5. Inventory Planning and Control

Introduction: Inventory as a Design and Decision Problem

Inventory is the stock of materials, components, work-in-process, finished goods, spare parts, and supplies held within or between stages of a supply chain. It exists because supply and demand rarely occur at the same time, in the same quantity, or at the same location. Inventory can therefore protect customer service and production continuity, but it also ties up capital, consumes space, becomes obsolete, and may conceal weaknesses in forecasting, purchasing, production, and distribution.

Inventory planning and control is consequently not a narrow exercise in calculating order quantities. It is the coordinated design of stocking policies that balance service, cost, risk, capacity, and strategic responsiveness. A production manager must decide what to stock, where to stock it, how much to order or produce, when to replenish it, and how to respond when demand or supply differs from expectations.

The subject connects forecasting, procurement, production scheduling, warehousing, transportation, finance, and customer service. A low inventory figure is not automatically evidence of good management. If it causes stockouts, expediting, line stoppages, or lost customers, the apparent saving may be illusory. Conversely, high availability achieved through excessive stock may weaken cash flow and hide process inefficiency. Effective control is therefore based on total supply-chain performance rather than on inventory reduction alone.

Learning Objectives

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

1. Explain the purposes, classifications, and costs of inventory.

2. Apply ABC–XYZ analysis to differentiate inventory policies.

3. Calculate and interpret EOQ, reorder point, safety stock, service level, and periodic-review decisions.

4. Explain how uncertainty affects inventory policies and performance.

5. Evaluate multi-echelon inventory and postponement decisions.

6. Diagnose the bullwhip effect and recommend mitigation measures.

7. Use digital information and analytics to improve inventory visibility and control.

8. Recognize operational, ethical, governance, and implementation risks in inventory decisions.

The Role and Economics of Inventory

Inventory performs several distinct functions. Cycle stock results from ordering or producing in batches. Safety stock protects against uncertainty in demand or replenishment time. Anticipation inventory is accumulated before predictable seasonal demand, planned promotions, strikes, or supply interruptions. Pipeline inventory is in transit or otherwise moving through the network. Decoupling stock separates production stages so that a disturbance at one operation does not immediately stop the entire system. Obsolete, damaged, returned, and slow-moving stock represents inventory that may no longer generate its intended economic value.

The principal inventory costs are interconnected. Purchase or production cost is the direct cost of acquiring the item. Ordering or setup cost is incurred when an order is placed or a production run is initiated. Holding cost includes capital cost, storage, insurance, handling, deterioration, shrinkage, and obsolescence. Shortage cost includes lost contribution, emergency transportation, production disruption, contractual penalties, and damage to customer relationships. In many industries, shortage costs are difficult to observe precisely; this uncertainty can lead managers either to overstock defensively or to underestimate service risk.

Inventory decisions should use relevant total cost. For example, a supplier offering a lower unit price may require a large minimum order, longer lead time, or lower delivery reliability. The resulting holding and shortage costs may exceed the purchase-price saving. Similarly, reducing a warehouse's stock without considering inventory held by suppliers, distributors, or other plants may simply shift inventory rather than remove it.

Important Terminology

Demand may be independent, as in a finished product sold to customers, or dependent, as in a component whose requirement is derived from a production schedule. Lead time is the elapsed time between placing a replenishment order and its usable receipt. Inventory position is usually calculated as on-hand inventory plus scheduled receipts minus backorders. A stockout occurs when required inventory is unavailable. A fill rate measures the proportion of demand supplied immediately from stock, whereas cycle-service level measures the probability of avoiding a stockout during a replenishment cycle. These measures are related but not identical.

A stock-keeping unit (SKU) is an individually managed item-location combination. Stock accuracy is the degree to which system records agree with physical stock. Inventory turnover measures annual usage or cost of goods sold relative to average inventory; days of supply expresses the same relationship in time terms. These indicators are useful, but neither should be maximized without reference to customer service and operating risk.

Segmenting Inventory: ABC–XYZ Analysis

A single policy for every SKU is inefficient because items differ in value and demand behavior. ABC analysis classifies items according to annual consumption value:

Annual consumption value = Annual demand × Unit cost.

Items are then ranked from highest to lowest value and grouped into policy classes. The precise boundaries vary, but an A class commonly contains a small proportion of items accounting for a large proportion of value; B items have intermediate importance; and C items represent many low-value items with relatively small total value. ABC analysis directs managerial attention, counting effort, approval controls, and supplier negotiation toward economically important items.

ABC classification alone is insufficient. A low-value item may have highly erratic demand, while a high-value item may be stable and easy to forecast. XYZ analysis classifies demand predictability. X items have relatively stable demand, Y items show variation or trend, and Z items are irregular or intermittent. Measures such as the coefficient of variation—standard deviation divided by mean demand—can support this classification, although intermittent demand requires additional care because a low mean can make the ratio misleading.

Combining the two dimensions produces a useful policy matrix. AX items are valuable and predictable, making them suitable for close planning and relatively precise replenishment. AZ items are valuable but erratic; they require careful risk analysis, possible supplier flexibility, and senior review. CX items may be controlled through simple systems and larger review intervals. CZ items can consume disproportionate administrative effort if managed individually, so standardization, vendor-managed inventory, substitution, or rationalization may be preferable.

The matrix is a decision aid rather than an automatic answer. Criticality, lead time, supply risk, product life cycle, regulatory requirements, and consequences of failure should be added to the analysis. A cheap bearing that stops an entire production line may warrant more control than its ABC class suggests.

Continuous-Review Models and the Economic Order Quantity

The economic order quantity (EOQ) identifies the order size that minimizes the sum of ordering and holding costs under simplifying assumptions. The basic model assumes known and constant demand, fixed ordering cost, constant unit price, instantaneous replenishment or known lead time, and no stockouts. Its formula is:

EOQ = √(2DS/H)

where D is annual demand, S is ordering or setup cost per order, and H is annual holding cost per unit. If holding cost is expressed as a percentage i of unit price C, then H = iC.

For example, suppose annual demand is 12,000 units, ordering cost is ₹600 per order, and annual holding cost is ₹12 per unit. EOQ is √(2 × 12,000 × 600 / 12) = √1,200,000, approximately 1,095 units. The model indicates a balancing point: larger orders reduce ordering frequency but increase average cycle stock; smaller orders do the reverse.

EOQ should not be treated as a mandatory order quantity. Quantity discounts, container sizes, production capacity, supplier minimums, transportation economics, shelf life, and cash constraints may justify another quantity. In production environments, the economic production quantity adjusts for the fact that replenishment occurs gradually while production continues. The basic logic remains a trade-off between setup and carrying costs.

Under continuous review, an order is triggered when inventory position reaches a reorder point. With constant demand and lead time:

Reorder point = Demand during lead time = dL,

where d is average demand per time period and L is lead time in the same units. If demand or lead time is uncertain, safety stock is added:

Reorder point = Expected demand during lead time + Safety stock.

The reorder point is not the order quantity. The former determines when to order; the latter determines how much to order. Confusing these decisions is a common operational error.

Uncertainty, Safety Stock, and Service Levels

Uncertainty may arise from demand variation, forecast error, supplier lead-time variation, quality failures, transportation disruption, or changes in production yield. Safety stock is an explicit buffer against such uncertainty. It should be calculated from the risk being protected, not added as an arbitrary percentage of demand.

If demand during lead time is approximately normally distributed and lead time is constant, safety stock can be estimated as:

Safety stock = zσLT,

where z is the standard-normal factor associated with the target cycle-service level and σLT is the standard deviation of demand during lead time. For example, a 95 percent cycle-service target uses z approximately 1.645. If daily demand has a standard deviation of 20 units and lead time is 4 days, assuming independent daily demand, σLT = 20√4 = 40 units. Safety stock at 95 percent service is approximately 65.8 units.

If lead time is variable as well as demand, a commonly used approximation for the variance of demand during lead time is:

σ²DLT = Lσ²d + d²σ²L,

where L is average lead time, σd is the standard deviation of demand per period, d is average demand per period, and σL is the standard deviation of lead time. A covariance term may be required when demand and lead time are correlated. The formula is an approximation and should be validated against actual data, especially for seasonal or intermittent demand.

Service level must be defined carefully. Cycle-service level is the probability that no stockout occurs during a replenishment cycle. Fill rate is the percentage of units demanded that are supplied immediately. A policy may have a high cycle-service level but a lower fill rate if occasional shortages are large, or a lower cycle-service level but a high fill rate if shortages are small. Managers should select the measure that reflects the customer's experience and the economics of shortage.

Increasing service level has a nonlinear cost. Moving from 90 to 95 percent may require a modest increase in safety stock, whereas moving from 98 to 99.5 percent can require a disproportionate buffer. Service targets should therefore be differentiated by customer importance, product criticality, margin, substitution possibilities, and contractual obligation rather than uniformly maximized.

Periodic Review and Practical Replenishment Policies

In a periodic-review system, inventory is assessed at fixed intervals and orders are placed to raise inventory position to a target level. The order quantity varies according to the observed position. If review interval is R and protection lead time is R + L, a target or order-up-to level can be expressed as:

S = Expected demand during (R + L) + Safety stock.

Order quantity = S − Inventory position.

The periodic system is administratively convenient when suppliers visit on fixed schedules or when many items are ordered together. Its disadvantage is that the item is exposed to demand during the review interval before another order can be placed; consequently, it commonly requires more safety stock than an equivalent continuous-review policy. A hybrid policy may use frequent review for A items and simpler periodic control for C items.

Common operational policies include the continuous-review (Q, R) policy, in which a fixed quantity Q is ordered at reorder point R; the periodic (P, S) policy, in which stock is reviewed every P periods and raised to S; and a min–max policy, in which replenishment is triggered below a minimum and raises inventory toward a maximum. The choice depends on demand characteristics, transaction cost, system capability, supplier arrangements, and the consequences of stockout.

When demand is intermittent, conventional normal-distribution formulas may be unreliable. Croston-type methods, bootstrapping, empirical lead-time demand distributions, and judgment supported by service-criticality analysis may be more appropriate. Forecasting and inventory policy must be treated as a combined problem: a forecast without an associated error distribution is insufficient for setting safety stock.

Multi-Echelon Inventory and Postponement

In a multi-echelon supply chain, inventory is positioned at several levels, such as supplier, plant, regional distribution centre, and customer-facing facility. Optimizing each location independently can create excessive total stock because every echelon protects itself against uncertainty. Multi-echelon inventory planning considers the interaction among stages and allocates safety stock where it provides the greatest service benefit at the lowest total cost.

An important distinction is between local and system-level service. A distribution centre may appear to need high safety stock if it ignores replenishment flexibility from a central plant. Conversely, centralizing all stock may reduce total inventory but increase response time and transportation risk. Decisions should examine demand correlation, lead times, capacity, transshipment options, ownership, and the value of postponing final configuration.

Postponement delays product differentiation until demand information is clearer. A manufacturer may produce a common base product and add colour, packaging, software, or region-specific components near the customer. This reduces the number of differentiated inventories and lowers obsolescence risk, although it may require flexible equipment, modular design, skilled labour, and rapid final-stage processes. The postponement point should be selected by comparing the cost of late customization with the cost and risk of holding finished variants.

Risk pooling is a related principle. Aggregating demand across locations or products can reduce relative variability when demands are not perfectly correlated. However, pooling can be limited by transportation time, customer expectations, regulatory constraints, and product-specific requirements. Centralization is not automatically superior; the network must be evaluated through total landed cost and service response.

The Bullwhip Effect and Inventory Coordination

The bullwhip effect is the amplification of demand variability as information moves upstream from customer to retailer, wholesaler, manufacturer, and supplier. Small changes in final demand can produce increasingly large fluctuations in orders, production, and replenishment. Causes include demand forecasting based on orders rather than consumption, order batching, temporary price promotions, rationing and shortage gaming, long lead times, and poor visibility.

The effect raises safety stock, capacity requirements, overtime, obsolescence, and expediting while reducing operational stability. A factory may interpret a distributor's unusually large order as genuine market growth even when the order merely reflects batch ordering or an attempt to secure scarce supply.

Mitigation requires coordination rather than isolated optimization. Useful practices include sharing point-of-sale and inventory data, shortening lead times, reducing order and setup costs, using stable pricing, limiting allocation gaming through transparent rules, collaborating on forecasts, and replenishing according to actual consumption. Vendor-managed inventory and collaborative planning, forecasting, and replenishment can improve alignment when governance, data quality, and accountability are strong. Information sharing alone does not eliminate the bullwhip effect if incentives continue to reward local targets that conflict with total-chain performance.

Technology, Analytics, and Control Architecture

Modern inventory control depends on reliable transaction data. Enterprise resource planning systems integrate purchasing, production, sales, finance, and inventory records. Warehouse management systems support location control, scanning, cycle counting, and task sequencing. Advanced planning systems can model constraints, scenarios, and multi-echelon policies. Barcodes and radio-frequency identification improve traceability, while Internet-of-Things sensors can monitor temperature, location, vibration, and condition for sensitive goods.

Analytics can segment SKUs, estimate lead-time distributions, detect abnormal consumption, identify dead stock, and simulate service-cost trade-offs. Machine-learning models may improve demand forecasts where sufficient, stable data exist, but they do not remove structural uncertainty or replace managerial judgment. A forecast algorithm can also reproduce bias caused by stockouts: observed sales may understate true demand because customers could not buy unavailable products.

Digital twins and simulation allow managers to test changes in review frequency, supplier reliability, network structure, and service targets before implementation. Control-tower dashboards can combine external signals, supplier status, orders, and inventory positions. Nevertheless, automation requires master-data governance, exception management, cybersecurity, access control, and clear responsibility for overrides. A technically sophisticated system using inaccurate bills of material, incorrect lead times, or unrecorded scrap will produce precise-looking but unreliable recommendations.

An Illustrative Case: MedEquip Components

Background

MedEquip Components is an illustrative manufacturer of diagnostic equipment. It manages approximately 4,000 purchased and manufactured SKUs. The company has experienced high inventory value, frequent emergency purchases, and occasional production stoppages caused by inexpensive electronic connectors and seals.

Situation and Analysis

An initial ABC analysis showed that 12 percent of SKUs represented about 78 percent of annual consumption value. XYZ analysis revealed that several A items had stable demand, while a group of lower-value Z items had intermittent demand and long supplier lead times. The company had been applying a common monthly review policy to all items. Buyers also placed large orders to obtain price discounts, while production planners frequently changed schedules in response to late customer orders.

A review of records found that the reported 95 percent service level was a fill-rate measure for shipments, whereas production planners were concerned with line-stoppage risk. The two measures had been treated as interchangeable. Lead-time variation was also omitted from safety-stock calculations. Finally, distributors ordered in batches near month-end, creating apparent demand peaks and contributing to upstream schedule instability.

Approach

MedEquip introduced differentiated controls. AX items moved to continuous review with statistically estimated reorder points and frequent cycle counting. AZ items received supplier-risk reviews, approved substitutes, and management attention rather than an automatic high stock target. CX items were placed under a simpler periodic-review policy. CZ items were rationalized, standardized, or made-to-order where feasible.

For critical connectors, the company calculated safety stock from both demand and lead-time variability and negotiated a supplier agreement involving smaller, more frequent deliveries. It also introduced a shared consumption signal with key distributors and replaced some month-end promotions with stable pricing. A postponement study showed that final labelling and region-specific packaging could be delayed until distribution centres, reducing finished-goods variety.

Outcome

The company’s intended outcome was not simply lower inventory. It sought to reduce obsolete stock while protecting production continuity. After a controlled pilot, management reported lower emergency purchasing, better visibility of critical items, and more stable supplier schedules. The pilot also exposed a data-governance problem: several lead times in the planning system reflected contractual promises rather than actual receipt history. Correcting this master data was essential before extending the policy.

Learning Points

The case illustrates that segmentation must combine value, variability, and criticality. It also shows why service-level definitions matter, why safety stock should reflect actual uncertainty, and why inventory control cannot be separated from supplier contracts, pricing, information sharing, and production scheduling. Most importantly, a policy change should be piloted with reliable measures and reviewed for unintended consequences.

Challenges, Governance, and Common Errors

Inventory models are simplified representations. Their results are weakened by nonstationary demand, promotions, product substitution, capacity constraints, correlated demand, supplier allocation, minimum order quantities, and inaccurate records. Normal-distribution assumptions may be inappropriate for intermittent or highly seasonal items. EOQ may encourage batch sizes that conflict with lean flow or shelf-life limits. A mathematically correct reorder point may still fail if physical transactions are delayed or inventory is inaccessible.

Common mistakes include setting safety stock as an arbitrary percentage, using average lead time while ignoring its variation, treating forecast error as the same as demand variation, optimizing each echelon separately, and measuring buyers only on purchase price. Other errors include allowing system parameters to remain unchanged after product or supplier conditions change, failing to record scrap and returns, and using high service targets to compensate for unreliable processes.

Governance should define who owns item master data, who approves service targets, how parameter changes are documented, and how exceptions are escalated. Inventory write-offs should be transparent rather than delayed to preserve apparent performance. Ethical practice requires accurate reporting of shortages, obsolescence, supplier quality, and customer commitments. In regulated sectors, traceability, shelf-life control, recall capability, and segregation of expired or quarantined stock are essential. Supplier agreements should not encourage distortion of demand or unfair allocation, and algorithms should be reviewed for hidden bias against smaller customers or suppliers.

Current Developments and Best Practices

Contemporary inventory management is moving toward differentiated, risk-based policies rather than blanket stock reduction. Resilience planning gives greater attention to dual sourcing, strategic buffers, flexible capacity, and scenario analysis. Sustainability adds questions about energy use in storage, waste, reverse logistics, packaging, and the carbon consequences of expedited transport. Circular supply chains require visibility of returns, repairable components, recovered materials, and uncertain reverse flows.

Effective practice generally includes the following principles:

• segment items by value, variability, criticality, and supply risk;
• define service measures in operational and customer terms;
• estimate demand and lead-time uncertainty from credible data;
• align order quantities with setup, transport, shelf-life, and cash realities;
• coordinate inventory across echelons rather than rewarding isolated optimization;
• reduce root causes of uncertainty through quality improvement, shorter lead times, and stable schedules;
• maintain accurate records through cycle counting and transaction discipline;
• review policies when demand, product life cycle, supplier performance, or network design changes; and
• combine automation with human accountability, exception management, and strong data governance.

Chapter Summary

Inventory planning balances availability, cost, risk, and responsiveness across the supply chain. Inventory serves cycle, safety, anticipation, pipeline, and decoupling purposes, but it also creates holding, ordering, shortage, and obsolescence costs. ABC–XYZ analysis differentiates policies according to economic importance and demand predictability, while criticality and supply risk refine the classification.

EOQ balances ordering and holding costs, but practical decisions must consider discounts, capacity, shelf life, and flow requirements. Reorder points determine when replenishment begins; order quantities determine how much is replenished. Safety stock protects against demand and lead-time uncertainty, and service level must be defined through measures such as cycle-service level or fill rate. Periodic-review systems offer administrative convenience but generally require protection over the review interval as well as lead time.

Multi-echelon planning positions inventory across the network, while postponement and risk pooling can reduce variety and obsolescence. The bullwhip effect demonstrates that local decisions and poor information can amplify variability upstream. Digital systems, analytics, sensors, and simulation improve visibility and decision quality only when supported by accurate data, governance, cybersecurity, and managerial judgment. The central lesson is that inventory control is a coordinated system of policies, information, processes, and incentives—not merely a formula for calculating stock.

Key Terms

ABC analysis: Classification of inventory according to annual consumption value.

Anticipation inventory: Stock accumulated for predictable future demand or disruptions.

Bullwhip effect: Amplification of demand variability as orders move upstream through a supply chain.

Cycle stock: Inventory created by ordering or producing in batches.

Cycle-service level: Probability of avoiding a stockout during a replenishment cycle.

Economic order quantity: Order size that balances ordering and holding costs under specified assumptions.

Fill rate: Proportion of demanded units supplied immediately from available stock.

Inventory position: On-hand stock plus scheduled receipts minus backorders.

Lead time: Time between placing a replenishment order and receiving usable supply.

Multi-echelon inventory: Inventory managed across several interconnected supply-chain stages.

Order-up-to level: Target inventory position used in periodic-review replenishment.

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

Reorder point: Inventory position at which replenishment is initiated.

Safety stock: Buffer held to protect against demand or supply uncertainty.

Stockout: Condition in which required inventory is unavailable.

Supply risk: Possibility that supply will be delayed, disrupted, insufficient, or nonconforming.

XYZ analysis: Classification of items according to demand predictability or variability.

Review Questions

Short Answer Questions

1. Distinguish cycle stock, safety stock, anticipation inventory, and pipeline inventory.

2. What is the difference between inventory position and on-hand inventory?

3. Explain why ABC analysis should be combined with XYZ analysis.

4. Differentiate cycle-service level from fill rate.

5. State two causes of the bullwhip effect.

Long Answer Questions

1. Explain the assumptions, formula, interpretation, and limitations of the EOQ model.

2. Discuss how demand and lead-time uncertainty influence reorder points and safety-stock decisions.

3. Compare continuous-review and periodic-review inventory systems.

4. Explain multi-echelon inventory planning and the role of postponement in reducing supply-chain risk.

5. Discuss how digital technologies can improve inventory control and why data governance remains essential.

Analytical / Application Questions

1. Annual demand for an item is 18,000 units, ordering cost is ₹450 per order, and annual holding cost is ₹9 per unit. Calculate EOQ and explain two practical factors that might justify using a different order quantity.

2. A component has average daily demand of 100 units, a four-day average lead time, daily demand standard deviation of 15 units, and lead-time standard deviation of one day. Using the approximate variance formula and z = 1.645, estimate safety stock and reorder point. State the assumptions used.

3. A distributor places very large orders at the end of each month even though customer consumption is stable. Diagnose the likely supply-chain consequences and propose an integrated plan involving information sharing, pricing, replenishment, and performance measures.

22. Multiple Choice Questions with Answers

Question 1

Which inventory category is primarily held to protect against uncertain demand or replenishment time?

A. Cycle stock

B. Safety stock

C. Pipeline inventory

D. Anticipation inventory

Correct Answer: B. Safety stock

Question 2

Annual demand is 10,000 units, ordering cost is ₹200 per order, and annual holding cost is ₹8 per unit. Which expression gives the EOQ?

A. √(10,000 × 200 × 8)

B. √(2 × 10,000 × 200 / 8)

C. 2 × 10,000 × 200 / 8

D. 10,000 × 8 / 200

Correct Answer: B. √(2 × 10,000 × 200 / 8)

Question 3

An item classified as AZ in an ABC–XYZ matrix is best described as:

A. Low-value and highly predictable

B. High-value and highly predictable

C. High-value and highly irregular

D. Low-value and moderately variable

Correct Answer: C. High-value and highly irregular

Question 4

If average daily demand is 80 units and lead time is 5 days, with no uncertainty or safety stock, the reorder point is:

A. 16 units

B. 85 units

C. 400 units

D. 4,000 units

Correct Answer: C. 400 units

Question 5

Why does a periodic-review system generally require protection over both the review interval and lead time?

A. Orders are always placed at the EOQ

B. Demand can occur before the next review and during replenishment

C. It eliminates all demand variability

D. It assumes suppliers provide instantaneous delivery

Correct Answer: B. Demand can occur before the next review and during replenishment

Question 6

Which statement correctly distinguishes fill rate from cycle-service level?

A. Fill rate measures probability of no stockout; cycle-service level measures units supplied

B. Both measures are always identical

C. Fill rate measures the proportion of demand supplied immediately; cycle-service level concerns avoiding stockout during a cycle

D. Fill rate applies only to production items

Correct Answer: C. Fill rate measures the proportion of demand supplied immediately; cycle-service level concerns avoiding stockout during a cycle

Question 7

Which practice is most likely to reduce the bullwhip effect?

A. Increasing order batches and using frequent promotions

B. Sharing point-of-sale consumption data and stabilizing replenishment

C. Extending supplier lead times

D. Forecasting only from upstream orders

Correct Answer: B. Sharing point-of-sale consumption data and stabilizing replenishment

Question 8

The main purpose of postponement is to:

A. Move all inventory to the supplier

B. Delay product differentiation until demand is clearer

C. Increase finished-product variety held at every location

D. Eliminate the need for forecasting

Correct Answer: B. Delay product differentiation until demand is clearer

Question 9

When both demand and lead time are variable, safety-stock analysis should generally:

A. Ignore lead-time variation

B. Use only annual purchase value

C. Consider the variability of demand during the replenishment lead time

D. Set stock equal to the EOQ

Correct Answer: C. Consider the variability of demand during the replenishment lead time

Question 10

Why can independently optimizing inventory at each echelon produce excessive total inventory?

A. Each stage may add its own buffer without considering network-wide protection

B. It always reduces safety stock to zero

C. It prevents any information sharing by definition

D. It makes demand perfectly predictable

Correct Answer: A. Each stage may add its own buffer without considering network-wide protection