Advanced Techniques of Theory of Constraints and Activity Based Costing for Scheduling of High Technology Production Lines Stefano Apolloni*, Marcello Lando*, Matteo M. Savino** * Department of and Industrial , University Federico II of Naples, Italy ** Department of Engineering, Sannium University, Benevento, Italy

Abstract One of the most important decisions that a manager should make is to determine a product mix to be scheduled able to maximise profits. To make right decisions he needs more accurate information about the optimal product mix and the restrictive bottlenecks of its company. The objective of this paper is to demonstrate as using the Activity Based Costing (ABC) approach together with the Theory of Constraints (TOC) philosophy we are able to locate an optimal solution to the product mix problem and the bottlenecks machines on the shop floor. A case study is provided to show the applicability of the proposed approach to a real case. Specifically we have analysed an aeronautical firm where, applying our methodology we have located a new layout able to reduce the total cost per unit during the amortization time.

1. Introduction The objective of this paper is to demonstrate as, using the Activity Based Costing (ABC) approach together with the Theory of Constraints (TOC) philosophy we are able to find an optimal solution to the product mix problem and, at the same time, to the optimisation of the production bottlenecks. Our contribution is a product mix decision model that uses activity based cost an theory of constraints information to improve the financial performance of a company. A case study is also given to show the applicability of the proposed approach to a real case. Specifically we have analysed an aeronautical firm where applying our methodology we have located a new layout able to reduce the total cost per unit by 34% during the amortization time. The proposed methodology will give managers more accurate information regarding the optimum product mix and critical bottlenecks of their companies. Infact By applying the TOC philosophy based on this information managers will be able to take the right actions that will improve the profitability of their companies. Specifically they will be able to observe the effects of several alternatives on the throughput of the whole system. In addition the proposed

methodology should help managers to prevent making decisions that sub optimise the system.

Th is may occur, for example, when the managers fix the productive capacity of a plant independently by its bottlenecks capacities.

2. Theory of Contraints and the effects of a bottleneck As succinctly put by Eliyahu Goldratt1 the primary goal of an enterprise is to make profit in the present and in the future but, in a enterprise, there are many obstacles that prevents from accomplishing this goal (the so called constraints/ bottlenecks). The Theory Of Constraints (TOC) is the management philosophy proposed by Goldratt that deals with managing these constraints; it is based on five steps focus on identifying the system constraints, exploiting it, subordinating the rest of the system to the needs of the bottleneck, improving the constraint and repeating the process continually. In a factory the bottlenecks are usually those machines or processes which control the throughput of the system; managing them – effectively and efficiently – yields higher system throughput. Many production control systems have been proposed to improve throughput in the past. Among them are the Materials Requirement Planning (MRP), Just-in-Time (JIT), , Constant Work in Process (CONWIP), and Drum-Buffer – Rope (DBR) systems. Successful manufacturing control procedures are required to identify and manage the system’s throughput, WIP, and cycle time where throughput is the number of final products produced per unit time by the system, WIP is the material within the system undergoing transformation into a final product, and cycle time is the average amount of time required for raw material to be transformed into a final product. An insufficient throughput leads, in fact, to unmet demand; an excessive WIP requires tying up excessive capital and an excessive cycle time leads to the loss of customer orders. In short, if any of these parameters are not managed properly, then the manufacturing centre loses money. These parameters are influenced by process variability, process time, process reliability, system bottlenecks, and the production control system used. The current manufacturing control systems may be classified into three categories. The first is MRP and its successor Manufacturing Resource Planning (MRPII). These control systems push materials into the production facility based on forecasted demand, and are thus known as push systems. In the second category of control systems, known as pull systems, the

1 Goldratt E.: “The Goal: A Process of Ongoing Improvement”, North River Press Inc., 1984

material is released into the production facility only when the demand for the end product

tri ggers it. Since the material is released into the system only when it is needed, these control system are also called JIT systems. The two popular implementations of JIT control systems are Kanban (card) control systems and CONWIP control systems. The third category of control systems is mixed control systems. In these systems, the pull and push control systems are used to manage certain segments of the production line. There is a great amount of literature evaluating the performance of these systems. Cook demonstrates that serial production systems using DBR results in greater average throughput and lower levels of WIP variance than when the same system is managed by kanban2. Guide in the analysis of a re – manufacturing facility proves that DBR results in a reduction in WIP and throughput variance compared to MRP3. Bonvik et al. in the analysis of CONWIP, kanban, and pull-push production control systems demonstrates that the pull-push systems carries the lowest WIP at any particular throughput with the kanban system generally carrying the highest WIP4.

3. Integrating Activity Based Costing (ABC) and Theory Of Constraints (TOC) ABC is a long term oriented analysis because it assumes that almost all of the costs of resources used in production are variable. However, in the short run, lot of them are fixed (for example the cost of labor). Another “weakness” of this theory is that it does not involve the system’s constraints so, especially in the short run, when, for example, the capacity of all the activities are fixed, an ABC analysis could be unreliable. TOC has, instead, a short time horizon because in the short run, being fixed the capacity of a plant, we have the bottleneck. ABC and TOC are, so, based on different time horizons; they have different hypothesis about labor and overhead costs and production capacity. Particularly, in the short run, labor and overhead costs can be assumed as fixed and TOC is able to give the right information, however, in the long run, all costs tend to be variable so ABC can reflect the expected costs in this time frame. In the end, since TOC and ABC are valid in different time horizons, they can complement each other.

2 Cook D.P.: “A Simulation comparison of traditional JIT an TOC manufacturing systems in a flow shop with bottleneck”; Production and Mangement, 1994 3 Guide V.D.R.: “ Scheduling using drum buffer rope in a remanufacturing environment”, International Journal of Production Research, 1996 4 Bonvik A.M., Couch C.E., Gershwin S.B.; “ A Comparison of production line control mechanism”, International Journal of Production Research, 1997

Before introduce the proposed model able to integrate ABC and TOC to determine the best

pro duct mix that maximizes a company profit, we have to introduce a definition of the term capacity. The largest asset that any manufacturer has, which allows it to make products, is its capacity. Although there is no unique way to define capacity; in the following paragraph we categorise it as5 (see figure 1): ‹ theoretical capacity defined as the maximum output a plant can produce in a specific period; ‹ practical capacity defined as the theoretical one adjusted for lost time due to non working day, plant breakdown, repairs and maintenance; ‹ normal capacity defined as the average output of a plant over an extended period; ‹ budgeted capacity defined as the estimated one that will be utilised in a specific time period

Figure 1: Definitions of capacity 4. Model Formulation One of the most fundamentals decisions that a company should make is the determination of the best product mix that maximises profits. Equation 1 reports the conventional approach to this problem:

5 Hill D, Kevin G, Glad E.; “Managing capacity”, Journal of cost management, 1994; Mc Nair C.J.; “ The hidden costs of capacity”, Journal of cost management, 1994

N Max!ƒ(si − mi − mani − ovr *si )*Xi i=1 subject to À N Œƒ miXi ≤∈acq.mat Œi=1 Œ N à maniXi ≤∈manodopera Œi=1 Œ + ŒXi ∈ N Õ

Equation 1: Conventional approach to a product mix problem Where:

Xi = number of product i that is produced in a specific period N = kinds of products that can be produced in the company

mi = material cost per unit of product Xi

si = selling price of one product Xi

mani = direct labour cost per unit of product Xi

⁄acq.mat = capital available for direct labour purchase

⁄manodopera = capital available for materials purchase ovr = overhead costs. We have calculated these by dividing the total overhead activity capacity by the total direct labour euros available

Since the new technologies and the automation have increased the percentage of the overheads on the new productions, the application of an only cost driver could negatively influence the obtainable solutions; besides a lot of activities are characterised by an increase of their costs not proportional to the number of produced unity but to their uses and, so, theyor costs haven’t to be assigned to each unity of product. In such cases Cooper and Kaplan have proposed to divide, using a criterion based on the type of costs assigned to the products, the activities in the following levels: ‹ unit level: their costs are exclusively depending from the number of realized products; ‹ batch level: these activities are performed every time a new production (i) starts so their costs have to be assigned only to the products of the batch i; ‹ product sustaining: they are performed to continue to produce and sell individual products so, to allocate their costs, we have to define a cost driver different by the volume of production because they are not affected by the level of production volume;

‹ facility sustaining: all the activities necessary for the plant survivor (the only way to eli minate the correspondents costs is to permanently close the plant) but not directly correlated to the plant’s output. Since such activities are common to all the products, the correspondents costs must be divided among the whole production6. Using this classification Gurses7 has obtained the following additional constraints (see eq 2)

N Np

ƒƒ c piu p X i ≤ Pp i=1 p=1 N Nq

ƒƒ cqiuqYi ≤ Pq i=1 q=1 N Ns

ƒƒ csius Zi ≤ Ps i=1 s=1 Equation 2: Constraints on the activities availability Where:

cpi = per unit usage of unit level activity P by product Xi

cqi = per unit usage of batch level activity Q by product Xi

csi = per unit usage of product sustaining activity S by product Xi

up = ratio, in a given time period, between budgeted and practical capacity of the activity p

us = ratio, in a given time period, between budgeted and practical capacity of the activity s

uq = ratio, in a given time period, between budgeted and practical capacity of the activity q

Np = total number of unit level activities

Nq = total number of batch level activities

Ns = total number of product sustaining activities

Pp = Practical capacity of each unit level activity Pq = Practical capacity of each batch level activity

Pp = Practical capacity of each product sustaining activity

Yi = number of batches of unit i produced in a give time period

Zi = one, if i is produced

6 In the following paragraph we have ignored these activities 7 Gurses A. P.; “An Activity –Based Costing and Theory of Constraints Model for Product- Mix Decisions”; Master of Science in Industrial and Thesis, Virginia Poytechnic Institute, June 1999

Zi = zero if i is not produced

To define the batch sizes Gurses has included the following constraint (see eq 3):

biYi ≥ X i Equation 3: Batch size constraint Where :

bi = batch size of the product i

In the end we can express a mix problem using equation 4.

N N Np N Nq N Ns

Max!ƒ(si − mi − mani )* X i − ƒƒ c piu p X i −ƒƒ cqiuqYi − ƒƒ csius Zi i=1 i=1 p=1 i=1 q=1 i=1 s=1 subject to À N Œƒmi X i ≤∈acq.mat Œ i=1 Œ N Œƒmani X i ≤∈manodopera Œ i=1 Œ N Np Œƒƒ c piu p X i ≤ Pp Œ i=1 p=1 Œ N Nq à cqiuqYi ≤ Pq Œ i=1 q=1 Œ N Ns Œ ƒƒ csius Zi ≤ Ps Œ i=1 s=1 Œb Y X Œ i i ≥ i ŒX ∈ N + Œ i Œ Œ Õ Equation 4: Proposed approach to a product mix problem Equation 4, nevertheless, allows us to define a theoretical solution since it is based on a splitting of all the overheads of a plant; being this very difficult we have analysed these indexes to simplify the problem.

Analysing these indexes we can note as the same can be valued, excluding csi, considering, as cost driver, the volume of production.

To evaluate csi, instead, we have to analyse the costs of the sale’s activities. In the following we have divide these in:

‹ transport costs (Ctrasp);

‹ warehousing costs (Cimm);

‹ costs (CMTK);

‹ commissions (Cprovv);

‹ duties (Csp.dog.).

Leaving out the duties – because it is possible to erase/reduce them producing closer to the output market – we have analysed the other cost drivers.

Ctrasp They are due to: o the item fragility; o the item price; o the item volume; o the item value; o the maximum waiting time of the market.

Cimm They are due to the item characteristics and to its value and, in the following, we evaluate them using the Wilson model.

CMTK; Cprovv They are in inverse relation to the life of an item.

Now we propose to evaluate csi as follows (Equation 5)

ctrasp + cimm + cMTK + c provv csi = X i X c = INT i trasp cap INT ( MT ) Voli X c = ms T i (Modello.Wilson) imm i 2

cMTK = Punit si X i

Equation 5: csi evaluation where:

capMT = Capacity of the choose transport

voli = volume of the item i m = Unitary warehousing costs of i, T = Mean warehousing time of i

Punit = Sales commission of i

5. A case study of an Hi Tech production line We define – especially in aeronautical industries – composite materials the ones produced using fibers normally weaved (frequently orientated) included in polymeric matrixes usually thermosetting (i.e. epoxy resins, phenolic resins, polyester resins). The composite works are, normally, obtained by stratification, of prepregs filled of their catalyst (stored, until their use, at about –20°C); this process allows to obtain different profiles characterized by a good accuracy shape and by a good surfaces finish so, generally, these works don’t have to be refined. The batching on the produced piece’s surfaces of coats and the opportunity to orient, on these surfaces, the prepregs allows to obtain a well optimized resistant section compared to local stress that, during the time, the piece have to stand. Furthermore the easy capability to be modeled, the forming technique which doesn’t request complex tools and/or complex cycles combined with the possibility of a piece sizing as function of local stress, and the possibility to obtain the piece directly from its mold to the high performances of prepregs when they have been polymerized has made the composites the better material usable to produce braces and, in general, part which have to ensure high specifications and low weight; in the last years the number of applications of composites is huge increasing so it is very important define a standard cycle and its cost. In this context our goal is, identified the main characteristics of the composites (see table 1), to evaluate the suitableness of a load/unload system of the bottleneck department.

Material Specific weight Breaking tensile Elastic modulus [kg/dm3] stress [daN/mm2] [daN/mm2] Light alloy (Al) 2,7 50 7000 Titan 4,87 110 11000 Carbon resin fabrics 1,38 70 6300 [90°] 1400 [45] Carbon resin tapes 1,38 170 [axial] 12000 [axial] 700 [90°] Table 1: main mechanical characteristics of composites compared with the ones of main materials used in aeronautic applications

Simulating the flow times we have found, as main bottleneck, the stratification unit: it is an au toclave and, in table 2, we report some of its resulting flow times (table 2)

Cycle Temperature gradient Temperature Polymer. Total cycle time [°C/1']: [°C] time [1'] [1'] Heating. Heating. Cooling Starting Working Ending Min Max Press Depress. max Min BMS 8-212N 2,8 0,6 2,8 20 197,0 20 150 277,6 405,2 10,0 10,0 BAC 5317-1 2,8 0,6 2,8 20 183,2 183,2 30 88,8 324,0 2,8 0,2 2,8 183,2 197,0 20 120 277,6 591,1 10,0 10,0 ……. ……. …… ….. …… …….. …….. …….. …… …… …… ……. BAC 5317-1* 2,8 0,6 2,8 20 183,2 183,2 58,8 294,0 2,8 0,2 2,8 183,2 197,0 20 120 247,6 561,1 10,0 10,0 SIKORSKY 4,4 0,6 4,4 20 205,4 20 180 263,5 555,7 10,0 10,0 BAC 5317-4 2,8 0,6 2,8 20 183,2 183,2 58,8 294,0 2,8 0,2 2,8 183,2 197,0 20 120 247,6 561,1 10,0 10,0 BMS 8-219D 4,4 1,1 2,8 20 138,8 20 90 159,5 239,8 10,0 10,0 BMS 8-256H 2,8 0,6 2,8 20 197,0 20 120 247,6 502,8 10,0 10,0 Mean processing time 195’ Table 2: Autoclave flow times

Table 3 reports others processing flow times

Processing Cycle time [1’] Cutting 5 Stratification 40 Other processings 100 Quality control 30

Table 3: others processing flow times

5.1 Identification of teh best plant/management solution Identified the bottleneck we have analised it to identify a plant solution able to minimize the cost of utilisation of the autoclave cost; in this context – discarded the hypotesis of new autoclave – we have analised the autoclave load/unload system to identify an its different shape and, afterwards, a different management logic.

5.2 Plant solution Since usually, given a polimerisation cycle, it is made up of items having different dimension and shape (see figure 2), and, at the same time, we use truck having a fixed configuration, we use, normally, its filling index is less than 45%.

Figure 2: Proposed truck This truck, actually used in several aeronautic firms, is a light aluminum truck (to minimise the power required to heat it) and has got three working plane mobile in the y direction; furthermore this truck has got the thermal and pressure control jacks in front, so we could minimise the time required to load/unload it. At the same time we have reengineered the load/unload area layout (see figure 3) obtaining ones able to allow us to load/unload during the polimerisation time.

Unload 1 Autoclave unload 2 Transfer 3 Truck unload 4 Tools transfer 5 Tools cleaning Load 11 Truck load 12 Truck parking 13 Autoclave load

Figure 3: Proposed load/unload area layout

5.3 Management solution To validate the proposed plant solution we have, primaly, identified some dynamic load rules, that (see figure 4) – given an input item (p), its dimension (n,m) and its working cycle (i) – we propose to verify, at input time of p (t0 ), if at the time t0+tprep (where tprep is the sum of all

the operation before polimerisation) there is – in the load/unload area –a truck (c*) containig ite ms having the working cycle (i) and if, on this truck, it is avaliable an area (D(c*) > (n x m) or there is an empty truck. To minimise the working time (load + polymerisation + unload) we have also fixed, given a truck dedicated to the cycle i, a maximum waiting time

Start

Identify cycle [i]

no Identify part [n,m]

no ∃ c() empty ∃ c*(i)

choose c* no D(c*)>n,m

Update D(c*)

Production

sì Other parts

Stop

Figure 4: Flow chart before polymerisation and a minimum truck loading rate. In the end we have simulated (see table 4) the system assuming: ‹ a random/discrete item’s input; ‹ one/two or three daily shifts; ‹ three different input time (5, 10 and 15 minutes); ‹ five different minimum truck loading rate (40, 50, 60, 70, 80 e 90%); ‹ two different maximum waiting time before polimerisation (4 and 8 hour).

EXP Input time Input type Max # of Working Loading rate (A) Waiting time items time [h]

[min] [min] [%] 1 5 Disc 120.000 160.000 60 4

2 10 Disc 120.000 160.000 60 4 3 15 Disc 120.000 160.000 60 4 4 5 Disc 120.000 160.000 70 8 5 10 Disc 120.000 160.000 70 8 6 15 Disc 120.000 160.000 70 8 7 5 Disc 120.000 310.000 60 4 8 10 Disc 120.000 310.000 60 4 9 15 Disc 120.000 310.000 60 4 10 5 Disc 120.000 310.000 70 8 11 10 Disc 120.000 310.000 70 8 12 15 Disc 120.000 310.000 70 8 13 5 Disc 120.000 460.000 60 4 14 10 Disc 120.000 460.000 60 4 15 15 Disc 120.000 460.000 60 4 16 5 Disc 120.000 460.000 70 8 17 10 Disc 120.000 460.000 70 8 18 15 Disc 120.000 460.000 70 8 19 15 Random 120.000 160.000 40 4 20 10 Random 120.000 160.000 40 8 21 10 Random 120.000 160.000 50 4 22 5 Random 120.000 160.000 50 8 23 10 Random 120.000 160.000 60 4 24 15 Random 120.000 160.000 60 8 25 10 Random 120.000 160.000 70 4 26 5 Random 120.000 160.000 70 8 27 5 Random 120.000 160.000 80 4 28 5 Random 120.000 160.000 80 8 29 15 Random 120.000 310.000 40 4 30 5 Random 120.000 310.000 40 8 31 15 Random 120.000 310.000 50 4 32 5 Random 120.000 310.000 50 8 33 15 Random 120.000 310.000 60 4 34 15 Random 120.000 310.000 60 8 35 5 Random 120.000 310.000 70 4 36 5 Random 120.000 310.000 70 8 37 5 Random 120.000 310.000 80 4 38 5 Random 120.000 310.000 80 8

39 15 Random 120.000 460.000 40 4 40 5 Random 120.000 460.000 40 8

41 10 Random 120.000 460.000 50 4 42 15 Random 120.000 460.000 50 8 43 10 Random 120.000 460.000 60 4 44 10 Random 120.000 460.000 60 8 45 5 Random 120.000 460.000 70 4 46 15 Random 120.000 460.000 70 8 47 5 Random 120.000 460.000 80 4 48 15 Random 120.000 460.000 80 8 Table 4: experimental test

Table 5 reports the obtained results EXP Worked Autocl. Util. (B) B >A B

22 7381 23,13 195 132

23 1297 7,93 35 121 24 924 11,96 28 112 25 6340 27,26 100 354 26 10270 67,73 61 473 27 11196 42,48 64 524 28 6264 66,10 52 400 29 11199 7,30 324 79 30 6728 8,44 200 34 31 11089 9,20 317 79 32 10623 16,87 353 22 33 6589 4,87 43 162 34 11731 32,77 466 39 35 8312 7,41 283 10 36 8587 35,12 137 327 37 10097 18,89 401 35 38 12022 37,72 429 48 39 8563 5,61 66 385 40 11196 14,46 64 524 41 15108 9,65 214 430 42 15108 19,31 214 430 43 12649 8,55 300 183 44 13958 29,78 201 460 45 3590 2,96 25 127 Table 5: Sperimental results (continue)

EXP Worked Autocl. Util. (B) B >A B

C f + (C ov * U * H ) C u = N

where:

‹ Cu = unitary production cost;

‹ Cf = fixed cost;

‹ Cov = variable cost; ‹ U = autoclave utilisation [%]; ‹ H = simulation length [min]; ‹ N = worked items. Table 6 reports the obtained results during (C) and after the amortization time (D) ⁄ EXP H. U Input type Workers Manpower Cu [ ] [#] cost. [⁄] C D 1 160.000 60 4 2 76.000,00 363,65 18,35 2 160.000 60 4 2 76.000,00 298,16 20,57 3 160.000 60 4 2 76.000,00 345,95 21,14 4 160.000 70 8 2 76.000,00 349,68 34,99 5 160.000 70 8 2 76.000,00 335,45 42,87 6 160.000 70 8 2 76.000,00 385,37 72,72 7 310.000 60 4 4 152.000,00 275,81 25,09 8 310.000 60 4 4 152.000,00 311,33 26,14 9 310.000 60 4 4 152.000,00 194,64 18,28 10 310.000 70 8 4 152.000,00 281,20 32,13 11 310.000 70 8 4 152.000,00 273,07 30,43 12 310.000 70 8 4 152.000,00 306,78 31,88 13 460.000 60 4 6 228.000,00 343,32 41,43 14 460.000 60 4 6 228.000,00 350,50 38,72 15 460.000 60 4 6 228.000,00 265,29 31,34 16 460.000 70 8 6 228.000,00 287,05 43,68 17 460.000 70 8 6 228.000,00 374,83 62,62 Table 6: obtained results (continue)

⁄ EXP H. U Input type Workers Manpower Cu [ ] [min] [%] [#] cost. [⁄] C D 18 460.000 70 8 6 228.000,00 596,30 86,97 19 160.000 40 4 2 76.000,00 597,37 27,23 20 160.000 40 8 2 76.000,00 299,47 23,97 21 160.000 50 4 2 76.000,00 231,43 17,33 22 160.000 50 8 2 76.000,00 300,68 27,01 23 160.000 60 4 2 76.000,00 1.648,65 91,21

24 160.000 60 8 2 76.000,00 2.337,45 151,30 25 160.000 70 4 2 76.000,00 353,53 34,92

26 160.000 70 8 2 76.000,00 239,26 42,57 27 160.000 80 4 2 76.000,00 207,44 27,02 28 160.000 80 8 2 76.000,00 390,89 68,41 29 310.000 40 4 4 152.000,00 200,68 20,31 30 310.000 40 8 4 152.000,00 335,79 35,55 31 310.000 50 4 4 152.000,00 204,44 22,28 32 310.000 50 8 4 152.000,00 220,87 30,72 33 310.000 60 4 4 152.000,00 337,28 30,70 34 310.000 60 8 4 152.000,00 214,02 41,82 35 310.000 70 4 4 152.000,00 270,52 27,49 36 310.000 70 8 4 152.000,00 295,20 59,96 37 310.000 80 4 4 152.000,00 234,45 34,39 38 310.000 80 8 4 152.000,00 213,09 45,07 39 460.000 40 4 6 228.000,00 272,57 36,67 40 460.000 40 8 6 228.000,00 220,59 40,17 41 460.000 50 4 6 228.000,00 158,59 24,89 42 460.000 50 8 6 228.000,00 168,39 34,69 43 460.000 60 4 6 228.000,00 188,09 28,39 44 460.000 60 8 6 228.000,00 193,77 49,05 45 460.000 70 4 6 228.000,00 638,84 76,16 46 460.000 70 8 6 228.000,00 301,46 58,20 47 460.000 80 4 6 228.000,00 259,22 55,75 48 460.000 80 8 6 228.000,00 214,70 57,68 Table 6: obtained results (continue) In the end tables 7a and 7b report the best obtained results (exp 41 during the amortization time, exp 21 after it)

EXP Input time. Input type H U Waiting Worked B [min] [min] [%] time items 21 10 Random 160.000 50 4 9.435 16,41 41 10 Random 460.000 50 4 15.108 9,65 Table 7a: best results ⁄ EXP B> A B< A Workers Manpower Cu [ ] [#] cost. [⁄] C D

21 200 258 2 76.000,00 231,43 17,33 41 214 430 6 228.000,00 158,54 24,84

Table 7b: best results These solutions have got the following likeness: ‹ The same value of U (50%); ‹ The same input type (random) ‹ The same input time (10 minutes); ‹ The same waiting time (4 h). In the end table 8 – given a fixed cost – compares the obtained management solutions with the current solution.

Now Exp 21 Exp 41 ∆ Entrata Unknown Random Random Autoclave utilization [h/day] 24 8 8 Annual production 20.000 9.435 15.108 Workers [#] 12 2 6 Trucks available 20 8 8 Autoclaves available 3 2 2 ⁄ ⁄ Cu ( ammortization time) ≈ 240 -- 158,54 -33,9% ⁄ ⁄ Cu ( post ammortization time) ≈. 40 17,33 -- -56,7%

Table 8: Comparison between obtained and current management solutions

6. References 1. Allen T. J., Scott Morton M. S. (a cura di); “Information Technology and the Corporation of the 1990s”, Oxford University Press, Oxford, 1994 2. Brausch J.M., Taylor T.C.; “Who is Accounting for the Cost of Capacity ?”, Management Accounting, 1997 3. Brimson J. A.; “Activity Accounting: An Activity-Based Approach”, John Wiley & Sons Inc., New York, 1991.

4. Bromwich, Bhimani; “Management Accounting: Pathways to Progress”, CIMA,

London, 1994. 5. Brown R. J., Yanuck R. R.; “Introduction to Life Cycle Costing”, The Fairmont Press/Prentice-Hall, Englewood Cliff (NJ), 1985; 6. Cooper R., Kaplan R. S. “Activity Based Systems: Measuring the Costs of Resource Usage”, Accounting Horizons, 1992 7. Cooper R., Kaplan R.S.; “The design of Cost Management Systems”, Harvard Business Review, 1991 8. Davemport, Short; “The new : Information Technology and Business Process Redesign”, Sloan Management Review, Summer 1990 9. Deming W.E.; “Out of the crisis”, MIT center for advanced engineering study, 1982 10. Deming. W.E.; “The new economics for industry, government, education”, MIT center for advanced engineering study, 1993 11. Demmy S., Talbott J.; “Improve Internal Reporting with ABC and TOC”, Management Accounting, 1998 12. Earl M., Khan B.; “How new is Business Process Redesign”; European Management Journal, 1994 13. Elftman, T.M.; “Examination of the effects of bottlenecks and production control rules at assembly station”, University of Florida, 1999 14. Goldratt E. M., Cox. J.; “The goal: a Process of Ongoing Impovement”; North River Press, Great Barrington, 1992 15. Goldratt E.; “What is this thing called the Theory of Constraints and how should it be implemented”, North River Press, 1990 16. Goldratt E.M., Cox J.; “The Goal”, London, 1984 17. Greenwood, Reeve; “Process Cost Management”, The Journal of Cost Management for the Manufacturing Industry, n°. 1, 1994 18. Gupta, Mahesh, Baxendale, Mc Namara; “Integrating TOC and ABC in a health care company”, Journal of Cost Management, 1997 19. Gurses A. P.; “An Activity –Based Costing and Theory of Constraints Model for Product- Mix Decisions”; Master of Science in Industrial and Systems Engineering Thesis, Virginia Poytechnic Institute, June 1999 20. Hammer M., Champy J.; “Reengineering the corporation”; Harper Business, 1993 21. Hammer M.; “Reengineering work: don’t automate obliterate”, Harvard Business

Review, 1990

22 . Harrison , Pratt; “A methodology for rengineering business”, Planning Review, vol 21, 1992 23. Heller R.; “The Leadership Imperative”, Truman Tally Books/Dutton, New York, 1995 24. Innes J., Mitchell F.; “Activity-Based Costing Research”, Management Accounting, 1989. 25. J.B. MacArthur; “Theory of Constraints and Activity Based Costing: friends or foe”, Journal of Cost Management, 1993 26. Johnson H. T., Kaplan R.S.; “Relevance Lost: The Rise and Fall of Management Accounting”, Harvard Business School Press, Boston, Massachusetts, 1987 27. Johnson H. T.; “Activity-Based Information: A Blueprint for World-Class Management Accounting”, Management Accounting, 1988. 28. Juran J.M.; “Quality policies and objectives”, in “Quality Control Handbook”, AAVV, Mc Graw Hill, 1979 29. Kaplan R. S., Norton D. P.; “The balanced scorecard. Measures that drive performance”, Harvard Business Review, 1992. 30. Kee R.; “Integrating ABC and Theory of Constraints to evaluate outsourcing decisions”, Journal of Cost Management, 1998 31. Klein; “IE’s fill facilitator rule in benchmarking operations to improve performance”, Industrial Engineering, 1993 32. Leavitt H. J., Whisler T. L.; “Management in the 1980s”, Harvard Business Review, 1988. 33. Lipnack J., Stamps J.; “Virtual Teams”, J. Wiley, New York, 1997. 34. MacArthur J.B.; “Theory of Constraints and Activity Based Costing: friends or foe”, Journal of Cost Management, 1993 35. Malone T. W., Yates J., Benjamin R. I.; “Electronic Markets and Electronic Hierarchies”, Communications of the ACM, 1987. 36. Miller J. G., Vollmann T. E.; “The Hidden Factory”, Harvard Business Review, 1985 37. Nanni A.; “Automation and Management Accounting in Japan”, John Wiley, New York, 1991 38. Player R. S., Keys D. E., “Lessons from the ABM battlefield: Getting off to the right start”, Journal of Cost Management, 1995 39. Porter M. E.; “Competitive Advantage”, The Free Press, New York, 1985.

40. Sakurai M.; “Target Costing and how to use it”, The Journal of Cost Management for

the Manufacturing Industry, 1989; 41. Shank, Govindarajan; “Strategic Cost Management”, The Free Press, New York, 1993 42. Smith J.J.; “TOC and MRPII, from theory to results”, 1994 43. Sponde C., E.O. Henke, M. Humble; “Using Activity Analysis to locate profitability drivers”; Management Accounting, 1994 44. Troxel, Weber,; “The Evolution of Activity-Based Costing”, The Journal of Cost Management for the Manufacturing Industry, 1990. 45. Turney; “Activity Based Management. ABM Puts Information to Work”, Management Accounting, 1992