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OMSAN LOJİSTİK
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Logistics Activity Profiling
Session 1 – Morning Defining the Problem Context - Supply Chain, Distribution Network - Single Warehouse Operations Logistics Activity Profiling Session 3 – Morning Process and Technology Alternatives Small Item Picking - Order Picking Methods - Forward Pick, Slotting, and Replenishment Mechanized Picking Systems Zone Picking Batch Picking Logistics Activity Profiling Session 2 – Afternoon Process and Technology Alternatives Pallet Storage Case Picking Manual Mechanized Session 4 – Afternoon Putting it all Together Developing the integrated design - Sizing the Functions - Defining Overall Facility Flow - Orienting Shipping and Receiving - Planning Aisle Patterns Integrating Human Activity Day 1 Day 2
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Warehousing—Do You Know? Logistics Activity Profiling
What is profiling? Why do we do it? (Hint: this is a trick question—it’s what we’re going to spend the next hour exploring!!) Ask class for some ideas on what profiling means Document key themes on flip chart/white board Refer back periodically
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Logistics Activity Profiling Definition
Profiling is an umbrella term used to define analysis you perform on your company’s key inventory data*, such as: Inventory physical characteristics Orders/order history SKU-level movement history In this session we’ll explore: Motivations—why profile? Inventory, orders, and history—what to do with them and why it’s important Creating an activity database (overview) * The official TPG term for this activity is “wallerin’ around in the data.” Note that when we say “product, inventory, and order profiling” we mean: Product = the SKU itself, including cube, weight, material handling class etc. Inventory means product/products in location in the building Order means everything to do with activity at several levels: Aggregate SKU/family level line/line makeup level
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Logistics Activity Profiling Motivations—Why Do It?
Gain insight into how processes might be designed Evaluate alternative operating methods Identify automation/mechanization opportunities Define inventory positioning rules (slotting) Increase space utilization and throughput capacity in an existing facility Consolidate distribution centers Improve order fulfillment efficiency Spotlight changes in inventory activity . . . End-of-life-cycle seasonal goods Changes from fast to slow movers, and vice versa Dead dogs
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Logistics Activity Profiling Which to Discuss First
Logistics Activity Profiling Which to Discuss First? The “Chicken-and-Egg” Problem SKUs Orders History Dimensions Patterns/Trends Lines Cube Pack/Storage Configurations Weight Single Pallets Handling Reqt’s Multiple Cases Broken Cases
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Logistics Activity Profiling Which to Discuss First
Logistics Activity Profiling Which to Discuss First? The “Chicken-and-Egg” Problem Orders History SKUs SKUs have dimensions from which you derive cubic volume and track weight . . . Dimensions Patterns/Trends Lines Cube Pack/Storage Configurations Weight Single Pallets Handling Reqt’s Multiple Cases Broken Cases
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Logistics Activity Profiling Which to Discuss First
Logistics Activity Profiling Which to Discuss First? The “Chicken-and-Egg” Problem Orders History SKUs Pack and storage configurations that tell you something about how they are stored and picked . . . Dimensions Patterns/Trends Lines Cube Pack/Storage Configurations Weight Single Pallets Handling Reqt’s Multiple Cases Broken Cases
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Logistics Activity Profiling Which to Discuss First
Logistics Activity Profiling Which to Discuss First? The “Chicken-and-Egg” Problem Orders History SKUs And handling requirements may affect pack/storage configurations and how orders can be sequenced and picked. Conversely, order patterns may dictate pack and storage configurations. Dimensions Patterns/Trends Lines Cube Pack/Storage Configurations Weight Single Pallets Handling Reqt’s Multiple Cases Broken Cases
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Logistics Activity Profiling Which to Discuss First
Logistics Activity Profiling Which to Discuss First? The “Chicken-and-Egg” Problem Orders History SKUs Dimensions Patterns/Trends Lines Cube Pack/Storage Configurations Weight Single Pallets Handling Reqt’s Orders are made up of lines; sometimes only one, sometimes hundreds . . . Multiple Cases Broken Cases
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Logistics Activity Profiling Which to Discuss First
Logistics Activity Profiling Which to Discuss First? The “Chicken-and-Egg” Problem Orders History SKUs The lines contain SKUs, which have cubic volume and weight and can come in a variety of pack and handling configurations . . . Dimensions Patterns/Trends Lines Cube Pack/Storage Configurations Weight Single Pallets Handling Reqt’s Multiple Cases Broken Cases
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Logistics Activity Profiling Which to Discuss First
Logistics Activity Profiling Which to Discuss First? The “Chicken-and-Egg” Problem Orders History SKUs And any and all of the SKU characteristics may vary dramatically by order type! Dimensions Patterns/Trends Lines Cube Pack/Storage Configurations Weight Single Pallets Handling Reqt’s Multiple Cases Broken Cases
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Logistics Activity Profiling Which to Discuss First
Logistics Activity Profiling Which to Discuss First? The “Chicken-and-Egg” Problem Orders History SKUs Dimensions Patterns/ Trends Lines Cube Pack/Storage Configurations Weight Historical data provides the foundation upon which profiling analysis is done Single Pallets Handling Reqt’s Multiple Cases Broken Cases
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Logistics Activity Profiling Inventory Characteristics
Physical Weight, cube and dimensions of each product Material handling requirements, e.g,: Full pallets (standard pallets, clamp loads) Roll stock (paper) Drums Cold (perishables, wine) Frozen (meats, ice cream) Population Total SKU counts Family/Group SKU counts Anticipated movement profile Stock/repeatable item Seasonal or “one-time use” goods First thought is often about velocity, but there are many other characteristics of inventory that will affect design Physical characteristics help map weight and cube per order/per line which can effect equipment and slotting decisions. Weight and cube become really important for leveraging WMS and transportation/manifesting systems. Population is interesting for things like Gross SKU counts (we have 85,000 SKUs to deal with . . .) SKU count by family (31 flavors will require 31 pick faces ) “Anticipated” profile means you identify a good as “seasonal” and make handling decisions as such. It’s in quotes because products don’t always behave like you expect. Gap example: “fashion” SKUs may have demand that goes on so long the item becomes a “basic.”
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Logistics Activity Profiling Order Data—Three Views
Identify demand patterns at the aggregate level: Daily Weekly Monthly Quarterly Annually Identify and model order/line characteristics Single-line orders Multi-line orders Pallet, case, and unit combinations Identify inventory-specific order characteristics: For all SKUs For individual SKUs For groups/families of SKUs
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Logistics Activity Profiling Aggregate Order Demand Data
Shipping cutoffs, system download windows, and a host of other events can cause spikes and dips in daily order activity Activity Time of Day Replenishment schedules to stores or regional facilities, customer “habits,” etc. can drive weekly patterns Activity Day of Week M TU W TH FR SA SU Reasons for looking at aggregate order data: Determine broad demand patterns (daily, weekly, monthly, annual, seasonal, lumpy) Determine number of incoming orders (daily, weekly, peak day, peak hour, etc.) Determine order arrival patterns/cutoffs: Same day shipping Next day shipping
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Logistics Activity Profiling Aggregate Order Demand Data
Analysis may show some clear indications of order variability during certain days or weeks of the month . . . Activity Day of Month . . . and show further trends at the quarterly level Activity JAN FEB MAR Q1
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Logistics Activity Profiling Aggregate Order Demand Data
The annual view can be an excellent indicator of seasonal peaks, especially when tracked over several years. Activity JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Months of the Year
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Logistics Activity Profiling Aggregate Order Data—Uses and Risks
The shape of the curve and its repeatability might cause you to design a facility to handle your June peak volume . . . . . . while negotiating 3rd party support for the October peak Activity JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Months of the Year
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Logistics Activity Profiling Aggregate Order Data—Uses and Risks
Be very careful when making a key, high-cost decision based on this form of analysis—the data could be skewed by one-time or rare events. Know your business—know your data!! This peak could be . . . Regular, repeatable, and expected Caused by an acquisition/expansion The result of your having the nation’s only remaining supply of pet rocks in a sudden, unexpected nostalgia craze!
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Logistics Activity Profiling Order/Line Characteristics—Shows Distributions
What’s important about order/line characteristics: What are the lines per order characteristics In this example, many single-line orders suggest batching opportunities. High single-line order demand might offer opportunities like assembly-line-style, high velocity pack stations Cube Per Order Lines Per Order
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Logistics Activity Profiling Order/Line Characteristics—Shows Pick Type Makeup
Pallet/Case
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Logistics Activity Profiling Order/Line Characteristics—Shows Pick Type Makeup
Full Case/Broken Case
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Logistics Activity Profiling Order/Line Characteristics—Shows Relationships
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Logistics Activity Profiling Order/Line Characteristics—Shows Daily Activity Patterns
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Logistics Activity Profiling SKU Characteristics
Enables you to look at SKU-level behavior . . . For all SKUs For individual SKUs For groups/families of SKUs And react to a number of potential outcomes: Demand patterns ABC classification Grouping opportunities
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Logistics Activity Profiling Identify Demand Patterns
Declining Inclining Consistent Seasonal Irregular
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Logistics Activity Profiling ABC Classification
Three words . . . Pareto, Pareto, Pareto!! “separating the critical few from the trivial many” The Pareto diagram is named after Vilfredo Pareto, a 19th century Italian economist who postulated that a large share of wealth is owned by a small percentage of the population1. This basic principle,often referred to as the “80-20” rule, translates well into a huge variety of applications. Why does this matter? Mathematical/statistical modeling and applications developed from Pareto’s principle can drive huge benefits to logistics and distribution operations. If the industry has not yet erected a monument to Pareto, it should!! Vilfredo Pareto 1source:
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Logistics Activity Profiling Typical Observations
20% of SKUs will generate 80% of activity, as measured in a variety of ways: Cube movement Line count Dollar value Staffing levels Of these populations, the “outliers” will have an even greater impact . . . 5% of SKUs often generate 50%-60% of activity 50% to 60% of SKUs often generate only 5% of activity
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Logistics Activity Profiling ABC Classification—Analysis Example
Pallet Layer Case
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Logistics Activity Profiling ABC Classification—Application Example
20,000 19,614 18,633 15,655 “C” Items: 649 SKUs Last 5% of movement Double-deep rack 15,000 Total Pallet Activity “B” Items: 97 SKUs Next 15% of movement With “A” items=95% of movement Block stack 4 -7 deep 10,000 5,000 “A” Items: 45 SKUs Top 80% of movement Fast-turn lanes at dock Block stack 7-9 deep 100 200 300 400 500 600 700 800 45 142 791 Cumulative SKU Count
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Logistics Activity Profiling ABC Classification—Application Example
20,000 17,450 16,577 15,000 13,949 “C” Items: 475 SKUs, 873 layers, 206 picks Last 5% of movement Double-deep rack Pick to pick area with DD reach truck Total Layer Activity 10,000 “B” Items: 177 SKUs, 2,628 layers, 624 picks Next 15% of movement With “A” items=95% of movement Push-back rack Clamp truck accessible 5,000 “A” Items: 139 SKUs, 13,949 layers, 3,537 picks Top 80% of movement 4-deep flow-through lanes Clamp truck accessible 100 200 300 400 500 600 700 800 139 316 791 Cumulative SKU Count
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Logistics Activity Profiling ABC Classification—Application Example (Traditional ABC)
70,000 68,302 64,553 60,000 “C” Items: 287 SKUs, 3,749 cases, 830 picks Last 5% of movement Double-deep rack rear locations or single-deep/turret truck access Pick to pick area 54,516 50,000 Total Case Activity 40,000 “B” Items: 303 SKUs, 10,037 cases, 2,377 picks Next 15% of movement With “A” items=95% of movement Double-deep rack rear locations or single-deep/turret truck access Pick to pick area 30,000 20,000 “A” Items: 201 SKUs, 54,516 cases, 10,322 picks Top 80% of movement 4-deep flow-through lanes Hand picked onto pallet jack or similar device 10,000 100 200 300 400 500 600 700 800 201 504 791 Cumulative SKU Count
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Logistics Activity Profiling ABC Classification—Application Example (Expanded ABC)
70,000 68,302 64,553 60,000 “C” Items: 287 SKUs, 3,749 cases, 830 picks Last 5% of movement Double-deep rack rear locations or single-deep/turret truck access Pick to pick area 54,516 50,000 47,776 Total Case Activity 40,000 “A3” Items: 71 SKUs, 6,740 cases, 1,382 picks Next 10% of movement All “A” items = 80% of movement Storage? Picking? 34,044 30,000 “A2” Items: 58 SKUs, 13,732 cases, 2,690 picks Next 20% of movement With “A1” items=70% of movement Storage? Picking? 20,000 “B” Items: 303 SKUs, 10,037 cases, 2,377 picks Next 15% of movement With “A” items=95% of movement Double-deep rack rear locations or single-deep/turret truck access Pick to pick area “A1” Items: 72 SKUs, 34,044 cases, 6,250 picks Top 50% of movement 4-deep flow-through lanes Hand picked onto pallet jack or similar device 10,000 100 200 300 400 500 600 700 800 72 130 201 791 Cumulative SKU Count
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Logistics Activity Profiling Grouping Opportunities
One form of grouping SKUs is “pair frequency,” in which you assess how often two (or more) SKUs are ordered together. These SKUs might be candidates to slot next to one another.
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Logistics Activity Profiling Grouping Opportunities (cont.)
Other grouping opportunities include . . . Store-specific Aisle specific Color/size/style Oversize/heavy Sortable/non-sortable Others?
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Logistics Activity Profiling Creating a Database
INV. MASTER Inventory Snapshots Average Inventory Levels ORDER MASTER Order Header Order Detail (Lines) ITEM MASTER Items Classification Item Weight Cases Per Pallet $Value Item Cube
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Logistics Activity Profiling Creating a Database (cont.)
HISTORICAL TRANSACTION DATABASE (CURRENT AND PREVIOUS YEARS) Static DAILY TRANSACTION DATABASE (CURRENT MONTH) Current Archive MS Access, other db 1 YEAR OF ORDERS (6 MONTHS IF NOT SEASONAL) INVENTORY SNAPSHOTS
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Logistics Activity Profiling Creating Reports
Current Static Archive MS Access, other db Consolidate and Calculate MS Excel, Other Reporting Tool Analyze (sort/rank) and Present
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Logistics Activity Profiling Session Wrap Up—Profiling Pays!!
Identify and operationally leverage behavior of . . Orders SKUs Activity Spot and quickly react to trends predict the future Make informed design decisions but remember . . . Be cautious about spending too much time on the analysis and getting mired in the data Data is all about yesterday . . . Know your business Know your data!! Wallerin’ in the data stimulates creative thinking. Keep your eyes open and your mind prepared for doing something different!!
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