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Outline

Outline. ideas of benchmarking DEA profiling. Purpose of the Course. warehouses and warehousing: means, not ends ends for students satisfy the course requirement prepare for thesis how to collect information, present, write an essay self-improve and self-actualize. Thesis.

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Outline

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  1. Outline • ideas of benchmarking • DEA • profiling

  2. Purpose of the Course • warehouses and warehousing: means, not ends • ends for students • satisfy the course requirement • prepare for thesis • how to collect information, present, write an essay • self-improve and self-actualize

  3. Thesis • a serious issue • certainly not something from cutting and pasting • not merely a collection of organized material • a step on generating knowledge • material read serving as the basis • key: your own thoughts • hard, but worthwhile training

  4. Term Project • the training for your thesis • just try your best, and don’t worry that much

  5. Benchmarking and Profiling

  6. Tasks for Senior Management of Warehouses • continuous improvement • setting objectives • absolute standard, e.g., 95% orders in 2 days, on average no more than 2.2 days • relative standard – benchmarking • profiling: pre-requisite of benchmarking • “soul” searching

  7. Steps for Benchmarking • identify the process to benchmark for e.g., most troublesome, most important • identify the key performance variables: efficiency (time, cost, productivity) and service level • document current processes and flows: physical activities and information flows • including resources required • identify competitors and best-in-class companies • decide which practices to adopt • see modifications

  8. Data Collected for Benchmarking Warehouses • performance benchmarking • inputs, e.g., • labor, investment, space, scale of storage, degree of automation • outputs • # of lines picked, level of value added service, # of special processes, quality of service, flexibility of service • broken case lines shipped, full case lines shipped and pallet lines shipped • process benchmarking • resources • procedure • results

  9. Difficulties of Benchmarking • intangible factors • how to measure factors such as degree of automation, level of value added service, quality of service, flexibility of service, etc. • incomparable factors • e.g., the comparison of quality of service with degree of automation

  10. Common Approaches for Intangible Factors • qualitative description, e.g., • different levels of sophistication of receiving

  11. Steps to World-Class Warehousing Practices

  12. Common Approaches for Intangible Factors • numerical values assigned to qualitative factors • quantitative measures for qualitative factors • e.g., quality of service by % of customers satisfied in 5 minutes, level of value added service by types of value added service provided

  13. Examples of Numerical Performance Indicators Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))

  14. Examples of Numerical Performance Indicators Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))

  15. PresentingIncomparable Factors degree of automation scale of operations • skipping comparison, e.g., the web graph for gap analysis • an example for 6 factors • best practices identified for benchmarking • the relative performance with respect to the best praes quality of service flexibility of service training of personnel level of value added service

  16. ComparingIncomparable Factors • various methods, e.g., Scoring, Analytic Hierarchy Process, Balanced Scorecard, Data Envelopment Analysis (DEA), etc.

  17. Data Envelopment Analysis (DEA)

  18. Comparing Incomparable Factors • data envelopment analysis (DEA): a technique to compare quantitative factors of different nature • providing a numerical value judging the distance from the best practices • some assumptions • numerical values of each factor, e.g., input1 = 5, input2 = 12, though input1 and input2 cannot be compared • linearity of effect, i.e., if 3 units of input give 7 units of outputs, 6 units of input give 14 units of output

  19. Idea of Data Envelopment Analysis (DEA) • W/H A and W/H B consume the same amount of resources • two types of incomparable outputs: apple and orange • which is better? A (4, 8) orange B (8, 4) apple

  20. Idea of Data Envelopment Analysis (DEA) • W/H C consumes the same amount of resources as W/Hs A and B do • How’s the performance of C relative to A and B? C (8, 8) A (4, 8) orange C (4, 4) C (6, 6) B (8, 4) apple

  21. Idea of Data Envelopment Analysis (DEA) • Given W/H A and B, for W/Hs that consumes the same amount of resources, the inefficient region is shown in RHS. • The efficiency of a warehouse that consumes the same amount of resources as A and B can be measured by the distance from the boundary of the date envelope. measurement of inefficiency A inefficient region B orange apple

  22. Idea of Data Envelopment Analysis (DEA) • efficient boundary from many warehouses that consume the same amount of resources inefficient region orange apple

  23. Idea of Data Envelopment Analysis (DEA) • efficient boundary from many warehouses that give the same amount of outputs and consume different values of incomparable resources banana and grapefruit banana inefficient region grapefruit

  24. Idea of Data Envelopment Analysis (DEA) • problem: situations for benchmarking often not ideal • different resources consumption for W/H • different outputs for W/H • for multi-input, multi-output problems, with W/H consuming different amount of resources and giving different amount of outputs, DEA • draws the efficient boundary • benchmarks a W/H with respect to these existing ones

  25. Idea of Data Envelopment Analysis (DEA) • multi-input, multi-output comparison • I decision-making units (DMUs), J types of inputs, K types of outputs • aij be the number of units of input j that entity i takes to give aik units of output k, j = 1, …, J and k = J+1, …, J+K • example: 2 DMUs; 2 types of inputs (grapefruit, banana); 2 types of outputs (apple, orange) • DMU 1: a11 = 1, a12 = 3, a13 = 5, and a14 = 2, i.e., DMU 1 takes 1 grapefruit, 3 bananas to produce 5 apples and 2 oranges • DMU 2: a21 = 2, a22 = 1, a23 = 3, and a24 = 4, i.e., DMU 2 takes 2 grapefruits, 1 banana to produce 3 apples and 4 oranges

  26. Idea of Data Envelopment Analysis (DEA) • rk = unit reward of type koutput, cj = unit cost of type j input • performance of DMU1 = (5r3+2r4)/(c1+3c2) • performance of DMU2 = (3r3+4r4)/(2c1+c2) • performance of DMUi defined similarly • given (aij) of the IDMUs, how to benchmark a tapped DMU with (aoj) for unknownrk and cj?

  27. Idea of Data Envelopment Analysis (DEA) • in general DEA finds the distance from the efficient boundary by a linear program purely making use of (aij) and (aoj) without knowing rk, nor cj • idea: similar to the construction of efficient boundaries in the simplified examples

  28. Studies Using DEA on Warehouses • de Koster, M.B.M., and B.M. Balk (2008) Benchmarking and Monitoring International Warehouse Operations in Europe, Production and Operations Management, 17(2), 175-183. • McGinnis, L.F., A. Johnson, and M. Villarreal (2006) Benchmarking Warehouse Performance Study, Technical Report, Georgia Institute of Technology.

  29. de Koster and Balk (2008) • inputs • # of direct FTEs • size of the W/H • degree of automation • # of SKUs • outputs • # of order lines picked/day • level of value-added logistics (VAL) activities • # of special optimized processes • % of error-free orders shipped out • order flexibility

  30. de Koster and Balk (2008) • 65 warehouses containing 140 EDCs • EDC: distribution centers in Europe responsible for the distribution for at least five countries there • composition • results

  31. Warehouse Performance Study in GIT • develop a single index to measure the performance of a warehouse • use data envelope analysis

  32. Examples from the Index – Warehouse Size • What are your inferences?

  33. Examples from the Index – Mechanization • What are your inferences?

  34. Profiling Examples Only

  35. Profiling • profile of the warehouse • define processes • status of processes • reveal status of warehouse • purposes • get new ideas on design and planning • get improvement • get baseline for any justification • remarks • use distributions, not means • express in pictures

  36. Various Profiles • indicators on every aspect • receiving, prepackaging, putaway, storage, order picking, packaging, sorting, accumulation, unitizing, and shipping

  37. Customer Order Profiling Customer Order Profile Order Mix Dist. Lines per order Dist. Cube per order Dist. Lines and Cube per order Dist. results from order profiling help design a warehouse, including its layout, equipment, picking methods, etc. Family Mix Dist. Full/Partial Mix Dist. Order Inc. Dist.

  38. Family Mix Distribution • implication: zoning by family

  39. Handling Unit Mix Distribution – Full/Partial Pallets • implication: good to have a separate picking area for loose cartons

  40. Handling Unit Mix Distribution – Full/Broken Cases • implication: good to have a separate picking area for broken cases

  41. Order Increment Distributions - Pallets • implication: good to have ¼ and ½ pallets

  42. Order Increment Distributions - Cases • implication: good to have ½-size cases

  43. Lines per order Distribution • implication: on the picking methods

  44. Lines and Cube per order Distribution • implication: on the picking methods

  45. Items Popularity Distribution • implication: on storage zones, golden, silver, bronze

  46. Cube-Movement Distribution • implication: small items in drawers or bin shelling; large items in block stacking, push-back rack

  47. Popularity-Cube-Movement Distribution • implication: on storage mode

  48. Item-Order Completion Distribution • implication: on mode of storage, e.g., warehouse within a warehouse

  49. Demand Correlation Distribution • implication: on zoning of goods

  50. Demand Variability Distribution • implication: variance of demand to set safety stock

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