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Improve Your Inventory Management

Your 6-step guide to optimizing inventory in Manhattan Active Inventory
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Improve Your Inventory Management
Challenges (11) Goals (11) 6 Steps Preview Outcomes Data FAQs (4)

Optimize Inventory Management in Manhattan Active Inventory

Many inventory operations struggle with common issues like frequent stockouts, excessive inventory, and slow movement of goods. Our platform helps you precisely identify the root causes of these inefficiencies within your processes. We provide clear guidance to implement practical improvements, leading to reduced operational costs, optimized stock levels, and improved fulfillment performance.

Download our pre-configured data template and address common challenges to reach your efficiency goals. Follow our six-step improvement plan and consult the Data Template Guide to transform your operations.

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Why Optimize Inventory Management in Manhattan Active Inventory?

Even with advanced systems like Manhattan Active Inventory, effective inventory management presents significant challenges for businesses aiming for peak operational efficiency. The complexities of global supply chains, fluctuating demand, and the need for precision across countless SKUs can lead to hidden inefficiencies that erode profitability. Inventory carrying costs, stockouts, excess inventory, and inefficient internal movements are not just minor inconveniences, they directly impact your bottom line and customer satisfaction.
Optimizing your inventory processes is not merely about reducing costs, it is about enhancing resilience, improving service levels, and ensuring that capital is not tied up in static assets. Understanding the true flow of inventory within your Manhattan Active Inventory system is crucial for identifying where these inefficiencies occur and for maintaining a competitive edge in today's dynamic market.

Unlocking Efficiency with Process Mining

Process mining offers a powerful, data-driven approach to dissecting and understanding your actual inventory processes within Manhattan Active Inventory. By focusing on the "Inventory Batch/Lot" as your case identifier, you gain a comprehensive, end-to-end view of each batch's journey, from its initial goods receipt to its eventual issue or consumption. This perspective moves beyond theoretical process maps, providing an accurate visual representation of every activity, decision point, and delay that occurs in reality.
You can uncover critical insights, such as unexpected deviations from standard put-away procedures, prolonged quality inspection times that delay stock availability, or inefficient internal transfers that add unnecessary cycle time. Process mining highlights where manual interventions are frequent, where bottlenecks consistently occur, and where processes diverge from compliance standards. This enables you to pinpoint the exact root causes of problems, rather than making assumptions. For instance, you might discover that specific warehouse locations consistently experience delays in put-away completion, or that certain SKU categories frequently undergo inventory adjustments due to recurring discrepancies.

Key Areas for Improvement and Optimization

Applying process mining to your Manhattan Active Inventory data can reveal numerous opportunities for improvement across your inventory lifecycle:

  • Put-away Efficiency: Analyze the time taken from goods receipt to stock being fully available in its designated location. Identify delays, unnecessary movements, or non-compliant storage practices.
  • Internal Transfer Lead Times: Optimize the flow of goods within your warehouse. Pinpoint reasons for prolonged transfers between bins or zones, ensuring products reach their picking locations faster.
  • Picking and Packing Performance: Streamline your order fulfillment by identifying bottlenecks in picking routes, packing stations, or dispatch processes. Reduce the overall cycle time for orders.
  • Inventory Accuracy and Adjustments: Understand the triggers and frequency of inventory counts and adjustments. Identify recurring discrepancies and their root causes, such as poor receiving practices or incorrect system entries.
  • Return Goods Processing: Evaluate the efficiency of handling returns. Reduce the time stock spends in quarantine or awaiting re-shelving, improving your ability to re-sell or dispose of items quickly.
    These insights empower you to make targeted changes that lead to substantial operational improvements and help you continuously improve Inventory Management.

Achieving Tangible Outcomes

By leveraging process mining to optimize your Inventory Management in Manhattan Active Inventory, you can expect a range of tangible benefits that directly impact your business performance:

  • Reduced Carrying Costs: By optimizing stock levels, minimizing excess inventory, and accelerating inventory turnover.
  • Minimized Stockouts: Improving inventory accuracy and streamlining fulfillment processes leads to better stock availability.
  • Faster Order Fulfillment: By eliminating bottlenecks in picking, packing, and dispatch, you significantly reduce customer lead times. This directly addresses "how to reduce Inventory Management cycle time".
  • Enhanced Operational Efficiency: Streamlining workflows reduces manual effort, improves labor utilization, and optimizes warehouse space.
  • Improved Compliance: Ensure adherence to internal policies and external regulations, especially concerning quality checks, hazardous material handling, or expiration date management.
  • Data-Driven Decision Making: Move away from gut feelings and make informed choices based on actual process execution data.

Getting Started with Your Inventory Optimization Journey

Embarking on your inventory optimization journey with process mining offers a clear path to transforming your operations. You no longer need to guess where inefficiencies lie; instead, you can rely on factual process data to guide your improvements. This approach provides clarity, actionable insights, and a framework for continuous improvement. Take the first step towards a more efficient, cost-effective, and compliant inventory management system powered by data from your Manhattan Active Inventory environment. Unlock the full potential of your supply chain and discover how to improve Inventory Management today.

Inventory Management Stock Optimization Warehouse Operations Supply Chain Logistics Inventory Control Material Handling Inventory Accuracy

Common Problems & Challenges

Identify which challenges are impacting you

Goods sit in receiving longer than necessary, delaying their availability for picking and increasing congestion in receiving areas. This leads to higher carrying costs, potential stock damage, and impacts overall order fulfillment times within Manhattan Active Inventory. ProcessMind maps the exact time from Goods Receipt Recorded to Put-away Completed, identifying bottlenecks in resource allocation, warehouse layout, or specific user activities. It helps pinpoint inefficient steps or zones causing these delays, enabling targeted process redesign for faster stock readiness.

Regular discrepancies between system records and physical stock lead to unreliable inventory data, causing stockouts, excess inventory, and inaccurate fulfillment promises. This directly impacts customer satisfaction and ties up capital in holding unnecessary stock within Manhattan Active Inventory operations. ProcessMind analyzes Inventory Count and Discrepancy Adjustment events to reveal patterns, frequency, and root causes of inaccuracies, such as specific locations, product types, or counting teams. It highlights process variations contributing to errors, allowing for improved counting procedures and better inventory accuracy.

Holding too much inventory ties up significant capital, incurs high storage costs, and increases the risk of obsolescence or damage. This can severely impact profitability and cash flow, despite using advanced systems like Manhattan Active Inventory. ProcessMind traces the lifecycle of inventory batches, correlating stock levels, movement frequency, and final disposition to identify items with unusually long dwell times or low turnover rates. It uncovers inefficient purchasing, overproduction, or slow internal processes contributing to excess stock.

Suboptimal picking paths and processes lead to wasted labor, increased picking times, and delays in order fulfillment, directly impacting customer satisfaction and operational costs. Even in Manhattan Active Inventory, manual planning or system misconfigurations can create inefficiencies. ProcessMind visualizes actual picking routes and times by analyzing Picking Initiated and Picking Completed events, revealing non-value-added movements, congestion points, and opportunities for route optimization or batch picking improvements. It helps streamline the picking process for faster, more cost-effective fulfillment.

Critical stockouts happen even when physical inventory is present, often due to poor visibility into stock location, status, or accessibility, leading to lost sales and customer dissatisfaction. This common problem can undermine the effectiveness of systems like Manhattan Active Inventory. ProcessMind analyzes the journey of inventory batches, correlating Goods Issue Recorded events with available stock data to uncover reasons for "phantom" stockouts, such as mislocated items, unavailable status, or process blocks. It reveals bottlenecks preventing available stock from being picked and packed.

Delays in moving stock between different warehouse locations or storage bins can bottleneck downstream processes, increase lead times, and reduce overall operational agility. This can negatively impact throughput in a Manhattan Active Inventory-driven warehouse. ProcessMind monitors the time taken between Stock Moved Internally events, highlighting prolonged transfers, frequent re-locations, or specific transfer paths that are inefficient. It identifies root causes like inadequate equipment, poor planning, or congested pathways, enabling optimized internal logistics.

Inefficient handling of returned goods leads to prolonged processing times, delaying restock or disposition, impacting inventory accuracy, and tying up valuable warehouse space. This problem can escalate costs and reduce customer satisfaction, even with robust systems like Manhattan Active Inventory. ProcessMind maps the entire Return Goods Processed workflow, identifying bottlenecks, multiple re-inspections, or unnecessary steps that prolong the cycle. It helps optimize the returns loop for faster turnaround and better resource utilization.

Poor put-away strategies or lack of insight into inventory movement patterns can lead to inefficient use of warehouse storage space, resulting in higher operational costs and increased travel distances. Even with Manhattan Active Inventory, physical layouts and process adherence dictate efficiency. ProcessMind analyzes Put-away Completed and Stock Moved Internally events alongside Storage Bin attributes to visualize actual stock placement versus optimal strategies. It reveals patterns of underutilized space or frequent re-shuffling that indicate suboptimal storage practices.

A high frequency of manual Inventory Discrepancy Adjustments indicates underlying issues in inventory control, such as poor receiving practices, inaccurate picking, or lack of proper cycle counting. This creates extra work, reduces trust in inventory data, and can mask more significant problems within Manhattan Active Inventory. ProcessMind identifies the frequency, type, and source of these adjustments, revealing which activities or users most often trigger them. It pinpoints where process breakdowns lead to incorrect stock levels.

Bottlenecks in the quality inspection process, from Goods Receipt Recorded to Quality Inspection Performed, can significantly delay the availability of new stock for put-away and subsequent picking. This directly impacts lead times and can lead to customer fulfillment delays, even within a sophisticated system like Manhattan Active Inventory. ProcessMind visualizes the flow and duration of the inspection phase, identifying specific stages, resources, or product categories that cause significant hold-ups. It helps streamline the inspection process to accelerate inventory readiness.

Excessive scrapping or disposal of inventory due to damage, obsolescence, or expiration represents a direct financial loss and indicates issues in inventory rotation, storage conditions, or demand forecasting. This is a critical cost center in any inventory operation, including those using Manhattan Active Inventory. ProcessMind tracks inventory batches through their lifecycle, correlating attributes like Expiration Date and Inventory Status with Stock Scrapped/Disposed events. It identifies patterns, root causes like prolonged storage, or inefficient first-in, first-out (FIFO) adherence.

Typical Goals

Define what success looks like

This goal aims to significantly reduce the time taken from when goods arrive to when they are properly stored and available in the warehouse. Faster put-away directly impacts inventory availability, reducing lead times for downstream processes and ultimately improving order fulfillment rates within Manhattan Active Inventory. It prevents bottlenecks at receiving docks and ensures stock is quickly accessible.ProcessMind identifies the exact bottlenecks and variations in the put-away process by analyzing event logs from 'Goods Receipt Recorded' to 'Put-away Completed'. It quantifies average and outlier durations, highlighting specific resources, locations, or SKU categories contributing to delays. This data empowers targeted improvements, potentially cutting put-away times by 15-20%.

Achieving this goal means decreasing the number of times inventory records must be manually corrected due to discrepancies between physical stock and system data. Fewer adjustments lead to higher inventory accuracy, reduced labor costs associated with investigations, and improved reliability of stock availability information, which is critical for effective inventory management in Manhattan Active Inventory.ProcessMind traces the complete lifecycle from 'Inventory Count Initiated' to 'Inventory Discrepancy Adjusted', revealing patterns and root causes of errors. By analyzing 'Quantity Change', 'Transaction Type', and 'User Performing Action' attributes, it uncovers activities or stages prone to errors, helping reduce discrepancies by 20-30% and improving overall data integrity.

This goal focuses on right-sizing the amount of inventory held across all locations. Optimizing holding levels reduces carrying costs, frees up working capital, and minimizes the risk of obsolescence, all while ensuring sufficient stock to meet demand. Achieving this balance is crucial for profitability and efficiency in Manhattan Active Inventory operations.ProcessMind provides a detailed view of inventory movements and holding durations by analyzing 'Inventory Batch/Lot' lifecycles. It identifies slow-moving or excess inventory based on 'Stock Value' and 'SKU Category' attributes, revealing opportunities for optimized ordering and storage strategies. This can lead to a 10-15% reduction in carrying costs.

The aim is to make the process of retrieving items from storage for orders as efficient as possible, minimizing travel time and manual effort. Streamlined picking routes lead to faster order fulfillment, lower labor costs, and improved picker productivity, directly impacting customer satisfaction and operational efficiency within Manhattan Active Inventory.ProcessMind maps the actual 'Picking Initiated' to 'Picking Completed' process flows, comparing them against optimal paths. By analyzing 'Warehouse Location', 'Storage Bin', and 'User Performing Action' attributes, it identifies non-value-added movements, backtracking, and inefficient sequences, enabling a 10-25% improvement in picking speed.

This goal targets situations where inventory is physically present but is reported as unavailable, leading to missed sales or delayed orders. Eliminating false stockouts ensures accurate real-time inventory visibility, preventing unnecessary expediting, improving customer satisfaction, and maximizing sales opportunities within Manhattan Active Inventory.ProcessMind can identify the root causes of false stockouts by analyzing the sequence of events leading up to 'Goods Issue Recorded' when stock should have been available. It correlates 'Inventory Status' and 'Warehouse Location' changes with actual stock movement, uncovering issues like misplacement, system lags, or unrecorded transfers, helping resolve such incidents by over 50%.

This goal focuses on reducing the time it takes to move inventory between different locations or bins within a warehouse or across warehouses. Faster internal transfers ensure stock is available where and when needed, preventing internal bottlenecks, improving replenishment efficiency, and maintaining optimal inventory flow across Manhattan Active Inventory.ProcessMind tracks the duration and paths of all 'Stock Moved Internally' events, highlighting delays, unnecessary steps, or resource constraints. By examining 'Warehouse Location', 'Storage Bin', and 'Movement Reason Code' attributes, it pinpoints inefficiencies, allowing organizations to cut internal transfer times by 15-30%.

The objective is to significantly reduce the cycle time for processing returned goods, from receipt to final disposition, e.g., restock, repair, scrap. Faster processing improves customer experience through quicker refunds or exchanges, reduces holding costs for return inventory, and accelerates inventory recovery or proper disposal within Manhattan Active Inventory.ProcessMind visualizes the 'Return Goods Processed' workflow, exposing delays and rework loops. By analyzing 'Transaction Type', 'Inventory Status', and 'User Performing Action' attributes, it identifies bottlenecks in inspection, quality checks, or system updates, enabling a 20-35% reduction in return processing lead times.

This goal aims to ensure that every square foot and cubic meter of warehouse space is used as effectively as possible. Optimizing storage utilization reduces the need for expensive expansion, improves accessibility of goods, and contributes to overall operational efficiency and cost savings in Manhattan Active Inventory environments.ProcessMind analyzes 'Put-away Completed' events in relation to 'Warehouse Location' and 'Storage Bin' attributes, identifying patterns of underutilized space or inefficient placement strategies. It can reveal frequent stock movements that indicate poor initial put-away decisions, helping to improve space utilization by 10-20%.

This goal focuses on reducing the reliance on manual intervention to correct inventory records. Fewer manual adjustments lead to improved data accuracy, reduced human error, better compliance, and significant time savings for staff, enhancing the reliability of inventory data within Manhattan Active Inventory.ProcessMind pinpoints the specific activities and conditions that necessitate 'Inventory Discrepancy Adjusted' events. By correlating these with preceding activities like 'Inventory Count Performed' or 'Goods Receipt Recorded' and attributes like 'Movement Reason Code', it identifies root causes, enabling a 25-40% reduction in manual adjustments.

The objective is to shorten the duration from when goods are received to when their quality inspection is completed and they are released for put-away. A reduced inspection lead time minimizes inventory holding in a non-saleable state, improves overall inventory flow, and ensures products are available faster for customers in Manhattan Active Inventory.ProcessMind tracks the precise time taken between 'Goods Receipt Recorded' and 'Quality Inspection Performed', and subsequent activities. It identifies bottlenecks, resource constraints, or delays associated with specific 'Supplier' or 'SKU Category' attributes, allowing for a 10-25% reduction in inspection times.

This goal aims to significantly lower the volume and value of inventory that must be scrapped or disposed of due to damage, obsolescence, or expiration. Minimizing scrappage directly reduces financial losses, improves resource utilization, and enhances sustainable inventory practices within Manhattan Active Inventory.ProcessMind analyzes all 'Stock Scrapped/Disposed' events, identifying patterns related to 'Expiration Date', 'SKU Category', 'Supplier', and 'Movement Reason Code'. It uncovers root causes such as inefficient storage, prolonged holding times, or poor forecasting, providing insights to reduce scrappage by 15-25%.

The 6-Step Improvement Path for Inventory Management

1

Download the Template
2

Export Your Data
3

Upload Your Dataset
4

Analyze Your Process
5

Implement Improvements
6

Monitor Your Process
1

Download the Template

What to do

Access the pre-built Excel template specifically designed for Inventory Management data, ensuring your initial data collection is structured correctly for ProcessMind.

View Data Template Guide Download Template
Why it matters

A standardized template streamlines data preparation, reducing errors and saving time, which is crucial for accurate process analysis from the start.

Expected outcome

A structured Excel template, ready to be populated with your Manhattan Active Inventory data.

WHAT YOU WILL GET

Pinpoint Inventory Inefficiencies and Boost Flow

ProcessMind visualizes your entire inventory management journey, revealing precise bottlenecks, reorder point issues, and slow-moving stock. Gain actionable insights to optimize stock levels and reduce operational costs.
  • Visualize actual inventory process flows
  • Identify stockout risks and excess inventory
  • Pinpoint inefficient inventory movements
  • Optimize stock levels for cost reduction
Discover your actual process flow
Discover your actual process flow
Identify bottlenecks and delays
Identify bottlenecks and delays
Analyze process variants
Analyze process variants
Design your optimized process
Design your optimized process

TYPICAL OUTCOMES

Realizing Inventory Management Excellence

These outcomes illustrate the significant improvements organizations typically realize by leveraging process mining to optimize their Inventory Management processes within Manhattan Active Inventory.

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Faster Stock Put-away

Average cycle time reduction

Organizations experience quicker movement of received goods to storage locations, reducing staging area congestion and improving stock availability for picking.
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Fewer Inventory Discrepancies

Reduction in manual adjustments

Pinpointing root causes of inventory errors drastically reduces the need for manual adjustments, leading to more accurate stock records and fewer operational disruptions.
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Optimized Inventory Levels

Reduction in average stock dwell time

By identifying slow-moving or excess inventory, companies can reduce carrying costs, free up warehouse space, and improve cash flow by optimizing stock holding periods.
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Streamlined Picking Efficiency

Average picking cycle time reduction

Analyzing picking routes and processes leads to faster order fulfillment, reducing labor costs and improving customer satisfaction through quicker delivery times.
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Lower Inventory Scrappage

Reduction in waste

Understanding the causes of damaged or expired goods allows organizations to implement preventative measures, significantly reducing costly inventory write-offs and improving profitability.
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Eliminate False Stockouts

Reduction in picking rejections

Identifying discrepancies between system and physical stock prevents situations where items are believed to be available but are not, improving order fulfillment reliability and customer trust.

Results vary based on process complexity and data quality. These figures represent typical improvements observed across implementations.

FAQs

Frequently asked questions

Process mining helps you visualize the actual flow of your inventory operations, revealing hidden bottlenecks and inefficiencies. It can identify where delays occur, such as in stock put-away or quality inspection, and highlight deviations from ideal processes. This data-driven insight enables targeted improvements to accelerate operations and reduce costs.

To begin, you typically need event logs containing a case identifier, activity, and timestamp for each inventory event. For Inventory Management, the "Inventory Batch/Lot" can serve as the case identifier. Relevant data includes movements, adjustments, put-aways, picks, and quality checks.

You can expect significant improvements such as accelerated stock put-away, reduced inventory discrepancy adjustments, and optimized inventory holding levels. Process mining helps streamline picking routes, eliminate false stockout incidents, and minimize costly inventory scrappage rates, leading to overall operational efficiency and cost savings.

The initial setup and data connection phase can take a few weeks, depending on data availability and complexity. Once data is flowing, the first insights and actionable recommendations can often be generated within 1-2 months. Continuous monitoring then provides ongoing optimization opportunities.

The primary requirement is access to your Manhattan Active Inventory data, typically via database exports, APIs, or data warehouse connections. You'll need a way to extract the event logs in a structured format suitable for process mining tools. No direct integration with the Manhattan Active Inventory system itself is usually needed for the analysis phase.

Yes, process mining excels at identifying the actual paths and deviations that lead to problems. By analyzing event data, it can pinpoint specific process steps or sequences contributing to discrepancies, stockouts, or delays. This provides a factual basis for root cause analysis and targeted solutions.

Absolutely, process mining can track the full journey of an inventory batch or lot through all stages. This includes put-away, picking, internal transfers, returns processing, and quality inspections. By seeing the end-to-end flow, you can optimize interconnected processes.

In addition to bottleneck identification, process mining helps enforce compliance with operational procedures and detect fraudulent activities. It also provides objective data for performance benchmarking and supports continuous improvement initiatives. You gain a transparent view of process variations and their impact.

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