Download presentation

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 Stage 1 measure Stage 3 Stage 4 Stage 5 Receiving unload, stage, & in-check immediate putaway to reserve immediate putaway to primary cross-docking prereceiving

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)) Financial Productivity Utilization Quality Cycle time Receiving Putaway Storage Order picking Shipping Total

14
**Examples of Numerical Performance Indicators**

Based on Table 3-4 Warehouse Key Performance Indicators (Frazell (2002)) Financial Productivity Utilization Quality Cycle time Receiving Cost / line Receipts / man-hr Dock utilization % of correct receipts processing time / receipt Putaway Cost /line Labor & equipment utilization % of perfect putaway Cycle time / putaway Storage Cost / item Inv / area Space utilization % of accurate record Inv. day Order picking Line picked % of correct picked lines Pick cycle time Shipping Cost / order Order shipped % of perfect shipments cycle time / order Total Cost / order, line, item Lines shipped --- % of perfect W/H orders Cycle time / order

15
**Presenting Incomparable Factors**

degree of automation flexibility of service level of value added service quality of service scale of operations training of personnel 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

16
**Comparing Incomparable 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? apple orange A (4, 8) B (8, 4)

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? apple orange A (4, 8) B (8, 4) C (8, 8) C (6, 6) C (4, 4)

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
**Idea of Data Envelopment Analysis (DEA)**

efficient boundary from many warehouses that consume the same amount of resources apple orange inefficient region

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
**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 k output, cj = unit cost of type j input performance of DMU 1 = (5r3+2r4)/(c1+3c2) performance of DMU 2 = (3r3+4r4)/(2c1+c2) performance of DMU i defined similarly given (aij) of the I DMUs, how to benchmark a tapped DMU with (aoj) for unknown rk 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), 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. Lines and Cube per order Dist. Cube per order Dist. Family Mix Dist. Full/Partial Mix Dist. Order Inc. Dist. results from order profiling help design a warehouse, including its layout, equipment, picking methods, etc.

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

51
**Item-Family Inventory Distribution**

implication: area assigned to different types of storage

52
**Handling Unit Inventory Distribution**

implication: different storage modes according to the number of pallets on hand

53
**Seasonality Distribution**

implication: shifting human resources and possibly space

54
**Daily Activity Distribution**

implication: shifting human resources and possibly space

55
**Activity Relationship**

implication: on layout

Similar presentations

Presentation is loading. Please wait....

OK

Multicriteria Decision-Making Models

Multicriteria Decision-Making Models

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google

Ppt on 5 electrical appliances Ppt on flags of the world Ppt on principles of peace building fund Ppt on second law of thermodynamics entropy Ppt on paintings and photographs related to colonial period lighting Ppt on national sports day Ppt on business environment nature concept and significance of the study Ppt on c language fundamentals Ppt on 3 idiots movie watch Ppt on carry save adder