A large fixed production lot may reduce setup frequency but typically creates which trade-off?

A. Lower inventory and higher responsiveness

B. Higher inventory and greater obsolescence exposure

C. No effect on capacity or inventory

D. Guaranteed improvement in delivery reliability

Correct Answer: B. Higher inventory and greater obsolescence exposure

Question 9

Which policy is most appropriate when an order exceeds existing ATP and requests delivery inside the frozen zone?

A. Promise it automatically to preserve sales volume

B. Ignore the order permanently

C. Conduct a controlled CTP and exception review

D. Add it to the forecast without checking capacity

Correct Answer: C. Conduct a controlled CTP and exception review

Question 10

Why should automated MPS recommendations remain subject to governance and human accountability?

A. Algorithms always produce identical schedules

B. Planning data may be biased or inaccurate and decisions may have contractual or ethical consequences

C. Technology cannot store inventory data

D. Human planners do not need system information

Correct Answer: B. Planning data may be biased or inaccurate and decisions may have contractual or ethical consequences