Digital Demand Planning and Materials Requirements Planning

Introduction

Materials management has moved from periodic, spreadsheet-based replenishment toward integrated, data-driven planning. Enterprise resource planning (ERP) systems connect demand signals, sales and operations planning (S&OP), production schedules, bills of material, inventory records, purchasing, manufacturing and capacity information in a common transactional environment. The purpose is not merely to automate calculations. It is to coordinate decisions so that the right materials are available in the right quantity, at the right time, and at an economically and operationally acceptable cost.

Demand planning estimates what customers are likely to require. S&OP reconciles that demand with supply, financial objectives and organisational capabilities. The master production schedule (MPS) converts the agreed plan into a time-phased statement of end-item production. Materials requirements planning (MRP) then translates the MPS and other demand into planned orders for components, raw materials and subassemblies. Capacity planning tests whether those plans can actually be executed.

The central managerial challenge is that each stage contains uncertainty and assumptions. A statistically accurate forecast can still produce poor material plans if inventory records are unreliable, lead times are unrealistic, bills of material are obsolete, lot-sizing rules are inappropriate or capacity constraints are ignored. Effective digital planning therefore combines quantitative models with disciplined data governance, cross-functional judgement and continuous feedback.

Learning objectives

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

1. Explain the relationship among forecasting, S&OP, MPS, MRP and capacity planning.
2. Apply basic forecasting methods and evaluate forecast error.
3. Construct and interpret an MPS and an MRP record.
4. Calculate gross requirements, projected available balance, net requirements, planned receipts and planned order releases.
5. Explain the effects of lead time, lot sizing, safety stock, scrap, yield and planning fences.
6. Analyse capacity implications and identify limitations of unconstrained MRP.
7. Evaluate ERP data, governance, artificial intelligence applications and implementation risks.

1. The integrated planning architecture

An integrated planning system typically operates at several planning horizons:

The planning flow is hierarchical, but information also flows upward. For example, a capacity bottleneck discovered during MRP may require revision of the MPS, S&OP plan or even the product promise made to customers. ERP systems support this feedback through common master data and linked records.

The principal inputs are:

• demand forecasts, customer orders and dependent demand;
• inventory balances, allocations, open orders and scheduled receipts;
• bills of material (BOMs) and product structures;
• item master data, including lead times, lot-sizing rules and safety stock;
• work-centre calendars, routings and available capacity; and
• planning policies such as make-to-stock, make-to-order or assemble-to-order.

A useful distinction is between independent demand and dependent demand. Independent demand arises outside the production structure, such as customer demand for finished bicycles. Dependent demand is calculated from the demand for another item, such as tyres required for those bicycles. Forecasting is normally applied to independent demand; MRP calculates dependent demand through BOM explosion.

2. Demand planning and forecasting

2.1 Demand signals

A digital demand plan may combine historical shipments, point-of-sale data, customer orders, quotations, promotions, market intelligence, economic indicators and salesperson judgement. These sources differ in reliability. Shipments may understate true demand when stockouts occurred, while customer orders may overstate final consumption if cancellation rates are high. The planner must distinguish actual demand from observed sales.

Forecasting is not a promise that demand will occur. It is an estimate used to prepare resources. A forecast should therefore be accompanied by a time bucket, unit of measure, product definition, probability or confidence information where available, and a statement of assumptions.

2.2 Basic time-series methods

For a simple moving average of the most recent n periods:

where F_t+1 is the forecast for the next period, A_t is actual demand in period t, and n is the number of periods averaged. This method assumes that recent observations are representative and gives equal weight to each included period. It responds slowly to trend changes, especially when n is large.

A weighted moving average is:

where each weight is non-negative and ∑w_i = 1. The weights express relative importance; the most recent period commonly receives the greatest weight.

Exponential smoothing is:

where 0 < α ≤ 1 is the smoothing constant. A high α responds rapidly to changes but may react excessively to random variation. A low α produces a smoother forecast but can lag behind a trend. The method is most suitable when the series has no strong, unmodelled trend or seasonality.

For a series with trend, a trend-adjusted method or regression model may be used. Seasonal products require seasonal indices or a model that explicitly represents periodic variation. Forecasting method selection should be based on out-of-sample performance, business interpretability and the consequences of error, not simply on the method that fits historical data most closely.

2.3 Forecast accuracy and bias

Forecast error for period t can be defined as:

A positive error indicates that actual demand exceeded the forecast. Common measures include:

MAD is expressed in demand units and is easy to interpret. MSE penalises large errors more heavily. MAPE is intuitive as a percentage but is unsuitable or unstable when actual demand is zero or very close to zero. In such cases, weighted absolute percentage error or scale-dependent measures are preferable.

Forecast accuracy alone is insufficient. A forecast may have small average error but persistent bias. Cumulative forecast error is:

A large positive or negative CFE suggests systematic underforecasting or overforecasting. Bias can cause chronic shortages, excess inventory or unnecessary capacity reservations.

Worked example: exponential smoothing

Demand for the last two months was 1,000 and 1,200 units. The forecast for the second month was 1,100 units. With α = 0.30, the forecast for month three is:

F₃ = 0.30(1,200) + 0.70(1,100) = 360 + 770 = 1,130 units.

The result is in units because both actual demand and the previous forecast are in units, while α is dimensionless. The calculation assumes that the demand series is being forecast at a consistent monthly frequency and that no separate seasonal adjustment is required.

3. Sales and operations planning

S&OP is a cross-functional process that balances demand, supply and financial objectives at an aggregate level. Participants normally include sales, marketing, operations, procurement, finance, product management and senior leadership. It is not simply a forecasting meeting. It is a decision process for resolving trade-offs.

A typical monthly S&OP cycle includes:

1. Data review: actual demand, inventory, capacity, service and forecast performance are analysed.
2. Demand review: the unconstrained demand plan is developed, including promotions, new products and market intelligence.
3. Supply review: production, procurement, labour, suppliers, inventory and capacity are assessed.
4. Reconciliation: alternative scenarios are evaluated for cost, revenue, service and risk.
5. Executive decision: one agreed plan is authorised and communicated.

Aggregate planning may use product families rather than individual stock-keeping units. Its alternatives include varying production rates, inventory, overtime, subcontracting, workforce levels and backorders. A simplified planning cost can be represented as:

This expression is a decision framework rather than a universal cost equation. Actual models must specify time periods, units, capacity, constraints and cost definitions. The lowest accounting cost may not be the best plan if it creates unacceptable supplier dependence, quality risk or customer-service failure.

The S&OP plan provides the policy context for the MPS. For example, S&OP may approve a level-production strategy for a product family, while the MPS determines which specific models will be produced in each week.

4. Master production scheduling

The MPS is a time-phased plan for producing or procuring specific end items. It is more detailed than S&OP but more aggregated than MRP. The MPS must be feasible with respect to demand commitments, available inventory, production capacity, material availability and planning policies.

Important MPS concepts include:

• Time buckets: days, weeks or months used for planning.
• Planning horizon: the future period covered by the schedule; it should extend at least through the longest relevant cumulative lead time.
• Demand time fence: a near-term period in which customer orders are treated as relatively fixed.
• Planning time fence: a period in which schedule changes require authorisation because they disrupt materials and capacity.
• Available-to-promise (ATP): the quantity that can be promised to new customers after considering inventory and scheduled production.

A simplified projected available balance is:

where PAB_t is projected available balance at the end of period t, MPS_t is scheduled production or receipt, and Demand_t is the demand consumed in that period. If safety stock is required, the schedule must ensure that projected balance does not fall below the approved safety-stock level, unless an authorised exception exists.

The MPS should not be treated as an automatic output of a forecast. It is a controlled managerial commitment. Changes to it can affect customer promises, supplier schedules, labour, machines, working capital and product freshness.

5. Materials requirements planning

5.1 Purpose and logic

MRP calculates when and how much of each dependent-demand item is needed. It uses a backward-scheduling logic: begin with the date and quantity of an end-item requirement, explode the BOM to identify component requirements, offset those requirements by lead time, apply inventory and lot-sizing rules, and generate planned order releases.

The classic MRP calculation uses:

1. Gross requirements: total demand for an item before considering available supply.
2. Scheduled receipts: orders already released and expected to arrive.
3. Projected available balance: expected inventory after receipts and requirements.
4. Net requirements: additional supply needed after available inventory and scheduled receipts.
5. Planned order receipts: quantities planned to arrive in the required period.
6. Planned order releases: quantities released earlier so that receipts arrive on time.

For period t, a general net-requirement calculation is:

where NR_t is net requirement, GR_t is gross requirement, SS_t is desired safety stock, PAB_t-1 is the previous period’s projected available balance, and SR_t is the scheduled receipt. This form assumes that the prior balance is available for use and that safety stock is expressed in the same units as the item.

After lot sizing, the planned receipt may be greater than the net requirement. If the planned receipt in period t is PORc_t, then:

The planned release is offset by the item's effective lead time. With a fixed lead time of L time buckets:

This assumes deterministic lead time and that the release-to-receipt interval is correctly represented in the ERP calendar. In practice, queue time, inspection, transport, supplier reliability and calendar exceptions may make effective lead time variable.

5.2 BOM explosion

Suppose one finished product requires three units of component A, and the MPS calls for 100 finished products in week 5. The gross requirement for A in week 5 is:

GR_A = 3 × 100 = 300 units.

If A itself requires two units of subcomponent B, the gross requirement for B is 600 units, subject to the timing of A’s planned order release. Thus, MRP is not simply multiplying all requirements by a BOM quantity in the same week. Each level is time-phased according to the parent item’s planned release and the component’s lead time.

BOM accuracy is essential. Engineering changes, phantom assemblies, alternate components, co-products, yield losses and effectivity dates require careful modelling. A BOM that is technically correct but not aligned with production practice will produce incorrect requirements.

5.3 Lot-sizing policies

Common lot-sizing policies include:

• Lot-for-lot: planned receipt equals net requirement. It minimises cycle inventory but may create frequent orders and setup costs.
• Fixed order quantity: each order is a predetermined quantity. It may reflect supplier pack sizes, machine batches or economic considerations.
• Periodic order quantity: requirements for a defined number of periods are combined.
• Minimum order quantity: the order cannot be below a supplier or process minimum.
• Economic order quantity: a theoretical trade-off between ordering and holding costs.

For the basic deterministic EOQ model:

where Q^* is economic order quantity in units per order, D is annual demand in units per year, S is ordering or setup cost per order, and H is annual holding cost per unit per year. The model assumes relatively constant demand, known costs, replenishment without shortages, and a quantity-independent unit price. These assumptions often do not hold fully in MRP environments, so EOQ should inform rather than mechanically determine every planned order.

Worked MRP example

A product has the following requirements for a component:

• Beginning projected inventory: 80 units
• Safety stock: 20 units
• Scheduled receipt in week 2: 40 units
• Gross requirements: week 1 = 50, week 2 = 70, week 3 = 40
• Lot-for-lot policy
• Lead time: one week

Week 1 has available supply of 80 units. To retain 20 units of safety stock, the net requirement is:

NR₁ = max(0, 50 + 20 - 80) = 0.

The projected balance after week 1 is 80 - 50 = 30 units.

For week 2, available supply before a planned receipt is 30 + 40 = 70 units. Therefore:

NR₂ = max(0, 70 + 20 - 70) = 20.

A planned receipt of 20 units is needed in week 2. The balance becomes 70 + 20 - 70 = 20 units.

For week 3:

NR₃ = max(0, 40 + 20 - 20) = 40.

A planned receipt of 40 units is required in week 3. With a one-week lead time, the week 2 receipt of 20 must be released in week 1, and the week 3 receipt of 40 must be released in week 2. This example assumes that all scheduled receipts arrive on time, inventory records are accurate, and no scrap or quality loss occurs.

6. Capacity planning and finite feasibility

Traditional MRP is often described as capacity-unconstrained: it determines material quantities and dates without automatically proving that machines, labour, suppliers or docks can support them. Capacity requirements planning (CRP) translates planned and released orders through routings into work-centre loads.

For a work centre, a simplified load calculation is:

If an order requires setup time s and processing time p per unit for quantity q, its load is s + pq. Total load is the sum across relevant orders. Available capacity depends on working time, number of resources, utilisation assumptions, planned downtime and efficiency. A simple available-capacity expression is:

The utilisation factor must be defined consistently. It may incorporate breaks, planned maintenance, absenteeism and expected efficiency; double-counting these factors will understate capacity.

When load exceeds available capacity, managers may reschedule orders, split lots, use overtime, subcontract, add shifts, revise routings, substitute materials or change customer priorities. Advanced planning and scheduling (APS) systems can perform finite-capacity scheduling, but sophisticated software cannot compensate for inaccurate routings or unrealistic standard times.

The distinction between MRP nervousness and genuine demand change is important. Nervousness is excessive rescheduling caused by small changes in demand, inventory, lead time or planning parameters. Time fences, order modifiers, firm planned orders and exception thresholds can reduce unnecessary disruption, but they may also reduce responsiveness. The correct balance depends on supply risk, product life cycle and customer-service requirements.

7. Planning parameters and master data governance

MRP results are only as reliable as the data and parameters that drive them. Key parameters include:

Safety stock is not a substitute for poor planning. A simplified statistical expression for demand safety stock during a constant lead time is:

where SS is safety stock, z is the standard-normal service factor, and σ_L is the standard deviation of demand during lead time. If period demand is independent with standard deviation σ_d and lead time is a constant L periods, then:

These formulas assume stable demand distribution, independent errors and constant lead time. If both demand and lead time vary, a more complete model is needed. One common expression, under independence, is:

where μ_d is mean demand per period and σ_L now denotes the standard deviation of lead time. Correlation between demand and lead time, non-normal demand and intermittent demand can invalidate this approximation. Safety-stock decisions should therefore consider service-level definitions, shortage consequences, obsolescence and cash constraints.

Data governance assigns ownership for item creation, BOM approval, unit-of-measure conversion, supplier lead times, inventory status, engineering effectivity and parameter changes. Controls should include validation rules, approval workflows, audit trails, duplicate-item prevention and periodic parameter review.

8. ERP execution and digital integration

In an ERP environment, MRP is generally run from a combination of transactional and master data. A planning run may create planned purchase orders, planned production orders, exception messages and rescheduling recommendations. These outputs become actual purchase or production orders only after review and conversion according to organisational authority.

Integration with other systems may include:

• customer relationship management for opportunities and forecasts;
• warehouse management for stock location and availability;
• manufacturing execution systems for production progress and quality;
• supplier portals and electronic data interchange for order confirmation;
• product lifecycle management for engineering changes; and
• business intelligence platforms for performance monitoring.

Important controls include role-based access, segregation of duties, version control, traceability of changes and reconciliation between physical and system inventory. A system that creates technically correct orders from unauthorised or manipulated demand is not a well-governed planning system.

Artificial intelligence and machine learning can support demand sensing, anomaly detection, parameter recommendation, supplier-risk prediction and exception prioritisation. Their value depends on data quality and operational adoption. A black-box forecast may be difficult to defend when planners need to explain a major purchase or production decision. Good practice includes maintaining a benchmark model, monitoring forecast drift, documenting input data, testing for bias, preserving human approval for high-impact decisions and measuring business outcomes rather than model accuracy alone.

9. Illustrative case: Meridian Appliances

Background

Meridian Appliances, a fictional mid-sized manufacturer, produces countertop food processors. It sells finished products through retailers and online channels. Demand is seasonal, promotional activity is frequent, and imported motors have a nominal six-week supplier lead time. The company’s ERP system contains MRP functionality, but planners frequently override recommendations.

Problem

The company experienced two apparently contradictory outcomes: finished-goods stockouts during promotions and excess inventory of motors after promotions ended. An investigation found that the demand forecast used shipment history without identifying retailer stockouts. Promotional uplifts were maintained in separate spreadsheets and were not consistently transferred to the MPS. The motor lead time in the item master was six weeks, although customs and inspection made the effective average closer to eight weeks. Several BOMs also omitted a minor connector used in a revised product version.

Analysis

The demand team reconstructed lost sales from retailer inventory data and created a baseline forecast plus separately documented promotional adjustments. S&OP compared three scenarios: level production, overtime during the peak and supplier consignment for motors. The supply review showed that final assembly had adequate monthly capacity, but the motor supplier had a firm monthly allocation.

The MPS was then frozen inside a three-week demand time fence and reviewed weekly outside that fence. MRP was run with the corrected BOM, an eight-week effective motor lead time and a supplier minimum order quantity. CRP revealed that a proposed promotional build exceeded testing-station capacity, so testing was moved to an approved alternate work centre.

Approach and outcome

The company introduced ownership rules for promotional demand, supplier lead times and BOM changes. It also used exception messages to distinguish shortages caused by demand increases from shortages caused by late purchase orders. After two planning cycles, the organisation reported fewer emergency purchases and improved promotion availability. Motor inventory initially increased because the revised lead time and minimum order quantity exposed previously hidden risk, but subsequent S&OP decisions reduced the risk through supplier collaboration rather than arbitrary parameter reduction.

Learning points

1. Forecast accuracy cannot be assessed correctly if stockout demand is treated as zero demand.
2. S&OP must connect commercial events to operational capacity and supplier constraints.
3. MRP cannot correct an inaccurate BOM or unrealistic lead time.
4. Capacity checks should occur before commitments become execution problems.
5. Inventory reduction is not always the same as planning improvement; risk-adjusted service and total cost matter.

10. Limitations, risks and best practices

MRP is powerful but not universally appropriate. It performs best where product structures are reasonably stable, requirements can be represented in time buckets, lead times are measurable and inventory transactions are disciplined. It is less effective when demand is highly intermittent, product customisation occurs very late, BOMs are unstable or replenishment is governed mainly by visual pull signals. Hybrid systems may combine MRP for long-lead and purchased items with kanban or reorder-point controls for repetitive, stable consumption.

Common failure modes include:

• treating the forecast as a fixed truth rather than a hypothesis;
• using shipment history without accounting for stockouts;
• allowing inaccurate inventory balances and negative stock;
• failing to close or cancel obsolete orders;
• applying one safety-stock policy to all items;
• ignoring supplier calendars, minimums and transport time;
• changing parameters without measuring consequences; and
• measuring planners only on inventory reduction, encouraging service failures.

Best practice is to segment items by value, variability, criticality and supply risk; establish clear parameter ownership; reconcile physical and system inventory; use exception-based management; review forecast bias and service performance; and connect planning decisions to cash, capacity, quality and customer outcomes. A mature planning function treats ERP as an organisational process supported by technology, not as a software installation alone.

Ethical and governance issues also matter. Demand manipulation to obtain favourable performance measures can distort supply plans. Supplier data and customer information require confidentiality and appropriate access controls. Automated recommendations should not conceal accountability, particularly when shortages affect essential products or when allocation decisions disadvantage particular customers without a defensible policy.

Chapter Summary

Digital demand planning begins with the disciplined interpretation of multiple demand signals and the selection of forecasting methods appropriate to the data and decision context. Forecast errors should be measured in units and percentages where suitable, while bias should be monitored separately. S&OP converts demand and supply analysis into an aggregate, cross-functional plan. The MPS translates that plan into specific end-item quantities and dates within a controlled planning horizon.

MRP uses the MPS, BOM, inventory records, scheduled receipts, lead times, lot-sizing rules and safety-stock policies to calculate time-phased dependent requirements. Gross requirements are offset by available supply to produce net requirements, planned receipts and planned releases. Capacity planning is essential because traditional MRP may generate a material plan that cannot be executed with available resources. ERP integration improves visibility, but it does not eliminate the need for accurate master data, governance and managerial judgement.

At postgraduate level, the key insight is that planning quality is a systems property. Forecast models, commercial decisions, engineering data, supplier performance, capacity assumptions and transactional discipline interact. Effective professionals therefore evaluate not only the arithmetic of MRP, but also the assumptions, risks, incentives and organisational controls behind each planned order.

Key Terms

Available-to-promise (ATP): Quantity that can be promised to customers after considering available inventory and scheduled supply.

Bill of material (BOM): Structured listing of components and quantities required to make an item.

Capacity requirements planning (CRP): Process of comparing workload generated by planned orders with work-centre capacity.

Dependent demand: Demand calculated from the requirement for a parent item.

Demand time fence: Near-term period in which demand is treated as relatively firm.

Enterprise resource planning (ERP): Integrated system supporting organisational transactions, master data and planning.

Exponential smoothing: Forecasting method that weights recent actual demand and the previous forecast.

Forecast bias: Persistent tendency to overforecast or underforecast.

Gross requirement: Total requirement before considering available inventory and scheduled receipts.

Independent demand: Demand originating from customers or external markets.

Lot-for-lot: Lot-sizing rule in which planned receipt equals calculated net requirement.

Master production schedule (MPS): Time-phased plan for specific end-item production or replenishment.

Materials requirements planning (MRP): Systematic calculation of dependent material requirements and planned orders.

Net requirement: Additional quantity needed after considering available supply and policy stock.

Projected available balance (PAB): Expected inventory balance after receipts and requirements in a time bucket.

Safety stock: Inventory held to protect against uncertainty in demand, supply or lead time.

Sales and operations planning (S&OP): Cross-functional process for reconciling demand, supply and financial objectives.

Scheduled receipt: Supply already ordered or released and expected to arrive in a specified period.

Time bucket: Planning interval, such as a day, week or month.

Review Questions

Short Answer Questions

1. Distinguish between independent demand and dependent demand.
2. What is the purpose of the master production schedule?
3. Define projected available balance in MRP.
4. State two limitations of MAPE.
5. Why is capacity requirements planning needed after an MRP run?

Long Answer Questions

1. Explain how forecasting, S&OP, MPS and MRP form an integrated planning hierarchy.
2. Compare moving average, weighted moving average and exponential smoothing, including their assumptions and limitations.
3. Explain the complete MRP calculation sequence from gross requirements to planned order releases.
4. Discuss how BOM accuracy, lead time, lot sizing and safety stock influence ERP planning results.
5. Critically evaluate the role of AI in digital demand planning and identify appropriate governance controls.

Analytical / Application Questions

1. A component has beginning inventory of 120 units, safety stock of 30 units, a scheduled receipt of 20 units in week 2, and gross requirements of 80 and 100 units in weeks 1 and 2. Under lot-for-lot planning, calculate net requirements and projected balances for both weeks.
2. A manufacturer’s MRP plan creates large loads at one work centre but unused capacity at another. Analyse at least four feasible managerial responses and explain the trade-offs among cost, service and risk.
3. Design a data-governance improvement plan for a company whose ERP has inaccurate BOMs, obsolete lead times and frequent inventory adjustments. Specify ownership, controls, metrics and review frequency.

Multiple Choice Questions with Answers

Question 1

Which demand type is normally calculated through BOM explosion in MRP?

A. Independent demand

B. Dependent demand

C. Promotional demand

D. Censored demand

Correct Answer: B. Dependent demand

Question 2

With actual demand of 1,200 units, previous forecast of 1,100 units and α = 0.30, what is the exponential-smoothing forecast?

A. 1,030 units

B. 1,100 units

C. 1,130 units

D. 1,200 units

Correct Answer: C. 1,130 units

Question 3

What is the primary purpose of S&OP?

A. To calculate every component requirement

B. To reconcile demand, supply and financial objectives

C. To replace all operational forecasts

D. To record warehouse transactions

Correct Answer: B. To reconcile demand, supply and financial objectives

Question 4

Under lot-for-lot planning, a net requirement of 75 units normally creates a planned receipt of:

A. 0 units

B. 50 units

C. 75 units

D. The economic order quantity in all cases

Correct Answer: C. 75 units

Question 5

If a planned order receipt is required in week 6 and the lead time is two weeks, when should the order be released?

A. Week 2

B. Week 4

C. Week 6

D. Week 8

Correct Answer: B. Week 4

Question 6

Which item is most directly required for BOM explosion?

A. The product structure and component quantities

B. The organisation chart

C. The annual financial statement

D. The customer satisfaction survey

Correct Answer: A. The product structure and component quantities

Question 7

A major limitation of traditional unconstrained MRP is that it may:

A. Ignore all inventory records

B. Generate a plan exceeding available capacity

C. Forecast only dependent demand

D. Prevent every schedule change

Correct Answer: B. Generate a plan exceeding available capacity

Question 8

Which forecast measure is expressed in the original demand units?

A. MAD

B. MAPE

C. Percentage bias only

D. Service factor

Correct Answer: A. MAD

Question 9

If beginning inventory is 80 units, gross requirement is 50 units and safety stock is 20 units, the week’s net requirement before scheduled receipts is:

A. 0 units

B. 10 units

C. 30 units

D. 50 units

Correct Answer: A. 0 units

Question 10

Which practice best supports trustworthy ERP planning?

A. Changing parameters without ownership

B. Using one safety-stock rule for every item

C. Maintaining approved BOMs, accurate inventory and auditable parameter changes

D. Ignoring supplier calendars

Correct Answer: C. Maintaining approved BOMs, accurate inventory and auditable parameter changes