Customer Service, Demand Planning and Logistics Integration

Introduction

Logistics performance is ultimately judged by the value experienced by the customer. A product that is inexpensive to manufacture but unavailable, late, damaged, incorrectly documented or difficult to return may create little customer value. Customer service therefore connects logistics operations with market expectations. Demand planning provides the information needed to position inventory and capacity, while integration ensures that purchasing, production, warehousing, transport, sales and finance make coordinated decisions.

This chapter examines customer-service standards, demand analysis, forecasting, segmentation, order-cycle performance and service quality. It then explains demand variability, the bullwhip effect, collaborative planning, forecasting and replenishment (CPFR), sales and operations planning (S&OP), and cross-functional coordination. The emphasis is not merely on achieving high service at any cost, but on designing a reliable, economically sustainable and ethically responsible service system.

Learning Objectives

By the end of this chapter, the learner should be able to:

1. Define customer service in logistics and establish measurable service standards.
2. Analyse demand using forecasting, segmentation and variability measures.
3. Calculate and interpret order-cycle and service-quality indicators.
4. Explain the causes and consequences of the bullwhip effect.
5. Describe CPFR, S&OP and the role of cross-functional integration.
6. Evaluate trade-offs between responsiveness, cost, resilience and sustainability.
7. Apply these ideas to a practical logistics improvement problem.

1. Customer Service as a Logistics Output

Customer service is the set of activities and performance outcomes through which an organisation makes its offering available, dependable and easy to use. In logistics, it includes the availability of the correct product, at the agreed location, in the required condition, at the promised time, with accurate information and appropriate after-sales support.

The traditional “seven rights” of logistics are delivery of the right product, in the right quantity and condition, to the right place, at the right time, for the right customer, at the right cost. The rights are a useful diagnostic framework, but they should not be treated as an excuse to promise every customer the maximum possible service. Service policies should reflect customer value, product economics, contractual commitments and operational capability.

Customer service has three stages:

• Pre-transaction service: policies, communication channels, credit terms, product information and order procedures.
• Transaction service: availability, order accuracy, delivery speed, reliability, documentation and condition of goods.
• Post-transaction service: installation, technical support, complaint handling, returns, repairs and recovery from failure.

A service standard should state the customer, product or segment covered; the performance measure; the target; the measurement period; exceptions; and the owner of the process. For example, “95% of standard stock orders for retail customers will be delivered complete within two working days” is more useful than “provide fast delivery.”

Service-quality dimensions

A logistics service can be evaluated through several dimensions:

Service quality is multidimensional. A company may have a short average delivery time but poor reliability, or high availability but frequent picking errors. Customers often value consistency more than an occasional exceptionally fast delivery.

2. Measuring Customer-Service Performance

Order cycle

The order cycle is the elapsed time from the customer’s order being received until the acceptable product is delivered and available for use. Its stages may include order entry, credit approval, allocation, picking, packing, dispatch, transport, receiving and verification.

Here, T_oc is order-cycle time, measured in hours or days; t_delivery is the time of acceptable delivery; and t_order is the time the valid order is received. The measure must specify whether weekends, public holidays, order-entry delays and customer-caused delays are included. A mean alone can conceal late orders, so median, percentile and maximum values are also useful.

Core service metrics

Order fill rate measures the proportion of ordered units supplied immediately from available stock:

It is appropriate when unit-level availability matters. A line fill rate instead measures order lines supplied in full; one large line and one small line receive equal weight. A case-fill or revenue-fill rate may be more appropriate where cases or financial value are the operational concern.

On-time delivery (OTD) is commonly calculated as:

The agreed window must be defined. “On time” might mean by a date, within a two-hour appointment, or before a customer’s production cutoff.

Perfect-order rate generally requires an order to be complete, on time, error-free and undamaged. A simple form is:

Because the order must satisfy all conditions, the rate is often lower than each individual component. Organisations should publish their precise definition rather than compare apparently similar figures.

Order accuracy measures the proportion of orders without picking, quantity, product, address or documentation errors. Damage rate may be expressed as damaged units divided by shipped units, or damaged consignments divided by total consignments. The denominator should match the decision being made.

Customer complaint rate can be stated per 1,000 orders:

This provides a comparable rate, but it does not capture customers who do not complain. Surveys, returns, social-media monitoring and account reviews should supplement complaint data.

Cost-to-serve and service trade-offs

Higher service may require more inventory, additional warehouse shifts, premium transport or decentralised facilities. The relationship is not necessarily linear: the first improvements may be inexpensive, while the last few percentage points can be very costly. Cost-to-serve analysis assigns relevant logistics costs to customers, channels, products and order behaviours. It can reveal that a customer with high sales revenue is unprofitable because of small, urgent, highly customised orders.

A sound policy does not automatically offer the same service to every customer. It establishes transparent segment rules and avoids discriminatory treatment unrelated to legitimate commercial requirements. Service differentiation should be communicated contractually and monitored for unintended exclusion.

3. Demand Planning and Forecasting

Demand planning is the structured process of estimating future customer requirements and translating those estimates into inventory, capacity, procurement, transport and workforce decisions. Demand is not identical to sales history. Sales may be constrained by stockouts, lost distribution, promotions, price changes or data errors. Planners should therefore distinguish observed sales from underlying demand.

A demand plan normally combines:

• historical transactions and inventory availability;
• customer, market and channel information;
• promotions, prices and product introductions;
• seasonality, trends and known events;
• sales-force and customer intelligence;
• statistical forecasts and managerial judgement.

Forecasting approaches

Qualitative methods include sales-force composite estimates, customer surveys, expert panels and the Delphi method. They are useful for new products, structural changes and markets with limited data, but can be affected by optimism, anchoring and political pressure.

Time-series methods use patterns in historical observations. A moving average for a window of n periods is:

Here, F_t+1 is the forecast for the next period and D_t is demand in period t. The method assumes that the recent average is informative and that no strong trend or structural break invalidates the window. A longer window smooths random variation but responds more slowly to change.

A weighted moving average is:

The weights w_i are non-negative unless a specialised method states otherwise. More recent periods commonly receive larger weights.

Exponential smoothing updates the forecast as follows:

α is the smoothing constant, D_t is actual demand and F_t is the previous forecast. A high α reacts quickly but may follow noise; a low value is stable but slower to recognise change. Basic exponential smoothing is most suitable when level is present without a systematic trend or seasonality. Trend- and seasonality-adjusted methods are needed when those features are material.

Causal models relate demand to explanatory variables such as price, advertising, weather or economic activity. They can support scenario analysis, but the relationship should not be interpreted as causal merely because two variables move together. Data quality, omitted variables and changing customer behaviour limit reliability.

Forecast error and bias

Forecast error for period t can be defined as:

A positive value means actual demand exceeded the forecast. Common measures are:

MAD is expressed in demand units; RMSE penalises large errors more heavily; MAPE is a percentage but becomes unstable or undefined when actual demand is zero or very small. Weighted or scaled measures are preferable for intermittent demand and for comparing products with different volumes. Forecast accuracy should be measured at the level where decisions are made and should distinguish random error from systematic bias.

A useful bias indicator is cumulative forecast error:

Persistent positive CFE suggests under-forecasting; persistent negative CFE suggests over-forecasting. Bias does not necessarily mean that a forecast is inaccurate in every period, but it signals a systematic planning problem.

Worked example: moving average and error

Actual weekly demand for three weeks is 100, 120 and 110 units. A three-week moving-average forecast for week four is:

If actual week-four demand is 125 units, the error is e₄ = 125 − 110 = 15 units. The forecast under-estimated demand by 15 units. This single observation does not establish a trend; several periods should be examined before changing the method.

4. Demand Segmentation and Variability

A single forecast and service policy for all products is usually inefficient. Segmentation aligns attention and resources with economic importance and demand behaviour.

ABC analysis ranks items by annual usage value:

A-items account for a relatively high share of value and usually require close review; B-items receive intermediate control; C-items have lower value and may use simpler procedures. ABC is a prioritisation technique, not a statement that C-items are unimportant to customers.

Other useful dimensions include:

• demand predictability or coefficient of variation;
• criticality to safety or production continuity;
• margin and strategic importance;
• lifecycle stage;
• lead time and supply risk;
• customer service requirement.

A common variability measure is the coefficient of variation:

σ is the standard deviation of demand and μ is mean demand, both in the same units. CV is dimensionless and allows relative variability comparisons, provided the mean is positive and not close to zero. A low-volume intermittent item can have a high CV even when its absolute variation is modest.

Segmentation can combine ABC and variability, producing policies such as frequent review and statistical safety stock for high-value stable items, specialist judgement for high-value erratic items, and simplified replenishment for low-value routine items. Criticality may override financial ranking: a low-cost component that stops a hospital or factory deserves exceptional attention.

5. Demand Variability, Inventory and Service

Demand variability and lead-time variability create the risk that available stock will not meet requirements during replenishment. Safety stock is inventory held to absorb uncertainty; it is not a substitute for removing avoidable process variation.

For a simple continuous-review system with normally distributed demand during lead time and a specified cycle-service probability, a common expression is:

ROP is the reorder point in units, μ_LT is mean demand during lead time, σ_LT is the standard deviation of demand during lead time, and z is the standard-normal value associated with the chosen cycle-service probability. The safety-stock component is zσ_LT. This model assumes reasonably stable parameters, independent replenishment lead time and demand assumptions appropriate to the chosen distribution. It should not be applied mechanically to intermittent or highly seasonal demand.

If demand per period has standard deviation σ_d and lead time is a fixed L periods, with independent demand across periods:

The square-root relationship follows from variance addition under independence. If lead time is variable, the variance must include both demand and lead-time uncertainty; practitioners should use an appropriate inventory model rather than simply inserting an average lead time.

The trade-off is fundamental: more safety stock can improve availability but increases capital, storage, obsolescence and environmental cost. The appropriate service level depends on margin, shortage consequences, replenishment speed and customer promise.

6. The Bullwhip Effect

The bullwhip effect is the amplification of order variability as information moves upstream from final demand to retailers, wholesalers, manufacturers and suppliers. Small changes in consumer demand can create large fluctuations in replenishment orders, production schedules and raw-material requirements.

Its major causes include:

1. Forecast updating: each stage forecasts from orders rather than shared consumption data.
2. Order batching: firms place infrequent large orders to reduce transaction or transport costs.
3. Price promotions: forward buying causes temporary order surges.
4. Shortage gaming: customers inflate orders when supply is rationed.
5. Long or unreliable lead times: planners order early and hold extra protection.
6. Poor visibility: partners cannot distinguish real demand from inventory movements.
7. Local optimisation: each firm pursues its own target rather than total-chain performance.

Consequences include excess inventory, stockouts, unstable production, overtime, obsolescence, inefficient transport and damaged supplier relationships. The effect is not inevitable. Sharing point-of-sale data, shortening lead times, reducing order batching, stabilising prices, allocating scarce supply transparently and coordinating replenishment can reduce it.

The bullwhip effect also demonstrates why forecast accuracy at one organisation is not enough. A distributor may forecast its customer orders accurately while those orders remain a distorted representation of consumer consumption.

7. Integration through CPFR and S&OP

Collaborative Planning, Forecasting and Replenishment

CPFR is a structured approach in which supply-chain partners collaborate on plans, forecasts and replenishment. A typical cycle is:

1. establish a trading relationship and collaboration agreement;
2. share strategy, calendars, constraints and relevant data;
3. create a joint business plan;
4. develop a baseline sales forecast;
5. identify exceptions and resolve forecast differences;
6. generate an order or replenishment forecast;
7. identify supply constraints and agree responses;
8. execute, measure and revise the plan.

The most valuable data are often point-of-sale demand, inventory positions, open orders, promotions, lead times and capacity constraints. CPFR does not mean sharing every commercially sensitive record. Data scope, ownership, access rights, confidentiality, cybersecurity, liability and dispute resolution should be agreed in governance documents.

CPFR can improve availability and reduce duplicated safety stock, but collaboration has costs. Partners need compatible data definitions, system integration, trust and staff time. A technically connected system can still fail if incentives reward local sales volume rather than sell-through or total supply-chain performance.

Sales and Operations Planning

S&OP is an internal, cross-functional process that reconciles demand and supply over a medium-term horizon, commonly monthly and extending several months ahead. It links strategy with execution without replacing detailed scheduling.

A mature S&OP cycle commonly includes:

• data gathering and statistical forecasting;
• demand review, including market intelligence and assumptions;
• supply review, including capacity, labour, inventory and supplier constraints;
• reconciliation of alternative scenarios and financial implications;
• executive review and approval of one integrated plan;
• implementation, monitoring and learning.

The output is not merely a forecast. It is an agreed set of decisions about production, purchasing, inventory, workforce, capacity, promotions, product introductions and customer commitments. Scenarios are especially valuable: for example, management can compare the cost and service effects of a base forecast, a promotion surge and a supplier disruption.

S&OP differs from CPFR in scope. CPFR is inter-organisational and focuses on collaborative trading relationships; S&OP is primarily an internal governance process, although it should incorporate supplier and customer realities.

8. Cross-Functional Coordination and Integrated Decision-Making

Integration means that functions share information, align objectives and make decisions that optimise the end-to-end system. It does not mean that every function has identical responsibilities. Sales understands customers and market events; marketing understands promotions and positioning; operations understands capacity; procurement understands supplier conditions; logistics understands inventory and transport; finance evaluates cash and profitability; information technology enables reliable data and workflows.

Common coordination mechanisms include:

• a single agreed demand plan with documented assumptions;
• clear definitions for “order,” “on time,” “available” and “forecast”;
• a common planning calendar and decision rights;
• exception-based management rather than attention to every routine item;
• cross-functional key performance indicators;
• integrated master data and product, customer and location identifiers;
• formal escalation of constraints and trade-offs;
• post-period review of forecast error, service failure and root causes.

Measures should avoid dysfunctional incentives. A sales team rewarded solely for bookings may create forward buying; procurement rewarded solely for purchase price may increase batch sizes and inventory; warehousing rewarded solely for labour productivity may damage picking accuracy. Balanced measures can include perfect orders, total cost-to-serve, inventory turns, forecast bias, cash-to-cash cycle, recovery time and customer retention.

Technology supports integration through enterprise resource planning, warehouse-management systems, transport-management systems, order-management systems, electronic data interchange, control towers and analytics. Artificial intelligence can assist with demand sensing, anomaly detection, promotion analysis and dynamic prioritisation. However, AI does not eliminate the need for sound definitions, representative data, human review and accountability. Models may reproduce historical bias, confuse promotion-driven sales with sustainable demand, or fail during structural breaks. Governance should require traceability, access control, privacy protection, cybersecurity, monitoring and a human override for material decisions.

9. Illustrative Case: Northstar Home Appliances

Background. Northstar Home Appliances supplies refrigerators and replacement parts through retailers and service contractors. Its stated target is 95% availability, but retailers report frequent stockouts of popular models and late deliveries of service parts. The company’s distribution centre holds high total inventory, while suppliers experience volatile orders.

Problem. The sales department frequently changes its forecast before promotions. Retailers place large orders at month-end to obtain volume discounts, and Northstar’s planners forecast from those orders rather than from retail sell-through. Procurement buys in large batches because supplier lead times are long and variable. Finance measures inventory value, sales measures revenue, and logistics measures shipment volume; no function owns the perfect-order outcome.

Analysis. A six-month diagnostic finds a 91% unit fill rate, 87% on-time delivery, and a 62% perfect-order rate. The perfect-order result is not the product of merely multiplying the three percentages because the failures overlap; Northstar must count orders satisfying all conditions. Forecast bias is positive for promoted models, indicating systematic under-forecasting. An ABC–variability analysis shows that several high-value models are relatively stable, while low-volume service parts are intermittent but operationally critical. Order batching and promotion peaks create artificial upstream volatility.

Approach. Northstar establishes a monthly S&OP meeting chaired by the operations director. Sales and marketing submit promotion calendars and assumptions; finance provides margin and working-capital scenarios; operations validates capacity; procurement reports supplier constraints. The company begins receiving retailer point-of-sale data for a pilot product family and creates a CPFR agreement with two major retailers. Volume discounts are redesigned to reduce extreme order batching. Service parts receive a criticality category in addition to ABC classification. Definitions for OTD, fill rate and perfect order are documented, and a cross-functional dashboard reports service, inventory, forecast bias and total cost-to-serve.

For stable high-value models, Northstar uses a statistically supported reorder point and reviews parameters monthly. For intermittent parts, it uses service-history information, supplier lead-time analysis and managerial review rather than blindly applying a normal-demand assumption. Promotion forecasts are separated from baseline demand and reviewed after each event.

Outcome. In the illustrative scenario, after two planning cycles the pilot achieves higher availability and lower emergency freight, while total inventory falls because duplicated protection is removed. The improvement is not attributed to forecasting alone: clearer promotions, reduced batching, shared data and coordinated decisions address the system causes. Northstar continues to monitor whether service improvements are sustained during peak season.

Learning points. First, high total inventory does not guarantee high customer service. Second, forecast error should be investigated for bias and root cause. Third, criticality can override financial ranking. Fourth, integration requires governance and incentives, not merely software. Finally, a service target should be evaluated together with cost, resilience and customer consequences.

10. Challenges, Ethics and Contemporary Practice

Demand plans can be distorted by deliberate optimism, political pressure, poor master data or sales incentives. Organisations should preserve an audit trail of assumptions and changes. Customer data used for demand sensing must be collected lawfully, used for a stated purpose and protected from unauthorised access. Data sharing between trading partners should respect confidentiality and competition law; collaboration must not become a mechanism for unlawful coordination of prices or market allocation.

Service differentiation should be transparent and commercially justified. Essential goods, medical supplies and products affecting vulnerable customers require stronger continuity planning and careful prioritisation during shortages. Algorithms used to allocate scarce stock should be explainable enough for affected customers and staff to understand the governing principles.

Contemporary demand planning increasingly incorporates real-time signals, e-commerce browsing and order data, weather, mobility, sensor information and external risk indicators. These signals can improve responsiveness but can also amplify noise. “Demand sensing” is most useful when it complements, rather than automatically replaces, a structured baseline forecast. Digital twins and scenario models support resilience analysis, while sustainability metrics encourage planners to consider emissions, packaging, waste and reverse logistics alongside speed and cost.

Best practice is therefore to:

1. define service promises in measurable, customer-relevant terms;
2. segment products and customers according to value, variability and criticality;
3. select forecasting methods appropriate to data and demand pattern;
4. monitor error, bias and forecast value added rather than accuracy alone;
5. share consumption and inventory information where governance permits;
6. use S&OP to reconcile demand, supply and financial decisions;
7. reduce avoidable variability before purchasing more safety stock;
8. align incentives with end-to-end outcomes;
9. protect data and document algorithmic decisions;
10. review assumptions after disruptions, promotions and product-life-cycle changes.

Chapter Summary

Customer service is a measurable logistics outcome encompassing availability, speed, reliability, accuracy, condition, information and recovery. Effective standards specify the customer segment, metric, target, time window and accountability. Order-cycle time, fill rate, on-time delivery, perfect-order rate, complaint rate and cost-to-serve provide complementary views; no single measure is sufficient.

Demand planning combines historical data, statistical forecasting, market knowledge and operational constraints. Moving averages and exponential smoothing are useful under stated assumptions, while error measures such as MAD, RMSE, MAPE and cumulative error reveal accuracy and bias. ABC, variability and criticality segmentation support differentiated policies. Demand and lead-time uncertainty influence safety stock and reorder points, but excess inventory is not a substitute for process improvement.

The bullwhip effect arises when order variability is amplified upstream through forecast updating, batching, promotions, shortage gaming, long lead times and poor visibility. Shared consumption data, stable policies and coordinated replenishment can reduce it. CPFR connects trading partners through collaborative planning and exception resolution; S&OP integrates internal demand, supply and financial decisions. Cross-functional coordination requires common definitions, aligned incentives, decision rights, reliable data and responsible technology governance.

Key Terms

Customer service: The performance and support experienced by a customer before, during and after fulfilment.

Order cycle: The elapsed time from valid order receipt to acceptable delivery.

Fill rate: The proportion of requested units, lines or orders supplied immediately, according to a defined denominator.

On-time delivery: Delivery completed within the agreed time window.

Perfect order: An order complete, on time, accurate and undamaged under a stated definition.

Cost-to-serve: The relevant cost of serving a particular customer, product, channel or order pattern.

Demand planning: The process of estimating future requirements and translating them into operational decisions.

Forecast bias: A persistent tendency for forecasts to overestimate or underestimate actual demand.

MAD: Mean absolute deviation, the average absolute forecast error in demand units.

RMSE: Root mean square error, a forecast-error measure that gives greater weight to large errors.

ABC analysis: Classification of items by annual usage value or another selected value measure.

Coefficient of variation: Standard deviation divided by mean, indicating relative variability.

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

Reorder point: Inventory position at which replenishment should be initiated.

Bullwhip effect: Upstream amplification of order variability relative to consumer demand.

CPFR: Collaborative planning, forecasting and replenishment among supply-chain partners.

S&OP: Sales and operations planning, a cross-functional process reconciling demand, supply and financial plans.

Demand sensing: Use of recent and external signals to improve short-term understanding of demand.

Perfect-order rate: The percentage of total orders meeting every defined perfect-order condition.

Cross-functional integration: Coordination of information, objectives and decisions across organisational functions.

Review Questions

Short Answer Questions

1. What are the pre-transaction, transaction and post-transaction dimensions of customer service?
2. Define order-cycle time and identify two policy choices that affect its measurement.
3. Why can MAPE be unsuitable when actual demand is zero or very small?
4. State three causes of the bullwhip effect.
5. Distinguish CPFR from S&OP.

Long Answer Questions

1. Explain how an organisation should design customer-service standards and evaluate the strengths and limitations of common service metrics.
2. Compare qualitative, time-series and causal forecasting methods, including the conditions under which each is useful.
3. Explain how ABC, variability and criticality segmentation can be combined to create differentiated inventory policies.
4. Analyse the causes, consequences and remedies of the bullwhip effect.
5. Describe an effective S&OP process and explain how cross-functional incentives can support or undermine it.

Analytical / Application Questions

1. A distributor supplied 9,400 of 10,000 ordered units immediately. Calculate the unit fill rate and explain why the result may differ from line fill rate.
2. Weekly demand is 80, 100 and 90 units. Calculate a three-week moving-average forecast for week four. If actual week-four demand is 120 units, calculate the forecast error and interpret its sign.
3. A company has high inventory but poor availability and large supplier-order fluctuations. Diagnose the likely integration problems and propose a coordinated improvement programme using service metrics, demand planning, CPFR or S&OP.

Multiple Choice Questions with Answers

Question 1

Which measure requires an order to satisfy all defined conditions such as completeness, timeliness, accuracy and absence of damage?

A. Perfect-order rate

B. Unit fill rate

C. Inventory turnover

D. Forecast bias

Correct Answer: A. Perfect-order rate

Question 2

Actual demand in three periods is 90, 120 and 150 units. What is the three-period moving-average forecast for the next period?

A. 110 units

B. 120 units

C. 130 units

D. 150 units

Correct Answer: B. 120 units

Question 3

In the forecast-error definition e_t = D_t − F_t, what does a positive error indicate?

A. The forecast exceeded actual demand

B. Actual demand exceeded the forecast

C. The forecast was exactly accurate

D. Demand was necessarily seasonal

Correct Answer: B. Actual demand exceeded the forecast

Question 4

Which situation is a direct cause of the bullwhip effect?

A. Sharing point-of-sale data

B. Reducing replenishment lead time

C. Order batching and promotional forward buying

D. Using common product identifiers

Correct Answer: C. Order batching and promotional forward buying

Question 5

What is the main purpose of ABC analysis?

A. To classify items by annual usage value for control prioritisation

B. To calculate transport distance

C. To determine customer satisfaction directly

D. To eliminate all demand variability

Correct Answer: A. To classify items by annual usage value for control prioritisation

Question 6

If mean demand is 200 units and standard deviation is 40 units, what is the coefficient of variation?

A. 0.02

B. 0.20

C. 1.25

D. 5.00

Correct Answer: B. 0.20

Question 7

Which statement best describes S&OP?

A. A warehouse picking method

B. An internal process reconciling demand, supply and financial plans

C. A customer complaint classification

D. A transport pricing formula

Correct Answer: B. An internal process reconciling demand, supply and financial plans

Question 8

Which forecasting method gives the next forecast as F_t+1 = αD_t + (1−α)F_t?

A. Simple exponential smoothing

B. ABC classification

C. Linear programming

D. Cycle counting

Correct Answer: A. Simple exponential smoothing

Question 9

Why should a low-value item sometimes receive high inventory-management attention?

A. Its unit cost automatically makes it an A-item

B. It may be operationally critical despite low financial value

C. Low-value items never have intermittent demand

D. It always has the shortest lead time

Correct Answer: B. It may be operationally critical despite low financial value

Question 10

Which practice most directly supports CPFR?

A. Each partner conceals inventory and consumption data

B. Partners share relevant demand information and resolve forecast exceptions jointly

C. All orders are placed only at month-end

D. Forecasts are prepared without promotion information

Correct Answer: B. Partners share relevant demand information and resolve forecast exceptions jointly