Download presentation

Presentation is loading. Please wait.

Published byCrystal Reeves Modified over 2 years ago

1
1 Outline ideas of benchmarking DEA profiling

2
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
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
4 Term Project the training for your thesis just try your best, and dont worry that much

5
5 Benchmarking and Profiling

6
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
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
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
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
10 Common Approaches for Intangible Factors qualitative description, e.g., different levels of sophistication of receiving Stage 1measureStage 3Stage 4Stage 5 Receiving unload, stage, & in-check immediate putaway to reserve immediate putaway to primary cross-dockingprereceiving

11
11 Steps to World-Class Warehousing Practices

12
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
13 Examples of Numerical Performance Indicators FinancialProductivityUtilizationQualityCycle time Receiving Putaway Storage Order picking Shipping Total Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))

14
14 Examples of Numerical Performance Indicators FinancialProductivityUtilizationQualityCycle time ReceivingCost / line Receipts / man-hr Dock utilization % of correct receipts processing time / receipt PutawayCost /line Putaway / man-hr Labor & equipment utilization % of perfect putaway Cycle time / putaway StorageCost / itemInv / areaSpace utilization % of accurate record Inv. day Order picking Cost / line Line picked / man-hr Labor & equipment utilization % of correct picked lines Pick cycle time ShippingCost / order Order shipped / man-hr Dock utilization % of perfect shipments cycle time / order Total Cost / order, line, item Lines shipped / man-hr --- % of perfect W/H orders Cycle time / order Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002))

15
15 Presenting Incomparable Factors 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 degree of automation flexibility of service level of value added service quality of service scale of operations training of personnel

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

17
17 Data Envelopment Analysis (DEA)

18
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., input 1 = 5, input 2 = 12, though input 1 and input 2 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
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) B (8, 4) apple orange

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

21
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. apple orange A B inefficient region measurement of inefficiency

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

23
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 grapefruit inefficient region

24
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
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 a ij be the number of units of input j that entity i takes to give a ik 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: a 11 = 1, a 12 = 3, a 13 = 5, and a 14 = 2, i.e., DMU 1 takes 1 grapefruit, 3 bananas to produce 5 apples and 2 oranges DMU 2: a 21 = 2, a 22 = 1, a 23 = 3, and a 24 = 4, i.e., DMU 2 takes 2 grapefruits, 1 banana to produce 3 apples and 4 oranges

26
26 Idea of Data Envelopment Analysis (DEA) r k = unit reward of type k output, c j = unit cost of type j input performance of DMU 1 = (5r 3 +2r 4 )/(c 1 +3c 2 ) performance of DMU 2 = (3r 3 +4r 4 )/(2c 1 +c 2 ) performance of DMU i defined similarly given (a ij ) of the I DMUs, how to benchmark a tapped DMU with (a oj ) for unknown r k and c j ?

27
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 (a ij ) and (a oj ) without knowing r k, nor c j idea: similar to the construction of efficient boundaries in the simplified examples

28
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), de Koster, M.B.M., and B.M. Balk (2008) Benchmarking and Monitoring International Warehouse Operations in Europe, Production and Operations Management, 17(2), McGinnis, L.F., A. Johnson, and M. Villarreal (2006) Benchmarking Warehouse Performance Study, Technical Report, Georgia Institute of Technology. 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 29 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
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
31 Warehouse Performance Study in GIT develop a single index to measure the performance of a warehouse use data envelope analysis

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

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

34
34 Profiling Examples Only

35
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
36 Various Profiles indicators on every aspect receiving, prepackaging, putaway, storage, order picking, packaging, sorting, accumulation, unitizing, and shipping

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

38
38 Family Mix Distribution implication: zoning by family

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

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

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

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

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

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

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

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

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

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

49
49 Demand Correlation Distribution implication: on zoning of goods

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

51
51 Item-Family Inventory Distribution implication: area assigned to different types of storage

52
52 Handling Unit Inventory Distribution implication: different storage modes according to the number of pallets on hand

53
53 Seasonality Distribution implication: shifting human resources and possibly space

54
54 Daily Activity Distribution implication: shifting human resources and possibly space

55
55 Activity Relationship implication: on layout

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google