Lecture 06 Warehouse Activity Profile
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1 .. Lecture 06 Warehouse Activity Profile Oran Kittithreerapronchai 1 1 Department of Industrial Engineering, Chulalongkorn University Bangkok THAILAND last updated: December 29, 2014 Warehouse v2.0: Activity Profile 1/ 34
2 Outline. 1 Concept of Warehouse Activity Profiling. 2 Master Data for Warehouse Activity Profile. 3 WAP Processes & Examples. 4 Case Study: Construction Material. 5 Overview of Key Performance Index source: General references [BH09, Mul94, Fra02] Warehouse v2.0: Activity Profile 2/ 34
3 Warehouse Activity Profiling What: improving warehouse by understanding natures & exploring patterns Idea: data mining with database program Item Master Statistic/KPI SKU detail SKUs & facility Location Master storage detail Warehouse Activity. Profiling Distribution Order Master in-out history Other layout put-away Structure? relationship pattern trend Warehouse v2.0: Activity Profile 3/ 34
4 Investigation = WAP Crime Investigation gathering evidences & witnesses understanding motivates selecting suspects capturing murder Warehouse Activity Profiling gathering data understanding patterns selecting causes & solution improving efficiency & productivity..questions & Data Information Success of profiling Warehouse v2.0: Activity Profile 4/ 34
5 Benefits of Warehouse Activity Profiling Understand demands & patterns layout, picking policy, labor management Calculate Key Performance Index (KPI) snap shot of warehouse Managing SKU select suitable equipment, package, slotting, default pick path Gather data for design Warehouse v2.0: Activity Profile 5/ 34
6 Master Data Item Master: database related to SKUs Location Master: database of inventory at all storage location Order Master:database of sale in-out to warehouse (100+MB) Questions What is data? obtaining data, time horizon, meaning of each column, wired data How each data set related? understanding big picture by interaction What is primary key of each set of data? understanding big picture by interaction Warehouse v2.0: Activity Profile 6/ 34
7 Profiling Data: Item Master General: SKU ID, description, vender ID Bulk break: break SKU, box per pallet Physical: volume, width (length height weight) Time: received date, expired date Ordering: min-max, response person Other: picking note, shipping note, lot #, equipment Example Sku ID Vendor ID Description Unit Unit Unit Unit length width height weight AAG47294 AAG PLNR, FAT LITTLE,BK source: Warehousing Science jb/wh/book/profile/activities/profilingexercise.html Warehouse v2.0: Activity Profile 7/ 34
8 Profiling Data: Location Master Header: date-time that data are retrieved Address: zone, aisle, section, position Unit: quantity, case, pallet Example Sku ID Zone Aisle Bay Level Position Qty Unit SPRSP A 102 A 2 B 20 Case AVE05731 A 102 A 5 B 12 Case WLJ36610 A 102 B 1 B 30 Case SPR5084SP A 102 B 3 B 10 Piece source: Warehousing Science jb/wh/book/profile/activities/profilingexercise.html Warehouse v2.0: Activity Profile 8/ 34
9 Typical Warehouse Address Warehouse v2.0: Activity Profile 9/ 34
10 Profiling Data: Order Master Header: order ID, customer ID, Detail: SKU ID, date, time, quantity (Qty), unit Note: largest database (200+MB) Example Sku ID Order Customer Order Date Time number number qty BRTTN :01:0 0 CMC5810-BE :45:00 KOK :41:00 SOF :01:00 source: Warehousing Science jb/wh/book/profile/activities/profilingexercise.html Warehouse v2.0: Activity Profile 10/ 34
11 Processes in WAP Define questions: What do you plan to improve (Pro VS Con)? Gather data: meaning of data & finding related data: Static: SKU related, layout-zone, std. time, cut-off time Dynamic: picker related, plan, OT, schedule Import data: connect with database, basic statistic analysis Check data: inconsistency, outlier clean-up data Analysis data: create & explain distribution (figure) Implementation: gap analysis, saving analysis Warehouse v2.0: Activity Profile 11/ 34
12 Basis Warehouse Statistic SKU Related Statistic # active SKU each month: difficulty & nature of warehouse # pallet receiving each day: workload from receiving Volume of SKU shipping each day: material handling # line per order: warehouse characteristics & channel Seasonality index: balance of workload Facility Related Statistic # new SKU each month: stability of warehouse Avg. inventory per SKU: workload, inventory policy # workers in each activity: division of labor Warehouse v2.0: Activity Profile 12/ 34
13 Examples of WAP Order mixed distribution: moving partial warehouse, re-range zone Channel mixed distribution: business analysis, outsourcing, direct shipment, Line per Order distribution: batch picking, Pallet order incremental distribution: requirement of equipment, sub-pallet Item popularity distribution: storage location, fast picking area, ABC analysis by pick Family pair analysis: SKU in same order zoning Warehouse v2.0: Activity Profile 13/ 34
14 Construction Material: Products Category: roof ( labor), cement (time limit), paint (flammable) roof tile grey cement home paint Warehouse v2.0: Activity Profile 14/ 34
15 Concept Master Data Process & Example Case Study KPI Construction Material: Sale Channel: retail ( margin), whole sale ( qty), project (know in advance) homemart retail Warehouse v2.0: Activity Profile wholesale construction project 15/ 34
16 Warehouse Layout never enough space space?, qty?, SKU? high labor turnover mgt?, worker?, task?, policy?, equipment? layout suck design?, inventory?, slotting? Warehouse v2.0: Activity Profile 16/ 34
17 Which category should be re-located? Warehouse v2.0: Activity Profile 17/ 34
18 Direct Shipment?: Cement or Roof Warehouse v2.0: Activity Profile 18/ 34
19 Number of line for batch picking Warehouse v2.0: Activity Profile 19/ 34
20 How to divide sub-pallet? Roof problems Roof problems: require high labor, small order qty, easy damage Question: should do sub-pallet? & how much Warehouse v2.0: Activity Profile 20/ 34
21 ABC analysis by total sale Warehouse v2.0: Activity Profile 21/ 34
22 ABC analysis by handling Warehouse v2.0: Activity Profile 22/ 34
23 Equipment selection Adopted from Frazelle, E Warehouse v2.0: Activity Profile 23/ 34
24 Cycle of inventory Warehouse v2.0: Activity Profile 24/ 34
25 Common Questions for WAP Area Questions Profiles Order picking batch size order mix dist. & shipping process picking tour line/order dist. shipping mode line/order dist. separation cubes/order dist. Receiving receiving mode order mix dist. & put-away separation put-away batch size lines/receipt dist. put-away tour lines/receipt dist. cube/receipt dist. Slotting zone definition popularity dist. storage selection & size item location cube volume profile popular/volume dist. order completing dist. demand correlation Adopted from Frazelle, E Warehouse v2.0: Activity Profile 25/ 34
26 Warning on WAP Yielding unexpected turns: profiling results general believes validate date & analysis new information & fact Spending too much time: Unavailable data: e.g., volume Over analysis: e.g., questions & profiling Profiling periodically: Data is inconsistency & dynamic WAP is non-stationary Warehouse v2.0: Activity Profile 26/ 34
27 What is KPI? What: a way to measure performance of organization/activity Important: indicate success of each activity evaluate main objectives measure progress of implementation (historical comparison) measure productivity & efficiency Issues: data collection, measurement, consistency Warehouse v2.0: Activity Profile 27/ 34
28 What are characteristics of good KPI? related to the organization objective & mission accepted by everyone (supervisor, customers, industry) historical comparable & controllable resources output logical & suggesting solution required minimal of works for collecting & analyzing Type of KPI? Financial related KPI: % warehousing cost per total cost, cost per shipped SKU Non-Financial related KPI: Warehouse v2.0: Activity Profile 28/ 34
29 KPI for Every Activity & Aspect Activity Financial Productivity Utilization Quality Cycle-Time Receiving receiving-cost receipts per % utilization % receipts receipts CT per line man-hour of dock accurate Put-away put-away cost put-away per % utilization % perfect put-away CT per line man-hour of labor put-away Storage storage cost inventory per % utilization % available inventory days per item square foot of cubic location on hand Picking picking cost lines per % utilization % perfect order pickper line man-hour of labor picking lines ing CT Shipping shipping cost order prepared % utilization % perfect warehouse per order for shipment of dock shipments order CT Total total cost shipped lines % utilization % perfect total wareper order per man-hour of capacity order house CT Adopted from Frazelle, E Warehouse v2.0: Activity Profile 29/ 34
30 Example of Non-Financial KPI Service Customer View: response time (order cycle time), shipment accuracy (correct qty/total qty), fill rate (qty shipped/ordered qty) Service Warehouse View: dock-to-stock time, inventory accuracy, % cross-docking order Productivity: lines per man-hour, cases per person-hour, cubic space utilization, equipment up-time Situation: lines shipped per SKU, inventory turnover, investment pick accuracy, % of new SKUs, % active SKUs, labor turnover, lines per order, total lines shipped per day Adopted from Hackman, S Warehouse v2.0: Activity Profile 30/ 34
31 Rating of Selected KPIs KPI/Rating Poor Sub-Par Par Superior Outstanding responding time (hrs) > <4 dock-to-store time (hrs) > <2 lines per man-hour < >50 cases per man-hour >250 cubic utilization (%) < annual lines per SKU < >400 inventory accuracy (% qty) > <0.05 inventory turn < >12.0 source Hackman, S. et al citehackman2001benchmarking,. Warehouse v2.0: Activity Profile 31/ 34
32 Using KPIs Project comparison: Idea: verify success of project with KPIs Limitation: wired KPI, standardization, spilling effect Historical comparison: Idea: compare KPIs across period e.g., month-by-month, year-by-year Limitation: situations (e.g., no picking, disruption), improving area Benchmark comparison: Idea: compare KPIs across organization Limitation: type & scale of warehouses, confidentiality, interpretation Method to compare: To-Be-Continue Warehouse v2.0: Activity Profile 32/ 34
33 Problems 1. In warehouse activity profile, why do we prefer Database software (e.g., MS Access) rather than Spreadsheet software (e.g., MS Excel) 2. Based on WPA workshop, how many SKUs in Zone A/ Aisle 145? 3. Based on WPA workshop, what is the average line/order of SKU located in zone A? Warehouse v2.0: Activity Profile 33/ 34
34 Reference [BH09] [Fra02] J. J. Bartholdi and S. T. Hackman. Warehouse & distribution science. Suply chain and logistics institute, Georgia institute of technology, E. Frazelle. World-class warehousing and material handling. McGraw-Hill Professional, [Mul94] D.E Mulcahy. Warehouse distribution and operations handbook. McGraw-Hill New York, Warehouse v2.0: Activity Profile 34/ 34
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