3. Process Analysis, Selection and Improvement

Introduction and context

Operations management is concerned not only with what an organization produces, but also with how work is designed, connected, measured, and improved. In logistics, the process may involve receiving an order, allocating inventory, picking and packing goods, arranging transportation, delivering the shipment, and managing returns. A process that appears satisfactory at an aggregate level may nevertheless contain long queues, unnecessary movement, repeated data entry, poor handoffs, or hidden capacity constraints.

Process analysis provides a disciplined way to make work visible. It identifies activities, decisions, resources, information flows, delays, and points of failure. Process selection then concerns the choice or design of an appropriate operating configuration, such as a functional process, batch process, line process, project process, or flexible technology-enabled process. Process improvement seeks to increase customer value while reducing time, cost, variability, risk, and waste.

For logistics managers, process analysis is especially important because performance depends on the coordination of multiple parties and locations. A delay in order confirmation can affect warehouse scheduling; an inaccurate inventory record can create expedites; and a transportation bottleneck can offset efficiency achieved in picking. The analysis must therefore consider the whole flow rather than isolated departmental performance.

Learning objectives

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

1. Explain the meaning, structure, and strategic significance of process analysis and process selection.

2. Construct and interpret flowcharts, SIPOC diagrams, and value-stream maps.

3. Calculate and apply cycle time, throughput, utilization, capacity, takt time, and Little’s Law.

4. Diagnose bottlenecks using the Theory of Constraints and related analytical methods.

5. Distinguish incremental continuous improvement from business process reengineering.

6. Evaluate process alternatives using cost, capacity, flexibility, quality, speed, risk, and customer-value criteria.

7. Apply process-improvement tools to logistics and supply-chain situations.

8. Recognize implementation, ethical, technological, and governance issues in process redesign.

Processes as systems of value creation

A process is an organized set of activities that transforms inputs into outputs for an internal or external customer. Inputs may include materials, information, labor, equipment, capital, and time. Outputs may be physical products, delivered services, decisions, or information. A process is more than a list of tasks: it includes the relationships among tasks, the sequence in which they occur, the resources used, and the rules governing decisions.

A useful process definition specifies five elements: the trigger that starts the process, the activities performed, the resources required, the output produced, and the customer or recipient of that output. For example, a warehouse replenishment process may be triggered by a reorder signal, use inventory data and labor, create a replenishment movement, and serve the picking operation.

Processes can be classified in several ways. Core processes directly create customer value, such as order fulfillment and transportation. Support processes enable core processes, such as equipment maintenance, recruitment, and information-technology support. Management processes establish direction and control, including forecasting, budgeting, compliance, and performance review. Processes can also be repetitive or non-repetitive, stable or highly variable, manual or automated, and local or cross-functional.

A process perspective differs from a functional perspective. A functional view asks whether warehousing, purchasing, or transport is performing well. A process view asks whether the complete customer order moves effectively from request to delivery. This distinction is critical because local optimization can produce system-wide deterioration. For example, purchasing may obtain a lower unit price by ordering large batches, while the resulting inventory increases storage cost, obsolescence, and working capital.

Making work visible: principal mapping methods

Flowcharts and process maps

A flowchart represents the sequence of activities and decisions in a process. Standard symbols commonly include rectangles for activities, diamonds for decisions, arrows for flow direction, and ovals for start and end points. Swimlane flowcharts add organizational lanes so that responsibility and handoffs become visible.

A process map should be created at an appropriate level of detail. A high-level map of order fulfillment may contain ten major stages, whereas a detailed map of a customs-documentation activity may show individual data fields and approval rules. Excessive detail can obscure the main problem; insufficient detail can hide rework and control failures.

When constructing a map, the analyst should identify the customer, define the start and end points, observe actual work, distinguish value-adding from non-value-adding activities, record waiting and rework, and validate the map with employees who perform the work. The documented procedure is not necessarily the actual process. Comparing the prescribed process with the performed process often reveals informal workarounds.

SIPOC analysis

SIPOC stands for Suppliers, Inputs, Process, Outputs, and Customers. It is a high-level diagnostic framework used before detailed analysis. A SIPOC for e-commerce fulfillment might identify suppliers such as merchants and inventory providers; inputs such as orders, stock data, packaging materials, and carrier information; the process as order release through dispatch; outputs as packed and documented shipments; and customers as buyers, retailers, or downstream distribution centers.

SIPOC is valuable because it establishes boundaries and prevents premature concentration on individual tasks. It also encourages analysts to ask whether inputs meet specified requirements and whether outputs satisfy customer needs. In logistics, supplier and customer definitions should include external partners and internal recipients, such as a transport department receiving freight-ready consignments from a warehouse.

Value-stream mapping

Value-stream mapping, associated with lean operations, examines the complete flow of materials and information required to deliver a product or service. It records process time, waiting time, inventory or work-in-process, information signals, and the relationship between customer demand and production or service activity.

The distinction between processing time and lead time is central. Processing time is the time during which work is actively performed. Lead time includes processing, waiting, movement, inspection, queueing, and delay. A process may have only a few minutes of actual work but several days of total lead time. The ratio of value-adding time to total lead time is therefore a useful indicator of flow efficiency, although value classification requires judgment.

A current-state map describes existing performance; a future-state map proposes a more effective flow. Future-state design may include demand-based release, reduced batch sizes, standardized work, fewer handoffs, improved information visibility, and controlled buffers. Value-stream mapping should not be treated as a drawing exercise. Its purpose is to generate operational decisions and a practical improvement plan.

Measuring process performance

Cycle time, lead time, and throughput

Cycle time is the time required to complete one unit or transaction at a particular activity or process. Lead time is the elapsed time from process initiation to completion, including waiting. Throughput is the rate at which completed units leave a process, commonly expressed as orders per hour, pallets per day, or shipments per week.

Capacity is the maximum sustainable output rate under specified conditions. Practical capacity is normally below theoretical capacity because of breaks, maintenance, changeovers, absenteeism, quality losses, and variability. A process may have high theoretical capacity but low effective capacity if it frequently experiences interruptions.

If a warehouse ships 480 orders during an eight-hour shift, its average throughput is 60 orders per hour. This number describes output, not necessarily the capacity of every activity. If picking can process 75 orders per hour but packing can process only 50, packing is likely to constrain the overall flow unless work is buffered or additional resources are deployed.

Utilization and bottleneck logic

Utilization is the proportion of available capacity being used. It can be represented as:

Utilization = Actual output or demand rate / Available capacity rate

A resource operating at 90 percent utilization may appear efficient, but high utilization leaves little protection against variation. As utilization approaches 100 percent, queues and waiting times can rise sharply. Consequently, the resource with the highest utilization is not always the only problem, but it is a strong candidate for investigation.

A bottleneck is a resource, policy, activity, or constraint that limits the output of the entire system. The bottleneck may be physical, such as a sorting machine, or managerial, such as an approval rule or delivery appointment policy. Bottleneck identification must consider the whole process and the time horizon. A station may be a bottleneck during the morning peak but not during the full day.

Little’s Law

Little’s Law relates work-in-process, throughput, and flow time:

L = λW

where L is average work-in-process, λ is average throughput, and W is average time in the system. If a cross-docking operation has an average of 240 pallets in process and processes 80 pallets per hour, average flow time is 240/80 = 3 hours.

Little’s Law is powerful because it applies to stable systems regardless of the specific arrival or service distribution. It does not, however, explain why work-in-process exists or whether the output is high quality. The variables must be measured over the same system boundary and time period. Managers should also recognize that reducing work-in-process without addressing the underlying constraint may simply move queues elsewhere.

Takt time and demand alignment

Takt time is the available production or service time divided by customer demand during that period. If a facility has 420 productive minutes and demand for 140 order lines, takt time is three minutes per order line. Takt time expresses the rhythm required to meet demand; it is not the same as actual cycle time. If cycle time exceeds takt time, capacity or work design must change. If cycle time is substantially below takt time, the process may have excess capacity unless flexibility or peak protection justifies it.

Process selection and configuration

Process selection concerns the design of the operating system that converts resources into outputs. The appropriate configuration depends on volume, variety, demand stability, customization, technology, capital intensity, and required responsiveness.

A functional or job-shop process groups similar resources together and supports high variety and low volume. It offers flexibility but usually involves complex routing and longer queues. Batch processes produce groups of similar items or transactions. They can improve efficiency but create waiting and setup costs. Line or flow processes arrange activities according to a standard sequence and are suitable for higher volume and lower variety. Continuous processes are highly standardized and capital intensive. Project processes are organized around unique outputs, such as a major warehouse construction or a complex supply-chain implementation.

Logistics operations often combine configurations. A parcel hub may use a line-like automated sortation system, functional maintenance resources, batch wave picking, and project-based network redesign. The objective is not to select one universal process type but to align process architecture with the order profile and service strategy.

Process selection should evaluate at least the following criteria:

• expected volume and demand variability;
• product or service variety and customization;
• required speed and delivery reliability;
• total cost, including labor, technology, inventory, and transition costs;
• scalability and flexibility;
• quality and traceability requirements;
• safety, regulatory, and environmental obligations;
• resilience under disruption; and
• compatibility with information systems and partner processes.

A low-cost process may be inappropriate if it cannot support promised service levels. Conversely, automation may be uneconomic where demand is volatile, product profiles change frequently, or the business lacks the capability to maintain the technology.

Improving the process: from diagnosis to intervention

Process improvement should proceed from a verified diagnosis rather than from a preferred solution. A practical improvement cycle includes defining the problem and customer requirement, measuring current performance, identifying causes, designing alternatives, testing changes, implementing the selected solution, and establishing controls. This logic is consistent with the DMAIC sequence—Define, Measure, Analyze, Improve, and Control—although organizations may use other improvement frameworks.

Theory of Constraints

The Theory of Constraints views every system as limited by at least one constraint. Its five focusing steps are:

1. Identify the system’s constraint.

2. Exploit the constraint by using its existing capacity as effectively as possible.

3. Subordinate other activities to the constraint, ensuring that non-constraint resources do not create excess inventory or unbalanced output.

4. Elevate the constraint by adding capacity, changing technology, revising policies, or redesigning work.

5. If the constraint moves, repeat the process and avoid allowing former assumptions to become permanent.

Suppose a distribution center’s dispatch dock limits daily shipments. Exploiting it may involve prioritizing complete orders, reducing paperwork delays, and scheduling carriers evenly. Subordinating may require upstream picking to release work according to dock capacity rather than maximum picking speed. Elevating could involve additional dock doors, appointment redesign, or automation. Improving picking alone will not increase total throughput if the dock remains constrained.

Continuous improvement and lean thinking

Continuous improvement involves repeated, incremental changes based on evidence and employee involvement. Lean thinking emphasizes customer-defined value, value-stream flow, pull, and the systematic removal of waste. Common forms of waste include unnecessary movement, transportation, waiting, excess inventory, over-processing, defects, overproduction, and underused human capability.

Kaizen events, standardized work, visual management, 5S, mistake-proofing, root-cause analysis, and statistical monitoring can support improvement. These techniques are not isolated tools. For example, 5S may improve workplace organization, but it will not resolve an inaccurate demand signal or a policy that creates excessive batch sizes.

Root-cause analysis should move beyond symptoms. The five-whys method asks why a problem occurred repeatedly, while cause-and-effect diagrams organize possible causes under categories such as people, methods, machines, materials, measurement, and environment. Data should be used to test suspected causes rather than merely confirm managerial opinion.

Business process reengineering

Business process reengineering, or BPR, involves fundamental rethinking and radical redesign of core processes to achieve major improvements in cost, quality, speed, or service. Unlike continuous improvement, which usually refines the existing process, BPR questions the assumptions underlying the process. It may eliminate handoffs, combine roles, use a single shared information record, redesign organizational boundaries, or create an entirely different fulfillment model.

For example, a traditional returns process may require a customer-service request, warehouse inspection, finance approval, and separate refund authorization. A reengineered process might use rules-based authorization, customer-uploaded evidence, integrated inventory and finance data, and automatic refund initiation for approved categories. The redesign changes both work and decision rights.

BPR carries greater disruption and implementation risk than incremental improvement. It is justified when the existing process is fundamentally misaligned with customer expectations, technology, or competitive requirements. It should not be used as a label for indiscriminate cost cutting or staff reduction.

A framework for evaluating alternatives

When alternatives are available, managers should compare them against explicit criteria. A weighted decision matrix can assign weights to criteria such as service reliability, cost, flexibility, implementation risk, sustainability, and scalability. Each alternative is scored, and the weighted results are reviewed alongside sensitivity analysis. Quantitative models may include capacity simulation, queuing analysis, total-cost-of-ownership analysis, and scenario analysis.

Financial evaluation should include both visible and hidden costs. A new warehouse-management feature may require software licenses, integration, training, data cleansing, process redesign, cybersecurity controls, and temporary productivity loss. Benefits may include fewer errors, reduced inventory, improved asset utilization, lower claims, and better customer retention. Payback period alone may undervalue resilience or regulatory benefits, so it should not be the only decision criterion.

Pilot testing is useful where uncertainty is high. A pilot should specify the hypothesis, performance measures, control conditions, duration, and decision rules. Improvements must be monitored after implementation because initial gains may disappear when attention shifts or demand conditions change.

Digital technologies and analytics

Digital tools can strengthen process analysis by capturing event timestamps, locations, scan data, exceptions, and resource status. Process mining reconstructs actual process paths from system logs and can reveal rework loops, variant paths, bottlenecks, and compliance deviations. It is particularly useful when the documented process differs from operational reality.

Warehouse-management systems, transportation-management systems, barcode scanning, RFID, robotics, sensors, and electronic data interchange can reduce manual intervention and improve visibility. Robotic process automation may handle repetitive data transfers, while predictive analytics can anticipate volume surges, equipment failures, or late deliveries. Artificial intelligence can support demand forecasting, slotting, route recommendations, and anomaly detection.

Technology does not automatically improve a process. Automating a poorly designed process may accelerate errors and make them harder to detect. Data quality, interoperability, explainability, cybersecurity, access control, and human oversight are therefore essential. Algorithms used for prioritization or workforce scheduling should be tested for unintended bias and should not remove accountability from managers.

Governance, ethics, and professional responsibility

Process redesign affects employees, suppliers, customers, and communities. Ethical analysis should consider job redesign, surveillance, privacy, fairness in workload allocation, accessibility, and the consequences of service prioritization. A carrier-selection algorithm that favors low cost without considering safety or labor standards may create unacceptable risks.

Governance requires clear process ownership, documented controls, version management, audit trails, segregation of duties, and escalation procedures. Regulatory requirements may concern customs, dangerous goods, occupational safety, data protection, product traceability, and environmental reporting. Process performance targets should not encourage employees to conceal defects, bypass safety checks, or manipulate timestamps.

A balanced performance system combines efficiency measures with quality, safety, compliance, employee well-being, and customer outcomes. The principle is simple: a process is not improved if it achieves a numerical target by transferring cost or harm to another stakeholder.

Illustrative case: redesigning order fulfillment at NorthStar Distribution

Background

NorthStar Distribution is a fictional regional distributor of maintenance supplies. It promises same-day dispatch for orders received before 14:00. The facility processes approximately 600 orders on a typical day, with substantial variation at month-end.

Situation and problem

Customers reported late dispatches despite management believing that the warehouse had adequate labor. A preliminary SIPOC identified suppliers including vendors, purchasing, and the sales-order system; inputs including stock records, orders, packaging, and carrier schedules; outputs including verified shipments; and customers including industrial clients and field-service teams.

A swimlane map showed that orders moved through release, picking, consolidation, packing, documentation, and dispatch. The current-state value-stream map revealed 18 minutes of active work but an average order lead time of 6.2 hours. Orders waited in three queues, and incomplete inventory information caused frequent re-picking.

Analysis

During the afternoon peak, picking processed 90 orders per hour, packing 72, and dispatch documentation 60. Average completed throughput was approximately 60 orders per hour. Dispatch documentation was therefore the effective constraint. Little’s Law indicated that an average of 360 orders in the fulfillment system at 60 orders per hour implied an average flow time of six hours, consistent with observed performance.

Further analysis found that documentation staff spent significant time re-entering carrier and customer information. The warehouse also released large waves every two hours, creating peaks followed by idle periods. Performance reports emphasized picker productivity rather than complete, on-time dispatch.

Approach and solution

NorthStar first exploited the constraint by standardizing document templates, eliminating duplicate data entry, and sequencing urgent orders according to carrier cutoff times. It then subordinated picking releases to the documentation and packing capacity rather than releasing the maximum possible work. A small pilot introduced continuous release of smaller order groups and barcode validation at packing.

The company subsequently elevated the constraint by integrating the order, inventory, and carrier systems and assigning cross-trained staff to documentation during the afternoon peak. A future-state map included one information record, smaller release intervals, a visual queue limit, and exception handling for inventory discrepancies.

Outcome

In the illustrative scenario, average work-in-process fell, lead time became more predictable, and the facility increased on-time dispatch without proportionally increasing labor. The key improvement did not begin with faster picking. It began with identifying the system constraint and aligning upstream work with it.

Learning points

The case demonstrates that local productivity is not equivalent to system throughput. It also illustrates the value of combining SIPOC for boundary definition, process mapping for handoff analysis, value-stream mapping for delay visibility, Little’s Law for diagnosis, and the Theory of Constraints for intervention. Finally, the case shows why process measures should reflect customer outcomes rather than isolated departmental activity.

Common challenges and mistakes

Several errors recur in process-improvement programs. Analysts may map the intended process rather than observing actual work; select a solution before defining the problem; confuse activity with value; or measure averages that conceal peak-period failure. Another mistake is to optimize a non-bottleneck resource while ignoring the constraint.

Organizations may also introduce too many measures, creating reporting effort without managerial insight. Poorly defined process boundaries can cause teams to exclude suppliers, customers, returns, or exception paths. Improvement teams sometimes underestimate change fatigue, training needs, data migration, and the political effects of shifting responsibilities.

A further risk is excessive reliance on automation. Automation is appropriate when work is repetitive, rules are stable, data are reliable, and the economic and risk cases are sound. Human judgment remains important where exceptions are complex, consequences are significant, or customer relationships require discretion.

Best-practice principles

Effective process analysis and improvement generally follow these principles:

• begin with customer requirements and a clearly defined process boundary;
• observe actual performance and distinguish facts from assumptions;
• analyze the complete flow, including information, exceptions, and external partners;
• use multiple measures rather than a single productivity indicator;
• identify and manage the system constraint before optimizing other resources;
• involve process performers in diagnosis and design;
• test changes through pilots where uncertainty is material;
• design controls, ownership, training, and review mechanisms before implementation;
• evaluate technology for total organizational impact, not novelty; and
• sustain improvement through standard work, visual performance review, and periodic reassessment.

Chapter Summary

Process analysis makes the movement of work, information, materials, and decisions visible. Flowcharts clarify sequence and responsibility; SIPOC establishes process boundaries; and value-stream mapping exposes waiting, inventory, information delays, and the difference between processing time and total lead time. Performance measures such as cycle time, throughput, utilization, takt time, capacity, and Little’s Law support evidence-based diagnosis.

Process selection must align volume, variety, flexibility, speed, quality, cost, technology, resilience, and regulatory requirements. Improvement may be incremental, using lean and continuous-improvement methods, or radical, using business process reengineering. The Theory of Constraints directs attention to the factor limiting system throughput: identify it, exploit it, subordinate other work, elevate it, and repeat when the constraint shifts.

Digital technologies, process mining, automation, analytics, and AI can improve visibility and decision-making, but only when data, governance, cybersecurity, human oversight, and ethical safeguards are addressed. Sustainable improvement requires customer-focused measures, cross-functional ownership, employee participation, controlled implementation, and attention to system-wide rather than local performance.

Key Terms

Process: A connected set of activities that transforms inputs into outputs for a customer.

Process analysis: Systematic examination of activities, resources, flows, performance, and causes of variation.

Process selection: Choice or design of an operating configuration suited to demand and service requirements.

Flowchart: A visual representation of activities, decisions, and sequence in a process.

SIPOC: A high-level map of Suppliers, Inputs, Process, Outputs, and Customers.

Value-stream mapping: Analysis of material and information flow, including processing, waiting, and inventory.

Cycle time: Time required to complete one unit or transaction at an activity.

Lead time: Total elapsed time from process initiation to completion.

Throughput: Rate at which completed units leave a process.

Capacity: Maximum sustainable output rate under specified conditions.

Utilization: Proportion of available capacity being used.

Bottleneck: Constraint that limits the output of the overall system.

Little’s Law: Relationship expressed as L = λW between work-in-process, throughput, and flow time.

Takt time: Available productive time divided by customer demand.

Theory of Constraints: Management approach focused on identifying and improving the system’s limiting constraint.

Continuous improvement: Ongoing incremental improvement based on evidence and participation.

Lean thinking: Approach focused on customer value, flow, pull, and waste reduction.

Business process reengineering: Fundamental redesign of a process to achieve major performance improvements.

Process mining: Reconstruction and analysis of actual process paths from digital event logs.

Standardized work: Documented best-known method for performing a task consistently and safely.

Review Questions

Short Answer Questions

1. Distinguish between cycle time and lead time.

2. What information does a SIPOC diagram provide?

3. State Little’s Law and define its variables.

4. Why can high utilization increase waiting time?

5. What is the difference between continuous improvement and business process reengineering?

Long Answer Questions

1. Explain how flowcharts, SIPOC, and value-stream mapping complement one another in process analysis.

2. Discuss the criteria that should guide process selection in a logistics organization.

3. Explain the five focusing steps of the Theory of Constraints with a suitable logistics example.

4. Analyze the role of takt time, throughput, utilization, and capacity in balancing an operating process.

5. Discuss the opportunities and governance risks associated with process mining, automation, and artificial intelligence.

Analytical / Application Questions

1. A fulfillment center has 300 orders in process and completes 50 orders per hour. Calculate average flow time using Little’s Law. If management wants a four-hour flow time at the same throughput, what average work-in-process level is required?

2. A process has three sequential activities with capacities of 80, 65, and 75 units per hour. Identify the bottleneck and explain two actions that could improve total throughput without merely increasing output at non-bottleneck activities.

3. A company is considering either incremental improvement of its returns process or a complete digital redesign. Develop a decision framework using customer value, cost, risk, flexibility, governance, and implementation considerations.

22. Multiple Choice Questions with Answers

Question 1

Which tool is most appropriate for defining the boundaries of a process before detailed mapping begins?

A. SIPOC

B. Control chart

C. Economic order quantity model

D. Break-even chart

Correct Answer: A. SIPOC

Question 2

A process has 240 units of work-in-process and an average throughput of 80 units per hour. According to Little’s Law, what is the average flow time?

A. 0.33 hours

B. 2 hours

C. 3 hours

D. 19,200 hours

Correct Answer: C. 3 hours

Question 3

A warehouse activity has a capacity of 70 orders per hour while the other sequential activities can process 90 and 85 orders per hour. Which activity is the likely bottleneck?

A. The 90-order activity

B. The 85-order activity

C. The 70-order activity

D. All activities equally

Correct Answer: C. The 70-order activity

Question 4

Which statement best distinguishes takt time from cycle time?

A. Takt time is actual completion time, while cycle time is customer demand rate

B. Takt time is the required rhythm based on demand, while cycle time is the time needed to complete a unit

C. Takt time includes waiting, while cycle time excludes all processing

D. They are alternative names for throughput

Correct Answer: B. Takt time is the required rhythm based on demand, while cycle time is the time needed to complete a unit

Question 5

Under the Theory of Constraints, what should a manager do after identifying the system constraint?

A. Increase output at every non-constraint resource

B. Exploit the constraint using its existing capacity effectively

C. Remove all process controls

D. Measure only departmental productivity

Correct Answer: B. Exploit the constraint using its existing capacity effectively

Question 6

Which situation most clearly represents business process reengineering rather than continuous improvement?

A. Rearranging tools to reduce operator movement

B. Reducing a batch size after a pilot test

C. Redesigning returns so integrated rules automatically authorize refunds and eliminate several handoffs

D. Updating a standard operating procedure

Correct Answer: C. Redesigning returns so integrated rules automatically authorize refunds and eliminate several handoffs

Question 7

A value-stream map shows five minutes of processing time and three hours of waiting. What does this primarily indicate?

A. The process has no capacity constraint

B. Lead time is being driven largely by non-processing delay

C. Processing time and lead time are identical

D. Throughput must equal five minutes per unit

Correct Answer: B. Lead time is being driven largely by non-processing delay

Question 8

Why may a process operating at 95 percent utilization experience disproportionately long queues?

A. High utilization removes all variability

B. Near-full utilization leaves little capacity to absorb variation

C. Utilization measures customer satisfaction directly

D. High utilization always indicates low demand

Correct Answer: B. Near-full utilization leaves little capacity to absorb variation

Question 9

Which risk is particularly relevant when automating a poorly designed logistics process?

A. Automation necessarily reduces data availability

B. Automation can accelerate errors and make them harder to detect

C. Automation eliminates the need for governance

D. Automation always lowers total cost

Correct Answer: B. Automation can accelerate errors and make them harder to detect

Question 10

NorthStar increases picking speed, but dispatch documentation remains the lowest-capacity stage. According to constraint logic, what is the most likely result?

A. System throughput will automatically increase in equal proportion

B. More work may accumulate before dispatch without solving the system constraint

C. Lead time will necessarily become zero

D. The documentation constraint will disappear because picking is faster

Correct Answer: B. More work may accumulate before dispatch without solving the system constraint