a sensitivity analysis of stochaux, inventory systems

33
"A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS" by Yu-Sheng ZHENG* and Xavier DE GROOTE** N° 92/16/TM * Department of Decision Sciences, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.. ** Assistant Professor of Operations Management, INSEAD, Boulevard de Constance, Fontainebleau 77305 Cedex, France. Printed at INSEAD, Fontainebleau, France

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Page 1: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

"A SENSITIVITY ANALYSIS OF STOCHAUX,INVENTORY SYSTEMS"

by

Yu-Sheng ZHENG*and

Xavier DE GROOTE**

N° 92/16/TM

* Department of Decision Sciences, The Wharton School, University of Pennsylvania,Philadelphia, PA 19104, U.S.A..

** Assistant Professor of Operations Management, INSEAD, Boulevard de Constance,Fontainebleau 77305 Cedex, France.

Printed at INSEAD,Fontainebleau, France

Page 2: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

A SENSITIVITY ANALYSIS

OF STOCHASTIC INVENTORY

SYSTEMS

Yu-Sheng Zheng *

Xavier de Groote **

October 1991

Revised, January 1992

Department of Decision SciencesThe Wharton SchoolUniversity of PennsylvaniaPhiladelphia, PA 19104-6366USA

** Technology Management AreaINSEADBoulevard de Constance77305 Fontainebleau CedexFRANCE

Page 3: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

Abstract

This paper derives sensitivity properties of the single item inventory model with stochastic

demand and positive setup costs. The anaivaissumbssiSsetheeffect of changes in the variability

of the leadtime demand. For the order-quantity/reorder-point systems, we prove that when the

variability of the leadtime demand increases: (i) the optimal order quantity increases, but,

somewhat contrary to the conventional wisdom, the optimal reorder point does not necessarilyincrease; (ü) the minimum cost of the inventory system increases, and it increases at a slower rate

than and converges to the cost of the optimal base-stock policy; and (iii) the cost performance

becomes more robust to the choice of order quantity. In addition, we also show that both the

optimal reorder point and the optimal order-up-to level decrease (increase) as the inventory

holding (shortage penalty) cost increases.

1. Introduction

It is common sense that, for the management of inventories, the variability of the leadtime demand

has a negative impact on the precision of control and on costs. An effective way of reducing

the variability of the leadtime demand is to shorten the leadtime itself. This reduction requises

effort and extra cost. With the current emphasis on "just-in-time" inventory management, and on

"time-based" competition, there are increasing needs for a more precise analysis of the effect of

the variability of the leadtime demand ou the control and performance of inventory systems. This

paper addresses this problem for one of the widely used stochastic inventory systems, the order

quantity/reorder point system.

The order quantity/reorder point system is also referred to as the (Q,r) system, where an order

of Q units is placed as soon as the inventory position (i.e., the inventory on hand plus on order minus

backorders) drops to the critical level r. This inventory control system is the simplest model that

simultaneously addresses economies of scale in order quantities, and a random (leadtime) demand.

The cost function of the (Q,r) system has been well known for about three decades, ever since the

publication of the popular textbook by Hadley and Whitin (1963). However, it had been perceived

to be too complex to analyse, and, until very recently, our knowledge of the optimal control of

titis system had been limited. We refer readers to Lee and Nahmias(1989) and Federgruen and

Zheng(1988) for recent reviews of the related literature.

From a modeling point of view, two simple inventory models, the EOQ model and the base stock

model, are special cases of the (Q,r) model. The EOQ model crystallizes the tradeoff between the

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inventory holding costs and the fixed ordering costs by assuming a deterministic demand. h is clear

that a positive (deterministic) leadtimc does not affect the EOQ model in any essential way because

of the predictable leadtime demand. The base stock model, on the other hand, stresses the balance

between the inventory holding costs and the shortage penalty costs by assuming away the fixed

ordering costs. In a base stock model, an order is placed to restore the inventory position to a fixed

"base stock" (or "order-up-to") level whenever a demand occurs. The optimal order-up-to level

can be determined by minimizing the expected inventory holding costs and shortage penalty costs,

which is essentially the newsboy problem -where the demand for newspaper is interpreted-as the

leadtime demand. The optimal order-up-to point, which is often referred to as the newsboy point,

enjoys a closed form solution. In that case, the effect of the variability of the leadtime demand is

relatively simple to analyze.

With little theoretical support, it has often been argued that in a (Q,r) system, the order

quantity should be determined by the EOQ formula in order to exploit the economies of scale, while

the reorder point should be set to the expected leadtime demand plus some safety stock reflecting

the variance of the leadtime demand and the service level (or the costs associated with shortage).

While this argument is intuitive and therefore has played an important role in popularizing the

model, it essentially dismisses the (Q,r) model by the rough functional segregation of the two control

parameters.

Our analysis is based on recent results of Zheng(1989), who proves for the first tinie that the

(Q,r) model can be viewed as an aggregation of a. variant of the EOQ model and the base stock

model. In his analysis, alter the reorder point is chosen optimally for any given order quantity, the

(Q,r) model reduces to a problem of finding the best tradeoff between the average ordering cost

and the average holding and penalty cost. The average cost of the model is shown to be the sum of

two components: the cost oi the optimal base stock policy and a cost function that is very similar

but smaller than that of the EOQ model (with backorder allowed). The analysis of these two

cost components has led to the establishment of 2 number of properties of the stochastic inventory

systems. In particular, it is shown in Zheng (1989) that the optimal order quantity of the (Q,r)

model is larger than the corresponding EOQ, that the EOQ is a good heuristic when economies

of scale prevail, that the cost of the (Q,r) model is bounded below by the maximum of the cost

of the EOQ model and the cost of the base stock model, and bounded above by the sum of these

costs, and that the expected total cost of the (Q,r) system is more robust to the choice of order

quantity than the corresponding EOQ system. By comparing a system with stochastic leadtime

demand with the corresponding deterministic EOQ system, these results provide first cut answers

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to the analysis of the effect of the variability of the leadtime demand.

The results presented in this paper augment the above results by further showing how changes

in the variability of the leadtime demand affect.the o ptimal control parameters and the system cost

performance. Specifically, we show that when the variability of the leadtime demand is increased:

(i) the optimal order quantity increases, but, somewhat contrary to the conventional wisdom, the

optimal reorder point does not necessarily increase; (ii) the minimum cost of the inventory system

increases, and it increases at a slower rate than and converges to the cost of the optimal base-stock

policy; and (iii) the cost performance becomes more robust to the choice of order quantity. We also

show that both the optimal reorder point and order-up-to level decrease (increase) as the inventory

holding (shortage penalty) cost rate increises, which complement the results on the effects of the

fixed order cost of Zheng(1989): namely, that the optimal order quantity increases and the optimal

reorder point decreases as the fixed ordering cost increases.

Two recent papers are relevant. Gerchack(1990) derives comparative statics for the (Q,r) model

for both backiogging and lost sale models. For bac.klogging models, some of his results are sim-

ilar to ours, but they appear with some qualifications which are not needed in our case. These

qualifications are apparently due to the use of an approximate cost function, which systematically

undercharges holding costs. Gerchack and Mossman(1988) investigate the effect of the leadtime de-

mand variability, and conclude that the relationship between the variance of the leadtime demand

and both the optimal reorder point and the optimal order quantity is not definit!ve. Again, this

discrepancy appears to be due to the use of the approximate cost function.

The paper is organized as follows. Section 2 describes the model and notation. The effect of

changes in the cost parameters is discussed in Section 3. Section 4 is devoted to the analysis of

changes in the variability of the leadtime demand. In section 5, we discuss the sensitivity of the

system performance to changes of the various problem parameters and the potential application of

the cost insensitivity to the choice of order quantity. Section 6 is a brief conclusion.

2. Model and notation

We consider a single-item continuous-review inventory system that faces a stationary stochastic

demand. Unless otherwise specified, the notation and assumptions are the sanie as in Zheng(1989).

The arrivai rate is denoted by Replenishment orders are delivered after a positive leadtime L.

All stockouts are backordered. The relevant costs are a fixed replenishment cost K for each order

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placed and proportional inventory holding (penalty) costs accumulating at constant rate h (p) per

unit stock (backorder) per unit time. The criterion is the (long run) average total cost per unit

time.

We assume the demand process and the leadtime are such that, under a (Q,r) policy, the

following three conditions are satisfied: (a) both the inventory position and inventory level processes

have a limiting distribution; (b) the fundamental equation IL = IP - D holds and IP and D are

independent, where IL and IP dente the steady state inventory level and inventory position

respectively, D the leadtime demand; (c) IP is uniform between r and r Q. It is. well known

that these conditions are satisfied if the demand process is a Poisson or a renewal process and the

leadtime is constant. These conditions can also be shown to hold when the leadtime is stochastic,

under certain assumptions (see Zipkin 1986), and when the demand process is a Markov-Chain-

Driven process (see Zipkin 1991). These conditions are required for the cost function (2) below to

hold. [Since the entire analysis of Zheng(1989) is based on (2), the assumption of constant leadtime

in Zheng(1989) can be replaced by the above three more general conditions.)

Let G(y) be the rate (per unit time) at which the conditional expected inventory (holding

and penalty) costs accumulate when the inventory position equals y. Let FO be the distribution

function (cdf) of the leadtime demand D. By condition (b) we have

G(y) = E[h(y - p(D - y)+] = (h p) jv F(x)dx - p(y - ED) (1)

where E denotes the expectation over the leadtime demand. It is well known that G(•) is a convex

function. By (c), the average cost function of a (Q,r) system can be written as:

c(Q, r) = AK r+Q G(y ) dy (2)

Equation (2) is exact when the cumulative demand can be modeled as a nondecreasing stochastic

process with stationary increments and continuous sample paths (see Browne and Zipkin 1991). It

is an adequate approximation when the demand is a counting process (and Q is not too small). We

treat both the reorder point and the order quantity as continuous decision variables.

Let y° be the minimum point of GO. It is well known that y° = F-1(.11-p). (Throughout

this paper, we assume, for notational convenience, that F(•) is continuous and strictly increasing.)

When K = 0, the base stock policies are optimal. In that case, y° is the optimal order-up-to level

and G(y°) is the optimal average cost. In the sequel, we refer to y° and G(y°) as the newsboy point

and the newsboy cost, respectively.

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Let r- and Q" denote the optimal reorder point and the optimal order quantity. The following

lemma is established in Zheng(1989).

Lemma 1 Q = Q" and r = if and oney G(r)= G(r + Q). r• < y° < r` + Q*.

The optimality equations in the above lemma are simply the first order condition of the cost

function. The lut part of the lemma follows from G(e) = G(r* + QI and the convexity of

function G.

3. Changes in the cost parameters

It has been shown (Zheng 1989) that the optimal order quantity increases and the optimal reorder

point decreases as the fixed ordering cost K increases. In order to obtain further comparative

statics results, we need to differentiate varions components of the model with respect to h and

p. For clarity, we will write expressions such as 8G(y)18h without explicitly incorporating the

dependency of G(y) on h in the notation. We also denote dG(y)/dy by G'(y).

Let the optimal order-up-to level be denoted by = r" + Q"). We first show that both

the optimal reorder point and order-up-to level decrease (increase) as the holding (penalty) cost

rate increases. Define a(y) = ERy — D)+ ]. Note that a(-) is a convex increasing function.

Proposition 1 Or* 18h < 0, 8r"18p > 0, OS" 18h < 0, 8S'/8p > O.

Proof: By totally differentiating the optimality condition G(r) = G(r + Q) = c(r,Q) with respect

to h at r = re , Q = Q*, we get

0G(r) , Or OG(r + Q) Or 8Q Oc+ G (r) ah

= ah + G (1. Q)( 7971 + = (3)Ohwhere the lut term has been simplified by recognizing that °c/& = 0 and &IN = 0 at r = r-,

Q = Q . From (1), we have G'(y) = (h + p)F(y) — p and Oc(y)/ah = a(y). From (2), aclah =

(g.+Q. a(y)dy)/Q". Solving for Or* 10h in (3), we get

ar•(f,.r.+Q. a( y ) dY)1(2" — a(**)Oh (h + p)F(r*) — p

Since a(y) is increasing, the numerator is positive. It follows from Lemma 1, F(r") < p/(h + p),

which implies that the denominator is negative. Thus, 8r"10h > O. The proof is similar for the

other three cases.

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4. Changes in the leadtime demand

In this section. we study how the variability of the leadtime demand affects the optimal order

quantity, the cost performance, and its sensitivity to the choice of order quantity. Throughout theanalysis, we compare systems with different mean and variance of leadtime demand, while assumingthat ail the other parameters (i.e., K, h, p and À) remain unchanged. Note that the cost function(2) depends on the leadtime demand only through the expected inventory cost function G.

We start by briefiy reviewing some useful results from Zheng (1989). To focus -on -the-role oforder quantity, we assume that the reorder point is chosen optimally for any given order quantity.Let r(Q) be the optimal reorder point for a given Q. The reorder-point is characterized by thefollowing lemme:

Lemme 2 For any Q > 0, r = r(Q) if and only if G(r) = G(r Q), and r(Q) < y° < r(Q) 1- Q.

Let C(Q) de min,.min,.c(Q,r). Let H(Q) G(r(Q)),Q > 0, with //(0) G(y°). Sincefrre))+Q G(y) dy = fo H(y)dy, the optimal order quantity solves

C(Q) = „ie.+ I QH(y)dy)IQ. (4)

H(Q) can be interpreted as the marginal (expected) inventory cost for the Q-th unit in a batch.

Since C(Q) is convex, the first order condition of (4) gives

C(Q. ) = H(Q"), (5)

which is simply one of the optimality equations in Lemma 1.

Let A(Q) QH(Q) — g H(y)dy. Condition (5) is then equivalent to the following lemma.

Lemma 3 Q = Q • if and only if A(Q) = ÀK.

Furthermore, we have

Lemme 4 H(•) and A() are increasing convez fonction. H(Q) > eQ, // 1(0) = 0 and H'(Q)

e as Q Do.

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Lemmas 3 and 4 are illustrated in Figure 1. The following lemma is geometrically obvious, therefore

its proof is omitted.

Lemma 5 For a > 1(< 1) and ci et;e001k4>W1H(Q).

[Figure 1 approximately here]

Before proceeding with the formai aaalysis, it is helpful to conjecture about our results on

the basis of the geometrical intuition given in Figure 1. The asymptotic line of H(Q), Hd(Q) =

hpQ/(h p), can be interpreted as the marginal inventory cost function of the system with de-

terministic leadtime demand. It is geometrically clear that the introduction of variability of the

leadtime demand increases the expected marginal inventory cost. It is also economically intuitive

that a more variable leadtime demand should yield a higher H-curve. With the asymptotic line

remaining the same, we expect a higher H-curve to be flatter. For fixed Q, A(Q) is the shaded

area between the H-curve and the horizontal line z H(Q). At the optimal order quantity Qe,

this area equals AK (Lemma 3). Therefore, a flatter H-curve, which makes the shaded area smaller

for fixed Q, requires a luger Q. Consequently, our first conjecture is that Q* increases with the

variability of the leadtime demand.

Furthermore, since Q" depends only on the shape but not on the absolute height of the H-

curve, it is conceptually helpful to decompose HO in two parts:

H(Q) = G(e) + 1-1.(Q)•

Let C* be the optimal average cost of the (Q,r) system, i.e., C" = minQ>o C(Q). Then, (4) can be

rewritten as

C'=G(y°)+ Cô

dewith C: = minc».0C.(Q) =) (AI( j Ho(y)dy)IQ. H.( . ) can be interpreted as the part of the

marginal inventory cost that can be used to trade off the ordering cost through the choice of order

quantity, and therefore is referred to as the controllable inventory cost. Note that H. is a

vertical shift of H. Therefore, a more variable leadtime demand would be associated with a flatter

H.-curve. Since H.(0) = 0, a flatter Hecurve means 11.(Q) is smaller. Hence, for any Q > 0 fixed,

110(Q) should decrease as the variability of the leadtime demand increases. This leads us to more

conjectures. First, as variability of the leadtime demand increases, the controllable inventory cost

110(Q) is diminishing, so is C. Therefore, the optimal average cost of the system, which is the

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sum of the newsboy cost and C:, should grow more slowly than the newsboy cost when variability

of the leadtim, demand increases, and it should be close to the newsboy cost when the variability

is high. Second, for more variable leadtime denund, the total average cost should be less sensitive

to the choice of order quantity because Co(Q) is smatier and it is the only part of the total cost

that is affected by Q. We proceed to prove these conjectures analytically.

To proceed with the analysis, we need a dear definition of the variability of the leadtime demand.

We assume in the sequel that the leadtime demand distribution takes the form of F(z)=

where p and a are its mean and standard deviation respectively, and 4i is strictly increasing. Then,the variability of the leadtime demand can be unambiguously represented by We will show howthe optimal control parameters and the optimal colts change as o changes. The above assumption is

certainly satisfied when the leadtime demand is Normal, which is often an adequate approximationwhen the (mean) leadtime demand is not too small. The above construction is also a special case

of the general notion of convex-ordering; see Rothschild and Stiglitz (1970) for a discussion.

We note that the variance of the leadtime demand is determined by both the demand process and

the leadtime. When the demand process is given exogenously, the variance of the leadtime demandis affected only by the leadtime. For deterministic leadtimes, a longer leadtime typically leads toa larger variance of the leadtime demand. When a Normal distribution is used to approximatethe leadtime demand, o is often assumed to be linear to f.t. When the leadtime is stochastic,the variance of the leadtime demand is determined by the variability of the demand process and

by the mean and the variance of the leadtime. More specifically, o can be computed by _using

the conditional variance formula (interested readers are referred to Zipkin 1991 for a detaileddiscussion).

The G-function can now be written as

G(y) = (h + p) 41(z-

)dz - p(y --00 o

= a[(h p) 4,(z)dz ---00

We observe that a change of p in (6) would only cause a horizontal shift of the G(•)-function, whichdoes not affect the newsboy cost nor the H(•)-function. The reexamination of equation (4) yieldsthe following proposition.

Proposition 2 For a given demand process, the optimal order quantity and the minimum cost of

the (Q,r) system are independent of the mean of the leadtime demand.

(6)

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Therefore, in the sequel, we set = 0 in (6) for notational simplicity. Moreover, we add the

subscript a to all the notation to indicate its dependency upon the leadtime demand variance

(subscript d instead of 0 is used to indicate zero variance of the leadtime demand, which can be

true either when the demand process dateininittic or the leadtime is zero).

It follows from (6) that

G.(0= aG1(Y10) (7)

where G t (y) = (h + p) poe 4(y)dy— py is the (shifted) expected inventory cost function when the

variance of the leadtime demand is 1.

Let e be the minimum point of Ga ( .). The following result is immediate.

Proposition 3 The newsboy cost grows linearly in a, Ge( g ) = crGi(e)•

Proof: By (7), e = agi.

The following lemma is crucial:

Lemma 6 110(Q) = all1(Q1e)

Proof: By definition of H, we need to show

Ga(ro(Q)) = eGi(ri(Q1cr))•

(8)

Since by (7) GrGi(ri(Q/a)) = Ga(ar i (Q1a)), (8) is equivalent to Ga(r„(Q)) = Gcr(ar1(Q1a)).Thus, it suffices to show ra (Q)= ar i (Q1a), which follows from the following equations

G«,(ar1 (Q1a)) = aGi( rt(Q la )) = GrGi( rt(Q/a)+ Q/a)

= G 5( 49r1(Q/a)+ Q)

where the first and the third equations are due to (7), and the second is by Lemma I.

Now, we are able to show

Corollary 1 For any flzed Q > 0, Hc,(Q) is increasing in o.

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Proof: It suffices to prove 11„(Q)> H1 (Q) for any o > 1. By Lemma 5, 111 (Q/al> Hi (Q)la for> 1. By Lemma 6, II„(Q)= a I 1 i (Q1a). Therefore. //e(Q) > HI(Q). q

Corollary 2 For any fixed Q > 0, 11,;(Q) and are deLrèosing in a; Hf, (Q) j 0 as a 1‘ oo.

Proof: Differentiating the equation in Lemma 6, we have II,,(Q)= I4(Q la). Fix Q > O. The factthat II:,(Q) is decreasing in a follows from the convexity of H1 . 110,e(Q) is decreasing in a because

H:,,,„(Q)(= 14(Q)) is decreasing and 1/,,,„(0) = O. The rest of the corollary is due to 14(0) = O.

Corollary 3 For fized Q > 0, A„(Q) is decreasing in a; A,,(Q) j 0 as cr j oo.

Proof: It follows from Corollary 2 and A,(Q) = Q11,1,(Q) that AgQ) j 0 as a j oo. The corollary

then follows from A,,(0) = 0 and A(Q) = g A'(y) dy. q

The effect of variability of the leadtime demand on the optimal order quantity is now clear.

The following proposition is a direct implication of Lemma 4 and Corollary 3.

Proposition 4 The optimal order quantity Q; increases in cr. Moreover Qa" j oo as a I oo.

We note for contrast that the reorder point is not necessarily mcreasing in o. , particularly whenp is not very large relative to h. Zheng(1989) provides counter examples which show that the

reorder point of a stochastic (Q,r) system can be lower than that of the corresponding EOQ system= 0).

Although the optimal order quantity grows with a, the following proposition shows that it growsat a rate slower than o..

Proposition 5 is decreasing in a, and I 0 as a j oo.

Proof:

ak = A0(C2;) = Q;14(Q;) — .10 He(Y)(1Y

= (72 [ 4-2-10 Hi(Q; /a ) — tnia Hi(Y)cild

= a2A1(Q;/a)•

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The proposition follows from the fact that A 1 is increasing and A i (0) = O. q

Then the following result is also immediate.

Proposition 6 (a )The optimal conteellable cost C",;„„ is decreasing in cr, and C;0 1, 0 as o j oo;

(b) The optimal mienne cost C; is increasing in a, but the increase is slower than the newsboy

cost, and C; crG i (yî) as a j oo.

Proof: (a) Since 110,,,(y) is decreasing in o, so is the controllable cost function Co(Q) and itsminimum By Lemma 8 in Zheng(1989), we have Co,,Q; < 2À/C, thus 1 0 as ex î oobecause Q; j oo as cr j oo.

(b) The cost function (4) and Corollary 1 imply that C; is increasing in o. C; ti aGi(e) becauseC; = Go (g) Co,,,, 0 as a j oo and Proposition 4. q

Fin*, we show that the total cost is more robust to the choice of order quantity when thedemand variance is larger. More specifically, we show that ea(a) c"ca•77) decreases as o in-

creases. This result augments the inequality ea (a)< ed(a) (where ea(a) = 1(a + 1/a)) establishedin Zheng(1989).

Proposition 7 For a > 0, ea(a) is decreasing in a, and eo (a) j 1 as o j oo.

Proof:

Co(aQ;) )tk + Ha(y)dy fe: ii,(y)dyeo(a)= —

C; aQ;C;

rce; Ha(Y)dY;" Q;Hu(Q7,)1 fia Ha(13Q;)d,C1

)-(1 +a Ho(Q;)1 r HHi0Q;,a)

+ J1 (zicy) di3)

where the third equation is due to (5) and the fifth Lemma 6. In view of Proposition 5, it sufficesto show that

clef Hi(AQ)H1(Q)

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is increasing in Q for > 1 and decreasing for d < 1. We show the former, the latter is similar.

For fi > 1,

;11(Q) = HuQ)[0e1(PQ)111(Q)—

where the inequality follows from

egh(RQ)] >

HI(PC2) Ht(AQ)14(Q) Ait(Q)

where the first inequality is due to the convexity of H1 and the second follows from Lemma

5. Sensitivity of the System Cost

In this section, we briefly discuss how the problem parameters affect the cost performance of a

(Q,r) system. It is well known that, due to the square root formula, the cost of the EOQ model

is robust to change in all its parameters (K, h, p, A). It would be interesting to know whether this

robustness carries over to the cost of the (Q,r) system.

First, since the ordering cost K affects only the cost function C., which is smaller than that

of the EOQ and diminishing as o increases, the system cost is robust, and it is more robust when

the leadtime demand is more variable. This can be verified formally as follows. Differentiating

(AI( fe; He (y)dy)IQ; with respect to K yields dC;IdK = XIQ;. Since Q; increases in o,dC;IdK decreases in o.

The effect of change in the holding cost h or the penalty cost p is more involved because the

change affects both the newsboy cost and Since the newsboy model does not enjoy the sanie

degree of insensitivity as the EOQ model, we do not expect that the average cost of the (Q,r)

system to be as robust to change in h or p, unless the effect of economies of scale dominates.

We can dso quantify, at least approximately, the effect of a change in the leadtime on the cost

performance of the (Q,r) system. A change in the leadtime affects both the mean and standard

deviation of the leadtime demand. Since the mean does not have any impact on the optimal average

cost, the system's cost would be affected only through the change of o. Assuming that the leadtime

demand is constant and o is linear in ft-, we conclude, by Proposition 6, that the change in the

system cost is sublinear in ././, and approximately linear in -IL when L (and hence o) is large.

When the leadtime is stochastic, not only its mean, but also its standard deviation would have an

impact on o. The reader is referred to Zipkin(1991) for a discussion.

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If a change in À does not cause a change in the leadtime demand, thon its effect on the system

performance is identical to that of K because À and K are symmetric in the cost function Ciee (2)).

This assumption, however, is very ite t practice. Typically, a change in the demand rate

induces a change in o. In this caue,thei0bal effect is more complex. Assuming that the leadtime

L is constant and the demand process is Poisson (but approximately Normal), we have o=

Therefore, the Newsboy cost changes linearly in f (Proposition 3); Cc, ,̀,, is affected by change of

both A and o. The effect of A on CZ, is sublinear in N/X. Since the effect of a on CZ„ is in the

opposite direction as indicated by Proposition 6(a), the total average cost C:„." must be sublimer to

VI In other words, the average cost of the (Q,r) system is more robust to change in the demand

rate than that of the EOQ model.

Finally, we comment on Proposition 7, which states that a larger variance in the leadtime de-

mand makes the average cost more robust to the choice of order quantity assuming the monter point

is always chosen optimally. Care is needed in interpreting this insensitivity result. In particular,

we cannot condude from this proposition that the average cost is insensitive to inaccurate data.

The reason is that when a suboptimal order quantity is chosen because of the inaccuracy ofsome

pazameter estimates, the reorder point is unlikely to be set optimally (with respect to the chosen

order quantity).

This proposition, and Theorem 4 in Zheng(1989), are motivated by their potentiel nsefulness

for analyzing multi-stage stochastic systems. For multi-stage systems with constant demand, such

an insensitivity property of the EOQ model has led to remarkable worst case performance bounds

for the power-of-two heuristic policies (Roundy 1985, 1986). Here, we briefly discuss how-there

insensitivity results for the (Q,r) model could be useful for similar analysis on some plausible policies

for multi-stage stochastic inventory systems. For multi-stage inventory systems where demands are

stochastic and where there are fixed costs for inventory replenishment at each stage, a plausible

control policy is to use an order-quantity/reorder-point policy at each stage. Since upstream stages

are the suppliers for their downstream stages, the order quantities of different stages should be

coordinated. For example, it is plausible to use the "integer ratio policy," under which the order

quantity of an upstream stage is restricted to an integer multiple of that of a downstream stage

(if the downstream stage is the only customer of the upstream stage); see e.g. De Bodt and

Graves(1985). Such a quantity coordination across different stages imposes a constraint on the

choice of order quantities, but not on the reorder points; that is, while the order quantity at a stage

may be required, for the global coordination, to deviate from its own "optimal" value the reorder

point remains "local", and can be chosen optimally for the choice of order quantity. Our insensitivity

13

Page 16: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

results indicate that such a quantity coordination would not cause a significant cost increase from

each stage's minimum cost. For systems with deterministic demands, Roundy(1986) showed by his

lower bounding theorem that the sum of the singiereage minimum cost of all the stages (with some

additional minor constraints on the order quantities i ni a. sumer vound on the system-wide average

cost, which in turn leads a very tight worst case performance of the power-of-two policies (whichare a subset of the integer ratio policies). For stochastic systems, a parallel lower bounding theorem

seems significantly more difficult to formulate. However, even in the absence of such a theorem, theheuristic policies that require quantity coordination across different stages are much encouraged byourInsensitivity results for the single-item (Q,r) system. For a serial system with Poisson demandat the lowest stage, an accurate cost evaluation procedure has been devised (Chen and Zheng 1991

a) for echelon stock order-quantity/reorder point policies. Numerical studies(Chen and Zheng 1991

b) show that for a fairly wide range of data these policies are indeed close to optimal. Research onworst case analysis of heuristic policies for multi-stage systems is in progress.

6. Conclusion

Combining the results of this paper with those of Zheng(1989), we now have a more completeunderstanding of the behavior of (Q,r) systems. By assuming that the leadtime demand is (ap-prcximately) Normal, a (Q,r) system can be fully characterized by the cost parameters K,h, p and

the demand parameters o. It is shown in Zheng(1989) that when K is large relative to cr, the

(Q,r) model behaves like the EOQ model in every respect, and that the optimal control parameters

and the optimal average cost of the EOQ model (with backorder allowed) are very good approxi-

mations of those of the (Q,r) model. The results of this paper show that when a is large relative

to K, on the other hand, the expected marginal inventory cost function Ha is relatively flat. In

that case. we can use a large order quantity to offset the ordering cost without incurring much

increase in the average inventory cost; consequently, the total average cost of the system is close to

the newsboy cost.

Strictly speaking, the (Q,r) model only applies to continuous review inventory systems with

unit demand. When the inventory system is reviewed periodically, or when the demand size is

random, the reorder point can be "overshot," in which case the control policy must be revised. A

well known inventory system that accommodates the overshot is the (s,S) system, where an order

is placed to increase the inventory position to S as soon as the inventory position drops to or below

s. Here s denotes the reorder point and S the order-up-to level. The order quantity is no longer

Page 17: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

(S-s), the difference between the order-up-to level and reorder point, but that plus the overshot,

which varies from order to order. Another variation that can accommodate overshot is the so

called (nQ, r) system, where an order is placed whenever the inventory position drops to or below

the reorder point r. The order quantity is chop te be an integer (n) multiple of Q such that the

inventory position is raised to between r+1 and r+Q. For recent analyses on these systems, see

Zheng(1990), Zheng and Federgruen(1991) and Zheng and Chen(1990). Although the (Q,r) model

appears as a special case of the more general (s,S) and (nQ,r) modela, the qualitative properties

derived for the former shed light on these general stochastic inventory systems. It should especially

be emphasized that when the overshot is not significant relative to the order quantity, the behavior

of (s,S) and (nQ,r) systems is similar to that of (Q,r) systems.

Acknowledgment

The research of the first author was supported in part by NSF grant DDM 9111183. The anthors

would also like to thank Professor Paul Zipkin for sharing part of his recent manuscript (Chapter 6

of Foundation of Inventory Management, Draft Version 0.1, November, 1991), in which proposition

3 of this paper is independently observed.

References

[1] Browne S. end P. Zipkin 1989 Inventory Models with Continuons, Stochastic Demands, Grad-

uate School of Business, Columbia University.

[2] Chen F. and Y.-S. Zheng(1991a) Evaluating the Performance of a Serial Inventory System

with Stochastic Demand, Working paper, Department of Decision Sciences, Wharton School,

University of Pennsylvania.

[3] Chen F. and Y.-S. Zheng(1991b) A Lower Bounding Framework for Stochastic Inventory Sys-

tems with Setup Costs Working paper, Department of Decision Sciences, Wharton School.

University of Pennsylvania.

[4] deBodt, M. A. and S. C. Graves(1985) Continuous Review Policies for a Multi-Echelon In-

ventory Problems with Stochastic Demand, Management Science, 31, 1286-1295.

15

Page 18: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

[5] Federgruen A. and Y.-S. Zheng 1988 A Simple and Efficient Algorithm for Computing Optimal

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[6] Gerchack 1990 Analytical Comparative Statics for the Continuous Review Inventory Mode&

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[8] Hadley, G. and T. M. Whitin 1963 Analysis of Inventory Systems, Prentice-Hall, Englewood

Cliffs, NJ.

[9] Lee, H. and Nahmias, S. 1989 Single product, single-location models, Chapter 2 in Handbook in

Operations Research and Management Science, Vol. 4: Logistics of Production and Inventory

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[11] Roundy, R. 1985 98% Effective Integer-Ratio Lot- Sizing for One Warehouse Multi-retailer

Systems, Management Science, 31, 1416-1430.

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Inventory Systems, Mathematics of Operations Research, 11, 699-727.

[13] Zheng Y. S. 1989 On Properties of Stochastic Inventory Systems, Management Science, to

appear.

[14] Zheng Y.-S. 1990 A Simple Proof for Optimality of (s, S) Policies in Infinite Horizon Inventory

Systems Journal of Applied Probability, to appear.

[15] Zheng Y.-S. and F. Chen 1990 Inventory Policies with Quantized Ordering, Naval Research

Logistics, to appear.

[16] Zheng Y.-S. and A. Federgruen, 1991 Finding Optimal (s,S) Policies is About as Simple as

Evaluating a Single Policy Operations Research 39, 654-665.

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Logistics Quarterly, 33 763-774.

16

Page 19: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

[18] Zipkin. P. 1991 Foundation of Inuentory Management, Version 1.0, Columbia University,

Manuscript to be published.

17

Page 20: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

Ce = H(Q*

0

Q*

Figure 1

HO, A() — functions and the optimality condition

Page 21: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

• INSEAD WORKING PAPERS SERIF.S

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B. Expen ECK/10 and

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89/49 Jean DERMINE

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89/66 B. SINCLAIR-DESGAGNÉ(TM.E,P)

89/67 Peter BOSSAERTS and(FIN) Pierre HILL/ON

"Technology strate*/ and international RADaperntions". October 1989.

"Equivalogre of simulation*: A graploapproach", November 1989.

Compiexity of simulation gondele: A araphthenretit apprentie, November 1989.

''MARS: A merl:ers and acquisitionsmanade" systtune, November 1989.

*On the reaulation of proceoreenentNovember 1989.

"Market miormignecture 'Mets ofRevenante« intervention in the forelegvienne market•, Dota aber 1989.

89/56 Wilfried VANHONACKER "On the practical usefednem of neetaaerutlysisand Lydia.PRICE mes". September 1989.

89/57 Taekwon KIM,Lars-Hendrik RÔLLERand Mihkel TOMBAK

89/58 Lars-Hendrik RÔLLER(FP,Thi) and Mihkel TOMBAK

89/59 Manfred KETS DE VRIES,(0I1) Daphna ZEVADI,

Alain NOEL andMihkel TOMBAK

89160 Enver YUCESAN and(TM) Lee SCHRUBEN

89/61 Suran SCHNEJDER and(An) Arnoud DE MEYER

"Market grevant and the diffusion ofundliprodeut technologies", September 1989.

"Strategic aspects of fleitelde productiontechnologies", October 1989.

"Locus of control and entrepreneurship: athree-country comparative study", October

1989.

"rantulation graples for design and analysis ofdberete event simulation modes", October

1989.

Interpreting end responding Io singeait.issues: The impact of national culture,October 1989.

1990

90/01 B. SINCLA1R-DESGAGNÉTM/F,P/AC

90/02 Michael BURDAEP

90/03 Arnoud DE MEYER

TM

90/04 Gabriel HAWAWINI andFIN/EP Erie RAIENDRA

90/05 Gabriel HAWAWINI andFIN/F,P Benrand IACQUILLAT

lhoavaidabie Moclauldlige, January 1990.

fflepeopenotie ColneolIllan, Conte ofAdjudant«, and thelbiloarionr of EuropeanManufacturing Fantileneent", lanuary 1990.

•Managegoeut of Counnunkatina inInternational lemme and Development',lanuary 1990.

"The Trnagronnitign or the %mnoFinancial Services Industry: Fon.Fragmentation to botegration*, lanuary 1990.

.E•rtell111 bey *latine: Tenant 1992and %your, Ume 1990.

Page 27: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

90/06 Gabriel HAWAWINI and "Integration of European Equity Markets:FIN/EP Erie RAIENDRA beplicatimm of Sine:nal Change fer Key 90/17 Nathalie DIERKENS "Information Asymmetry and F.qvity

Market Participants tel and Beyond 1992".lanuary 1990.

FIN Reviaed lanuary 1990.

90/18 Wilfried VANHONACKER "Magagerial Draisine Raies and the90/07 Gabriel IlAWAWINI "Stock Market A/101111dieli and the Pricing of MKT F.alimadon of Dynainit Sales Restes»FIN/EP Fsmity on the Tokyo Stock Exrhange",

lanuary 1990.Model •, Revised lanuary 1990.

90/19 %th JONES and "Tbe KIWI of Computer Inlemention and90/08TM/EP

Tawfik JELASSI andB. SINCLAIR-DESGAGNÉ

"Modelling *ah MCDSS: What »Mn«Effiles?". January 1990.

TM Tawfik JELASSI Tati: Saurante ou Rariaining Unicorne",February 1990.

90/09 Albedo GIOVANNINI "Capital Controls and International Trade 90/20 Tawfik JELASSI, "An introdection ta Croup Draisine »dEP/I1N and lac WON PARK Finance", lanuary 1990. TM Gregory KERSTEN and Nrantiation Support", Fehnary 1990.

Stanley ZIONTS90/10 Joyce BRYFR and "The Impact of Language Theorim on MSTM 'nova JELASSI Dialog", ;gentry 1990. 90/21 Roy SMITH and "Recollait:radium of Global Sec:trille*

FIN Ingo WALTER Industry in the 199re.Fenaary 1990.9011I Enver YUCF.SAN *An Overview of Frequency DemainTM Mea:odelette for Simulation Sensitivity 90/22 logo WALTER •Farepaan Pinamitibligration mut Ils

Analysis", Januar, 1990. FIN Impticatiomt for the Uilltd States•, February1990.

90/12 Miehael BARDA "Feellaral Change. Unemployment Renefitsand Iligh Unemployment: A U.S.-European 90/23 Damien NEVEN "EEC lotettratioe Mem& 1992: SomeComparium", lanuary 1990. EP/SM Distrbutineal Aspacts", Revimd December

192990/13 Soumitra DUITA and •Apprandetate %menine about Temporal1/11 Shashi SHEKHAR Commtraints in Real Time Planning and 90/24 Lars Tyge NIELSEN "Positive Prime in cArm". lanuary 1990.

Search", /anuary 1990. FIN/EP

90/14TM

Albert ANGEHRN andHana-Jakoh LÜTH1

•Ireseal Interactive Modelling and IntelligentDSS: Petting llwory hen Practice". January

90/25FIN/EP

Lars Tyge NIELSEN "Elisa.» et Fmailihrione in CAPM",January 1990.

1990.90/26 Charles KADUSHIN and •Wily netwericiam Faits: Double lands »d

90/15TM

Arnaud DE MEYER,Dirk DESCHOOLMEESTER,Rudy MOENAERT and

"The Intered Technological Renewal of a%miens Unit with a Mature Technology".January 1990.

OB/BP Miehael BRIMM the Limilatiosta of Shed» Networke,February 1990.

Jan BARBE 90/27 Abbas FOROUGH1 and •NSS Sol:Mun Io Meer N'inaction1111 Tswfik IELASSI Sheaddim Mets', February 1990.

90/16 Richard LEVICH and "Tas-Driven Regolatory Drag: EuropeanFIN logo WALTER Financial Center: in the 1990's", lemme 90/211 Anmud DE MEYER 'Tic Manufacturing Contstiontiou te

1990. TM Innovation", February 1990.

Page 28: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

90/40 Manfred KETS DE VRIES "Leaders « the Coach: The case of Roberto90/29 Nathalie DIERKENS "A Discuesion of Correct Mesures of OB Calvi', April 1990.I1N/AC Information Asymmetry", January 1990.

90/30 Lies Type NIELSEN "Te Expetted U$Ifty of Portfolios of9n/41

FIN/EPGabriel HAWAV/INI,117115k SWARY and

"Capital Market Reaction to theAnortarscement of Internat, Rauking

FIN/EP Assets", Mare 1990. lk HWAN JANG Legidatinn", Mach 1990.

90/31 David GAUTSCIII and "What Devinions U.S. Retail Manias?". 90/42 Joel STECKEL and "Crocs-Vandatind Remmena Marker inMKT/EP Roger BETANCOURT Fehmary 1990. MKT Wilfried VANHONACKER Marke/MI Research". (Revised April 1990).

90/32 Srinivamn BALAK- "Informatima Asymmetry, Adverse Selection 90/43 Ruben KORA1CZYK and "FAmity Ride Prends and the Peint of

SM RISHNAN and and John-Ventures: Throry and Evidence". FIN Claude VIALLET Foreign Excluante Risk', May 1990.

Mitchell KOZA Revised, la cary 1990.90/33 Carco SIEHL, 'The Rale of Rites of lei:ration in Service 90/44 Gilles AMADO, .01191dienould Chante nad CulturalOB David BOWEN and Deliver'''. Match 1990. 011 Claude FAUCHEUX and Rendes: Franco-American Contres", April

Christine PEARSON

90/45

André LAURENT

Soumitra DUTTA and

1990.

elategratint Case RIO« and Rode Rend

90134t1N/EP

Jean DERMINE "The Cakes fana Fitmpean RankineInter:ration, a Cali for a Pro-Active

TM Piero BONISSONE Reasoning: The tàoaulhstic Correction",May 1990.

Compention Poney". April 1990.90/46 Soma MAKRIDAKIS "Exponentiel Surmandswinerra Met of

90/35 Jae Won PARK •Changing Uncertahtty and the Time- TM and Michèle HIBON Initial Values and Leva Falletioes an Post-

EP Varying Risk Premier in the Tenu Structureof Nominal Inter'« Rates". Decenther 1989,Revised March 1990. 90/47 Lydia PRICE and

Semple Foreca tien Ancem".

Improper Samprame in IdifferilMKT Wilfried VANHONACKER Experimente: Linitalianme the Use of

90/36 Arnaud DE MEYER "An %Wied lame:tee of Meta-Arniyais Reonita9119191‘18

TM Masurfacturing Stratégies in European Updating", Reviaed May 1990.Industry", April 1990.

90/4* lm WON PARK "The Infornation in the Tenu Structure of

90/37TM/013/SM

William CATS-BARIL "Executive Information Systems: Developingas Approach Io Open the Possibles", April

EP latere* Rates: Out-andinsole ForecadingPerformance*, lune 1990.

1990.90/49 Soumitra DUTTA "Approenate Remet ky /valloRY

90/38 Wilfried VANHONACKER "Managerial bectent >barioler and the TM Answer Na Quinine', lune 1990.

MKT Estimation of Dynamic Sales RespmrseModela", (Revised Fentary 1990). 90/50

EPDaniel COHEN andchartes WYPLOSZ

"Price and Trade Fifeets et Exchange RatesFlactuations and die Nana of Poney

90/39

TM

Louis LE BLANC andTawfik JELASSI

'An Englue« and Section Metbodologyfor Expert System Shelh", May 1990.

Coonfonation", April 1990.

Page 29: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

90/51 Michael BURDA and "Grass Labeur Market Flows in Vampe: 90/63 Sumwers GHOSHAL and "Organising Coussinet« Analyais Systeme,EP Chartes WYPLOSZ Some Stylized Falls". lune 1990. SM Eleanor WESTNEY Augias 1990

90/52 Lars Tyge NIELSEN "The Mie of Inlimite Menus". lune 1990. 90/64 Sumantra GHOSHAL "Intentai Differentiation and CorporateFIN SM Performance: Cam of the Multinational

Corporation", Angle 199090/53 Michael Borda "The Consequences of German &momieEP and Mnnelary Union". /une 1990. 90/65 Chades WYPLOSZ "A Note ou the Real Exclunme Rate Effect of

EP German Unification", Anon 199090/54 Damien NEVEN and "Rumens Financial Regulation:El' Colin MEYER Framework for Pulky Analysis". (Reviaed 90/66 Snumitra DUTTA and "Computer Support for Strategic and Tartkal

May 1990). TM/SE/11N Nem BONISSONE Munia" in Mergero and Acquisitions",September 1990

90/55 Michael BURDA and "Intertempond Prices and the US TradeEP

90/56

Stefan GERLACH

Damien NEVEN and

Balance". (Reviaed JuIy t990).

*The Structure and Determistants of Fast-West

90/67TNI/SE/F1N

Sombra DUTTA andPiero BONISSONE

ilategratkig Prier Canes and Expert Knowledge lna Menem and Ammiabitomn Re:domina System',September 1990

EP Lars-Hendrik RÔLLER Trade: A Preliminary Analysis of theManuficturbse Serine". lut), 1990 90/68 Saumura DUTTA P'renseumult leilhodology for Enlumina theami

TM/SE Business Impact et Atticial Intelligence90/57 fars Tyge NIELSEN Comma Knowledge of a Multivariate Aggregate ApOications", Sepresmar 1090FIN/EP/ Statistie". July 1990TM 90/69 Sonmitra DUTTA "A Medd Bir Temporal Remanie« kt Medical

TM Expert &Siffle% Sesternber 199090/511 Lam Tyge NIELSEN Cannna Knowledge of Price and Expected CosiFIN/EP/TM in an OliRopelktic Market". August 1990 90/70

TMAlbert ANGEHRN "'Triple C': A Mena Interactive MCDSS',

September 199090/59 Lean DERMINE and "Feemornies of Seule andFIN Lam-Hendrik RÔLLER Steve in the French Mutuel Fumés (SICAV) 90/71 Philip PARKER and "Campedtive Efforts in Diffistion Modela: An

Industry". Auguu 1990 MKT Hubert GATIGNON Empirical Analysée'. September 1990

90/60 Pen 1Z and 'An Interactive Group Occitan Aid for 90/72 Enver YÛCESAN 'Andy* tif Marli« Chia Ugine 5anndationTM Tintin JELASSI Muttiohjective Proldents: An Empirical TM Grapk Modela", October 1990

Autrement", September 1990

90/61 Pankaj CHANDRA and "Modela for the Emilasittion of Manufacturing90/73TM

Arnoud DE MEYER andKasra FERDOWS

"Remaniant the Bardera in Manufacturing",October 1990

TM Mihkel TOMBAK %adule, August 1990

90/62 Damien NEVEN and "Public relief Towards TV Break:Ming in the 90/74 Summum GHOSHAL and "Rognidte Cemplexity: O. Headquartem-FP Menno VAN DUK Netherlands", August 1990 SM Nitin NORRIA Subeithary Rebtirms in MNICs". October 1990

Page 30: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

9onsMKT

Roser BETANCOURT andDavid GAUTSCHI

'Ibo Orrai et Relia Artividem Coarekals,Mensorment ma Evillente. October 1990

9047FIN/Er

Lam Tyne NIELSEN

911/70 Wilfried VANHONAC/CER 'Men Bond Doride* Behavione and the Yetinution 9011011 Susan C. SCHNEIDER andMKT of Dreemdaelta gemma» Modeae. 00/MKT Reinhard ANGELMAR

Revive/ October 1990»Wied F.R. KETS DE VRIES

tern Wilfried VANHONACKER nada; Ihe Kart &Bembo ai Site Respire as OBMKT Advertisima: Am Aarettalisn-ludemendent

Aeletendoliiee T. October 1990 90190 Philip PARKERMKT

90/18 Micked BURDA and *Embaume Rate Drineirs and CauveryEIP Stefan GERLACH Unification: The Ostmark - DM Rate.

October 1990

90/79 Mil GARA •Interencen ai* an Ustimown Noise Lied in aTM Bernes% haceur, October 1990

90/1111 Ale GARA and •Gaine Sarre Data I.• laterearet about hercha»TM Robert WIN/CUIR Rellanionet Omober 1990 1991

Tarvfd 1ELASSI "Dm niant ar INItnr: Bien et Orientations desTM Brevet Interactifs d'Aide II In Decisbee 9101 Luk VAN WASSENHOYE.

October 1990 ni/St Leonard FORTUIN andPaul VAN BEFJC

901113 Otaries WYPLOSZ Ideeetery Union rd Fiscal Poliry Diodes',"EP November 1990 91/113

7M/5111Lut VAN WASSF.NHOVE,Leonard FORTU1N and

9063 Nathalie DIUICENS and noiormatien Myestretry and Corporate Paul VAN BEEKnNrni Bernard SINCLAIR-DESGAGNE Commaitatiem Remdts of • Pilet Sonde,

Noverdber 1990 91103 Pekka HIETALA adFIN Tien ILOYMNIEMI

9014 Phifip M. PARKER "The Effort d Advertitina on Price and Quality:MKT Ibo Ormaehie Industry Rerithed• 91104 Lars Tyge NIELSEN

December 1990 FIN

Avijit GHOSH end •Opetri Tiadas and Lerma be Competitire 91105 Srs SCHNEIDERMKT Varna TIBREWALA lelerkeee November 1990 OB

*MMEPITM

Olivier CADOT andBernard SINCLAIR-DESGAONE

°nyder, and Stoss in roides; November 1990 MAKOB

Minfœtt Kers DE VRIES,Daum MILLER andAlain NOEL

•Faisten•• of EssualBwinas in CAMA: M'aberNasadif. Decendier 1990

`Cognai» in Organientioeud Andria: 11bo'sMien the Semer Revient December 1990

CE0 Celle* Ti gratta and OtherTes fier the Bard Rama; December 1990

'hire ihraticity Dr:amies mer the AdoptionLiterie: Am Empiriud Study; December 1910

'0perational Itamtemitffla Mont fer Marge«Thon %y Mielermary 1991

*Oparediond Remo* enrovirommotehinurop 1991

Dividend herease in Rem IssueThur, »I Bedonne towesery.1991

"Tweetued Separallma Parme Structure amiRelsemesse lantary 1191

9Mlegettiag beneterke ia Or .ekami PM

gledeeilledies the Leolereretep Welter«Applicage, et tbe Sieste* Relideeelip imervireMine Imam 1990 09111. mvised Apal 19901

Page 31: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

91/07 Olivier CADOT "levain to Inmadvent Countriem: A farallosicalEr Sem," lanuary 1991 91/19

MKT

Vikas TIBREWALA end • Aggregate Test of Purtimse Regularite,Bruce BUCHANAN March 1991

91/08 Charles WYPLX/SZ "Pola-Refone Emt and West: CapitalEr Aemennlotioe and the Labour Mubility 91/20 Darius SABAVALA and Monitoring Short-Rome Changes in Purrhasing

Construisit," lanuary 1991 MKT Vika* TIBREWALA Behaviour", March 1991

91/09 Spyroe MAKRIDAKIS •What eau we Leoni from navrer, Felnuary 1991 91/21 Sumatra GHOSHAL, Internât Conammiration within biNns:TM SM Harry KORINE and lemme of Formel Structure Verses lalegrative

Gabriel SZULANSKI Promues*, April 199191/10 Luc Van WASSENHOVE end "Integrating Schedeing with Batching andTM C. N. P01111 Let-Seing: A Revive or Alliamilkulla and 91/22 David GOOD, "EC lekgratian nad Me Stream of the Fromm-

Causplenity •, Febnery 1991 EP Lers-Hendrik RÔLLER and American Aies* ladmetries: Implications ferRobin S1CKLFS Efficient" and Wren*, April 1991

91/11 Luc VAN WASSENHOVE et al. "Muid-Item in Caparitated Mugi-StageTM Serial System", February 1991 91/23 Spyros MAKRIDAKIS and "Kemmel SeeldRiagtlie Effect of Initial

TM Michèle HEION Vaine" and Lem Fewetimature Foot-Semple91/12 Albert ANGEHRN •Interpretadve Computer Intelâmame: A Link Forweerting Areemey;111.111 1991 (Revision ofTM between Umm, Modela and Metbods in ose. 90146)

February 199191/24 Louis LE BLANC and •A. %Orient ArresmarMWChoice Modela for

91/13EP

Michael BURDA •Ulm and 'Muet Markets à Czechariovidia andthe E."-GDR: A Twin Stedy", February 1991

1M Tawfik IELASS1 Software Eveleaden anifillkoMnr, May 1991

91/25 Luk N. VAN WASSENHOVE and "Tradaelle What Trtieothr April 1991

91/14 Roger BETANCOURT and 'ne Output of Rata At-livides: Fumai Shi/7m Charles CORBEITMKT David GAUTSCHI Evident •, February 1991

91/26 Luk N. VAN WASSENHOVE and "Sangle Media- Sdrifi bilai tenaillée Total Lat.98/15

OBManfred F.R. KETS DE VRIES •Expérang the Mytb about Ratinant Organiusiows

and Emcmives", March 19911M C.N. POTTS %kWh• April 1991

91/27 Nathalie DIERKENS •A Dimmion of Cornet Mandes of Informatisa91/16 Arnaud DE MEYER and •Faeteties of the Vehme: L'endive Senweary of FIN Asyremetry: 1be Lime or Myes and ISIMInf`sTM East. FERDOWS et.al. the 1990 Interea6unal Manufacturing Futures

Survey", March 1991Maint or Ibe Importance of the Amr( Sawcture ofthe Me. May 1991

91/17 Dirk CATTRYSSE, "Ileuriaties for Ibe Marrie letvizing and 91/20 Philip M. PARKER •A Note au 'Aéreribia« and the Prim lad QualityTM Roda KUIK,

Marc SALOMON and&Madelin Noble. laids Setup Times •, March 1991 MKT of Oplometrie Services', hume 1991

Luk VAN WASSENHOVE 91/29 Tswfik JELASSI and 'An t'aphte] Moly of an Interactive, SeamiamTM Abbas FOROUGH1 Orieded Compoterised %sertirai« Support System

91/18 C.N. POTTS end •Approximnalion Algorithmes fer Scinda« a Sm* (NSS)", lune 19911111 Luk VAN WASSENHOVE Machine M Milhatime Total Lute Work•,

Match 1991

Page 32: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

91/30 Wilfried R. VANHONAC10ER and MM« Meta-Anallysis Rimes in Bayeniatt Updating:MKT Lydie J. PRICE lbe Erne Cal Profilent", lune 1991 91143 Sumatra OHOSHAL and %W in Trammieend Combattiez: The

SM Christopher BARTLETT Managemeat Challenge', September 199191131 Reraul KARJR and "leader Tratfieg Ruinaient muid the StockFIN Theo VERMAELEN Mana% Jase 1991 91/44 Summar* GHOSHAL and Inabilated Innevation I. the IfIfferantioded

SM Nilin NOHRIA Network. MnItinetietur, September 1991

91/32 Stman C. SCHNEIDER 'Orgamisadonel Sememaking: 1992• , lune 1991011 91/43 Philip M. PARKER "The F/feet et Adverfidad es Frite and Quality:

MKT As Fmapirical Study of Eye Examinalions, Sweet

91/33 Miction' C. BURDA and *Germe Trade Usinas gifler Umilication Third Lama muid Self-Decevers", September 1991EP Michael FUNKE Dere, Wace Marin/mea Mossopolistarr

lutte 1991 91/46 Philip M. PARKER *Prelog Santals in Mante whig tem*MKT Flatikifies". October 1991

91/34 Jean DERMINE *The BIS Noeud fer the Memerenleat of InterestFIN Rate Reit, Some ?Wells", lune 1991 91/47 Philip M. PARKER Sady of Price Ehiseleky Dynamite Usai

MKT Parsianonions Redmeneent/Naltiode Fareham

91/33FIN

Jean DERMINE "The Radeau of Finacial Servies in the F.C,Centrai:sten er %kraal Antemear lune 1991

Diffames Modela•, October 1991

91/414 H. tandis LABEL and 9Mourgerial Ineentive amillhaviromantal

91/36 Albert ANGEHRN •Suppertiat Modelaient Decktie« Mana: New EP/TM Bernant SINCLAIR-DESOAONE Cooludiemee. Oember I991

TM Perspeedves mad New Systems", Auget 199191/49 Bernard SINCLAIR-DESGAGNB "Me Net-Onler Afflua» MmItiendu

91/37 Mao WALTER and 'The latrodactim of Universel Bakin in Canada: TM FradgekAgent TreblearttOmber 1991

ET Hugh THOMAS An Emme Study", Auget 199191/30 Luk VAN WASSENHOVE end Mea Gien h Yeer blegataring Strelfear

911311 Ingo WALTER and "Natimml Ilad Global Canspetitivemeas of New York SM/TM Charles CORBETT October 1991

EP Anthony SAUNDERS City as a %amis! Center', Mea 199191/31 Piao M. PARKER Mimais Aatatlf Made Modela Sarre

91/39 Mg° WALTER andAnthony SAUNDERS

"Reeantiguratien of Rankine and Capital Marketshi Eastern Emme. Augura 1991

MKT Empiried Goddelinee, Odober 1991

91/32 Michel BURDA and 'Mana Caplet Imeatieent and Menti« lia an

91140 Luk VAN WASSENHOVE. 'A Set Fartitieming Renne fer the Gemerefued EP Charles WYPLOSZ Integrated Fitropee, October 1991

TM Dirk CATTRYSSE and Awdeament Profiles", August 1991Marc SALOMON 91/53 Michael BURDA end • aimer Marna and German Integration: Sente

EP Charles WYPLOSZ Ifiemette •, Ocaber 1991

91/41TM

Luk VAN WASSENHOVE,M.Y. KOVALYOU and

'A nie Pelyeandal Approwienation Stigma forSchee/Mg a Male Maitise to Minimise Total 91/34 Albert ANGEHRN •Sanie Adage: An Alternative Frimaire* for

C.N. POTTS Weigilted Lite Work", Annal 1991 TM Caentleter-Aided Raban Maldnor. October 1991

91/42 R. WEfTZ and "Solving A Mani-Ctiteria Allocation Freedom.:TM Tawfik IELASS1 A DIPCfRi011 Support System Apprends",

August 1991

Page 33: A SENSITIVITY ANALYSIS OF STOCHAUX, INVENTORY SYSTEMS

91/55 Robin HOGARTH,EP/SM Claude MICHAUD,

"Leenevity of Business Firmus A Four-StaseFreinework for Analysis", November 1991

92/03OB

Manfred F.R. KETS DE VRIES 'Ibn Fansily Film An Owen', Maenso•,lanuary 1992

Yves DOZ andLudo VAN DER HEYDEN 92/04 Philippe HASPESLAGH and "Maine Acquisitions Work", /anuary 1992

SM David MM/SON91/56 Bernard SINCLAIR-DESGAGNETM/EP

"Aspirations an Emmen» Development',November 1991 92/05 Xavier DE GROOTE "Fledhally and Prend Dives* in Loteniap

TM Modela". lamas, 1992 (revised)91/57 Lydda PRICE 'Ibo Indirect Efforts of Neptive Information enMKT Attitude ange", November 1991 92/06 Theo VERMAELEN and "Enencial Innevatien: Self Tender Offen I.• the

FIN Kees COOLS January 199291/58 Manfred F. R. KETS DE VRIES "Leaders Who Go Cran", November 1991OB 92/07 Xavier DE GROOTE "lb. Fleoldlity of Production Prenne A

TM Gemmi Framework", January 1992 (rened)91/59 Paul A. L. EVANSOB

91/60 Xavier DE GROOTE

"Managemeat Development as Glue Technology",November 1991

`Flesildity nad MarketiegIblannfaceung

92/08TM

Luk VAN WASSENHOVE,Leo KROON andMarc SALOMON

•End and Approdemlion Ateritleas for theOperatiend Fimed ber ved Sebaddin Profilent",Jarasary 1992

TM Coordintin", November 1991 (revised)

91/61 Arnaud DE MEYER "Prend Development in ne Textile Markimery92/091M

Luk VAN WASSENHOVE,Roelof KUBC and

"Statistired Se r» MIllikne for lAtaingProlden", lennety

TM Industry". November 1991 Mare SALOMON

91/62 Philip PARICER and *Sperifying Cempatitive Efforts in Diffusion 92/10 Yves DOZ and •Repainap Ermandlinness: A Proroge ofMET Hubert GATIGNON Mn»: An Empirerai Anoblie, November 1991 SM Heinz THANHEISER Orgemisntiond Renewe •, Jamary 1992

91/63 Michael BURDA 'Sem New Indes ou the Interindmstry Wein 92/11 Enver YUCESAN and •Che the hernetability of Vesifying StretteralEP Sentare frein the Gemme Soeioneesesnir Pond•

December 199117S1 &bridon JACOBSON ?reperdes et pinne End Simulation Models",

February 1992

91/64 Jean DERMINE "InerrationnEntion of FInamen1 Mark». 92/12 Gabriel HAWAWINI "Valentin of Cie-Bender Meilen endFIN Ef&ioscy and Stanley', December 1991 FIN Andeilimer, Penny 1992

12,1 92/13 Spyros MAICRIDAICIS and ail. IdeCenepotiliseu A Budget Related1M Michèle HIBON et.al. Fapieieal Fonmens Study", Felnuary 1992

92/01 Wilfried VANHONACKER "CONPRO4DOGIT: A New Brand amoire Medd111KT/EPI7AI Imerporating a Coasideration Set Formation 92/14 Lydda PR/CE "Inde.' Cluster Ondin with NORMIX

Pronee, Jonas, 1992 MKT Population Atembenialp Prembalities",Febtuary 1992

92/02 Wilfried VANHONACKER "Ibn Drin» et tbe Considoration Set ForeudemMICT/EP/TM Promu A Radient Medelling Penipartivo

Sun Nemosical Rente", lersdary 1992