Material Planning and Replenishment Systems

Introduction

Material planning and replenishment determine when, where, and how much inventory should be available across a supply network. Their purpose is not simply to increase stock availability. A well-designed system coordinates demand, purchasing, production, distribution, capacity, supplier performance, working capital, and customer service.

Inventory decisions are made at several levels. Strategic decisions determine network structure, sourcing policy, postponement points, and technology architecture. Tactical decisions establish planning parameters, order policies, inventory targets, and supplier agreements. Operational decisions release purchase orders, production orders, transfer orders, and replenishment signals.

Modern organizations rarely rely on one method. A manufacturer may use material requirements planning (MRP) for components, distribution requirements planning (DRP) for warehouses, enterprise resource planning (ERP) for transaction integration, and Kanban or vendor-managed inventory (VMI) for repetitive, stable-demand items. The managerial challenge is to design a coherent hybrid system rather than to select a fashionable technique in isolation.

Learning Objectives

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

1. Explain the logic and information requirements of MRP, DRP, ERP, JIT, Kanban, VMI, continuous replenishment, postponement, and collaborative planning.
2. Calculate planned orders, safety stock, reorder points, and basic distribution requirements under stated assumptions.
3. Select replenishment methods according to demand characteristics, product structure, lead time, volume, and supply-network configuration.
4. Diagnose common causes of nervousness, excess inventory, shortages, and poor schedule adherence.
5. Design an integrated replenishment process with appropriate governance, performance measures, and digital controls.

1. The Logic of Material Planning

Material planning translates an anticipated requirement into time-phased supply decisions. The basic planning relationship is:

Gross requirement is the expected demand during a planning period. Available supply includes usable on-hand inventory, scheduled receipts, and, depending on the system, confirmed transfers. Safety stock is a planned protection quantity. The equation is a planning identity, not a universal ordering rule: it must be supplemented by lot-sizing, lead-time, yield, capacity, and order-policy rules.

A replenishment system must answer five questions:

• What is required?
• When is it required?
• What supply is already available?
• When must an order be released to meet the requirement?
• What constraints or risks could invalidate the plan?

The answers depend on the demand environment. Independent demand is generated externally, such as customer demand for finished goods. Dependent demand is derived from another item’s requirement, such as the number of wheels required for a planned number of bicycles. Forecasting is central to independent demand; explosion from a bill of material is central to dependent demand.

Important planning data

Data quality is a design issue, not merely an information-technology issue. An inaccurate bill of material can create systematic shortages even when the planning algorithm is correct. Incorrect lead times produce either premature inventory or late supply. Unrecorded scrap, substitutions, engineering changes, and stock held in quarantine can make the book balance differ from physical reality.

2. Material Requirements Planning

MRP is a time-phased planning technique for dependent-demand items. It starts with the master production schedule (MPS), explodes requirements through the bill of material (BOM), offsets them by lead time, and applies inventory and order policies.

2.1 MRP logic

For each item and time bucket, MRP generally performs the following sequence:

1. Determine the gross requirement from the parent item or independent demand.
2. Add any required safety stock.
3. Subtract projected available inventory and scheduled receipts.
4. Calculate the net requirement.
5. Apply the lot-sizing rule.
6. Offset the planned receipt by the item’s lead time to obtain the planned order release.
7. Pass the dependent requirement to the next lower BOM level.

The projected available balance can be represented as:

where PAB_t is projected available balance in period t, SR_t is the scheduled receipt, POR_t is the planned order receipt, and GR_t is the gross requirement. All quantities must use the same unit and period definition. The equation assumes that receipts are usable in the stated period and that inventory losses, yield variation, and substitutions are either negligible or explicitly modeled.

A net requirement occurs when projected availability would fall below the specified safety-stock level. If the lot-for-lot rule is used, the planned receipt equals the net requirement. Under a fixed-order-quantity rule, the receipt may be the smallest permitted multiple or fixed quantity that covers the requirement.

2.2 Worked MRP example

An assembly requires two units of component C. The MPS calls for 40 assemblies in period 3 and 30 in period 4. Component C has 10 units on hand, no scheduled receipts, a safety stock of 5 units, and a one-period lead time. Use lot-for-lot ordering.

Gross requirements are 80 units in period 3 and 60 units in period 4. In period 3, the available stock before ordering is 10. To finish at or above safety stock, the planned receipt is:

The projected balance after the receipt is 5 units. In period 4:

Thus, planned receipts are 75 units in period 3 and 60 units in period 4. With a one-period lead time, planned releases are 75 units in period 2 and 60 units in period 3. The example assumes perfect supply, no scrap, and a reliable MPS. If component yield is 95%, the gross requirement must be adjusted or the order quantity increased according to the organization’s yield policy.

2.3 MRP strengths and limitations

MRP is particularly effective when demand is dependent, product structures are known, lead times are reasonably stable, and the organization has a credible production schedule. It provides visibility of future shortages and coordinates thousands of components more systematically than individual reorder decisions.

Its weaknesses arise when assumptions are treated as facts. MRP can generate excessive changes when the MPS is unstable, create unrealistic plans when capacity is ignored, and propagate errors through multi-level BOMs. “Nervousness” describes frequent changes to planned orders caused by demand revisions, timing shifts, cancellations, or parameter instability. Time fences, order modifiers, frozen schedules, exception messages, and capacity checks can reduce—but not eliminate—this problem.

MRP is also not automatically a scheduling system. Basic MRP is often infinite-capacity planning; it may calculate material availability without proving that machines, labor, docks, or suppliers can execute the plan. Capacity requirements planning and finite-capacity scheduling are therefore needed where bottlenecks are significant.

3. Distribution Requirements Planning

DRP extends time-phased planning to distribution networks. It determines replenishment requirements for regional warehouses, distribution centers, and sometimes retail locations. The logic resembles MRP, but the demand relationship is geographical rather than primarily product-structural.

A DRP record commonly contains forecast or order demand, beginning inventory, scheduled receipts, planned transfers, safety stock, and projected available balance. A warehouse’s planned receipt becomes a planned shipment from an upstream facility. The upstream facility then aggregates requirements from multiple destinations and plans production or procurement.

DRP is valuable when:

• stock is positioned at multiple locations;
• transport lead times matter;
• customer service differs by market;
• inventory must be deployed across a network; and
• central production or procurement must see regional requirements.

Reorder-point calculation

For a continuous-review item with approximately stable demand, a basic reorder point is:

where d is average demand per unit of time, L is replenishment lead time in the same time unit, and SS is safety stock in units. The term dL is cycle or lead-time demand. This formula assumes that demand and lead time are represented adequately by their averages and that replenishment is triggered when inventory position reaches the reorder point.

If demand during lead time is uncertain but lead time is treated as fixed, a common statistical approximation is:

where z is the desired service-factor quantile, σ_d is the standard deviation of demand per period, and L is lead time in periods. This is appropriate only when demand observations are reasonably independent, the chosen service measure corresponds to the selected z-value, and lead time is sufficiently stable. If both demand and lead time vary, a more general approximation is:

where σ_L is the standard deviation of lead time. Then SS = zσ_DL. The formula assumes independence between demand and lead time; correlation requires an additional covariance term.

For example, average weekly demand is 100 units, lead time is 2 weeks, weekly demand standard deviation is 20 units, and the target factor is z = 1.65. Then safety stock is approximately 1.65 × 20 × √2 = 46.7 units, usually rounded according to the organization’s inventory policy. The reorder point is approximately 247 units: 200 units of expected lead-time demand plus 47 units of safety stock. The result is not a guarantee of a particular fill rate unless the statistical assumptions and service interpretation are valid.

4. ERP as an Integrating Platform

ERP integrates master data, transactions, planning, finance, procurement, manufacturing, sales, quality, and logistics in a common system. ERP does not replace planning judgment. Rather, it provides a shared data and transaction architecture through which different planning methods can operate.

An ERP replenishment process may connect a customer order to demand management, MPS, MRP, purchase requisitions, supplier orders, goods receipt, invoice verification, inventory valuation, and financial settlement. The integration reduces duplicate data entry and supports traceability.

Important ERP governance controls include:

• role-based authorization for changing BOMs, suppliers, and planning parameters;
• approval workflows for purchase and production orders;
• revision control for engineering changes;
• audit trails for master-data changes;
• segregation of duties between ordering, receiving, and payment;
• cycle counting and reconciliation between physical and system stock; and
• exception dashboards for shortages, overdue receipts, and parameter anomalies.

ERP implementations often fail because organizations automate unstable processes, underestimate master-data work, or configure the system around departmental preferences rather than end-to-end flow. The system should distinguish planning proposals from committed orders and should make assumptions visible to planners.

5. Lean Replenishment: JIT and Kanban

Just-in-time (JIT) is a management philosophy emphasizing the elimination of waste, short and stable flow, quality at source, small lots, reliable processes, and production only in response to actual or closely synchronized demand. JIT is not equivalent to “zero inventory.” Some inventory is necessary to absorb uncertainty, protect critical operations, and decouple processes. The objective is to remove avoidable inventory and process instability.

Kanban is a visual or electronic authorization mechanism that controls replenishment. A downstream process withdraws material, and that withdrawal signals the upstream process or supplier to replenish the quantity consumed. Thus, Kanban is a pull-control mechanism, whereas an MRP release is typically schedule-driven or push-oriented.

A basic container-based Kanban quantity can be estimated as:

where n is the number of containers or Kanban cards, D is average demand per unit time, L is replenishment lead time, T is a review or handling factor if used by the organization, α is a protection factor, and C is container capacity. Because practices differ, organizations must define whether T is additive or whether total replenishment time is represented directly. The result is rounded upward to a whole container. The formula assumes reasonably stable demand, known replenishment time, and a controlled container quantity.

Kanban works best with repetitive demand, short and predictable replenishment, standardized work, visible consumption, and reliable quality. It is less suitable for highly erratic demand, unique engineered products, long and unreliable supplier lead times, or items whose container quantity is economically or physically inappropriate. Hybrid systems are common: MRP plans long-term requirements while Kanban controls day-to-day movement of stable components.

6. Vendor-Managed Inventory and Continuous Replenishment

Under vendor-managed inventory, the supplier monitors agreed customer inventory and takes responsibility for proposing or executing replenishment within defined rules. Ownership of stock may remain with the supplier until consumption, but ownership and physical control must be stated explicitly in the contract.

VMI requires:

• accurate and timely inventory and sales data;
• agreed minimum, maximum, or target levels;
• clear ownership and liability rules;
• service, quality, and delivery commitments;
• exception-management procedures; and
• mechanisms for resolving forecast, allocation, and obsolescence disputes.

Continuous replenishment is a broader process in which replenishment decisions are based on frequent updates of consumption, inventory, forecasts, and supply status. It may be supplier-managed or jointly managed. The central principle is not a particular legal arrangement but a regular, data-based replenishment cycle that reduces batching and improves visibility.

VMI can reduce the bullwhip effect by replacing isolated orders with consumption-based information. However, it does not remove total inventory from the system. It may shift inventory upstream, improve availability, or reduce duplicated safety stocks, but the physical risk remains somewhere in the network. It can also create dependency, confidentiality concerns, and disputes about forecast bias or service responsibility.

7. Postponement and Decoupling

Postponement delays product differentiation until demand information is more reliable. Examples include final packaging, labeling, configuration, or localization near the customer. A firm may hold generic modules centrally and complete the final variant after receiving a market-specific order.

Postponement is effective when products share common components, customization can be performed quickly, demand is uncertain at the variant level, and the delayed operation is economically feasible. It reduces the number of finished-product stock-keeping units and may lower obsolescence. Its limitations include additional handling, specialized late-stage capacity, possible longer customer lead times, and the risk that the postponement point is chosen without considering total cost.

The decoupling point is the location in the supply chain where forecast-driven activity changes to order-driven activity. Upstream processes may use forecasts and MRP; downstream processes may use customer orders, Kanban, or configured-to-order logic. Choosing this point requires analysis of lead time, product variety, process flexibility, transport, and customer service.

8. Collaborative Planning and Replenishment

Collaborative planning, forecasting, and replenishment (CPFR) coordinates trading partners through shared information, agreed forecasts, exception resolution, and joint replenishment decisions. Collaboration should be selective rather than universal. It is most valuable where partners have significant interdependence, demand variability, high stockout costs, long replenishment times, or promotional distortions.

A practical CPFR cycle is:

1. Establish collaboration scope, roles, data rights, and performance measures.
2. Exchange baseline forecasts and identify differences.
3. Reconcile assumptions about promotions, events, capacity, and market conditions.
4. Create a jointly agreed forecast or exception list.
5. Translate the plan into orders, production, and distribution requirements.
6. Monitor actual consumption and revise only when evidence warrants change.
7. Review service, inventory, forecast error, and root causes.

Collaboration can fail when partners share data but not decisions, when incentives reward local cost rather than total supply-chain performance, or when one party uses shared information opportunistically. Trust therefore requires governance, confidentiality, transparent definitions, escalation rules, and balanced scorecards.

9. Choosing an Appropriate Replenishment Architecture

No single system is best for every item. A useful segmentation considers demand predictability, value, criticality, volume, product structure, lead time, and supply risk.

The choice should be based on total cost and service, not on the label attached to the method. Relevant costs include purchase price, ordering, setup, transport, handling, storage, capital, obsolescence, shortage, expediting, quality failure, and information-system administration.

10. Integrated Process Design

A robust replenishment architecture normally contains the following layers:

• Demand layer: orders, forecasts, promotions, projects, and service commitments.
• Planning layer: MPS, MRP, DRP, safety stock, deployment, and capacity checks.
• Execution layer: purchase orders, work orders, transfer orders, Kanban signals, and shipment confirmations.
• Control layer: inventory accuracy, exception management, approvals, supplier performance, and financial reconciliation.
• Learning layer: forecast-error analysis, parameter review, root-cause analysis, and continuous improvement.

Figure: Integrated replenishment planning flow with feedback from execution performance to future planning.

The architecture should distinguish normal flow from exceptions. Planners should not spend equal effort on every item. Exception thresholds can identify projected stockouts, overdue orders, demand changes beyond tolerance, capacity overloads, unusual usage, and master-data inconsistencies.

11. Performance Measurement

A balanced scorecard prevents a narrow focus on inventory reduction. Useful measures include:

• Fill rate: proportion of demand fulfilled immediately from available stock, subject to a defined denominator and time period.
• Cycle service level: probability that no stockout occurs during a replenishment cycle.
• Inventory turnover: annualized cost of goods sold divided by average inventory value.
• Days of supply: inventory units or value divided by average daily demand or cost, with the denominator clearly defined.
• Forecast error: measured using an agreed metric such as mean absolute deviation or weighted absolute percentage error, while recognizing that percentage measures behave poorly near zero demand.
• Schedule adherence: actual completion compared with planned completion.
• Supplier on-time-in-full performance: receipts meeting both date and quantity criteria.
• Obsolescence and expiry: value or quantity becoming unusable because of age, engineering change, or market decline.

Metrics require definitions. A high inventory turnover ratio can coexist with poor customer service if inventory is concentrated in the wrong locations. Likewise, a high fill rate may be purchased through excessive safety stock. Managers should examine trade-offs and root causes rather than reward isolated indicators.

12. Technology, Analytics, and Artificial Intelligence

Cloud ERP, application programming interfaces, electronic data interchange, warehouse-management systems, radio-frequency identification, Internet-of-Things sensors, and control towers can improve event visibility and transaction speed. Their value depends on process discipline and data quality.

Analytics can segment items, estimate lead-time distributions, detect abnormal consumption, identify supplier risk, and recommend parameter changes. Machine-learning forecasts may improve performance where demand contains nonlinear patterns or rich external drivers. However, forecasts generated by complex models remain estimates. Planners must test bias, stability, explainability, data leakage, and performance under structural changes.

AI governance should address access to commercial data, cybersecurity, intellectual property, automated-order authorization, auditability, and human accountability. An AI system should not automatically release high-value or safety-critical orders without suitable controls. Models should be monitored for drift, and overrides should be recorded for learning rather than treated as unexplained failure.

13. Case Study: Replenishment Redesign at Meridian Home Systems

Background. Meridian Home Systems assembles modular air-treatment units. It operates one plant, two regional distribution centers, and approximately 400 retail customers. The company used annual forecasts, large monthly purchase orders, and manual spreadsheet allocation. Components were frequently unavailable at the plant while finished products accumulated at one distribution center.

Problem. Customer fill rate was 88%, average inventory had risen by 24%, and planners frequently expedited motors and control boards. The BOM contained obsolete revisions, supplier lead times were not updated, and regional warehouses placed orders independently. Management initially proposed adopting a single “real-time AI replenishment system.”

Analysis. A cross-functional team classified items by demand and supply characteristics. Finished units were forecast at the family level, but variants were highly uncertain. Common housings and motors had repetitive demand; control boards had long and variable lead times; packaging was inexpensive and easy to postpone. The team also found that inventory records differed from physical counts by 6–11% for several high-volume items.

Approach. Meridian first corrected BOM revisions, introduced cycle counting, and established supplier lead-time ownership. It implemented MRP for plant components and DRP for transfers to the two distribution centers. Stable housings and fasteners were replenished through electronic Kanban with fixed containers. Critical control boards used time-phased planning and a reviewed safety-stock policy. Final labeling and selected packaging were postponed until regional orders were known. For two strategic suppliers, Meridian established VMI with shared consumption data and weekly exception reviews. A CPFR pilot was introduced with its largest retail customer to coordinate promotions.

Outcome. After two planning quarters, the company reported an improved fill rate, fewer emergency shipments, and lower finished-goods variety stock. The largest improvement did not come from the algorithm; it came from accurate master data, synchronized regional requirements, and clearer ownership. Inventory reduction was measured only after service had stabilized. The company retained human approval for changes to critical-item parameters and supplier commitments.

Learning points. First, replenishment methods should be matched to demand and supply characteristics. Second, data governance is a prerequisite for automation. Third, postponement can reduce variant risk without eliminating responsiveness. Fourth, collaboration requires explicit rules and mutual performance measures. Finally, a hybrid architecture is often more effective than a single universal method.

14. Challenges, Ethics, and Governance

Replenishment decisions have ethical and social consequences. Pressure to reduce inventory may transfer risk to suppliers through unreasonable lead times, unpaid buffer stock, or unilateral forecast changes. VMI and CPFR involve commercially sensitive data and therefore require access controls and fair-use agreements. Automated systems may prioritize high-margin customers during shortages, raising questions of transparency and contractual fairness.

Environmental performance should be incorporated into replenishment design. Smaller, more frequent deliveries may reduce inventory but increase transport emissions. Excess safety stock can create waste, especially for perishable or rapidly obsolete products. Supplier selection should consider labor standards, resilience, emissions, and responsible sourcing, not only unit price and lead time.

Governance should specify who owns parameters, who can override recommendations, how exceptions are escalated, and how decisions are audited. Critical materials may require dual sourcing, approved substitutes, continuity plans, and scenario exercises. Resilience is not simply holding more stock; it is the capability to sense disruption, respond quickly, and recover at acceptable cost.

Best-Practice Guidelines

1. Stabilize and govern master data before automating replenishment.
2. Segment items by demand, value, criticality, lead time, and supply risk.
3. Use MRP for dependent requirements and DRP for time-phased network deployment.
4. Use Kanban where consumption and replenishment are sufficiently stable.
5. Treat safety stock as an explicit risk decision, not as a hidden planning error.
6. Separate forecast, order, and execution signals while maintaining traceability.
7. Combine inventory, service, capacity, quality, cost, and sustainability measures.
8. Review lead times, lot sizes, yields, and safety stocks regularly.
9. Use postponement when commonality and late customization are economically feasible.
10. Introduce VMI or CPFR only with clear data, ownership, incentives, and governance.
11. Design exception management so planners focus on material risks.
12. Retain human accountability for high-impact automated decisions.

Chapter Summary

Material planning and replenishment systems coordinate demand, inventory, capacity, suppliers, and distribution over time. MRP explodes dependent demand through the BOM and creates time-phased planned orders. DRP applies similar logic to the deployment of inventory across distribution locations. ERP integrates transactions and planning data but cannot compensate for poor processes or inaccurate master data.

JIT emphasizes flow and waste reduction, while Kanban provides a pull signal for stable repetitive replenishment. VMI and continuous replenishment share consumption information and shift or coordinate replenishment responsibility. Postponement delays product differentiation to reduce variant risk, and CPFR aligns trading partners through shared forecasts and exception management. Effective organizations combine these approaches according to item and network characteristics.

The central managerial principle is fit. A replenishment design must reflect demand uncertainty, lead time, product structure, capacity, service expectations, cost, risk, sustainability, and governance. Performance should be evaluated through balanced measures rather than inventory reduction alone. Digital technologies and AI can improve visibility and analysis, but their value depends on data quality, accountability, cybersecurity, and disciplined decision processes.

Key Terms

Material requirements planning (MRP): Time-phased planning of dependent-demand materials based on an MPS, BOM, inventory, and lead times.

Distribution requirements planning (DRP): Time-phased planning of inventory deployment across distribution locations.

Enterprise resource planning (ERP): Integrated platform linking organizational transactions, data, planning, and financial processes.

Master production schedule (MPS): Planned timing and quantity of end-item production.

Bill of material (BOM): Structured specification of components and quantities required for a parent item.

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

Independent demand: Demand generated externally by customers or markets.

Planned order release: Order authorization timed to meet a future planned receipt.

Safety stock: Inventory held to protect against uncertainty in demand, supply, or lead time.

Reorder point: Inventory-position threshold that triggers replenishment under a reorder-point policy.

Just-in-time (JIT): Operating philosophy focused on synchronized flow, waste reduction, and reliable processes.

Kanban: Pull-based authorization signal for replenishing consumed material.

Vendor-managed inventory (VMI): Arrangement in which the supplier monitors and manages agreed customer inventory replenishment.

Continuous replenishment: Frequent, data-based replenishment using current consumption and inventory information.

Postponement: Delaying product differentiation until demand information is more certain.

Decoupling point: Location where forecast-driven activity changes to order-driven activity.

Collaborative planning, forecasting, and replenishment (CPFR): Joint process for coordinating forecasts, plans, and replenishment among partners.

Bullwhip effect: Amplification of demand variability as information moves upstream through a supply chain.

Inventory position: On-hand inventory plus relevant incoming supply minus committed or allocated demand.

Exception management: Focusing managerial attention on deviations that exceed defined limits.

Review Questions

Short Answer Questions

1. Distinguish independent demand from dependent demand and give one example of each.
2. What information is required for an MRP system?
3. Explain why Kanban is described as a pull-control mechanism.
4. State two conditions under which postponement is useful.
5. Why does ERP implementation require master-data governance?

Long Answer Questions

1. Explain the complete MRP process from the MPS through planned order release, including the role of the BOM, inventory records, lead times, and lot-sizing.
2. Compare MRP, DRP, JIT, and Kanban in terms of demand type, planning horizon, information requirements, and limitations.
3. Discuss the design requirements, benefits, and risks of vendor-managed inventory and continuous replenishment.
4. Explain how postponement and the decoupling point influence product variety, inventory, responsiveness, and operating cost.
5. Design a balanced performance-measurement system for an integrated replenishment operation.

Analytical / Application Questions

1. A component has average weekly demand of 150 units, a fixed lead time of 3 weeks, weekly demand standard deviation of 25 units, and a service factor of 1.65. Calculate safety stock and reorder point using the fixed-lead-time approximation, and state the assumptions behind your result.
2. A manufacturer experiences frequent MRP rescheduling, obsolete finished goods, and shortages of common components. Diagnose at least four possible causes and recommend a hybrid replenishment architecture.
3. A retailer and supplier are considering VMI for promotional products with highly uncertain demand. Develop a governance design covering data sharing, inventory ownership, exception rules, performance measures, and safeguards against excess or obsolete stock.

Multiple Choice Questions with Answers

Question 1

Which planning system primarily explodes dependent demand through a bill of material?

A. MRP

B. DRP

C. Kanban

D. CPFR

Correct Answer: A. MRP

Question 2

A product requires 3 units of a component for each finished unit. If the schedule calls for 20 finished units, what is the component gross requirement before inventory adjustments?

A. 17 units

B. 20 units

C. 23 units

D. 60 units

Correct Answer: D. 60 units

Question 3

Which formula represents the basic reorder point when lead time is fixed?

A. ROP = safety stock ÷ demand

B. ROP = average demand × lead time + safety stock

C. ROP = annual demand ÷ order quantity

D. ROP = lead time − safety stock

Correct Answer: B. ROP = average demand × lead time + safety stock

Question 4

Which condition most strongly supports Kanban?

A. Highly unique products with unpredictable demand

B. Stable repetitive demand and reliable replenishment

C. No visibility of consumption

D. Long, highly variable supplier lead times

Correct Answer: B. Stable repetitive demand and reliable replenishment

Question 5

What is the primary purpose of postponement?

A. To increase finished-product variety stock

B. To eliminate all safety stock

C. To delay differentiation until demand information improves

D. To replace all forecasting with annual ordering

Correct Answer: C. To delay differentiation until demand information improves

Question 6

In VMI, which activity is typically performed by the supplier?

A. Monitoring agreed customer inventory and proposing replenishment

B. Auditing the customer’s financial statements

C. Setting the customer’s retail prices

D. Eliminating the customer’s ownership policies

Correct Answer: A. Monitoring agreed customer inventory and proposing replenishment

Question 7

Average weekly demand is 100 units, fixed lead time is 2 weeks, and safety stock is 40 units. What is the reorder point?

A. 140 units

B. 200 units

C. 240 units

D. 300 units

Correct Answer: C. 240 units

Question 8

Which statement about ERP is most accurate?

A. ERP automatically makes inaccurate master data reliable

B. ERP integrates transactions and planning but still requires governance

C. ERP is only a warehouse picking system

D. ERP eliminates the need for managerial judgment

Correct Answer: B. ERP integrates transactions and planning but still requires governance

Question 9

What does the bullwhip effect describe?

A. Reduction of demand variability at every upstream stage

B. Amplification of demand variability moving upstream

C. A method for calculating safety stock

D. A visual signal used in Kanban

Correct Answer: B. Amplification of demand variability moving upstream

Question 10

Which measure best combines the requirements of quantity and delivery timing from a supplier?

A. Inventory turnover

B. Forecast bias

C. Supplier on-time-in-full performance

D. Warehouse space utilization

Correct Answer: C. Supplier on-time-in-full performance