Manufacturing Strategies for SCM 2001.12.8 MAI Lab. Seminar Park Jung Joon.

Manufacturing Strategies for SCM

2001.12.8

MAI Lab. Seminar

Park Jung Joon

What is the right supply chain for your product? A simple framework can help you figure out the answer

M. L. Fisher*

*Operations and Information Management, University of Pennsylvania’s Wharton School, Philadelphia, USA

Havard Business Review, March/April(1997) 105-106

MAI-LAB Seminar 3/36

Introduction The U.S. food industry was wasting $30 billion annually because of

poor coordination among Supply chain Excess of some products and a shortage of others owing to an inability

to predict demand Before devising a supply chain, consider the nature of the demand for

products Product life cycle, demand predictability, product variety, market

standards for lead time, service , and stocks… If one classifies products on the basis of their demand pattern, they

fall into one of two categories; functional or innovative Functional product

With small demand variance and low profit margin Innovative product

With uncertain demand, high variety, and high profit


Functional versus innovative products:differences in demand

Aspects of demand Functional

(Predictable demand)

Innovative

(Unpredictable demand)

Product life cycle More than 2 years 3 months to 1 year

Contribution margin* 5% to 20% 20% to 60%

Product variety Low (10 to 20 variants per category)

High(often millions of variants per category)

Average margin of error in the forecast at the time production is

committed10% 40% to 100%

Average stockout rate 1% to 2% 10% to 40%

Average forced end-of season markdown as percentage of full price

0% 10% to 25%

Lead time required for made-to-order products

6 months to 1 year 1day to 2 weeks

* The contribution margin equals price minus variable cost divided by price and is expressed as a percentage


Sport Obermeyer and Campbell Soup Company

Sport Obermeyer is a major supplier of fashion skiwear Each year, 95% of its products are completely new design for which dema

nd forecasts often err by as much as 200% Because the retail season is only few months long, the company has little t

ime to react if it misguesses the market Campbell Soup Company

In contrast, only 5% Campbell’s products are new each year. Sales are highly predictable, allowing Campbell to achieve a nearly perfec

t service level by satisfying more than 98% of demand immediately Sport Obermeyer : the goal was reducing “stockouts costs” through increased

speed and flexibility Campbell Soup Company : the goal was physically efficiency For example, PC, cars, apparel, ice cream, coffee, cookies and children’s car s

eats etc


Physically efficient versus market responsive supply chain

Physically efficient process Market-responsive process

Primary purpose Supply predicable demand efficiently at the lowest possible

cost

Respond quickly to unpredictable demand in order to minimize stockouts, forced markdowns, and obsolet

e inventory

Manufacturing focus Maintain high average utilization rate

Deploy excess buffer capacity

Inventory strategy Generate high turns and minimize inventory throughout the chain

Deploy significant buffer stocks of parts or finished goods

Lead-time focus Shorten lead time as long as it doesn’t increase cost

Invest aggressively in ways to reduce lead time

Approach to choosing suppliers

Select primarily for cost and quality Select primarily for speed, flexibility, and quality

Product-design strategy Maximize performance and minimize cost

Use modular design in order to postpone product differentiation

for as long as possible


Matching Supply Chains with Product


Example- Automobile and Computer industry

If you choose the cars Colors, interior features, drive train configuration and other

option… About 20 millions versions. But desired options entailed an 8 weeks wait for delivery.

BMW Z3 roadster – innovative product Volkswagen – functional product

The computer industry 20 years ago, IBM System /360 mainframe

14-month lead time : for physical efficiency But now, PCs

Just one day lead time possible : but they retain its emphasis on a physically efficiency


Conclusion

28 types of toothpaste Procter & Gamble

Simplifying many of its product lines and pricing Toothpaste is a product category in which a move to the left –from

innovative to functional- make sense

Campbell’s Efficient Supply of Functional products National Bicycle’s success is a good example of a responsive supply

chain achieved through avoiding uncertainty

A quantitative analysis of relationships between product types and supply chain strategies

D. Li* and C. O'Brien**

* School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK

** School of Mechanical, Materials, Manufacturing Engineering and Management, University of Nottingham, Nottingham NG7 2RD, UK

International Journal of Production Economics

Volume 73, Issue 1 31 August 2001 Pages 29-39


Literature Reviews

What is the right supply chain for your product? A simple framework can help you figure out the answer

March/April (1997) 105¯116, Harvard Business Review.

M.L. Fisher Integrated decision modeling of supply chain efficiency

March 1999, International Journal of Production Economics Dong Li and Christopher O'Brien

Inventory models involving lead time as a decision variable May 1994, Journal of the Operational Research Society M. Ben-Daya and A. Raouf Department of System Engineering, King Fahd University of Petro

leum & Miner, Dhahran, Saudi Arabia


Introduction(1/3)

The performance of many Supply chain(SC)

The conceptual mode (Fisher's research) Products are classified into two categories,

Functional products - with small demand variance and low profit margin

Innovative products - with uncertain demand, high variety, and high profit

SC strategies are classified into two processes A physically efficient process to supply predictable demand

efficiently at the lowest possible cost A market-responsive process to provide quickly response to

unpredictable demands


Introduction(2/3)

Fig.1. Fisher's conceptual model on matching SC strategy to products


Introduction(3/3)

Companies are facing SCM problems Past - Qualitative strategies Now - Quantitative operational decisions

The focus in this paper Impacts of operational parameters

Value adding capacity, demand uncertainty, material cost Explore the influence of significant operational parameters

Three manufacturing strategies Manufacturing to order (MTO) Manufacturing from stocks (MFS) Manufacturing to stocks (MTS)


Modeling Manufacturing Strategies(1/2)

생산전략 설 명MTO

(Make To Order)

설계 즉 형상이 완료되어 있는 제품을 고객의 주문에 근거하여 생산하는 방식

MTS(Make To

Stock)

완제품 재고를 창고나 진열대에 두고 고객이 즉석에서 가져가거나 요구하면 배달해주는 가전제품 , 생활용품 등 성숙기의 제품에 적용되는 생산전략

ATO(Assemble To

Order)

최종제품 직전의 조립품과 각종 옵션 부품 등을 제작하여 재고로 가져가고 , 고객의 요구에 맞추어 최종조립 및 도색 , 포장을 단시간에 할 수 있는 능력을 보유하는 생산전략

ETO(Engineering

To Order)

고객이 제품의 기획단계에서부터 참가하는 형태로 제품의 설계부터 생산완료에 필요한 전체 시간을 기다려준다 . 첨단제품 , 특수제품제작

Job-Shop 범용적인 공작기계군을 갖추고 다품종 소량의 부품을 가공하는 구역 . 전용 라인에 비교하여 자동화가 지연되고 있으나 , FMC, FMS 의 도입에 의해 그 효율이 진행되고 있다


Modeling Manufacturing Strategies(2/2)

Trading off the contribution of each performance criterion Profitability, Responsiveness, Reliability

Time is a key factor in this model to measure SC efficiency All decision variables are measured by time unit Inventory level : a number of days of supply Cycle time can be linked to stocks

Manufacturing Strategies Represent SC processes

MTO A physically efficient process

MFS A market responsive process

MTS A physically responsive process


Model assumptions

Model assumptions

Performance is measured by a weighted-sum of values related to three criteria-profit, lead time and delivery promptness. The performance values are the gaps(dissatisfaction level) between target and planned values. Demand uncertainty is represented by the probabilities of planned stocks failing to meet demands. SCM strategies are represented by three manufacturing strategies: manufacturing to order (MTO), manufacturing from stocks (MFS) and manufacturing to stocks (MTS) The expenses of enhancing responsiveness are represented by the cost of reducing production times, i.e., lead time crashing costs(usually from excessive capacity and overtime working payment, etc.)


Definitions of the parameters and variables in the model(1/2)

Terms Definitions

PRE, RLT, RDT Finished product sale price ($1000), required delivery lead time (30 time unit) and limited delivery delay (5 time unit)

Gl , Wl Gaps and weight to the lth criteria (W1 – profit, 0.35; W2 – lead time,0.35; W3 – delivery promptness, 0.30)

ETC, ELT, EDT Expected total cost($), lead time and delivery delay (time unit)

Crt, Crd Unit costs reducing production lead time ($600/per time unit) and production delay ($800/per time unit)

PLL, PDL Maximum production lead time (10 time unit) and production delay (5 time unit)

PLS, PDS Minimum production throughput time (0.1 time unit) and production delay (0.1 time unit)

PL, PD Planned production lead time and production delay (time unit)

HCm, HCf Total stock carrying cost of materials and finished products ($)

PCm, PCf Total penalty for delayed supplies from materials stocks and finished product stocks ($)


Definitions of the parameters and variables in the model(2/2)

Terms Definitions

Cm, Cp Materials purchasing price ($1000), fixed production costs ($400)

LTs, LTp Lead time components resulting from materials supply and production (time unit)

Hm, Hf Unit stock carrying cost of materials and finished products ($0.03/time unit)

Pm, Pf Unit delay penalty of materials supply and finished products stock supply ($0.04/time unit)

MT, FT Expected inventory level of materials and finished product in time (time unit)

MD, FD Expected supply delay resulting from unavailable materials and finished products stocks (time unit)

SL, SD Expected lead time (10 time unit) and delay of supplier (1.5 time unit)

Um, Uf Uncertainty of demands for materials and finished products


Strategies of the SC process


Strategies of the SC process-MTO

LTs=SL

SD

Cm

MaterialsSupply

MaterialsSupply

MaterialsInput

MaterialsInput

ProductManufacturing


ProductOutput

ProductOutput

ProductDelivery

ProductDelivery

MT=0

MD=SD

HCm=0

PCm=MD · Pm·Cm

LTp=PL

PD

Cp

FT=0

FD=PD

HCf=0

PCf=FD · Pf · PRE

부품재고비용 = 0

MD=SD

재고수송비용 = 0

완제품재고비용 = 0

FD=PD


부품리드타임= 공급자의 리드타임

제품리드타임= 계획된 제품 리드타임


Strategies of the SC process-MFS

LTs=0

SD

Cm

MaterialsSupply

MaterialsSupply

MaterialsInput

MaterialsInput



ProductOutput

ProductOutput

ProductDelivery

ProductDelivery

MT=SL+SD

MD=(SL+SD)·Um

HCm=MT · Hm · Cm

PCm=MD · Pm · Cm

LTp=PL

PD

Cp

FT=0

FD=PD

HCf=0

PCf=FD · Pf · PRE

부품재고비용 존재MD=(SL+SD)·Um

재고수송비용 존재

완제품재고비용 = 0

FD=PD


부품리드타임= 0 ( 이미 정의 되어있다 )

제품리드타임= 계획된 제품 리드타임


Strategies of the SC process-MTS

LTs=0

SD

Cm

MaterialsSupply

MaterialsSupply

MaterialsInput

MaterialsInput



ProductOutput

ProductOutput

ProductDelivery

ProductDelivery

LTp=0

PD

Cp

FT=PL+PD

FD=(PD+PL)·Uf

HCf= FT · Hf · PRE

PCf=FD · Pf · PRE

MT=SL+SD

MD=(SL+SD)·Um

HCm=MT · Hm · Cm

PCm=MD · Pm · Cm

부품재고비용 존재MD=(SL+SD)·Um


완제품재고비용 존재FD=(PD+PL)·Um


부품리드타임= 0 ( 이미 정의 되어 있다 )

제품리드타임= 0 ( 이미 정의 되어있다 .)


Supply chain strategy optimization model

Min PE=Wl·Gl

s.t. ETC-G1=PRE

ELT-G2=RLT

EDT-G3=RDT

PLs<PL<PLL

PDs<PD<PDL (1)

ETC=Production time reducing cost

+Production delay reducing cost

+Inventory holding cost

+Delaypenalty + Fixedcosts

=Crt(PLL-PL)+Crd(PDL-PD)

+HCm+HCf+PCm+PCf+Cm+Cp (2)

ELT=Sum of the lead time

components resulting

from materials supply

and production

=LTs+LTp (3)

EDT=Sum of the delivery delays

resulting from unavailable

supplies of materials and

finished products

=MD+FD (4)


Analysis of matching product types to manufacturing strategies

Functional and innovative products By Fisher A sensitivity analysis Primary factors to identify the two product types

Profit margin and demand uncertainty

However, product profit with SC strategies Affect the product manufacturing cost and product price So we focus demand uncertainty and value-adding capacity(rather

than profit margin)

Value-adding capacity (VC)

Value-adding capacity=Materials cost / Finished product price (5)


Interaction between SC strategies and operational environment


Impact of demand uncertainty (Uf=Um) and value adding index on the performanc

e of the strategies (VC ¯ Value-adding capacity, PE ¯ Performance)

Impact of demand uncertainty and value adding index(1/3)



Impact of demand uncertainty (Uf Um)

and value adding index


Summary of the results of the sensitivity analysis(1/3)


Discussion(1/2)

DU of materials and finished products are at lower levels MTS always performs better than the other two strategies and the

physical efficient process is the last choice However DU increase, the performance of MTO and MFS surpass

es the performance of MTS process.

The ranking of the performance is highly dependent on VC when DU are at a high level (e.g., Uf=Us=0.3)

This implies that, at some demand uncertainty levels, it is flexible to employ different strategies for pursuing high performance

It has been noticed that, when demand uncertainty is high and value-adding capacity is low (e.g. Uf, Us>0.3 and VC<0.90), MTO performs much better than the others as the strategy tries to avoid any wastes in stocks at the expense of longer lead times


Discussion(2/2)

The DU of finished products (Uf) is at the higher levels and the DU of materials (Um) is at the lower levels (e.g., Uf=0.3, 0.5 and Us=0.01, 0.05, 0.1)

MFS performs much better than MTO, although MTS performs similarly when value-adding capacity is high (e.g., VC>0.95)

Market responsive process is suitable to novelty product with unpredictable demand and high profit margin

Higher levels of materials DU usually lead to MTO performing better than MTS

High uncertain materials demand, the material stocks are probably ineffective and the non-stock strategy with acceptable lead times may surpass the MTS strategies

The MTS strategy can still be the best choice if the gain of the MTS process from lead time reduction can offset the loss from wastes in stocks

This may explain the higher performance of MTS than MTO in the situations of high value-adding capacity (possibly leading to acceptable profit) and high materials demand uncertainty as shown in Table 3 (Nos. 6 and 7)


Conclusion

Fisher's research Manufacturing to stocks process (named physical responsive process in th

is research) is analysed as well. But, Some are different some extents (physical efficient process matching

functional products) Difference in definitions of the process

Further research More operational factors such as supplier lead times and delays...


Concluding remarks

A quantitative view of interactions between some key operational parameters and the performance of different SC strategies

A tool to investigate operational conditions quantitatively in the design of appropriate SC strategies

Qualitative Analysis Quantitative Analysis

Manufacturing Strategies for SCM 2001.12.8 MAI Lab. Seminar Park Jung Joon.

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