Nof S.Y. Springer Handbook of Automation

Подождите немного. Документ загружается.

Nanomanufacturing Automation References 945

surfaces by tip induced anodization in atomic force

microscopy, Appl. Phys. Lett. 67, 1295–1297 (1995)

53.3 D.M. Schaefer, R. Reifenberger, A. Patil, R.P. An-

dres: Fabrication of two-dimensional arrays of

nanometer-size clusters with the atomic force mi-

croscope, Appl. Phys. Lett. 66, 1012–1014 (1995)

53.4 T.Junno,K.Deppert,L.Montelius,L.Samuelson:

Controlled manipulation of nanoparticles with an

atomic force microscope, Appl. Phys. Lett. 66(26),

3627–3629 (1995)

53.5 P. Avouris, T. Hertel, R. Martel: Atomic force mi-

croscope tip-induced local oxidation of silicon:

kinetics, mechanism, and nanofabrication, Appl.

Phys. Lett. 71, 285–287 (1997)

53.6 R. Nemutudi, N. Curson, N. Appleyard, D. Ritchie,

G. Jones: Modiﬁcation of a shallow 2DEG by AFM

lithography, Solid-State Electron. 57/58,967–973

(2001)

53.7 S.J. Ahn, Y.K. Jang, S.A. Kim, H. Lee, H. Lee: AFM

nanolithography on a mixed LB ﬁlm of hexadecy-

lamine and palmitic acid, Ultramicroscopy 91,171–

176 (2002)

53.8 E. Dubois, J.-L. Bubbendorff: Nanometer scale

lithography on silicon, titanium and PMMA re-

sist using scanning probe microscopy, Solid-State

Electron. 43, 1085–1089 (1999)

53.9 M. Sitti, H. Hashimoto: Tele-nanorobotics using

atomic force microscope, Proc. IEEE Int. Conf. Intell.

Robot. Syst. (Victoria 1998) pp. 1739–1746

53.10 M. Guthold, M.R. Falvo, W.G. Matthews, S. Paulson,

S. Washburn, D.A. Erie, R. Superﬁne, F.P. Brooks Jr.,

R.M. Taylor II: Controlled manipulation of molecu-

lar samples with the nanomanipulator, IEEE/ASME

Trans. Mechatron. 5(2), 189–198 (2000)

53.11 A.A.G. Requicha, C. Baur, A. Bugacov, B.C. Gazen,

B. Koel, A. Madhukar, T.R. Ramachandran,

R. Resch, P. Will: Nanorobotic assembly of two-

dimensional structures, Proc. IEEE Int. Conf. Robot.

Autom. (Leuven 1998) pp. 3368–3374

53.12 L.T. Hansen, A. Kühle, A.H. Sørensen, J. Bohr,

P.E. Lindelof: A technique for positioning nanopar-

ticles using an atomic force microscope, Nanotech-

nology 9, 337–342 (1998)

53.13 G.Y. Li, N. Xi, M. Yu, W.K. Fung: 3-D nanomanip-

ulation using atomic force microscope, Proc. IEEE

Int. Conf. Robot. Autom. (Taipei 2003)

53.14 G. Li, N. Xi, H. Chen, C. Pomeroy, M. Prokos:

Videolized atomic force microscopy for interactive

nanomanipulation and nanoassembly, IEEE Trans.

Nanotechnol. 4(5), 605–615 (2005)

53.15 G.Li,N.Xi,M.Yu,W.-K.Fung:Develop-

ment of augmented reality system for AFM-based

nanomanipulation, IEEE/ASME Trans. Mechatron.

9(2), 358–365 (2004)

53.16 H.Chen,N.Xi,G.Li:CAD-guidedautomated

nanoassembly using atomic force microscopy-

based nonrobotics, IEEE Trans. Autom. Sci. Eng.

3(3), 208–217 (2006)

53.17 H. Chen, N. Xi, W. Sheng, Y. Chen: General

framework of optimal tool trajectory planning

for free-form surfaces in surface manufacturing,

J. Manuf. Sci. Eng. 127(1), 49–59 (2005)

53.18 J. Zhang, N. Xi, G. Li, H.-Y. Chan, U.C. Wejinya:

Adaptable end effector for atomic force microscopy

based nanomanipulation, IEEE Trans. Nanotech-

nol. 5(6), 628–642 (2006)

53.19 A. Javey, J. Guo, D.B. Farmer, Q. Wang, D. Wang,

R.G.Gordon,M.Lundstrom,H.Dai:Carbonnano-

tube ﬁeld-effect transistors with integrated ohmic

contacts and high-k gate dielectrics, Nano Lett.

4(3), 447–450 (2004)

53.20 A. Bachtold, P. Hadley, T. Nakanishi, C. Dekker:

Logic circuits with carbon nanotube transistors,

Science 294, 1317–1320 (2001)

53.21 I.A. Levitsky, W.B. Euler: Photoconductivity of

single-walled carbon nanotubes under CW il-

lumination, Appl. Phys. Lett. 83, 1857–1859

(2003)

53.22 L. Liu, Y. Zhang: Multi-wall carbon nanotube as

a new infrared detected material, Sens. Actuators

A 116, 394–397 (2004)

53.23 J.A. Misewich, R. Martel, P. Avouris, J.C. Sang,

S. Heinze, J. Tersoff: Electrically induced optical

emission from a carbon nanotube FET, Science 300,

783–786 (2003)

53.24 L. Valentini, I. Armentano, J.M. Kenny, C. Can-

tanlini, L. Lozzi, S. Santucci: Sensors for sub-ppm

gas detection based on carbon nanotube thin

ﬁlms, Appl. Phys. Lett. 82, 4623–4625 (2003)

53.25 J. Kong, N.R. Franklin, C. Zhou, M.G. Chapline,

S. Peng, K. Cho, H. Dai: Nanotube molecular

wires as chemical sensors, Science 287, 622–625

(2000)

53.26 G.Y.Li,N.Xi,M.Yu,W.K.Fung:Augmentedreality

system for real-time nanomanipulation, Proc. IEEE

Int. Conf. Nanotechnol. (San Francisco 2003)

53.27 L. Liu, Y. Luo, N. Xi, Y. Wang, J. Zhang,

G. Li: Sensor referenced real-time videolization of

atomic force microscopy for nanomanipulations,

IEEE/ASME Trans. Mechatron. 13(1), 76–85 (2008)

53.28 K.W.C. Lai, N. Xi, C.K.M. Fung, J. Zhang, H. Chen,

Y. Luo, U.C. Wejinya: Automated nanomanufactur-

ing system to assemble carbon nanotube based

devices, Int. J. Robot. Res. (IJRR) 28(4), 523–536

(2009)

53.29 K.W.C. Lai, N. Xi, U.C. Wejinya: Automated pro-

cess for selection of carbon nanotube by electronic

property using dielectrophoretic manipulation, J.

Micro-Nano Mechatron. 4(1), 37–48 (2008)

53.30 M.S. Arnold, A.A. Green, J.F. Hulvat, S.I. Stupp,

M.C. Hersam: Sorting carbon nanotubes by elec-

tronic structure using density differentiation, Nat.

Nanotechnol. 1, 60–65 (2006)

53.31 P.G. Collins, M.S. Arnold, P. Avouris: Engineer-

ing carbon nanotubes and nanotube circuits using

electrical breakdown, Science 292, 706–709 (2001)

Part F 53

946 Part F Industrial Automation

53.32 R. Krupke, F. Hennrich, H. von Lohneysen,

M.M. Kappes: Separation of metallic from semicon-

ducting single-walled carbon nanotubes, Science

301, 344–347 (2003)

53.33 R. Krupke, S. Linden, M. Rapp, F. Hennrich:

Thin ﬁlms of metallic carbon nanotubes prepared

by dielectrophoresis, Adv. Mater. 18, 1468–1470

(2006)

53.34 M. Yu, M.J. Dyer, G.D. Skidmore, H.W. Rohrs,

X. Lu, K.D. Ausman, J.R.V. Ehr, R.S. Ruoff: Three-

dimensional manipulation of carbon nanotubes

under a scanning electron microscope, Nanotech-

nology 10, 244–252 (1999)

53.35 T. Fukuda, F. Arai, L. Dong: Assembly of

nanodevices with carbon nanotubes through

nanorobotic manipulations, Proc. IEEE 91, 1803–

1818 (2003)

53.36 N.G. Green, A. Ramos, H. Morgan: AC electrokinet-

ics: a survey of sub-micrometre particle dynamics,

J. Phys. D: Appl. Phys. 33, 632–641 (2000)

53.37 M. Dimaki, P. Boggild: Dielectrophoresis of car-

bon nanotubes using microelectrodes: a numerical

study, Nanotechnology 15, 1095–1102 (2004)

53.38 J. Li, Q. Zhang, N. Peng, Q. Zhu: Manipulation of

carbon nanotubes using AC dielectrophoresis, Appl.

Phys. Lett. 86, 153116–153118 (2005)

53.39 A. Subramanian, L.X. Dong, J. Tharian, U. Senn-

hauser, B.J. Nelson: Batch fabrication of carbon

nanotube bearings, Nanotechnology 18, 075703

(2007)

53.40 A. Subramanian, T. Choi, L.X. Dong, D. Poulikakos,

B.J. Nelson: Batch fabrication of nanotube trans-

ducers, Proc. 7th IEEE Conf. Nanotechnol. (IEEE-

NANO2007) (Hong Kong 2007)

53.41 R.Resch,C.Baur,A.Bugacov,B.E.Koel,A.Mad-

hukar, A.A.G. Requicha, P. Will: Building and

manipulating three-dimensional and linked two-

dimensional structures of nanoparticles using

scanning force microscopy, Langmuir 14(23), 6613–

6616 (1998)

53.42 J. Israelachvili: Intermolecular and surface forces

(Academic Press, London 1991)

53.43 G.Y. Li, N. Xi, M. Yu, W.K. Fung: Modeling of 3-D

interactive forces in nanomanipulation, IEEE/RSJ

Int. Conf. Intell. Robot. Syst. (Las Vegas

2003)

53.44 P.M. Garth: Nonlinear programming: Theory,

algorithm and applications (Wiley, New York

1983)

53.45 M. Avriel: Nonlinear Programming: Analysis

and Methods (Prentice Hall, Englewood Cliffs

1976)

53.46 J.C. Giddings: Field-Flow Fractionation: Analysis of

macromolecular, colloidal, and particulate mater-

ials, Science 260, 1456–1465 (1993)

53.47 H. Morgan, N. G. Green: AC Electrokinetics: col-

loids and nanoparticles (Research Studies Press

Ltd. Hertfordshire 2003)

53.48 T.B. Jones: Electromechanics of Particles (Cam-

bridge Univ. Press, Cambridge 1995)

53.49 Y.Li,D.Mann,M.Rolandi,W.Kim,A.Ural,S.Hung,

A. Javey, J. Cao, D. Wang, E. Yenilmez, Q. Wang,

J.F.Gibbons,Y.Nishi,H.Dai:Preferentialgrowthof

semiconducting single-walled carbon nanotubes

by a plasma enhanced CVD method, Nano Lett. 4(2),

317–321 (2004)

53.50 J. Zhang, N. Xi, H. Chen, K.W.C. Lai, G. Li:

Design, manufacturing and testing of single car-

bon nanotube based infrared sensors, IEEE Trans.

Nanotechnol. 8(2), 245–251 (2009)

53.51 J. Zhang, N. Xi, H. Chen, K.W.C. Lai, G. Li: Pho-

tovoltaic effect in single carbon nanotube based

Schottky diodes, Int. J. Nanopart. 1(2), 108–118

(2008)

53.52 J. Zhang, N. Xi, K. Lai: Fabrication and testing of

a nano infrared detector using a single carbon

nanotube (CNT), SPIE Newsroom (2007), online at:

http://spie.org/x8489.xml

53.53 J.Zhang,N.Xi,L.Liu,H.Chen,K.W.C.Lai,G.Li:

Atomic force yields a master nanomanipulator, IEEE

Nanotechnol. Mag. 2(2), 13–17 (2008)

53.54 G. Li, N. Xi, D.H. Wang: Probing membrane proteins

using atomic force microscopy, J. Cell. Biochem. 97,

1191–1197 (2006)

53.55 G. Li, N. Xi, D.H. Wang: Investigation of angiotensin

II type 1 receptor by atomic force microscopy

with functionalized probe, Nanomed. Nanotech-

nol. Biol. Med. 1(4), 302–312 (2005)

53.56 G.Li,N.Xi,D.H.Wang:Insitusensingandmanip-

ulation of molecules in biological samples using

a nano robotic system, Nanomed. Nanotechnol.

Biol. Med. 1(1), 31–40 (2005)

Part F 53

947

Production, S

54. Production, Supply, Logistics and Distribution

Rodrigo J. Cruz Di Palma, Manuel Scavarda Basaldúa

To effectively manage a supply chain it is necessary

to coordinate the ﬂow of materials and information

both within and among companies. This ﬂow goes

from suppliers to consumers, as it passes through

manufacturers, wholesalers, and retailers. While

materials and information move through the

supply chain, automation is used in a variety of

forms and levels as a way to raise productivity,

enhance product quality, decrease labor costs,

improve safety, and even to perform tasks that go

beyond the precision and reliability of humans.

A rapid development in information technology

has transformed not only the way people work

and interact with each other; electronic media

enable enterprises to collaborate on their work

and missions within each organization and with

other independent enterprises, including suppliers

and customers.

Within this chapter, the focus is on the main

beneﬁts of automation in production, supply, lo-

gistics, and distribution environments. The ﬁrst

Section centers on machines and equipment

automation for production. The second section

focuses on computing/communication automation

for planning and operations decisions. Finally,

the last section highlights some considerations

regarding economics, productivity, and ﬂexibil-

ity important to bear in mind while designing an

automation strategy.

54.1 Historical Background ........................... 947

54.2 Machines and Equipment Automation

for Production...................................... 949

54.2.1 Production Equipment

and Machinery............................ 949

54.2.2 Material Handling and Storage

for Production and Distribution .... 949

54.2.3 Process Control Systems

in Production ............................. 950

54.3 Computing and Communication

Automation for Planning

and Operations Decisions ...................... 951

54.3.1 Supply Chain Planning ................. 951

54.3.2 Production Planning

and Programming ....................... 952

54.3.3 Logistic Execution Systems............ 953

54.3.4 Customer-Oriented Systems.......... 953

54.4 Automation Design Strategy .................. 954

54.4.1 Labor Costs

and Automation Economics .......... 954

54.4.2 The Role of Simulation Software.... 954

54.4.3 Balancing Agility, Flexibility,

and Productivity ......................... 954

54.5 Emerging Trends and Challenges ........... 955

54.5.1 RFID Technology in Supply Chain

and Networks ............................. 955

54.6 Further Reading ................................... 958

References .................................................. 959

54.1 Historical Background

Automation isany technique, method or system ofoper-

ating or controlling a process without continuous input

from an operator, thereby reducing human intervention

to a minimum. Many believe that automation of supply

chain networks began with the use of personal comput-

ers in the late 1970s, while others date it back to the

use of electricity in the early 1900s. The fact is that,

regardless of when we place the beginning of automa-

tion, it has changed the way we work, think, and live

our lives. Children are now in contact with automation

from the day they are born, such as automated ma-

chines that monitor the vital signs of premature infants.

As people grow older, they continually have contact

with automation via automatic teller machines (ATM s),

Part F 54

948 Part F Industrial Automation

Table 54.1 Supply chain evolution at a food-products production and service company

Before supply chain After supply chain design Advantages

Forecasting/

ordering

The company determines the

amount of nuts that a customer

will expect in its food products.

The company and its customer share

sales forecasts based on current

point-of-sale data, past demand

patterns, and upcoming promotions,

and agree on an amount and schedule

to supply.

Forecasting accuracy,

collaborative

replenishment

planning

Procurement The company phones its

Brazilian ofﬁce and employees

deliver the orders in person to

local farmers, who load the raw

nuts on trucks and deliver them

to the port.

The company contacts its Brazilian

ofﬁce by email, but employees

still must contact local farmers

personally.

Enhanced

communication

Transportation The shipping company notiﬁes

the company when the nuts have

sailed. When the nuts arrive in

a US port, a freight-forwarder

processes the paperwork to clear

the shipment through customs,

locates a truck to deliver them

to the company plants, and

delivers the nuts to the company’s

manufacturing plant, although it

may be only half-full and return

empty, costing the company extra

money.

Shippers and truckers share up-to-

date data online via a collaborative

global logistics system that

connects multiple manufacturers

and transportation companies and

handles the customs’ process. The

system matches orders with carriers

to assure that trucks travel with full

loads.

Online tracking,

transportation cost

minimization

Manufacturing The nuts are cleaned, roasted,

and integrated with various food

products manufactured by the

company according to original

production forecast.

Production forecast is updated based

on the current demand for product

mix, and products are manufactured

under lean manufacturing and

just-in-time principles.

Capacity utilization,

production efﬁciency

Distribution The products are packed,

and trucks take them to the

company’s multiple warehouses

across the country, from where

they are ready to be shipped to

stores. However, they may not be

near the store where the customer

needs them because local demand

has not been considered.

After food products are inspected

and packed at the plant, the company

sends the products to a third-party

distributor, which relieves the

company of a supply chain activity

not among its core competencies.

The distributor consolidates the

products on trucks with other

products, resulting in full loads and

better service.

Enhanced distribution

planning, inventory

management and

control.

Customer If the company ordered too

many nuts, they will turn soft

in the warehouses, and if they

ordered too few, the customer

will buy food products with nuts

elsewhere.

The company correctly knows

the customer’s needs so there is

neither a shortage nor an oversupply

of the products. Transportation,

distribution, warehousing, and

inventory costs drop, and product

and service quality improve.

Increased customer

satisfaction. Cost

minimization, proﬁt

maximization

Part F 54.1

Production, Supply, Logistics and Distribution 54.2 Machines and Equipment Automation for Production 949

self-operated airplanes, and self-parking cars. In the in-

dustrial realm, automation now can be seen from such

simple tasks as milking a cow to complex repetitive task

such as building a new car. Computing and communi-

cations have also transformed production and service

organizations over the past 50 years. While working

in parallel, error recovery, and conﬂict resolution have

been addressed by human workers since early days of

industry, they have recently been transformed by com-

puter into integrated functions [54.1].

Automation is the foundation of many of society’s

advances. Through productivity advances and reduc-

tions in costs, automation allows more complex and

sophisticated products and services to be available to

larger portions of the world’s population. The evolution

of supply chain management at a company as an au-

tomation example of the production, supply, logistics,

and distribution is described in Table 54.1.Anexample

of such evolution for a speciﬁc company is discussed

in [54.2].

54.2 Machines and Equipment Automation for Production

54.2.1 Production Equipment

and Machinery

Automation has been used for many years in manufac-

turing asa wayto increase speed of production, enhance

product quality, decrease labor costs, reduce routine

labor-intensive work, improve safety, and even to per-

form tasks that go beyond the precision and reliability

of normal human abilities [54.3, 4]. Examples of au-

Fig. 54.1 Industrial robotics in car production (courtesy of

KUKA Robotics)

tomation range from the employment of robots to install

a windshield on a car, machine tools that process parts

to build a computer processor, to inspection and testing

systems for quality control to make certain the precise

amount of cereal is in each box of cornﬂakes. An ex-

ample of industrial robots for car production is shown

in Fig.54.1.

There are many reasons that explain why automa-

tion has become increasingly important in today’s

production systems. The main justiﬁcations arise from

the relative strengths that automation provide in com-

parison with humans within a production environment.

Automated machines are able to perform systematic

and repetitive tasks, requiring precision, storing large

amounts of information, executing commands fast and

accurately, handling multiple tasks simultaneously, and

conducting dangerous/hazardous work [54.5,6].

However, in spite of the numerous beneﬁts and ad-

vantages automation may offer, it is not always the best

solution and in some cases it is not even a feasible

one. In situations with elevated levels of variation, short

product lifecycles, or highly customized production,

a solution involving automation can be unnecessarily

complex and expensive when compared with a tra-

ditional manual process. Under these circumstances,

the beneﬁts of a manual process, such as the ﬂexi-

bility and lower capital requirements, begin to gain

relative advantage when compared with an automated

process.

54.2.2 Material Handling and Storage

for Production and Distribution

Material handling equipment plays an important role in

the automation of production, storage, and distribution

systems, by interconnecting the ﬁxed or ﬂexible work-

Part F 54.2

950 Part F Industrial Automation

Fig. 54.2 Inertial guidance automatic guided vehicle (courtesy of

the Jervis B.Webb Company)

stations that compose them. Typical automated material

handling systems include conveyors for moving prod-

uct in a general direction inside a facility, sorters and

carousels for distributing products to speciﬁc locations,

and automated storage and retrieval systems (ASRS)

for storage and automated guided vehicles (AGVs) for

transporting materials between work stations[54.7]. For

instance, AGVs play an important role in the paper in-

dustry, where moving roles quickly and efﬁciently is

critical. The key is not to damage rolls during this han-

dling process. An example of an AGV in the paper

industry is shown in Fig.54.2.

Conveyors are believed to be the most com-

mon material handling system used in production

and distribution processes. These systems are used

when materials must be transferred in relatively large

quantities between speciﬁc machines or workstations

following a predetermined path or route. Most of these

systems use belts or wheels to transfer materials hori-

zontally or gravity to move materials between points at

different heights. Frequently within production or dis-

tribution systems several types of conveyors are utilized

in a combined manner, constituting conveyor networks

or integrated systems.

In more sophisticated conveyor networks, sorters

are utilized. A sorter consists of an array of closely

coupled, high-density diverters used to sort units (ma-

terials, products, parts, etc.) to speciﬁc lanes for further

consolidation. Consequently, in addition to the transfer

functionality supported by regular conveyor systems,

sorter mechanisms provide, by means of sensors,

a classiﬁcation capability that makes them especially

attractive for highly variable, small-size shipments.

Examples of operations that utilizesorters fortheir ship-

ments are Federal Express, United Parcel Services, and

Amazon.

Kimberly-Clark claims that a sorter mechanism im-

plemented at one of its distribution facilities in Latin

America has improved the truck loading operation time

from 1–3h to 20 min. With capacity of 200cases per

minute and fully customizable logic (i.e., it can be pro-

grammed to follow a balanced sorting sequence per

dock or a sorting sequence by customer orders), this

sorter has signiﬁcantly increased truck rotation at the

Kimberly-Clark distribution center versus the previous

manual system.

54.2.3 Process Control Systems

in Production

Production systems can be designed with different lev-

els of automation. However in all automated production

systems, even at the lowest levels of sophistica-

tion constituted by automated devices such as valves

and actuators, a control system of some kind is re-

quired.

At the individual machine level, the automatic con-

trol is executed by computer systems. An example is

computer numerical control (CNC), which reads in-

structions from an operator in order to drive a machine

tool. At the automatic process level there are two main

types of control systems, the programmable logic con-

troller (PLC) and the distributed control system (DCS).

These systems were initially developed to support

distinctive process control functions. PLCs began re-

placing conventional relay/solid-state logic in machine

control while DCSs were merely a digital replacement

of analog controllers and panel-board displays. How-

ever, the technology of both types of process control

systems has evolved over the years and the differences

in their functionalities have become less straightfor-

ward. Further up in the hierarchy, at the production cell

level, cell controllers provide coordination among indi-

vidual workstations by interacting with multiple PLCs,

DCSs, and other automated devices. At the highest

production automation level, control systems such as

manufacturing control systems and/or integrated plant

systems initiate, coordinate, and provide visibility of

the manufacturing operation of all other lower control

levels [54.6,8].

Part F 54.2

Production, Supply, Logistics and Distribution 54.3 Planning and Operations Decision Automation 951

54.3 Computing and Communication Automation for Planning

and Operations Decisions

54.3.1 Supply Chain Planning

To manage the supply chain effectively it is necessary

to coordinate the ﬂow of materials and information both

within and between companies (e.g., [54.9]). The focus

of the supply chain planning process is to synchronize

activities from raw materials to the ﬁnal customer. Sup-

ply chain planning processes strive to ﬁnd an integrated

solution for the strategic, tactical, and operational activ-

ities in order to allow companies to balance supply and

demand for the movement of goods and services.

Information and communication technology (ICT)

plays a vital role in supplychain planning by facilitating

the ﬂow of information and enhancing the cooperation

between customers, suppliers and third party partners.

As show in Fig.54.3, intranets can be used to integrate

information from isolated business processes within the

ﬁrm to help them manage their internal supply chains.

Suppliers

Order

processing

Procurement

Customers

Retailers

Logistics

services

Distributors

Planning &

scheduling

Production

Inventory

Shipping

Product

development

Enterprise

intranet

extranet

Marketing

Partner

enterprises

Fig. 54.3 Intranet and extranet for supply chain planning

Access to these private intranets can also be extended to

authorized supplies, distributors, logistics services, and

to retail customers to improve coordination of external

supply chain processes [54.10].

Electronic data interchange (EDI) and other similar

technologies not only save money by reducing or elimi-

nating human intervention and data entry, but also pave

the way for collaboration initiatives between organiza-

tions. An example would be vendor-managed inventory

(VMI) where customers send inventory and consump-

tion information to suppliers, who schedule deliveries

to keep customer inventory within agreed upon ranges.

VMI not only provides beneﬁts for the customer but

also increases demand visibility for the supplier and

leads to cost savings and reduced inventory investment

for the supplier. As shown in Fig.54.4, EDI can sig-

niﬁcantly improve productivity. A typical illustration

is the case of Warner-Lambert that increased its prod-

Part F 54.3

952 Part F Industrial Automation

Operational

system

ERP

SCM

manufacturing

planning

Review and

comments

Data

warehouse

Retail link

Sales data

at POS

Forecast

Inventory

plan

Retailer Producer

Retailer

server

Producer

server

EDI

Fig. 54.4 Producer and retailer EDI application

ucts’ shelf-ﬁll rate at its retailer Wal-Mart from 87%

to 98% by EDI application, earning the company about

US$ 8 million a year in additional sales [54.11].

ICT is key to the strategic, tactical, and operational

analysis required for supply chain planning. Transac-

tional ICT helpsto automate theacquisition, processing,

and communication of raw data related to historic and

current supply chain operations. This data is utilized

by ICT systems for evaluating and disseminating de-

cisions based on rules; for example, when inventory

reaches a certain level, a purchase order for replenish-

ment is automatically generated [54.12]. The data from

Human resource management

Accounting and financial management

Production

scheduling &

re-scheduling

Inventory

planning &

control

Distribution

planning

Material

requirement

planning

Procurement

Suppliers

Customers

Marketing

studied

Product

development

& quality assurance

Price &

promotion

ERP analytics

Sales

Order entry

Fig. 54.5 A general framework of ERP implementation

ICT systems can also be used for simulating and opti-

mizing a system. An example could be where ICT data

is used tosimulate the impact on aprocess with different

levels of work in process on total productivity.

54.3.2 Production Planning

and Programming

As the rate of technological innovation increases,

maintaining a competitive cost structure may rely

heavily on production efﬁciency generated by an ef-

fective production planning and programming process.

For large-scale, global enterprises where traditional

MRPI (material resource planning (1st generation)) and

MRPII (material resource planning (2nd generation))

approaches may not be sufﬁcient, new enterprise re-

source planning (ERP)solutions are beingdeployed that

not only link all aspects from the bill of materials to

suppliers to customer orders, but also utilize different

algorithms in order to efﬁciently solve the scheduling

conundrum. A general framework of ERP implementa-

tion is shown in Fig.54.5.

Off-the-shelf solutions such as those provided by

software supplier SAP advance planner and optimizer

(APO) provide automatic data classiﬁcation and re-

trieval, allowing key measures to be retrieved for

strategic, tactical, and operational planning. However,

typical ERP systems are rigid and have a difﬁcult

time adjusting to the complexity in demand require-

ments and constant innovation of the product portfolio

Part F 54.3

Production, Supply, Logistics and Distribution 54.3 Planning and Operations Decision Automation 953

mix. Global companies that compete in diverse market-

places may choose to address these issues by building

their own large-scale optimization models. With a solid

database structure, these models are able to adapt

to continuous changes in portfolios and can incorpo-

rate external inﬂuences on demand, such as market

trends in promotions and advertisement. Over the past

decade, there has been a shift in focus from busi-

ness functions to business processes and further into

value chains. Nowadays, enterprises focus on the ef-

fectiveness of the operation that requires functions

to be combined to achieve end-to-end business pro-

cesses [54.13].

54.3.3 Logistic Execution Systems

Logistic execution systems (LES), seek to consolidate

all logistic functions, such as receiving, storage, inven-

tory management, order processing, order preparation,

yard management and shipping into an integrated pro-

cess. The LES can be designed to communicate with

the ﬁrms enterprise network and external entities such

as customers and suppliers.

The LES might be part of the ERP system or oth-

erwise should communicate with it through interfaces

in order to interact with other modules such as ﬁnance,

purchasing, administration, and production planning.

Usually it also communicates with customers, suppli-

Yard management

Controls dock activities

and schedules dock

appointments to avoid

bottlenecks

Cross docking

Incoming shipments

directed

to shipping dock to fill

outgoing orders without

put away and picking

Order tracking

Tracks inbound and

outbound shipments

Order management

Orders added,

modified, or cancelled

in real time

Customer labeling and

packaging

Special packaging;

bar coding

Warehouse management

Warehouse optimization

Slotting optimizes

placement of items

Labor management

Plans, manages, and

reports on performance

of warehouse personnel

Fig. 54.6 A warehouse management system with decision support systems

ers, and carriers via EDI or web-based systems with the

purpose of sharing logistic information relevant to all

parties such as order status, trucks availability, conﬁr-

mation of order reception, etc.

A LES is usually composed of a warehouse man-

agement system (WMS) complemented with a labor

management system (LMS) and a transportation man-

agement system (TMS). Essentially WMS issues, man-

ages, and monitors tasks related to warehouse operation

performance. A WMS improves efﬁciency and produc-

tivity of warehousing operations usually supported by

(1) barcode and (2) radiofrequency data communica-

tions technologies. Both of these technologies provide

a WMS with instantaneous visibility of warehouse op-

erations, facilitating precise inventory control as well as

accurate knowledge of labor and equipment resources

availability. The addition of labor and transportation

management in a LES has expanded the span of WMS

functionality to the point of practically embracing every

logistic function from receiving to the actual shipping

of goods. An example of a WMS is shown in Fig.54.6.

54.3.4 Customer-Oriented Systems

Many times, automation is thought to only apply to the

production environment. However, there are a myriad

of examples where other critical non-production-related

processes have been automated, such as order entry,

Part F 54.3

954 Part F Industrial Automation

inventory management, customer service, and product

portfolio management. The goal of these applications

is to improve upon the customers’ experience with

suppliers.

For many companies, the customer experience is

a very tangible and measurable effect that should be

viewed through the eyes of the customer. The goal for

the supplier is to be easy to do business with. For this

reason, many companies have decided to automate how

the customer exchanges information related to sales and

logistics functions. One example of this automation is

the use of cellular phone messaging technology to co-

ordinate in real time to the customer the status of their

order throughout the delivery process.

54.4 Automation Design Strategy

54.4.1 Labor Costs

and Automation Economics

Competitive dynamics and consumer needs in today’s

marketplace demonstrate the need for manufacturer

ﬂexibility in response to the speed of change. Modern

facilities must be aligned with the frequent creation of

new products and processes and be prepared to man-

age these changes and resulting technological shifts.

Therefore, one of the most difﬁcult issues now facing

companies is identifying a manufacturing strategy that

includes the optimal degree of automation for a given

competitive environment.

AsshowninFig.54.7, increased production costs,

including the mitigation of possible labor shortages, is

one of the initial reasons companies look towards au-

tomation. However, production costs alone are usually

not sufﬁcient to justify the cost of investment. Produc-

tivity, customer response time, and speed to market are

many times key factors to the success of automating

Production unit cost

Production volumes

Fixed

Flexible

Manual

Hard automation

Manual manufacturing

Robotic

manufacturing

Fig. 54.7 Automation tradeoffs based on production vol-

umes

a production process and must be measured in order to

effectively determine the impact on costs and expected

revenues in the future.

54.4.2 The Role of Simulation Software

Determining the beneﬁts of automation may prove to

be a challenge, especially when there are complex rela-

tionships among processes. Will there be improvement

in throughput and will this be sufﬁcient to justify the

required investment? With the availability of software

such as ARENA by Rockwell Automation, simulation

can be used as a tool to test different possible scenarios

without having to make any physical changes to an ex-

isting system. This tool can be especially useful in cases

where the required investment for automation is high

and theexpected beneﬁts arenot easily measured. In ad-

dition, the visual simulation capabilities related to this

type of software facilitates the analysis of the process as

well as obtaining top management support.

54.4.3 Balancing Agility, Flexibility,

and Productivity

Typical business concerns such as increasing sales and

operating proﬁt are always considered key performance

drivers that lead the investment strategy within an or-

ganization. However, with the complexity that can be

found within certain marketplaces, a local niche or

a well-developedglobal market, theneed for sustainable

growth has increasingly become one of the most impor-

tant aspects when determining a competitive strategy.

A major competitive concern in the global market

is agility in order to react to a dynamic market. To

maintain agility between autonomous and geograph-

ically distributed functions signiﬁcant investment in

automated error detection is required to facilitate recov-

ery and conﬂict resolution [54.1].

This complexity has adjusted the traditional return-

on-investment (ROI) approach to automation invest-

Part F 54.4