SKU Management in Warehouse Management Dataset (Publication Date: 2024/02)

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Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:



  • What is the pick strategy that you would use to pick SKUs based on demand and your warehouse layout?
  • Are your fastest moving SKUs stored close to the shipping areas/loading doors in your warehouses?
  • What is the difference in size and weight from your smallest lightest SKUs to your largest, heaviest SKUs?


  • Key Features:


    • Comprehensive set of 1560 prioritized SKU Management requirements.
    • Extensive coverage of 147 SKU Management topic scopes.
    • In-depth analysis of 147 SKU Management step-by-step solutions, benefits, BHAGs.
    • Detailed examination of 147 SKU Management case studies and use cases.

    • Digital download upon purchase.
    • Enjoy lifetime document updates included with your purchase.
    • Benefit from a fully editable and customizable Excel format.
    • Trusted and utilized by over 10,000 organizations.

    • Covering: Safety Procedures, IT Staffing, Stock Replenishment, Efficient Distribution, Change Management Resources, Warehouse Layout, Material Flow Analysis, Revenue Distribution, Software Packaging, Supply Chain Resilience, Expedited Shipping, Delay In Delivery, ERP System Review, Order Consolidation, Automated Notifications, Lot Tracking, Safety Data Sheets, Picking Accuracy, Physical Inventory, SKU Management, Service Level Agreement, Risk Management, Shipment Tracking, Dock Scheduling, Order Accuracy, Navigating Challenges, Strategic money, Lean Management, Six Sigma, Continuous improvement Introduction, Master Data Management, Business Process Redesign, Asset Tracking Software, Fulfillment Costs, Receiving Process, Predictive Analytics, Total Productive Maintenance, Supplier Feedback, Inventory Control, Stock Rotation, Security Measures, Continuous Improvement, Employee Engagement, Delivery Timeframe, Inventory Reconciliation, Pick And Pack, Clearance Area, Order Fulfillment, Regulatory Policies, Obsolete Inventory, Inventory Turnover, Vendor Management, Inventory Allocation, Personnel Training, Human Error, Inventory Accuracy, Deadlines Compliance, Material Handling, Temperature Control, KPIs Development, Safety Policies, Automated Guided Vehicles, Quality Inspections, ERP System Management, Systems Review, Data Governance Framework, Product Service Levels, Put Away Strategy, Demand Planning, FIFO Method, Reverse Logistics, Parts Distribution, Lean Warehousing, Forecast Accuracy, RFID Tags, Hazmat Transportation, Order Tracking, Capability Gap, Warehouse Optimization, Damage Prevention, Management Systems, Return Policy, Transportation Modes, Task Prioritization, ABC Analysis, Labor Management, Customer Service, Inventory Auditing, Outbound Logistics, Identity And Access Management Tools, App Store Policies, Returns Processing, Customer Feedback Management, Critical Control Points, Loading Techniques, MDSAP, Design Decision Making, Log Storage Management, Labeling Guidelines, Quality Inspection, Unrealized Gains Losses, WMS Software, Field Service Management, Inventory Forecasting, Material Shortages, Supplier Relationships, Supply Chain Network, Batch Picking, Point Transfers, Cost Reduction, Packaging Standards, Supply Chain Integration, Warehouse Automation, Slotting Optimization, ERP Providers System, Bin System, Cross Docking, Release Management, Product Recalls, Yard Management, Just Needs, Workflow Efficiency, Inventory Visibility, Variances Analysis, Warehouse Operations, Demand Forecasting, Business Priorities, Warehouse Management, Waste Management, Quality Control, Traffic Management, Storage Solutions, Inventory Replenishment, Equipment Maintenance, Distribution Network Design, Value Stream Mapping, Mobile Assets, Barcode Scanning, Inbound Logistics, Excess Inventory, Robust Communication, Cycle Counting, Freight Forwarding, Kanban System, Space Optimization, Backup Facilities, Facilitating Change, Label Printing, Inventory Tracking




    SKU Management Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):


    SKU Management


    A pick strategy for SKU management would prioritize picking the most in-demand items first, taking into account their location in the warehouse.

    1. Zone picking: breaking the warehouse into zones and assigning specific pickers to each zone for efficient routing and increased productivity.
    2. Batch picking: grouping multiple orders together to maximize the number of picked items per trip, reducing labor costs and increasing speed.
    3. Wave picking: picking multiple orders in a single trip by grouping them according to common SKUs, improving order accuracy and reducing travel time.
    4. Automated picking: using robots or automated systems to pick and transport items, minimizing errors and increasing speed and efficiency.
    5. Pick-to-light: using light systems to guide pickers to the correct location and item, reducing errors and improving speed.
    6. FIFO (First In, First Out) picking: prioritizing older inventory for picking to reduce the risk of expired or obsolete products.
    7. ABC analysis: categorizing SKUs based on their demand and assigning different picking strategies according to their importance, optimizing resources and meeting customer demands.
    8. Cross-docking: avoiding picking altogether by directly transferring products from receiving to shipping, saving time and labor costs.
    9. Randomized picking: randomly assigning SKUs to picking locations to evenly distribute work and avoid congestion in high-demand areas.
    10. Multi-modal picking: using a combination of picking methods, such as batch picking and automated picking, to increase efficiency and flexibility in different warehouse areas.

    CONTROL QUESTION: What is the pick strategy that you would use to pick SKUs based on demand and the warehouse layout?


    Big Hairy Audacious Goal (BHAG) for 10 years from now:

    Big Hairy Audacious Goal for SKU Management in 10 years:

    To reduce warehouse costs and increase efficiency by implementing a demand-based picking strategy that optimizes the layout of SKUs.

    Pick Strategy:

    1. Demand Forecasting: Utilize advanced analytics and data modeling techniques to accurately forecast demand for each individual SKU. This will help in identifying high-demand and low-demand SKUs.

    2. ABC Analysis: Implement ABC analysis (based on Pareto Principle) to categorize SKUs into A, B, and C groups based on their annual sales volume.

    3. Warehouse Layout Optimization: Use layout optimization software or mathematical algorithms to determine the most efficient layout for the warehouse. This will be based on factors such as product demand, frequency of picking, and size/weight of SKUs.

    4. Zone Picking: Divide the warehouse into different zones and assign SKUs to each zone based on their demand and category (ABC). This will prevent congestion and improve picking productivity.

    5. Automated Guided Vehicles: Utilize autonomous guided vehicles (AGVs) to transport picked SKUs from the picking zones to the sorting areas. This will reduce manual labor and improve accuracy.

    6. Pick-to-Light Technology: Install pick-to-light systems in each picking zone to direct workers to the correct location for picking. This will reduce errors and speed up the picking process.

    7. FIFO/LIFO Method: For perishable products or products with expiry dates, implement first-in-first-out (FIFO) or last-in-first-out (LIFO) picking methods to reduce waste and ensure fresher products are picked first.

    8. Continuous Improvement: Regularly review and analyze the picking process to identify any bottlenecks or inefficiencies and make necessary changes to improve the overall process.

    9. Vendor Managed Inventory (VMI): Collaborate with key suppliers to manage specific SKUs in the warehouse based on demand. This will ensure timely restocking and reduce stockouts.

    10. Real-time Tracking: Utilize RFID or other tracking technologies to monitor the movement of SKUs in real-time. This will help in identifying any delays or issues in the picking process and take immediate action to address them.

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    SKU Management Case Study/Use Case example - How to use:



    Synopsis:

    The client in this case study is a large e-commerce company specializing in the sale of various consumer goods. With the rapid growth of online shopping, the company has experienced a surge in demand for their products. This has led to an increase in the number of SKUs (stock keeping units) to be managed in their warehouse. The company is facing challenges with efficient picking and fulfillment of orders due to the large number of SKUs. The existing pick strategy is inadequate in meeting the demand and results in long wait times, errors in picking, and a decrease in customer satisfaction. The client is seeking a solution that can optimize the picking process based on demand and the layout of their warehouse.

    Consulting Methodology:

    To address the client′s challenges, our consulting team adopted a data-driven approach based on the principles of demand-based picking and warehouse layout optimization. This methodology involved five key steps:

    1. Data Gathering and Analysis: The first step was to gather data on the company′s sales history and inventory levels. We also analyzed the warehouse layout and the picking process to identify any bottlenecks or inefficiencies.

    2. Demand Segmentation: To understand the demand patterns of the SKUs, we performed a demand segmentation analysis. This involved grouping the SKUs based on their demand levels, such as high, medium, and low demand.

    3. ABC Analysis: Using the Pareto Principle, we conducted an ABC analysis to categorize the SKUs based on their contribution to the overall sales. This helped us prioritize the SKUs and focus on the ones with the highest sales volume.

    4. Optimal Pick Strategy Design: Based on the demand segmentation and ABC analysis, we designed an optimal pick strategy that takes into consideration the demand for each SKU and their placement within the warehouse. The goal was to reduce the time and effort required for picking while improving accuracy and speed.

    5. Implementation and Monitoring: The final step involved implementing the new pick strategy and monitoring its effectiveness. We worked closely with the warehouse staff to ensure a smooth transition and provided training on the new process. Regular monitoring was done to track the KPIs and make any necessary adjustments.

    Deliverables:

    1. Demand Segmentation Report: This report provided insights into the demand patterns of the SKUs and helped in identifying the most critical SKUs.

    2. ABC Analysis Report: The ABC analysis report categorized the SKUs based on their contribution to sales and helped in prioritizing the picking process.

    3. Optimal Pick Strategy Design: A detailed document outlining the new pick strategy, along with the rationale behind it and any necessary changes to the warehouse layout.

    4. Training and Implementation Plan: A comprehensive plan for training the warehouse staff on the new pick strategy and its implementation.

    Implementation Challenges:

    The main challenge faced during the implementation of the new pick strategy was resistance from the warehouse staff. They were used to the existing pick process and were initially hesitant to adopt a new strategy. To overcome this, we involved the staff in the design process and provided training and support during the implementation phase. Regular communication and updates on the progress and benefits of the new pick strategy also helped in gaining their trust and cooperation.

    KPIs:

    1. Order Fulfillment Time: The time taken from when an order is received to when it is ready for shipment.

    2. Picking Accuracy: The percentage of correct items picked in an order.

    3. Wait Times: The time an order waits in the picking queue before being fulfilled.

    4. Customer Satisfaction: Measured through customer feedback surveys and ratings.

    Management Considerations:

    1. Technology Solutions: Implementing a Warehouse Management System (WMS) or a Warehouse Control System (WCS) can help in automating the picking process and improving efficiency.

    2. Data-Driven Decision Making: Regular analysis of data on demand and inventory levels can help in optimizing the pick strategy and ensuring it remains effective over time.

    3. Continuous Monitoring: Regular monitoring of KPIs and benchmarks helps in identifying any issues or inefficiencies and taking corrective action.

    4. Staff Training and Engagement: Involving the warehouse staff in the design process, providing adequate training, and recognizing their efforts can help in gaining their support and improving morale.

    Citations:

    1. Optimizing SKU Management in a Multi-Channel Warehouse by Ehrhardt + Partner Solutions. Retrieved from: https://www.ehrhardt-partner.com/en/knowledge/archive/december-2015/optimizing-sku-management-in-a-multi-channel-warehouse/

    2. The Key to Optimizing Pick Strategies in Warehouses by Fortna Inc. Retrieved from: https://blog.fortna.com/the-key-to-optimizing-pick-strategies-in-warehouses

    3. Demand-Based Picking – How businesses can improve their order processing and productivity by ProShip. Retrieved from: https://proshipinc.com/resources/demand-based-picking/

    4. Efficient SKU Management in Warehouse Operations by Supply Chain 24/7. Retrieved from: https://www.supplychain247.com/article/the_case_for_efficient_sku_management_in_warehouse_operations

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