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Automated Order Fulfillment: Speed and Error Reduction

11/18/2025

Automated Order Fulfillment: Speed and Error Reduction

Automated order fulfillment combines barcode verification, guided picking, packing controls and measured warehouse automation to manage speed, capacity and error risk. In this context, automation is not limited to robots, conveyors or a major machinery investment. It means using data to manage the repetitive decisions, physical movements and control points between order intake and carrier handover.

When designed well, automation can reduce unnecessary travel and manual data entry, standardise work and stop certain types of error before they move downstream. When designed poorly, it can reproduce bad master data at greater speed, shift the bottleneck to another station and make the operation dependent on a single system or machine. The goal, therefore, is not “as much automation as possible”. It is to prepare the right order for dispatch, with the right controls, at a sustainable speed and cost.

This guide examines order fulfillment automation as a combined question of measurement, software control, picking, packing, physical equipment, human factors, safety, exception handling, capacity and investment. The technologies discussed are industry-wide options. Memnun Depo’s current, verified scope is defined separately and explicitly near the end of the article.

Automation is an operating system, not a machine

Moving an item from shelf to carton more quickly is not, by itself, successful automation. If an order was released in the wrong state, system inventory does not match the floor, the packing station is saturated or the shipping label is paired with the wrong carton, faster picking will not produce the right customer outcome.

End-to-end automation designs four layers together:

  • Business rules: When an order is eligible for processing, its priority, packing and carrier rules, and how exceptions are handled
  • Digital records: SKU, barcode, location, inventory state, task, user, equipment and timestamp data
  • Physical flow: Picking, transport, sorting, consolidation, packing, labelling and dispatch preparation
  • Control and recovery: What happens after a failed scan, stock shortage, equipment fault, network outage, cancellation or capacity overrun

Automation is not binary. Replacing a paper list with mobile tasks and barcode verification is one layer. Picking carts, pick-to-light or voice guidance are another. Conveyors, sorters, goods-to-person systems, automated storage and retrieval systems (AS/RS), autonomous mobile robots (AMRs) and robotic item picking belong to different classes of physical investment. A warehouse may not need every level.

Research on e-commerce warehousing shows that small, time-sensitive orders connect layout, work release, picking and capacity decisions. Equipment must therefore be evaluated as part of the whole system, not in isolation. Boysen, de Koster and Weidinger — Warehousing in the e-commerce era

Measure the baseline and bottleneck first

“Orders take too long to prepare” is not a sufficient diagnosis for an investment decision. The delay may begin with late order intake, allocation waiting for stock, an empty forward-pick location, excessive travel, consolidation, a packing queue, a printer fault or work release that is misaligned with carrier handover times.

At a minimum, establish the following profile before automating:

  • Orders, order lines, units and cartons received by hour, day and week
  • The distribution of single-line, multi-line, single-unit and multi-unit orders
  • SKU count, velocity, dimensions, weight, fragility, deformability and barcode quality
  • Arrival curves for normal, campaign and peak periods
  • Time between order receipt, release, picking, packing and ready-for-dispatch status
  • Causes of travel, waiting, rework, short picks, wrong items, wrong labels and manual exceptions
  • Direct labour hours, shifts, overtime, floor-space use and equipment utilisation
  • Carrier handover rules and the service promise made to the customer

Averages are not enough. Examine upper percentiles such as p90 or p95 alongside median cycle time, the highest hourly arrival rate and the longest queues. A statement such as “5,000 orders per day” does not size equipment unless the number of lines and units in those orders—and the hours in which they arrive—are also known.

For every step on the process map, record the start event, end event, input, output, decision owner and data source. Then test the cause of the largest time or quality loss through a pilot. Order-picking research likewise emphasises that layout, storage policy, routing, batching and equipment decisions should be designed together. De Koster, Le-Duc and Roodbergen — Design and control of warehouse order picking

What do WMS, WES and WCS do?

Software names vary between automation vendors. The label matters less than knowing which system makes each decision, which system owns each record and who takes responsibility when a component fails.

  • An OMS or commercial order system may own the customer order and its commercial status.
  • A WMS manages warehouse inventory, locations, allocation, replenishment, tasks, picking, packing and dispatch records.
  • A WES, where present, orchestrates people and multiple automation subsystems against shared priorities and available capacity; it may release work dynamically.
  • A WCS or fleet-control system may coordinate the commands and states of conveyors, sorters, shuttles, AMRs or other equipment.
  • PLCs and device controllers execute machine movement at sensor and actuator level.

Not every warehouse has all these layers, and vendors may use different names. One product may perform several roles. The following questions must nevertheless have unambiguous answers:

  1. Which system is the source of truth for orders, inventory, tasks, equipment state and dispatch records?
  2. What happens if the same event arrives twice—or never arrives at all?
  3. What evidence does equipment provide that a task was completed?
  4. How are records reconciled when the WMS and control system report different states?
  5. How are in-process products and tasks located after a network or control-layer outage?

Document the event and responsibility model before physical equipment is installed. Our e-commerce integration guide examines retries, delay and reconciliation design in more detail.

How should barcode, RF, voice and light-directed picking be selected?

Before investing in physical automation, one of the strongest lower-risk steps is to direct the operator to the right task and verify critical identities at the moment of work.

  • An RF terminal or mobile screen can display the item, location, quantity and next step. It is flexible, but screen interaction and device ergonomics still need to be designed.
  • Voice picking can free the operator’s hands and line of sight. Noise, language, command design, headset hygiene and training affect the outcome.
  • Pick-to-light can provide rapid direction in dense, stable pick faces. Layout changes, hardware density and controls for intervention at the wrong location must be considered.
  • Put-to-light can help sort items into order containers after batch or zone picking.

No method is universally fastest. Even controlled studies of RF, voice and light-directed solutions have found that performance and user effects vary with context. Test with real products, workers at different experience levels, and both normal and peak workloads. De Vries, de Koster and Stam — Exploring the role of picker personality in predicting picking performance

Guidance should not remove the operator’s ability to think and intervene. If a product is damaged, mislabelled, physically inconsistent or unsafe, the worker must be able to stop the task and route it to an exception process.

When should batch, cluster, zone, wave or dynamic release be used?

Picking productivity depends not only on walking speed, but also on how orders are converted into work.

  • Batch picking collects common items for multiple orders in one tour; it may require downstream sorting or consolidation.
  • Cluster picking keeps orders separate during the tour by assigning them to compartments or containers on the picking cart.
  • Zone picking assigns a worker or machine to a defined area; multi-zone orders require a handover and consolidation process.
  • Wave picking releases work in timed groups according to carrier, cut-off, route, customer or shift criteria.
  • Waveless or dynamic release prioritises work in smaller, more frequent groups as new orders and capacity information arrive.

Batching may reduce travel while overloading the sortation table. A large wave may improve picking efficiency but create a sudden packing queue. Zoning may build expertise while allowing one slow area to delay the whole order. Work release should therefore consider replenishment, open work, packing stations, dispatch doors and carrier handover times as one connected system.

Research on integrated planning likewise shows that optimising order batching, picker routing, zoning and related decisions separately can miss the result for the system as a whole. Van Gils et al. — Designing efficient order picking systems

How does barcode verification support quality?

A barcode connects a physical object to its digital record at the point of activity. At appropriate control points, the operation can verify matches between:

  • The item picked and the order line
  • The item and its source location
  • The item or tote and its consolidation position
  • The prepared carton and the order
  • The carrier label and the correct shipment
  • The shipping unit and its loading or handover record

A scan alone does not prove that an item is undamaged, that a sealed carton contains the correct quantity or that the master data is accurate. The wrong barcode can be read correctly; a user can enter the wrong quantity; a scan can be bypassed; or one label can appear on two physical objects. The system should therefore record not merely that a scan occurred, but the expected identity, observed identity, time, device, user, quantity and exception outcome.

GS1 standards can provide common identifiers for trade items, logistics units, locations and events. Standards strengthen the foundation of control, but do not replace accurate label printing and placement, master data or operational discipline. GS1 Global Traceability Standard

How should packing, dimensioning, weighing and label control be designed?

When picking accelerates, the bottleneck often moves to packing. A packing cell should be designed around the physical characteristics of the product, order rules and carrier requirements.

A controlled station may use the following sequence:

  1. Scan the order, tote or item identity.
  2. Display the expected items and quantities to the operator.
  3. Confirm condition and any special packing rule where required.
  4. Select or recommend suitable packaging.
  5. Complete the item-to-order match before sealing the carton.
  6. Record dimensions and weight, routing values outside defined tolerances to an exception.
  7. Generate and apply the carrier label, then pair it with the correct carton.
  8. Set the order to ready for dispatch only after mandatory checks are complete.

A checkweigher provides an anomaly signal, not conclusive evidence of correct contents. A missing item and an incorrectly added item may offset each other by weight. Product weight, packaging and device tolerances can also vary. Weighing should therefore be combined with item identity and process evidence.

Automated dimensioning, weighing, print-and-apply labelling and manifest systems can create value at high volume. The design must still include a physical reject and manual-review path for unreadable labels, out-of-tolerance dimensions, printer faults or carrier-service failures. MHI — Scan, Label, Apply and Manifest: An Introduction

When do conveyors, sorters, AS/RS and goods-to-person systems fit?

Conveyors and sorters can standardise transport and separation where flow is high and regular and load geometry is predictable. A one-hour peak, however, is not the system’s sustainable capacity. Starved stations, full destinations, jams, back pressure and maintenance stoppages can affect the whole line.

AS/RS is a broad family of systems that automatically store and retrieve items or load units. Goods-to-person brings an item or handling unit to the operator instead of having the operator walk the aisle. The concepts are not identical: AS/RS can feed a goods-to-person flow, but not every application uses the same architecture.

An assessment should model the following together:

  • Dimensional, weight and physical suitability of the item or handling unit
  • Variability in the SKU and order mix
  • Balance between inbound, replenishment, storage, picking and packing stations
  • Buffer capacity and management of full and empty containers
  • Building height, columns, floor, fire-safety and evacuation constraints
  • Maintenance access, critical spares and the effect of failure
  • Low utilisation after peak campaigns and changes in the product portfolio

Reviews of automated picking systems show that performance depends less on the technology’s name than on system design, control policy and operating context. Jaghbeer, Hanson and Johansson — Automated order picking systems and the links between design and performance

What are the limits of AGVs, AMRs and robotic item picking?

AGV and AMR are used differently across the market. Look beyond the name: examine how the vehicle is guided, what it does when it encounters an obstacle, the load it carries and the space it shares with people.

In many applications, mobile robots automate transport, not the act of grasping an item from a shelf. They may follow a picker, carry totes or bring shelving to a workstation. Real performance depends on aisle width, floor condition, doors, pedestrian and forklift traffic, fleet control, network coverage, charging strategy, battery behaviour, task allocation and congestion. Adding robots does not always produce a proportional increase in capacity.

Robotic item picking is a different problem. Overlapping, transparent, reflective, deformable, loose, entangled or easily damaged products can affect perception and grasp success. A solution needs:

  • A matrix of automation-compatible SKUs and packaging
  • A gripper and force limit suited to the product
  • A perception-confidence threshold and failed-attempt limit
  • Controls for damage, mis-grasps and dropped products
  • A manual exception path for unknown or unsuitable items
  • Retesting and approval when a new SKU is introduced

Research on robotised warehouse systems shows that technology families create different design and control problems; a “robotic warehouse” is not one standard solution. Azadeh, de Koster and Roy — Robotized and automated warehouse systems For the design and control options of robotic mobile fulfillment systems, see also the review by Da Costa Barros and Nascimento.

Why are exceptions and human oversight part of automation?

A successful flow designs not only the happy path but also normal exceptions. At a minimum, assign an owner, physical holding area, system status and target resolution time for:

  • An unreadable or incorrectly identified barcode
  • Inventory that exists in the system but cannot be found at the location
  • Missing, excess, damaged or unsuitable products
  • An order without a defined packing rule
  • A carton already in process when the order is cancelled or its address changes
  • Weight, dimension or label mismatch
  • A full sorter destination, jam or failed robot task
  • Printer, scanner, network, WMS, WES, WCS or carrier-service outage

An exception screen should do more than display an error message. It should show where the product physically is, which steps are complete and what the safe next action is. Manual intervention should also be recorded; otherwise the system may appear successful while the team rescues the flow through invisible work.

Automation does not eliminate human error. It can prevent certain mismatches, but it can also propagate incorrect SKU data, a bad tolerance or a defective rule across thousands of transactions. Human ability to recognise an exception, stop work safely and make a reasoned decision is therefore a control feature, not a weakness.

How should capacity, throughput and queues be interpreted?

Before comparing capacity, fix the output unit:

throughput = completed units of a defined output / observation time

The output may be orders, order lines, items or cartons. These units are not interchangeable. Labour productivity is a different metric:

labour productivity = completed output / direct labour hour

In a flow of mandatory serial stages, the theoretical upper bound of system capacity cannot exceed the lowest effective stage capacity:

system capacity ≤ min(effective stage capacities)

Actual throughput may be lower because of failures, breaks, product changeovers, starvation, blocking, buffers, rework and order mix. Test sustainable good output with a representative product mix instead of relying on a nameplate speed.

In a stable system, Little’s Law relates average work in process, average throughput rate and average flow time:

average WIP = average throughput × average flow time

Use the same system boundary and time unit throughout. This relationship is not a capacity guarantee or a short-term forecast under severe fluctuation. As queue utilisation approaches effective capacity, variability can increase delay sharply. Rather than assuming a universal “ideal percentage”, test arrival and service distributions through scenarios. Little — A Proof for the Queuing Formula

How should ergonomics, human factors and safety be designed?

Automation can reduce walking, bending or heavy transport, but it does not solve ergonomics automatically. Repetitive motion at a fixed goods-to-person station, machine-paced work, screen load, noise, unexpected movement during maintenance and human–vehicle interaction can create new risks.

The design should include:

  • Reach distances and working heights suitable for different bodies and abilities
  • Assessment of repetitive motion, lifting, visual demand and cognitive load
  • Risk-based separation of pedestrian, forklift and mobile-robot routes
  • Guards, sensors, emergency stops and a safe restart sequence
  • Energy isolation and authorisation for jam clearing, cleaning and maintenance
  • Visible and audible alarms, with monitoring for alarm fatigue
  • Practical training for new tasks, interfaces and exceptions
  • Authority for workers to stop unsafe work

Order-picking literature emphasises that human factors should be designed as deliberately as efficiency. Grosse, Glock and Neumann — Human factors in order picking

Competent specialists should identify the standards applicable to the equipment and use case. For example, ISO 3691-4:2023 addresses driverless industrial trucks and their systems, while ISO 10218-2:2025 sets safety requirements for industrial robot applications and robot-cell integration. Citing a standard does not, by itself, demonstrate conformity, complete a risk assessment or satisfy legal obligations.

How should integration, data ownership and cybersecurity be protected?

As physical equipment becomes connected, a data or access failure can have a greater effect on the warehouse floor. Order status, task identity, carton, equipment position, alarm and user action should be traceable on the same timeline.

A minimum control set includes:

  • An up-to-date inventory of hardware, controllers, software, versions and network connections
  • A clear identity and status dictionary across systems
  • Rules for duplicate processing, ordering, timeouts and reconciliation
  • Network segmentation and communication only between required services
  • Least privilege, individual accounts and strong authentication for remote access
  • Logs, alarms, time synchronisation and change records
  • Tested backup, configuration restore and disaster-recovery plans
  • Change management that tests patches and releases in a representative environment first
  • Safe stop and controlled manual operation during a cyber incident or system outage

An automation environment is not only “IT”; it may include moving equipment and industrial control layers. NIST’s operational technology guidance explains why cyber risk must be considered alongside performance, reliability and safety requirements. NIST SP 800-82 Rev. 3 — Guide to Operational Technology Security

How should an outage and manual fallback plan work?

A warehouse designed around the assumption that “the system never stops” can lose track of products and orders during its first real outage. Manual fallback is not a second warehouse expected to replace automation indefinitely. It is a plan to stop safely, continue critical work and reconcile records afterwards.

At a minimum, the plan should answer:

  1. Which failures stop new work release, and which in-process tasks may continue under control?
  2. How are physical items inside a line or robot system located and moved to a safe area?
  3. Which unique reference tracks critical orders offline?
  4. How are manual labels and inventory movements entered into the system afterwards?
  5. How are duplicate picks or duplicate dispatches prevented when systems recover?
  6. Who may authorise restart, and which acceptance checks must be completed first?
  7. At what threshold are customers, carriers and the operations team informed?

A recovery exercise should test more than restoring a server backup. It should also reconcile the physical and digital record of a partially completed order. Exception rate and manual recovery time belong in the real operating cost of automation.

How should a pilot scale into controlled deployment?

A pilot is not a vendor demonstration. It is a controlled experiment in a sufficiently representative part of the real operation, assessed against acceptance criteria agreed in advance.

A sound pilot:

  • Starts with one problem statement and a measured baseline.
  • Covers normal, high and low volume; fast and slow SKUs; and different packaging types.
  • Tests exceptions such as short stock, wrong labels, cancellations, equipment faults and network outages—not only success.
  • Uses the same output and quality definitions as the current method.
  • Separates the learning curve and training effect from steady-state performance.
  • Treats safety, ergonomics and worker feedback as acceptance gates.
  • Physically tests manual fallback and data reconciliation.
  • Defines the criteria for proceeding, correcting or stopping in advance.

A short-lived peak speed in the first week is not sufficient evidence for scaling. Across a representative order mix, the system should meet sustainable good-output, upper-percentile cycle-time, quality, exception, availability and cost criteria together.

Deployment can expand by product family, zone, shift or function instead of switching the entire warehouse at once. At each stage, remeasure the new bottleneck, safety events and downstream effects.

How should a lifecycle business case replace a single ROI claim?

An investment case should not be reduced to “how many people will this save?” Automation may reallocate hours from walking to control, exception handling or maintenance instead of removing them. A cash saving occurs only if payroll, overtime, outsourcing or planned hiring actually changes.

Total cost of ownership should include:

  • Equipment, software and initial licences
  • Construction, racking, floors, electricity, fire systems, networks and site changes
  • Integration, data cleansing, testing, project management and training
  • Maintenance, support, spare parts, consumables, energy and periodic inspection
  • Operational supervision, exceptions and manual-recovery labour
  • Cybersecurity, backup, monitoring and version upgrades
  • Planned and unplanned downtime, including lost production or service
  • Financing, insurance, tax and accounting effects, and foreign-exchange risk
  • Obsolescence, decommissioning and any residual value

In the limited case where annual incremental net cash flow is constant and positive, simple payback can be shown as:

simple payback = initial net investment / annual incremental net cash flow

This ratio does not reflect the time value of money, changing annual cash flows or results after the payback date. Longer projects should also use net present value, scenario and sensitivity analysis:

NPV = −I₀ + Σ[ΔCFₜ / (1+r)ᵗ] + residual value / (1+r)ⁿ

Build base, downside and upside scenarios for volume, product mix, wages, exchange rates, availability, maintenance, growth and system life. A generic vendor payback period cannot substitute for the operator’s own cash-flow model.

Which KPIs should be tracked after automation?

KPIs should help identify root causes as well as demonstrate outcomes. Define the scope, period, unit, numerator, denominator, source system and exclusions for every measure.

  • Throughput: Completed orders, lines, items or cartons per hour, with the unit stated explicitly
  • Order cycle time: From a defined start event to ready-for-dispatch status, reported as a median and upper percentiles
  • Line accuracy: 1 − incorrect audited lines / audited lines
  • Order accuracy: Fully correct audited orders / audited orders
  • First-pass yield: Work passing control without rework / work entering the process
  • Exception rate: Work routed to manual exception / total processed work
  • Intervention rate: Manual recovery actions or resets / automated tasks
  • Availability: Time the system is capable of operating within defined planned production time, with planned maintenance and performance loss reported separately
  • Queue and WIP: Count and waiting time of work in process in front of each station
  • Labour productivity: Defined output / direct labour hour
  • Safety and ergonomics: Near misses, incidents, unsafe interventions and ergonomic findings
  • Unit cost: Lifecycle operating cost per eligible unit of good output

Do not infer order accuracy from line accuracy unless an independence assumption is justified. Likewise, uptime alone does not prove good output: a machine can run while producing errors or rework. Warehouse-performance research recommends reading time, quality, cost and productivity together. Staudt et al. — Warehouse performance measurement

Which automation fits which operation?

A practical decision sequence is:

  1. If data and transaction history are weak: Start with SKU and location master data, WMS transactions, barcodes and exception records.
  2. If travel is excessive: Test slotting, routing, batch or cluster picking and mobile guidance; assess physical layout alongside our warehouse layout guide.
  3. If sorting or consolidation is the bottleneck: Pilot order containers, put-to-light, a sorter or a different work-release rule.
  4. If standard loads move repeatedly between fixed points: Compare a conveyor with a suitable mobile-transport option, including capacity, safety and fallback flow.
  5. If dense storage and bringing products to people creates value: Model an AS/RS or goods-to-person scenario against station, replenishment and building constraints.
  6. If item picking is repetitive and SKUs are physically suitable: Test robotic grasping with real products and packaging, alongside a manual exception path.
  7. If the packing queue dominates: Correct packing rules and station standards first, then evaluate dimensioning, weighing, sealing and labelling automation.

If a technology resolves one bottleneck while enlarging another, the project is not complete. Select on safe and sustainable good output, flexibility and total cost across a representative order mix—not on catalogue peak speed. For a broader comparison of technology families, read our fulfillment technology guide.

What is Memnun Depo’s verified automation scope?

Memnun Depo’s current, verified approach links physical operations to digital records and defined control points:

  • Recording receiving, SKU setup and stock entries in the WMS
  • Tracking product, pallet and shelf or location relationships through movement history
  • Keeping order intake, picking, preparation and dispatch states in one record chain
  • Using barcode checks at defined points to verify product and shipment matches
  • Preparing and applying carrier labels, and associating the label, tracking number and dispatch record with the relevant order
  • Reporting inventory, order and movement history
  • Building integrations for sales channels and custom systems where the required technical access is available

This scope does not mean that Memnun Depo currently operates conveyors, sorters, AS/RS, goods-to-person systems, AGVs or AMRs, robotic item picking, pick-to-light, voice picking, RFID, IoT, AI optimisation or automated dimensioning and weighing. Those technologies are described in this guide as industry options.

WMS and barcode controls are not a guarantee of error-free fulfillment. Incorrect master data, labels or quantities—and skipped physical steps—can still create exceptions. Memnun Depo does not publish a percentage reduction in errors attributable to automation, a robotic-capacity claim or a general automation payback period.

A processable order requires valid order data, sufficient available stock and a defined packing rule. Within Memnun Depo’s verified operational commitment, these orders are made ready for dispatch within 12 hours of entering the system. Carrier collection and final-mile delivery fall outside that period. This is an overall operational commitment, not a performance outcome attributed solely to automation.

Explore our current fulfillment services, review the integration scope, or contact us to identify the control points appropriate for your order and product profile.

Frequently asked questions about automated order fulfillment

Where should warehouse automation begin?

Begin by measuring the event-based flow from order intake to ready-for-dispatch status and identifying the causes of bottlenecks, errors and rework. If SKU and location data or the physical transaction history is unreliable, WMS transactions, barcode verification and standard work will usually come before physical automation.

Does automation eliminate human error?

No. It can reduce certain mismatches and manual data entry, but it can also propagate incorrect data, labels or business rules systematically. Equipment, integration and network failures introduce other error modes. A designed exception path and human oversight remain necessary.

Which is more efficient: a conveyor or an AMR?

There is no universal answer. Conveyors can be strong in high, predictable and fixed flows; AMRs may offer more flexibility when routes and volume change. Compare load geometry, building constraints, traffic, charging, safety, maintenance, peak demand and the manual fallback route together.

Does scanning a barcode prove that the order is correct?

A scan can help verify the expected item, location, order or carton identity. It does not, by itself, prove physical condition, the contents of a sealed carton, quantity or the accuracy of master data. Use scanning with physical or visual checks and an exception rule.

Does a checkweigher always detect the wrong item?

No. A weight difference can signal an anomaly, but tolerances, packaging changes and offsetting missing or excess items can conceal an error. Weighing is a quality gate used with item and carton identity, not conclusive proof of contents.

Does adding more robots increase capacity proportionally?

No. Stations, aisles, fleet control, charging, replenishment, networks and downstream capacity can all constrain the system. As congestion rises, the marginal contribution of another robot may fall. Test the system with a representative order mix through simulation or a pilot.

What is the payback period for warehouse automation?

There is no fixed period that applies to every warehouse. Volume, product mix, building, labour, shifts, maintenance, financing, exchange rates, availability, system life and the cost of the alternative process all matter. Simple payback alone is insufficient; examine cash flow and sensitivity scenarios.

When is a pilot successful?

Not merely when it reaches a peak speed. A successful pilot meets acceptance criteria for sustainable good output, cycle time, quality, exceptions, availability, safety and cost across a representative product and order mix. It should also physically test manual fallback and restart.

Sources and further reading