ERP, APS and Digital Planning Systems

Introduction and context

Master Production Scheduling (MPS) has evolved from a periodic spreadsheet exercise into a digitally integrated planning capability. In contemporary manufacturing, the MPS is normally created, checked, released and monitored through an enterprise resource planning (ERP) system, an advanced planning and scheduling (APS) system, or a connected combination of both. These technologies do not eliminate the need for managerial judgement. Rather, they provide a structured environment in which demand, material availability, capacity, inventory policy and operational priorities can be represented and reconciled.

The strategic issue is not simply whether an organisation owns an ERP or APS package. The more important question is whether the digital planning system represents the business accurately enough to support feasible, responsive and economically sound decisions. Poor master data, inappropriate parameters, weak integration and uncontrolled overrides can produce a highly automated but unreliable MPS.

ERP systems generally provide transactional integration and a common database for finance, sales, procurement, inventory, production and distribution. APS systems normally add specialised planning and scheduling capabilities, including finite-capacity planning, constraint analysis, optimisation, scenario comparison and detailed sequencing. Cloud platforms, application programming interfaces (APIs), industrial Internet of Things (IIoT) data and digital twins increasingly connect planning with real-time operational information.

This chapter examines the functionality, architecture, governance and implementation of these systems from an MPS perspective.

Learning objectives

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

1. Explain how ERP and APS systems support MPS.
2. Distinguish transaction processing, planning, scheduling and execution functions.
3. Evaluate data integration and master-data governance requirements.
4. Explain the role of planning parameters and finite-capacity logic.
5. Assess digital twins, cloud platforms and interoperability standards.
6. Identify implementation, cybersecurity, organisational and model-risk issues.
7. Recommend controls and performance measures for a reliable digital planning process.

1. The digital planning architecture

A useful architecture separates four related layers:

The layers should not be interpreted as completely separate software products. Modern ERP suites may contain embedded APS functions, while specialist APS products may integrate with ERP, manufacturing execution systems (MES), warehouse systems and supplier platforms.

The central principle is one planning model, many operational views. Sales may view available-to-promise quantities, production may view work-centre loads, procurement may view component requirements, and finance may view inventory and service implications. These views should be derived from consistent demand, supply and master-data definitions.

A simplified planning flow is:

Demand signals → Demand management → MPS → MRP and capacity checks → Detailed schedule → Execution feedback → Updated plan

The flow is iterative rather than strictly linear. A capacity shortage discovered during scheduling may require a change to the MPS, an alternative bill of material, overtime, subcontracting, rescheduling or a customer-priority decision.

2. ERP support for MPS

2.1 Core ERP functionality

An ERP-supported MPS typically uses the following objects:

• independent demand, such as forecasts and customer orders;
• dependent demand generated from bills of material;
• inventory balances and inventory status;
• scheduled receipts and open purchase or production orders;
• item, location and planning parameters;
• work centres, calendars and available capacity;
• bills of material, routings and operation times;
• lot-sizing, safety-stock and lead-time rules;
• planning horizons, time fences and order policies.

The ERP performs a critical transformation: it converts a commercial requirement into a time-phased production and replenishment plan. At the MPS level, the principal output is the planned quantity of end items or important components by time bucket. MRP then explodes the MPS through the product structure to calculate lower-level requirements.

A simplified projected available balance relationship is:

where PAB_t is projected available balance at the end of period t, SR_t is scheduled receipt quantity due in period t, MPS_t is the planned MPS receipt or completion quantity in period t, and D_t is demand in period t. The equation assumes that all quantities are expressed in the same unit, receipts are available within the relevant period, and inventory records are accurate. If safety stock is required, a shortage condition occurs when PAB_t falls below the specified safety-stock level rather than only when it becomes negative.

For a simple lot-for-lot policy, the planned receipt may be set equal to the net requirement. Under other policies, such as fixed-order quantity or economic order quantity, the receipt may exceed the immediate net requirement.

2.2 Time fences and planning stability

ERP systems commonly divide the planning horizon into zones:

• Frozen zone: changes are restricted because materials, labour or customer commitments are already being arranged.
• Slushy or semiflexible zone: changes are possible but require review.
• Liquid zone: the plan can be changed relatively freely.

Time fences reduce nervousness, the phenomenon in which small demand or inventory changes cause repeated rescheduling. However, excessive freezing can make the plan unresponsive, while insufficient control can create costly expediting and cancellations. A time fence should therefore reflect actual procurement lead times, production lead times, customer commitments and the cost of change.

2.3 Available-to-promise and capable-to-promise

ERP systems often provide available-to-promise (ATP), which considers uncommitted inventory and planned supply. Capable-to-promise (CTP) extends this logic by checking whether capacity and materials can support a new order. CTP is particularly important where production is make-to-order, capacity is constrained or product configurations are complex.

ATP and CTP are not merely customer-service tools. They create a feedback loop between commercial commitments and the MPS. If sales promises quantities without reference to the planning model, the MPS becomes a retrospective attempt to fulfil infeasible commitments.

3. APS: extending ERP planning

ERP planning is often strongest in material coordination and transaction integrity. APS adds analytical depth where decisions depend on finite capacity, alternative resources, sequence-dependent setups, priorities, constraints and multiple scenarios.

3.1 Characteristic APS capabilities

Important APS capabilities include:

1. Finite-capacity planning: planned work is restricted by available machine, labour, tooling or supplier capacity.
2. Constraint-based scheduling: operations are placed only when required materials, resources and precedence conditions are satisfied.
3. Scenario planning: alternative demand, capacity, sourcing and inventory assumptions can be compared without immediately changing the operational plan.
4. Optimisation: the system may minimise tardiness, setup time, overtime, inventory, transportation cost or a weighted combination of objectives.
5. Multi-site planning: supply and demand can be balanced across plants, warehouses, suppliers and distribution centres.
6. Exception management: planners focus on shortages, overloads, lateness, instability and policy violations rather than reviewing every order manually.

An APS does not automatically produce a superior plan. An optimisation result is only as meaningful as its objective function, constraints, data and assumptions. For example, a model that minimises changeovers may create unacceptable customer lateness; a model that maximises utilisation may increase work-in-process and reduce responsiveness.

3.2 Infinite and finite capacity

An infinite-capacity plan assumes that required capacity is available and reports overloads after the plan is generated. A finite-capacity plan prevents or limits assignments beyond available capacity. Infinite planning is useful for rapid material feasibility analysis and rough-cut studies. Finite planning is more realistic for bottlenecked environments, but it requires reliable calendars, operation times, alternative resources and sequencing rules.

A basic work-centre load calculation is:

where Load_w,t is the required processing time at work centre w in period t, O(w,t) is the set of operations assigned to that work centre and period, q_o is the quantity processed by operation o, and r_o is the standard processing time per unit. If r_o is measured in hours per unit, load is measured in hours. The calculation assumes that operation times are applicable to the product, quantities are correctly assigned, and setup, queue, downtime and efficiency effects are either included or separately modelled.

Utilisation can be expressed as:

This ratio is a diagnostic, not a universal performance target. A value above 1 indicates overload under the stated assumptions. Sustained utilisation near 100% may be undesirable because variability, downtime and urgent work require protective capacity.

3.3 Optimisation and managerial judgement

APS optimisation usually requires an objective function. A simplified weighted objective may be represented as:

where T is tardiness, S is setup cost or time, I is inventory-related cost, O is overtime or outsourcing cost, and α, β, γ and δ are management-defined weights. This expression is conceptual unless each measure is precisely defined and converted into commensurable units, usually monetary cost or a validated penalty score.

The weights embed strategic choices. A hospital-equipment manufacturer may assign high priority to due-date performance, while a high-volume commodity plant may place greater emphasis on setup reduction. Managers must review whether the objective function reflects actual business priorities and whether the system is exploiting unintended incentives.

4. Data integration and master-data governance

4.1 The planning data chain

MPS reliability depends on the quality and timeliness of interconnected data. Important interfaces include:

• customer relationship management and order-management systems;
• forecasting and demand-planning applications;
• ERP item, inventory, procurement and production records;
• APS planning models;
• MES confirmations and machine-status data;
• warehouse-management systems;
• supplier portals and electronic data interchange;
• quality, maintenance and laboratory systems;
• finance and costing systems.

Integration can be batch-based, event-driven or near real-time. Batch integration is often adequate for stable planning cycles and may be easier to govern. Event-driven integration improves responsiveness but increases complexity, monitoring requirements and the risk of inconsistent states.

4.2 Master data as a managerial asset

Master data includes relatively stable definitions used repeatedly across transactions. In MPS, this includes item identifiers, units of measure, product structures, routings, work centres, calendars, lead times, lot sizes, yields, scrap factors, safety stocks, sourcing rules and planning policies.

Master-data governance should define:

• ownership of each data domain;
• approval and change-control procedures;
• effective dates and revision management;
• validation rules and mandatory fields;
• data-quality measures;
• audit trails and segregation of duties;
• procedures for obsolete, duplicate or superseded records.

A practical data-quality framework assesses accuracy, completeness, consistency, timeliness, uniqueness and traceability. A routing with an accurate processing time but an obsolete work-centre assignment is not operationally reliable. Similarly, an apparently correct inventory balance may be unusable if its status, location or unit of measure is wrong.

4.3 Governance of bills of material and routings

A bill of material should reflect the product definition used by production, procurement and costing. Engineering changes need controlled effectivity dates so that the MPS does not plan a configuration before its components or process instructions are available. Routings should represent realistic operation sequences, setup requirements, yields, parallel resources and quality holds.

Data governance is not a one-time cleansing project. It is a continuing operating capability supported by ownership, monitoring and periodic review. The frequency of review should be risk-based: bottleneck resources, high-value items, regulated products and frequently changed products require stronger controls.

5. System parameters and planning policies

Parameters translate organisational policy into system behaviour. Common examples include:

Parameter selection should be evidence-based. A nominal lead time is not necessarily the same as the average observed lead time, and an average may not protect against variability. Parameters should be reviewed using historical performance, supplier agreements, process capability and strategic policy.

Safety stock is often estimated using demand and supply uncertainty. For a simple independent-demand case with a constant replenishment lead time, one common relationship is:

where SS is safety stock in units, z is the standard-normal service-factor corresponding to the desired cycle-service probability, and σ_LT is the standard deviation of demand during lead time. This formula assumes a suitable probability model, stable statistical estimates and that safety stock is intended to protect against demand uncertainty represented by σ_LT. If both demand and lead time vary, or if errors are correlated, a more complete model is required; the simple expression should not be applied mechanically.

Parameter governance should include simulation or sensitivity analysis. For example, planners can test how a 10% increase in demand, a supplier delay or a reduction in available capacity changes service, inventory and overtime.

6. Digital twins and operational feedback

A digital twin is a digital representation of a physical asset, process, product or system that is connected to data about the physical counterpart. In production planning, a digital twin may represent a plant, line, work centre, material flow, product family or supply network.

The value of a planning twin lies in its ability to support:

• what-if analysis before changing the live plan;
• comparison of planned and actual cycle times;
• identification of bottleneck migration;
• estimation of the effects of downtime or maintenance;
• testing of new product introductions and layout changes;
• closed-loop learning from execution results.

A digital twin is more than a visual dashboard or three-dimensional model. It requires defined data relationships, update mechanisms, model assumptions, validation and a decision purpose. A detailed twin with poor event timestamps or inaccurate routings may provide less value than a simpler, well-governed model.

The feedback loop should distinguish planned, scheduled, released, started, completed, scrapped and held quantities. Confusing these statuses can cause the planning system to assume that supply is available when it is not. Event latency also matters: a delay between a machine stoppage and its representation in the APS can create a plan that is already infeasible.

7. Cloud platforms and interoperability

Cloud-based planning platforms provide scalable computing, centralised access, managed upgrades and easier collaboration across sites and suppliers. They can support scenario calculations that would be expensive to run on local infrastructure. However, cloud adoption changes the risk profile rather than eliminating risk.

Key considerations include data residency, contractual service levels, identity management, backup and recovery, integration ownership, vendor lock-in, upgrade governance and exit strategy. A company should know how it would retrieve its data and continue critical planning if the provider became unavailable.

Interoperability concerns whether systems can exchange data with consistent meaning. Technical connectivity alone is insufficient. Two systems may both use the field “quantity” while applying different units, statuses, calendars or effectivity rules.

Important interoperability mechanisms include APIs, message queues, event streams, electronic data interchange and industrial communication standards. Data contracts should specify field definitions, units, valid values, timing, ownership, error handling and versioning. Common identifiers for items, locations, orders and resources are essential.

A robust integration design includes reconciliation controls. For example, the number and quantity of open production orders in an APS extract should be compared with the authoritative ERP record. Exceptions should be logged, investigated and resolved rather than silently discarded.

8. Implementation risks and controls

ERP and APS implementations are socio-technical transformations. Major risks include:

Process misalignment

Organisations may automate inconsistent or conflicting planning practices. Process mapping and agreement on planning responsibilities should precede configuration.

Poor data migration

Legacy systems often contain duplicate items, obsolete bills, informal spreadsheets and unexplained codes. Migration should use profiling, cleansing, test loads, reconciliation and business sign-off.

Over-customisation

Excessive custom code increases cost, upgrade difficulty and dependence on specialist knowledge. Standard functionality should be preferred unless a documented business requirement justifies deviation.

False precision

A system may display schedules with minute-level precision even though demand, processing times and supplier reliability are uncertain. Users should distinguish model precision from forecast accuracy.

Change resistance and workarounds

If the system does not reflect operational realities, employees may maintain shadow spreadsheets. These workarounds reduce data integrity and obscure accountability. Training, user involvement and rapid issue resolution are therefore control mechanisms, not merely adoption activities.

Cybersecurity and resilience

Connected planning systems are exposed to credential theft, ransomware, malicious changes, data exfiltration and denial of service. Controls should include least-privilege access, multifactor authentication, network segmentation, secure interfaces, patch management, immutable backups, monitoring and tested recovery procedures.

Algorithmic and model risk

Optimisation and AI tools may encode biased priorities, use stale data or produce recommendations that users cannot interpret. Human approval thresholds, explanation requirements, scenario testing and audit logs are appropriate for material decisions.

Regulatory and ethical concerns

Traceability, segregation of duties, product safety, environmental reporting and data-protection obligations may apply. Workforce data from sensors or digital systems should be collected proportionately and governed transparently. Automation should not be used to conceal unsafe workloads or transfer unreasonable risk to operators and suppliers.

9. Performance measurement and control

A digital MPS should be evaluated through outcomes, not software utilisation alone. Useful measures include:

• schedule adherence;
• customer service level and due-date performance;
• forecast error and forecast bias;
• inventory turns and days of supply;
• planned-versus-actual lead time;
• capacity overload frequency;
• expedite and reschedule frequency;
• schedule nervousness;
• data-quality exception rate;
• planning-cycle time;
• percentage of recommendations accepted, modified or rejected;
• recovery time after system or integration failure.

Metrics should be interpreted together. Higher schedule adherence may result from excessive inventory or large frozen windows. Better machine utilisation may coincide with longer queues and poorer delivery performance. Balanced measurement should connect customer, operational, financial, data and resilience outcomes.

A useful governance rhythm includes a daily exception review, a weekly production and capacity review, a monthly parameter review and a periodic strategic scenario review. Escalation thresholds should be explicit, such as a bottleneck overload above a defined limit, a material shortage within the frozen zone or repeated integration failure.

10. Illustrative case: integrating ERP and APS in a medical-device plant

Background

The following is a fictional illustrative case. MedEquip Components manufactures configurable medical-device assemblies at one plant. Its ERP system records orders, inventory, purchasing and production transactions. The MPS is maintained by planners using ERP outputs and spreadsheets. The plant has two sterilisation cells and a precision machining centre that frequently constrain throughput.

Problem

Customer orders are often accepted using ATP based mainly on inventory and open orders. The ATP calculation does not adequately consider sterilisation capacity, engineering-change effectivity or quality holds. Planners frequently reschedule production, while production supervisors maintain local priority lists. The plant experiences late deliveries, excess work-in-process and emergency procurement.

A data review finds that:

• 8% of active items have duplicate descriptions;
• several routings use standard times established five years earlier;
• quality-hold inventory is included in some planning extracts;
• the sterilisation calendar omits planned maintenance;
• the APS interface transmits order quantities but not all status codes.

Analysis

The problem is not primarily a lack of scheduling intelligence. It is a combination of weak master data, incomplete integration and conflicting priorities. A finite-capacity APS model would otherwise produce misleading results because it would treat unavailable inventory as usable and overstate sterilisation capacity.

The plant maps data ownership: engineering owns bills and routings; operations owns calendars and standard times; quality owns inventory-status definitions; supply chain owns planning policies; information technology owns interface monitoring. Each domain receives validation rules and approval responsibilities.

Approach and solution

The implementation proceeds in stages:

1. Cleanse and rationalise item, bill, routing and status data.
2. Establish effectivity controls for engineering changes.
3. Integrate quality status, maintenance calendars and actual production confirmations.
4. Configure the APS with finite capacity at the sterilisation cells and machining centre.
5. Introduce a frozen MPS horizon based on material and production commitment times.
6. Create a CTP process for customer orders requiring constrained resources.
7. Use scenario planning to compare overtime, subcontracting, revised due dates and product-family sequencing.
8. Establish daily exception management and weekly cross-functional review.

The objective function gives priority to regulatory due dates and customer service, while assigning explicit penalties to overtime, subcontracting, inventory and sequence changes. Planners retain authority to approve exceptions, but every override is recorded with a reason code.

Outcome

After stabilisation, schedule changes become less frequent, quality-hold stock is excluded from available supply, and capacity conversations shift from informal escalation to quantified scenarios. The plant does not eliminate all lateness; instead, it identifies infeasibility earlier and makes trade-offs visible. Management learns that the APS created value only after ERP data definitions and governance were corrected.

Learning points

The case demonstrates five principles. First, integration quality is a planning capability. Second, finite capacity requires credible calendars and operation times. Third, CTP links commercial promises to operational reality. Fourth, scenario analysis supports managerial judgement but does not replace it. Fifth, implementation success depends on ownership, controls and behavioural change as much as on software functionality.

11. Best-practice framework

A mature digital MPS capability generally follows these practices:

1. Define a single, documented planning process and decision calendar.
2. Establish data owners and measurable quality standards.
3. Separate demand, supply, capacity and execution statuses clearly.
4. Use finite-capacity logic for genuine bottlenecks and appropriate approximations elsewhere.
5. Configure time fences and exception thresholds based on operational economics.
6. Validate objective functions with planners, operations, finance and customer-facing teams.
7. Test interfaces through reconciliation, not only technical connectivity tests.
8. Use sandbox scenarios before changing the live plan.
9. Maintain human accountability for material customer, safety and compliance decisions.
10. Review parameters, models and cybersecurity controls continuously.
11. Design cloud and integration architectures for resilience and portability.
12. Measure outcomes across service, cost, inventory, capacity, data and risk.

Chapter Summary

ERP systems provide the transactional foundation for MPS by integrating demand, inventory, purchasing, production and financial information. APS systems extend this foundation through finite-capacity planning, constraint-based scheduling, optimisation, scenario analysis and multi-site coordination. The distinction between infinite and finite capacity is important, but neither approach is reliable without accurate calendars, routings, lead times, inventories and status definitions.

Master-data governance is a continuing managerial responsibility. Parameters such as lot sizes, lead times, safety stocks, calendars and time fences translate policy into system behaviour and must be reviewed against evidence. Digital twins can connect planning models with execution data, while cloud platforms improve scalability and collaboration but introduce dependency, cybersecurity and continuity concerns. Interoperability requires common semantics, identifiers, data contracts and reconciliation controls.

Implementation risks include poor migration, over-customisation, false precision, workarounds, cyber threats and algorithmic model risk. Successful organisations treat ERP, APS and digital planning not as isolated IT projects but as governed socio-technical systems. The ultimate test is improved decision quality: feasible commitments, controlled change, transparent trade-offs, reliable execution feedback and resilient performance.

Key Terms

ERP: An integrated system that records and coordinates enterprise transactions and resources.

APS: An advanced planning and scheduling system that analyses constraints, capacity and alternatives.

MPS: A time-phased plan for producing or procuring important end items or modules.

MRP: Material requirements planning that derives dependent requirements from planned supply and product structures.

Finite-capacity planning: Planning that restricts assignments to available resource capacity.

Infinite-capacity planning: Planning that assumes capacity is available and reports overloads afterward.

Master data: Reusable definitions such as items, bills, routings, calendars and planning parameters.

Data governance: Policies, ownership and controls that maintain data quality and accountability.

Time fence: A boundary controlling the degree to which planned orders may be changed.

ATP: Available-to-promise quantity based on usable supply and existing commitments.

CTP: Capable-to-promise quantity based on material and capacity feasibility.

Digital twin: A connected digital representation of a physical asset, process or system.

Interoperability: The ability of systems to exchange and correctly interpret information.

Data contract: An agreed specification of fields, meanings, units, timing and error handling.

Schedule nervousness: Frequent and disruptive changes to planned orders or schedules.

Exception management: Directing planner attention to significant deviations or risks.

Model risk: The risk that a planning model produces unsuitable results because of flawed assumptions, data or design.

Master-data owner: The accountable role responsible for the definition, quality and change control of a data domain.

Review Questions

Short Answer Questions

1. What is the principal difference between ERP and APS functionality in MPS?
2. Define finite-capacity planning and state one situation in which it is useful.
3. Why are calendars and routings part of master-data governance?
4. What is the purpose of a planning time fence?
5. Distinguish ATP from CTP.

Long Answer Questions

1. Explain how demand, ERP transactions, MPS, APS scheduling and execution feedback form an integrated planning cycle.
2. Discuss the risks of implementing APS with inaccurate master data and propose governance controls.
3. Evaluate the advantages and limitations of cloud-based planning platforms for a multi-site manufacturer.
4. Explain how a digital twin can support MPS decisions and identify the conditions required for its credibility.
5. Discuss how optimisation objectives can create unintended operational behaviour and how managers should govern them.

Analytical / Application Questions

1. A work centre has 360 available hours in a week. Three planned operations require 120 units at 1.5 hours per unit, 80 units at 1.2 hours per unit and 50 units at 2 hours per unit. Calculate the load and utilisation, then recommend an action if the work centre is a bottleneck.
2. An organisation’s APS shows adequate supply, but the shop floor reports shortages. Analyse at least four possible integration or master-data causes and design a reconciliation control.
3. A plant must choose between overtime, subcontracting and delayed delivery after a bottleneck failure. Design a scenario-planning framework that incorporates customer service, cost, capacity recovery and governance requirements.

Multiple Choice Questions with Answers

Question 1

Which function most directly distinguishes APS from basic ERP material planning?

A. Recording supplier invoices

B. Finite-capacity and constraint-based scheduling

C. Maintaining the general ledger

D. Creating employee payroll records

Correct Answer: B. Finite-capacity and constraint-based scheduling

Question 2

In the relationship PAB_t = PAB_t-1 + SR_t + MPS_t − D_t, what does D_t represent?

A. Demand in period t

B. Available capacity in period t

C. Planned setup time

D. Safety stock in period t

Correct Answer: A. Demand in period t

Question 3

A work centre has 400 available hours and a planned load of 500 hours. Its utilisation is:

A. 80%

B. 100%

C. 125%

D. 900 hours

Correct Answer: C. 125%

Question 4

Which item is normally master data for MPS and APS?

A. A one-time customer payment

B. A work-centre calendar

C. A posted sales invoice

D. A completed bank reconciliation

Correct Answer: B. A work-centre calendar

Question 5

What is the main purpose of a frozen planning zone?

A. To prevent all future forecasting

B. To restrict disruptive changes near execution

C. To eliminate the need for capacity planning

D. To increase the number of engineering changes

Correct Answer: B. To restrict disruptive changes near execution

Question 6

CTP improves on ATP by additionally considering:

A. Only historical sales revenue

B. Capacity and material feasibility for a new order

C. Employee attendance records only

D. The organisation’s tax position

Correct Answer: B. Capacity and material feasibility for a new order

Question 7

Which statement best describes a digital twin in production planning?

A. A static product catalogue with no operational data

B. A connected digital representation used to analyse a physical system

C. A spreadsheet used only for annual budgeting

D. A cybersecurity password database

Correct Answer: B. A connected digital representation used to analyse a physical system

Question 8

Why is technical connectivity insufficient for interoperability?

A. Systems must also share consistent meanings, units and statuses

B. Connectivity prevents all data validation

C. Interoperability requires every system to use identical hardware

D. APIs cannot transmit quantities

Correct Answer: A. Systems must also share consistent meanings, units and statuses

Question 9

Which is an example of model risk?

A. A planner documents an approved override

B. An optimisation model produces infeasible schedules because its calendar is wrong

C. A supplier confirms a purchase order

D. A work order is correctly closed after production

Correct Answer: B. An optimisation model produces infeasible schedules because its calendar is wrong

Question 10

A key control for cloud-based planning continuity is:

A. Removing all user authentication

B. Avoiding data backups to reduce cost

C. Maintaining tested recovery procedures and an exit strategy

D. Allowing uncontrolled customisation by every user

Correct Answer: C. Maintaining tested recovery procedures and an exit strategy