Introduction

Modern warehouses generate large amounts of data every day. Every purchase receipt, inventory movement, stock transfer, picking activity, shipment, return, adjustment, and order creates information that can be stored and analyzed. Warehouse equipment, barcode scanners, RFID systems, WMS platforms, ERP systems, and IoT devices can generate even more data.

However, simply collecting large amounts of data does not automatically improve warehouse performance. The organization must be able to transform raw data into useful information and then use that information to make better decisions. This is the purpose of warehouse data analytics.

Warehouse data analytics involves examining warehouse and inventory data to identify patterns, trends, relationships, problems, opportunities, and potential future outcomes.

For example, a warehouse may discover from its data that 40% of picking errors occur in one particular zone. Management can investigate that area and determine whether the problem is caused by poor labeling, incorrect storage locations, inadequate training, or an inefficient layout.

Analytics therefore changes warehouse management from simply asking “What happened?” to also asking “Why did it happen?”, “What is likely to happen next?”, and “What should we do?”

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Meaning of Warehouse Data Analytics

Warehouse data analytics is the process of collecting, processing, examining, and interpreting warehouse-related data to support operational and strategic decision-making.

Warehouse analytics can examine:

• Inventory levels.
• Inventory movements.
• Orders.
• Picking activities.
• Receiving.
• Shipping.
• Returns.
• Warehouse capacity.
• Labor productivity.
• Equipment utilization.
• Supplier performance.
• Customer orders.

The goal is to turn raw operational data into meaningful information.

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Data versus Information

It is important to distinguish between data and information.

Data consists of raw facts.

For example:

Order 1001 — 10 chairs — 9:30 AM

Order 1002 — 15 desks — 10:15 AM

These are individual pieces of data.

When the organization analyzes many transactions and determines that the average order contains 12 items, this becomes useful information.

Therefore:

Data → Processing → Analysis → Information → Decision

This process is fundamental to warehouse analytics.

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Sources of Warehouse Data

Warehouse data can come from many sources.

Warehouse Management Systems

WMS platforms generate information about receiving, storage, picking, packing, and shipping.

ERP Systems

ERP systems provide information about purchasing, sales, finance, inventory, and suppliers.

Barcode Systems

Barcode scanners generate transaction records whenever products or locations are scanned.

RFID Systems

RFID systems can generate information about the movement and identification of tagged products.

IoT Sensors

Sensors can provide information about temperature, humidity, equipment condition, location, and energy usage.

Transportation Systems

Transportation systems can provide information about deliveries, routes, travel time, and vehicle utilization.

Customer Orders

Sales orders provide information about product demand and customer behavior.

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Importance of Warehouse Analytics

Warehouse analytics helps management understand what is happening inside the warehouse.

It can help answer questions such as:

• Which products are selling fastest?
• Which products are slow-moving?
• Which warehouse areas are congested?
• Which employees or processes have low productivity?
• Where are most picking errors occurring?
• How accurate is inventory?
• How long does order fulfillment take?
• Which suppliers frequently deliver late?
• How much warehouse capacity remains?
• Which products are likely to become obsolete?

These answers support better decisions.

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Inventory Analytics

Inventory analytics involves analyzing inventory-related information to improve stock management.

It can examine:

• Inventory quantities.
• Inventory turnover.
• Stock movement.
• Stock aging.
• Stockouts.
• Overstock.
• Safety stock.
• Reorder levels.
• Demand patterns.
• Inventory value.

Inventory analytics helps organizations balance two competing risks:

Too little inventory

versus

Too much inventory

Too little inventory can cause stockouts and lost sales, while excessive inventory increases holding costs and the risk of obsolescence.

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Inventory Turnover Analysis

Inventory turnover measures how frequently inventory is sold or consumed during a period.

A commonly used formula is:

Inventory Turnover = Cost of Goods Sold ÷ Average Inventory

For example, suppose a company has:

Cost of Goods Sold = KSh 12,000,000

Average Inventory = KSh 3,000,000

Inventory turnover:

12,000,000 ÷ 3,000,000 = 4 times

This means the average inventory is turned over approximately four times during the period.

Higher turnover may indicate efficient inventory movement, although extremely high turnover can also indicate that inventory levels are too low.

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Days Inventory Outstanding

Days inventory indicates approximately how long inventory remains before being sold or consumed.

A simplified calculation is:

Days Inventory = 365 ÷ Inventory Turnover

If inventory turnover is 4:

365 ÷ 4 = 91.25 days

This means inventory remains in the organization for approximately 91 days on average.

Managers can use this measure to identify slow-moving inventory.

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Stockout Analysis

A stockout occurs when a required product is unavailable.

Analytics can identify:

• How frequently stockouts occur.
• Which products experience the most stockouts.
• Which locations experience stockouts.
• How long stockouts last.
• The possible sales impact.

For example, if Product A experiences stockouts every month, management may investigate:

• Forecasting accuracy.
• Supplier lead time.
• Reorder level.
• Safety stock.
• Demand variability.

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Overstock Analysis

Overstock occurs when an organization holds more inventory than is reasonably required.

Analytics can identify products that:

• Have low demand.
• Have remained in inventory for a long time.
• Are approaching expiry.
• Have declining sales.
• Have become obsolete.

For example, if 1,000 units of a product have been stored for 18 months and only 50 units have been sold, management should investigate whether the product was over-purchased.

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Slow-Moving Inventory

Slow-moving inventory consists of products that move through the warehouse relatively slowly.

Slow-moving inventory can increase:

• Storage costs.
• Capital tied up in stock.
• Risk of damage.
• Risk of obsolescence.
• Risk of expiry.

Analytics can identify slow-moving products and support decisions such as:

• Discounting.
• Promotions.
• Supplier returns.
• Transfers.
• Alternative uses.
• Disposal.

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Fast-Moving Inventory

Fast-moving inventory consists of products with high demand and frequent warehouse movement.

These products may require:

• Convenient storage locations.
• Higher replenishment frequency.
• Higher safety stock.
• Faster picking processes.

Analytics can help identify fast-moving products so that they can be positioned strategically.

For example, products that are picked hundreds of times per day may be placed closer to the packing area.

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ABC Inventory Analytics

ABC analysis classifies inventory according to its relative importance or value.

A common classification is:

A items — high-value or high-impact items.

B items — medium-value or medium-impact items.

C items — lower-value items.

Analytics can help determine which products fall into each category.

Management can then apply different control levels to each group.

For example, A items may require frequent monitoring and accurate records, while C items may require simpler controls.

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Warehouse Productivity Analytics

Warehouse productivity analytics evaluates how effectively warehouse resources are being used.

Common measures include:

• Orders processed per hour.
• Lines picked per hour.
• Units picked per hour.
• Receiving lines per hour.
• Packing productivity.
• Labor utilization.
• Equipment utilization.

For example, if a picker processes 100 order lines during an eight-hour shift:

100 ÷ 8 = 12.5 lines per hour

Management can compare this result against historical performance or established standards.

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Picking Analytics

Picking is often one of the most important warehouse activities to analyze.

Analytics can identify:

• Picking speed.
• Picking accuracy.
• Travel distance.
• Picking errors.
• Most frequently picked products.
• Picking performance by zone.
• Picking performance by shift.

For example, if one warehouse zone consistently has a lower picking rate, management can investigate whether products are poorly positioned or whether the picking process is inefficient.

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Order Fulfillment Analytics

Order fulfillment analytics examines how effectively customer orders move from receipt to shipment.

Important measures include:

Order cycle time

The time between receiving an order and completing fulfillment.

Order accuracy

The percentage of orders shipped correctly.

On-time fulfillment

The percentage of orders completed within the required time.

Perfect order rate

A measure that may consider whether an order was complete, accurate, undamaged, and delivered on time.

These metrics help organizations evaluate customer service.

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Data Visualization

Data visualization involves presenting information through graphical formats so that users can understand patterns and trends more easily.

Instead of examining thousands of rows in a spreadsheet, managers can view:

• Charts.
• Graphs.
• Maps.
• Tables.
• KPI cards.
• Trend lines.
• Heat maps.

Visualization allows important information to be understood quickly.

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Why Data Visualization Matters

Consider two ways of presenting warehouse performance.

A report may contain 10,000 lines of transactions.

Alternatively, a dashboard may show:

Orders Today: 4,500

Orders Completed: 4,100

Pending: 400

Picking Accuracy: 98.5%

Inventory Accuracy: 99.1%

Average Order Cycle Time: 2.4 hours

The dashboard makes the most important information immediately visible.

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Common Visualization Types

Bar Charts

Useful for comparing categories.

For example, comparing monthly warehouse throughput.

Line Charts

Useful for showing trends over time.

For example, inventory levels over six months.

Pie or Proportion Charts

Can show the relative composition of categories, although they should be used carefully when there are many categories.

Heat Maps

Useful for identifying high and low activity areas.

For example, a warehouse heat map may show which storage locations experience the highest picking activity.

Tables

Useful when users need exact values rather than visual patterns.

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Warehouse Dashboards

A warehouse dashboard is a visual interface that displays important operational information.

A dashboard should focus on information that managers actually need to make decisions.

A warehouse dashboard may display:

• Inventory levels.
• Orders received.
• Orders completed.
• Pending orders.
• Picking accuracy.
• Receiving performance.
• Shipment status.
• Stockouts.
• Warehouse capacity.
• Productivity.

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Operational Dashboard

An operational dashboard is designed for day-to-day management.

For example, a warehouse supervisor may need to see:

Orders received today

Orders picked

Orders pending

Employees active

Equipment status

Shipping backlog

This information allows immediate operational action.

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Strategic Dashboard

A strategic dashboard focuses on longer-term performance.

Senior management may monitor:

• Warehouse operating costs.
• Inventory turnover.
• Customer service levels.
• Productivity trends.
• Capacity utilization.
• Return rates.
• Long-term inventory growth.

Strategic dashboards support planning rather than immediate operational decisions.

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Dashboard Example

Suppose a warehouse dashboard shows:

The manager immediately notices that 250 orders remain pending and capacity utilization has reached 87%.

Further analysis may reveal that a particular picking zone is creating a bottleneck.

The dashboard therefore acts as a starting point for investigation.

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Predictive Analytics

Predictive analytics uses historical and current data to estimate what may happen in the future.

Traditional reporting asks:

What happened?

Diagnostic analytics asks:

Why did it happen?

Predictive analytics asks:

What is likely to happen?

Prescriptive analytics goes further by asking:

What should we do?

These levels can work together.

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Predictive Analytics in Warehousing

Predictive analytics can be used to forecast:

• Product demand.
• Stockouts.
• Equipment failures.
• Warehouse workload.
• Labor requirements.
• Delivery delays.
• Inventory requirements.

For example, if historical data shows that demand for a particular product increases every December, predictive analytics can estimate the expected demand for the next December.

The organization can then increase inventory before the expected increase.

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Predictive Stockout Analysis

Suppose an organization identifies that Product A normally sells 100 units per week.

Current inventory is 250 units.

Supplier lead time is two weeks.

Analytics may determine that the organization is at risk of running out of stock before the next replenishment arrives.

Management can therefore order additional inventory.

Predictive analytics helps the organization act before the problem occurs.

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Predictive Maintenance

Predictive maintenance uses equipment data to estimate when maintenance may be required.

Suppose an automated conveyor has sensors measuring:

• Motor temperature.
• Vibration.
• Operating hours.
• Power consumption.

Analytics may detect that vibration has increased significantly compared with normal operating levels.

The organization can schedule maintenance before the conveyor fails.

This reduces unexpected downtime.

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Forecasting Warehouse Workload

Analytics can also estimate future warehouse workload.

Suppose historical data shows:

Monday = 5,000 order lines.

Tuesday = 4,500.

Wednesday = 4,700.

Thursday = 6,000.

Friday = 7,500.

Management can use this information to plan staffing levels.

More employees may be scheduled during high-volume periods.

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Decision-Support Systems

A decision-support system, or DSS, is a computerized system that helps managers analyze information and make decisions.

A DSS may combine:

• Data.
• Reports.
• Models.
• Forecasts.
• Rules.
• Analytics.

The system does not necessarily make the final decision.

Instead, it provides useful information and recommendations to support management judgment.

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Example of a Warehouse Decision-Support System

Suppose a product’s demand is increasing.

The system analyzes:

• Historical demand.
• Current stock.
• Supplier lead time.
• Safety stock.
• Purchase price.
• Warehouse capacity.

It recommends:

“Increase replenishment quantity by 20%.”

The manager reviews the recommendation and decides whether to accept it.

This is decision support.

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Prescriptive Analytics

Prescriptive analytics goes beyond predicting what might happen.

It evaluates possible actions and recommends an appropriate response.

For example:

Prediction:

Product A is likely to run out within seven days.

Prescriptive recommendation:

Order 500 units from Supplier X because Supplier X has the shortest lead time and sufficient capacity.

This makes analytics more useful for operational decision-making.

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Warehouse Capacity Analytics

Analytics can be used to monitor warehouse capacity.

For example:

Total warehouse capacity = 10,000 pallet positions

Occupied positions = 8,500

Capacity utilization:

8,500 ÷ 10,000 × 100 = 85%

Management can monitor whether the warehouse is approaching its practical capacity.

High utilization may indicate:

• Congestion.
• Difficulty locating products.
• Reduced movement efficiency.
• Need for additional space.

However, 100% physical utilization is not always desirable because warehouses require operational space for movement, staging, receiving, packing, and other activities.

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Supplier Performance Analytics

Warehouse analytics can also evaluate suppliers.

Metrics may include:

• On-time delivery rate.
• Quantity accuracy.
• Defect rate.
• Lead-time reliability.
• Return rate.

Suppose Supplier A delivers 98% of orders on time while Supplier B delivers only 75%.

Management may investigate Supplier B’s performance.

This can influence procurement decisions.

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Customer Analytics

Customer order information can also provide useful warehouse insights.

Analytics can identify:

• Most frequently ordered products.
• Seasonal demand.
• Order frequency.
• Order sizes.
• Geographic demand.
• Return patterns.

For example, if customers in one region consistently order certain products, inventory can potentially be positioned closer to that market.

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Data Quality

Analytics is only as reliable as the data being analyzed.

This is commonly expressed as:

“Garbage in, garbage out.”

If warehouse data is incorrect, analytics can produce misleading conclusions.

For example, if the system records 500 units when the warehouse physically has 300, an inventory report based on that data will also be incorrect.

Data quality therefore requires:

• Accuracy.
• Completeness.
• Consistency.
• Timeliness.
• Validity.

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Data Cleaning

Data cleaning involves identifying and correcting inaccurate, incomplete, duplicated, or inconsistent information.

For example, a product may appear in the system as:

Office Chair

and elsewhere as:

Office Chairs

If these records represent the same item but are treated as different products, analytics may produce inaccurate results.

Standardized master data reduces such problems.

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Real-Time Analytics

Real-time analytics allows information to be analyzed as transactions occur.

For example, when a product is scanned during shipment, the inventory level can immediately change.

Management can then see the updated stock level.

Real-time analytics is particularly useful in high-volume warehouses where conditions change rapidly.

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Historical Analytics

Historical analytics examines past data.

It can help identify:

• Long-term trends.
• Seasonal patterns.
• Historical errors.
• Productivity changes.
• Supplier performance.
• Inventory trends.

Historical analysis is often the foundation for forecasting.

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Example: Using Analytics to Solve a Warehouse Problem

Suppose TechNova notices that customer complaints about incorrect orders have increased.

Management examines warehouse data.

The analysis shows:

Overall picking accuracy: 97%

Zone A: 99%

Zone B: 98%

Zone C: 92%

Further analysis shows that Zone C contains many visually similar products.

The warehouse team investigates and discovers that several products have unclear labels.

Management improves labeling and reorganizes the storage locations.

After implementation, Zone C picking accuracy increases to 98.5%.

This example demonstrates how analytics can support continuous improvement.

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Analytics and Continuous Improvement

Analytics should not be treated as a one-time activity.

A continuous improvement cycle can be:

Measure → Analyze → Identify Problem → Implement Improvement → Measure Again

For example:

Initial picking accuracy = 96%.

Analysis identifies poor product labeling.

New labels are introduced.

Picking accuracy increases to 99%.

The organization continues monitoring performance.

Analytics therefore supports continuous improvement.

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Warehouse Analytics and Business Central

Microsoft Dynamics 365 Business Central can provide business data related to sales, purchasing, inventory, warehousing, finance, and other processes.

For example, a Business Central implementation may contain information about:

• Item quantities.
• Item movements.
• Sales transactions.
• Purchase transactions.
• Warehouse receipts.
• Warehouse shipments.
• Inventory valuation.
• Customers.
• Vendors.

A functional consultant should understand how these transactions generate data and how organizations can use that information for reporting and decision-making.

For example, if management wants to identify slow-moving inventory, the consultant needs to understand which inventory transactions and item-related information can be used to support that analysis.

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Data Analytics Implementation Process

A practical warehouse analytics process can involve:

Data Collection

Gather data from WMS, ERP, scanners, sensors, and other systems.

Data Validation

Check whether the data is accurate and complete.

Data Preparation

Clean, organize, and structure the data.

Analysis

Identify patterns, trends, relationships, and anomalies.

Visualization

Present important findings through reports, dashboards, and charts.

Decision-Making

Use the findings to determine appropriate actions.

Monitoring

Measure the results after implementing the decision.

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Common Warehouse Analytics KPIs

Important warehouse KPIs may include:

Inventory Accuracy

Measures how closely system inventory matches physical inventory.

Order Accuracy

Measures how many customer orders are fulfilled correctly.

Picking Productivity

Measures picking output relative to labor time.

Order Cycle Time

Measures the time required to fulfill an order.

Inventory Turnover

Measures how frequently inventory is sold or consumed.

Stockout Rate

Measures the frequency of inventory shortages.

Warehouse Capacity Utilization

Measures how much warehouse capacity is being used.

On-Time Shipment Rate

Measures the proportion of shipments completed on schedule.

Return Rate

Measures the proportion of products returned.

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Common Mistakes in Warehouse Analytics

Organizations may make several mistakes when using analytics.

One mistake is collecting large amounts of data without identifying what decisions the data will support. More data does not automatically mean better decisions.

Another mistake is relying on inaccurate master data. If product numbers, quantities, locations, or transaction records are incorrect, reports will also be unreliable.

Organizations may also focus on too many KPIs. A dashboard containing hundreds of indicators may overwhelm managers instead of helping them. Good analytics focuses on the information most relevant to the decision being made.

Another mistake is analyzing performance without investigating causes. For example, discovering that picking productivity has fallen is only the beginning. Management should investigate why it has fallen.

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Good Practices for Warehouse Analytics

Organizations should establish clear data definitions.

Data should be collected consistently.

Master data should be maintained accurately.

KPIs should have clearly defined formulas.

Reports should focus on decision-relevant information.

Dashboards should be simple enough to understand quickly.

Historical trends should be compared with current performance.

Analytics should identify both problems and opportunities.

Managers should investigate the causes behind unusual results.

Predictions should be treated as estimates rather than guarantees.

Analytics results should be reviewed after actions are implemented.

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Key Takeaways

Warehouse data analytics involves collecting, processing, analyzing, and interpreting warehouse information to support better decisions.

Warehouse data can come from WMS, ERP, barcode systems, RFID, IoT sensors, transportation systems, sales orders, and other operational sources.

Data becomes useful when it is transformed into meaningful information that supports decisions.

Inventory analytics helps organizations understand stock levels, inventory movement, turnover, stockouts, overstock, and slow-moving inventory.

Inventory turnover measures how frequently inventory is sold or consumed during a period.

Picking analytics can identify productivity problems, travel inefficiencies, and picking errors.

Order fulfillment analytics evaluates the speed, accuracy, completeness, and reliability of customer-order processing.

Data visualization uses charts, graphs, tables, heat maps, and other visual tools to make warehouse information easier to understand.

Dashboards provide managers with a consolidated view of important operational indicators.

Operational dashboards support daily warehouse decisions, while strategic dashboards support longer-term management decisions.

Predictive analytics uses historical and current information to estimate future events such as demand increases, stockouts, equipment failures, and workload changes.

Prescriptive analytics goes further by recommending actions that may address predicted problems.

Decision-support systems combine data, analysis, models, and recommendations to assist managers in making informed decisions.

Data quality is essential because inaccurate, incomplete, inconsistent, or outdated data can produce misleading analytical results.

Warehouse analytics can be used to evaluate inventory, suppliers, customers, warehouse capacity, labor productivity, equipment, and order fulfillment.

Analytics supports continuous improvement through the cycle of measure, analyze, improve, and measure again.

The ultimate purpose of warehouse analytics is not merely to produce reports. Its purpose is to turn warehouse data into actionable knowledge that helps managers reduce costs, improve inventory control, increase productivity, improve customer service, identify risks, and make better operational and strategic decisions.