The challenges of capacity planning

243

International Journal of Production Economics, 30-31 (1993) 243-264 Elsevier

The challenges

of capacity

Nils Arne Bakke” and Roland

planning

Hellbergb

a Gesellschaft fur Technologie Transfer, mbH, Hannover, German?, b Linjegods ax, Norway and institute of Technology, Linkoping. Sweden

Abstract An Increasing number of Industrial compames are forced to reorganize then capactty planmng as today’s competitive environment renders traditional methods obsolete. Capactty planmng plays a key-role. e.g. m Improving due date observance and shortemng delivery times, and is a precondition when rapid and cost efficient adjustments of capactty to market-fluctuation are necessary Thts paper identifies weaknesses of traditional approaches to capactty planning and suggest a number of remedies. The emphases is placed on the specttic problems of companies producing complex, assembly intensive products m small lots. The empirical basts of the paper is mamly collected from four case studtes. The case studies were conducted as research projects and consultancy work durmg the years 198991992. Addittonally, materials from detailed case studtes of 50 industrial compames made by Gesellschaft fur Technologie Transfer 198881992 is utthzed The paper deprcts capacity planning as a complex activity with broader methodologtcal and orgamzational ramrfications. An encompassing approach is necessary as optimtzed solutions of isolated problems concerning, e g. scheduhng technology. lot-stze dectstons, etc., are not able to guarantee an overall functioning capacity planning. The paper suggests theoretical and/or practical changes m the following seven areas: (1) master scheduling, (2) the level of detail and length of the planning horizon, (3) the scheduhng technology, (4) the maintenance of the planning parameters, (5) capactty planning in pre-productton departments, (6) integration of subcontractors in capacrty planning, (7) order release and start of manufacturing.

1. The problem area Traditionally, for most firms, a highly sophisticated capacity planning was not mandatory, as market fluctuations could be handled satisfactorily through investments in inventories and/or over-capacity. The protection given by production for inventory secures an even and balanced load and high capacity utilization. Today, the costs of this strategy seem too high for most firms, both in terms of costs (inventory, etc.) and inflexibility in relation to changin-632g customer preferences (long lead times, high costs of customization, etc.). Investments in over-capacity enable a rapid and flexible adjustment to changing demand. This strategy is, however, seldom a viable alterna-

Correspondence to: Nils A. Bakke, nologie

Transfer,

mbH,

0925-5273/93/$06.00

Hannover,

Gesellschaft Germany.

10 1993 Elsevier

Science

fur Tech-

Publishers

tive as the cost of the required slack resources is too high to remain competitive. Today, new strategies combining cost efficiency and flexibility are required. The new strategies expose the internal operations directly to changes in market demand. Through cutting inventories and lead time at all stages of the order cycle, the companies are forced to respond directly to customer orders with minimal buffers of inventory and time (see e.g. Ref. Cl]). This leads to permanent capacity problems as the companies typically are experiencing fluctuations between overload and idleness. Firstly, the companies experience problems with inferior due date performance because of bottleneck problems. Additionally, reduced profitability is likely, due to over-proportional high costs induced by coping with overload. Secondly, the companies lose money due to idle resources fixed in the short term. Thirdly materials management may represent

B.V. All rights

reserved

244

N.A. Bukkr.

R. Hrllhm,:

The challengrs

a problem. Due to the uncertainties attached to long supplier lead times materials management simultaneously experiences problems of high inventories and lack of critical materials and components. As purchased components and services have increased their share of total value added, there is a growing need for new institutional relationships between networks of firms with partly integrated capacity planning systems. These developments force capacity planning into the focus of attention of industrial companies, as a strategic activity with a major influence on their competitiveness. However, both the significance and the complexity of capacity planning vary considerably between companies. Our focus is on companies producing fairly complex, assembly intensive and customized products in small lots. The main case studies referenced to in the text include production of advanced maritime equipment; ship refrigerators, pumps (Norway), paper machinery (Germany), powerstation turbines (Germany), and production of large dumpers (Canada). This kind of companies has important common characteristics influencing the task of capacity planning: l A high due date observance is an important competitive parameter. l The mix of end-products to be produced may fluctuate considerably. l The customization implies that a considerable share of the delivered end products are unique, creating a variable load for the construction department and the departments preparing new items for manufacturing and assembly. (The Bill of Materials (BOM), routings and operation times, NC-programs, etc. must be updated or generated.) l Periodic capacity deficits are likely to arise in pre-production departments as the demand for customization fluctuates. This may result in overdue start of manufacturing as the release of work orders is slowed down. Sometimes this may cause idleness at bottleneck work-centers as an insufficient number of work orders are available.

of crcpucitlt

plunmng

The fluctuating mix of end products and the unstable character of the BOMs may strongly affect the mix of items to be manufactured. As a result bottleneck resources in manufacturing are often variable and difficult to predict. l Due to the high number of items manufactured in small quantities at irregular intervals it is difficult to establish a perfect flow-oriented layout. Although considerable improvements can be realized, e.g. through the use of group technology. the complexities of job-shop production are difficult to eliminate completely. l MRP systems play a key role in the planning processes. KANBAN or other repetitive production planning and control techniques have only a limited applicability in these production environments. The OPT ideas [2] represent a more relevant approach, as the correct identification of bottlenecks is a precondition for a dynamic capacity planning. Although traditional input-output models may have some relevance to the following discussion we are not considering them in detail as they put limited weight on our main topic. the dynamic regulation of capacity (e.g. Refs. [3-51). Thus, for the studied type of company capacity planning is important for all departments involved in the completion of customer orders. The aim of the paper is to develop a theoretical and practical framework of capacity planning for companies facing these challenges. Section 2 discusses merits and shortcomings of traditional approaches. Section 3 outlines the basic components of a revised approach to capacity planning. Section 4 evaluates the wider relevance of the suggested improvements, and places them within a broader theoretical framework. Although the need for an encompassing approach to the problem area is stressed, the main emphasis is put on problems related to manufacturing. Capacity planning for other activities, having their own peculiarities, deserve a more detailed treatment than offered in this contribution. l

N.A. Babe,

R. Hellherg,iThe

2. Current capacity planning 2.1. General principles

of capacity planning

The master production schedule (MPS) plays a key role for the capacity planning of companies of the type studied in this paper. The MPS specifies how much and in what future periods each major product is to be completed. To transform an MPS into capacity requirements different procedures can be used. Below four different techniques are described. They differ primarily in the level of detail of input and output data [6]. (1 j Capacity planning using over all factors (CPOF). CPOF is a simple approach to rough-cut capacity planning. As time phased MRP-data are not utilized, the required input comes only from the MPS. This method is based upon planning factors derived from standards or historical data for end products. When these planning factors are applied to the MPS data, overall labour or machine-hour capacity requirements can be estimated. This overall estimate is thereafter allocated to individual work centers on the basis of historical data on shop workloads. (2) Capa& bills. The capacity bill procedure provides a much more direct link between individual end products in the MPS and the capacity required for individual work centers. It takes into account any shifts in product mix. Consequently, it requires more data than the CPOF procedure. BOM and routing data are required, and direct labour- or machine-hour data must be available for each operation. The bill of capacity indicates the total standard time required to produce one end product in each work center required in its manufacture. (3) Resource projiles. Neither the CPOF nor the capacity bill procedure takes into account the specific timing of the projected workloads at individual work centers. In developing resource profiles lead time data is taken into account to provide timephased projections of the capacity requirements for individual production facilities. The re-

challenges

of capacity planning

245

source profile procedure requires BOM, routing, time standard information and production lead time for each end product and component part. (4) Capacitjs requirements planning (CRP). Capacity requirements planning differs from the resource profile procedure in four respects. First, CRP utilizes the information produced by the MRP explosion process, which includes consideration of actual lot sizes and expected lead times for both open and planned work orders. Second, the gross to net feature of the MRP system takes into account the production capacity already stored in the form of inventories of both components and assembled parts. Third, the current status of all WIP in the shop is considered, so only the capacity needed to complete the remaining work on open work orders is computed. Fourth, CRP takes into account the demand for service parts, other demands that may not be accounted for in the MPS, and any additional capacity that might be required by MRP planners (e.g. scrap, item record errors, etc.). To accomplish this, the CRP procedure requires the same input information as the resource profile procedure plus information on MRP planned orders and the current status of open work orders at individual work centers. In practice most firms still seem to be using the simpler methods (l-2), sometimes combined with elements of more sophisticated methods (334)’ Hence, the process of capacity planning typically includes the following three steps: (1) Establish and maintain a production program in the form of an MPS. It consists usually of a mix of prognosis orders, orders under negotiation and confirmed orders. (2) By MPS-changes an update of the load profiles of all affected departments is required. In the horizon exceeding 2-3 month’s information about load is kept on a fairly aggregated level. Usually the load is broken down to department level, e.g. milling, grinding and assembly (cf. methods l-2 above). To distribute the load in time ‘Based on a study of 50 companies in 6 European countries made by Gesellschaft fur Technologie Transfer in the years 1989%1992. See also Ref. [16, p. 641.

246

N.A. Bakke,

R. HellherylThe

simplified scheduling techniques, enabling a check of load-capacity relationship on a monthly basis 4-8 months into the future, are utilized. Some make a more detailed check of expected critical resources, that is expensive machinery or work-centers that traditionally have been bottlenecks. They than usually look at maximum 2-6 months into the future (cf. method 3-4). Detailed profiles for all work-centers are as a rule available only for a 2-8 weeks’ horizon. (3) After completing step 1 and 2 overload or underload or even both are identified. Then the task is to adjust one or more variables to create an acceptable balance. By overload the simplest solution is to negotiate delivery dates, creating balance by postponing orders into the future. Unfortunately, in today’s competitive environment this is often equal to losing the order. This makes capacity increasing actions mandatory. By idle capacity the problem is to reduce the fixed costs, which is difficult on short notice. This is obvious for machinery, buildings, etc. Yet, in practice these constraints also pertain to labour, either due to legal restrictions or because the skills of the labour force represent important competitive resources. 2.2. Shortcomings

in use

But how do these methods function in practice? Let us look at a summary of the experiences from the case studies. (Table 1 shows some key characteristics from 3 of the studied firms.) (I) The Master Production Schedule is unreliable. The MPS is the basis for all the following steps of capacity planning. Failures at this stage severely limit the reliability of the later steps. Considering this the companies seem to put surprisingly small weight on maintaining a correct master-schedule: between involved de0 The communication partments is sporadic. This regularly leads to non synchronized decisions. E.g. sales accepts orders or delivery dates without the consent of production.

challenyes

of capucity

plannmy

Due to long lead-times uncertain parts of the master-schedule must be released for manufacturing. One manufacturer (dumpers) based the whole plan on forecasts, making the customization as modifications on already finished products. Generally the companies experienced dynamically changing delivery dates, without satisfactorily update routines. Two of the companies did not practice any regular update of requirements at items level when customer orders get new delivery dates. The low update frequency of the MPS may result in plans almost without value at following stages. In company C our analyses showed that the best customer order was 3 months late, the worst 1 year late according to information stored in their planning systems. As many orders in reality had been renegotiated it was impossible in any simple way to time phase the capacity requirements and to set priorities at the shop floor. The uncertainties of the MPS were generally acknowledged in all companies, contributing to a low level of commitment to derived plans, especially in the production departments. (2) The division of planning levels and plamzing horizons irl different systems increases uncertainties. All studied companies originally operated with differentiated planning horizons. The different planning horizons are handled in separate systems producing information with a different degree of detail. Within this framework the middle to long term planning (1-12 months) is done with a simplified BOM and aggregated description of the resources (the MRP-system). The short term planning (&8 weeks) is made with actual work orders, complete routings and calculated operations times for individual operations. Within this short horizon the planning systems are able to give a relatively reliable picture of the load at work center level (typically a shop-floor control system loosely coupled with the MRP-system). This separation has a number of unfortunate consequences: l A capacity-load balance at an aggregated level based on simplified BOM’s may cover

N.A. Bakke, R. Hellberg/ The challenges of capacity planning Table 1 Some key characteristics

of the studied

247

firms Company Maritime Equipm.

A

Company B Paper Machinery

Company Powerst.

May 91

Sept 92

C

Update frequency MPS Capacity balancing level Horizon of detailed profile Horizon for subcontracting

No reg. update Aggregated 2 weeks 1-3 weeks

2 weeks Aggreg. 4 weeks 14 weeks

Daily Work center 10 months 10 months

No reg. update Aggregated 4 weeks 14 weeks

Labour planning: - horizon - detail

2-3 months Aggregated

10 months Work center

2-3 months Aggregated

10% deviation

2-3 months Aggreg. 22% dev.

2% dev.

27% deviation

21% 26%

29% 39%

6% 7%

11% 72%

Capacity loss due to early start of work orders”

2-3%

4-5 %

1%

Unknown

Capacity planning of preproduction departments

No

No

Yes

No

Yes No No

Yes Yes No No

Yes Yes Yes Yes

Yes No No No

Capacity parameter bottlenecks

accuracy

in

Completed work orders: ~ no. W.O. > 10 days early _ no. W.O. > 10 days late

Scheduling technology: - forw. and backward. - load scheduling - bottleneck sched. - bottleneck customer order netscheduling “Figures based on limited random

No

sample.

devastating unbalances at work-center level. Company B had in May 1991 a 270,000 hour capacity and a 267,000 hour load the next 8 months. This was regarded as an optimal balance. However, when broken down to work-center level and scheduled by the authors, three resources had more than 150%, five resources more than 120% overload. A simulation of the production program forecasted an expected average delay of 22.7 work days.2 The problem with aggregated plans is the low precision, as ‘The simulations mentioned were conducted by the authors using FAST (Factory Analysis and Simulation Tool) [17], developed by Gesellschaft fur Technologie Transfer mbH, Hannover. A detailed description of the simulation techniques are the topic of another paper. The analysis functions of FAST are described in Ref. [9].

0

decisions concerning capacity at workcenter level are chronically inaccurate. The short horizon of the detailed planning causes a late identification of bottlenecks and under-utilized resources. At this stage it is usually too late to reach good results concerning due date observance and utilization. Secondly the costs of handling the unbalances discovered are considerable. Overtime, subcontracting and short-time work have to be organized at very short notice, generating extra cost compared with middle to long-term capacity adjustments. Additionally the short horizon increases the risk of making false decisions. Apparent bottlenecks in the short term have a middle to long term capacity surplus, and apparent non-bottlenecks have a long term overload.

248

N.A. Bukke, R. Hellhrrg,i The chu/lenge.s of‘ capacity

Fig. 1. Area of application of today’s dominant nations of MRP and shop-floor control-systems.

combi-

Bottleneck capacity is not utilized and work is taken away from non-bottleneck resources. The separation of planning horizons creates a deadlock as none of the systems support detailed decisions concerning capacity at work center level in the required time horizon. Figure 1 illustrates the dilemma. Required is a system including the whole planning horizon with uniform and fairly detailed information. Today’s dominant combinations of MRP and shop-floor control-systems cannot satisfy this requirement.3 (3) Poor scheduling technologies. To make a reliable time phased capacity planning the load must be correctly scheduled. All the studied firms had problems generating realistic plans because of poor scheduling technology. l All the firms used scheduling algorithms presupposing fixed lead-times (transit times).4 The capacity planning suffers from this because it is impossible to predict the distribution of load on the individual resources in time. The material flow never behaves in the simplified way presupposed by scheduling algorithms based on fixed lead-times. This regularly leads to unexpected temporary bottlenecks or idleness at different resources. Consequences are e.g. (1) 3This conclusion is supported by detailed studies of 15 different MRPII systems and more than 50 installations of the same systems in 6 European countries (Gesellschaft fur Technologie Transfer 1988-1992). 4The lead time per operation is computed as the processtime plus a fixed work-center dependent transit time. The process time is dependent on the unit processing time of the actual item at the considered machine (resource) and the lot-size. Hence the process-time is variable. However, the transit time is fixed. It is usually composed of a buffertime plus transport time.

planrmg

loss of valuable capacity at bottlenecks and increased costs due to unplanned idleness, (2) delays because of unexpected large amounts of WIP.’ Not surprisingly, one of the companies had very bad relations to the work force because of inaccurate planning of overtime and shorttime work. The problems of predicting workflow were exaggerated by a low order release frequency and the “optimization” of lot-sizes (work orders with identical items were processed together). The large batches flow unpredictably through the factory, as they cause short-time fluctuations between overload and idleness at work-center level. This practice increases the inaccuracy of work flow predictions based on fixed lead-times scheduling. This frequently led to a combination of overtime and short-time work at the same workcenter within the same week. l The release of individual work orders is not coordinated with the net-requirements of complete customer orders. There are two main reasons for this loss of connection. Firstly most work orders are customeranonymous usually covering the requirements of the actual item for a number of customer orders with delivery dates distributed over a longer time horizon. Secondly the scheduling technology and the order release logic do not optimize synchronization of all work-orders (items) belonging to the same customer order. These deficits lead to an unsynchronized delivery of parts to assembly and thereby a not cash-flow optimized use of available capacity. Capacity is invested today making items needed months into the future, instead of critical items belonging to customer orders with close delivery dates. This is illustrated in Fig. 2. Caused by one big lot an unnecessary ‘Due to the unexpected material flow, as a protection the companies usually increase planned lead-times. As an unintended consequences also the WIP increases. A backwards scheduling with long lead-times means an early start with large time buffers for every operation, which then causes a hrgh WIP. For a critique of scheduling algorithms based on fixed lead-times see e.g. Refs. [2, 11. 171.

N.A. Bakke,

Capacity per day: Big-lot:

R. Hellberg/

The challeuyes

of capacity

249

plaminy

56 h 1277 standard hours/ 1915 pcs.

03.06.

Fig. 2. “Big-lot” and capacity

05.68.

07.18.

89.12.lI~2.

13.04.

22.06.

24.08.

requirement

bottleneck emerges. Because of this lot the discrepancy between available capacity (capacity curve) and capacity required (target output curve) increases by a jump. The lot is covering the net-requirements of an item for a six-month period. By splitting this work order into smaller lots synchronized with the assembly of the individual customer orders the bottleneck disappears as parts of the load are moved further into the future. The unsynchronized delivery of parts to assembly causes longer assembly lead-times, increased WIP at all stages and a higher risk of late delivery. Figure 3 displays the distribution of completion dates of manufactured items for one customer order. According to plan they should all have been completed on day 580. l The companies were not able to simulate consequences of different capacity increasing

actions. Because of the many variables to take into consideration there is a need for comfortable simulation tools to evaluate different plan-alternatives. E.g. simulate the consequences of increased capacity, changed delivery dates, changed lot-sizes, etc. Without such instruments time constraints and the limited cognitive capacity of decisions makers inevitably lead to decisions characterized by large uncertainties.’ (4) Neither capacity nor load are accurately known.

Because available capacity is not systematically measured over longer time-periods, the companies plan with a 2&25% inaccuracy at 6This is in accordance with well known insights of organizational theory. See e.g. Refs. [18, 191.

N.A. Bakke, R. He//berg/ The chdlenges

250 GTT

Unsynchronized

oj ~upucity planning

delivery of parts to assembly for a CUSP.order

40 % 30-

20.

I

584

589

I

I

594

Fig. 3. The distribution of completion dates of manufactured items for one customer order. (According to plan they should all have been completed on day 580.)

a number

of key resources.7 Uncertainties of the same scale were found in the load as the data acquisition systems generally showed a modest reliability. The (flexibility of) qualifications of personnel were only superficially known in the studied firms. This makes it difficult to utilize available flexibility of labour in the capacity planning process, e.g. systematically move people from under utilized to bottleneck work centers. (5) Lack of capacitll planning fbr construction and pre-production actitlities. These departments are characterized by a very rough capacity planning not included in the MRP-systems. All the studied companies experienced server difficulties completing a num‘This finding is also confirmed by the results of 50 casestudies made by Gesellschaft fur Technologie Transfer in the years 1988-1991. Only three firms reached an accuracy within O-10% deviation at all key resources over a period of 3-12 months.

ber of important customer orders because of failures of planning in these areas. Periodically a number of items could not be released to manufacturing because of temporary capacity deficits in the pre-production departments. (6) Problems of increasing capacity through subcontracting. The internal uncertainties concerning where and when capacity is needed, make the companies unable to generate realistic middle to long term schedules for sub-contractors. Because the planning systems deliver detailed information only for 2%&week horizon, knowledge about what items to subcontract is not available at an early stage. Due to uncertainties concerning available capacity and data-acquisition even within the shorter horizon capacity requirements were unreliable in the studied firms. Therefore a number of other methods outside the systems were utilized as well. E.g. capacity deficits were identified through the growth of considerable amounts of WIP or the

N.A. Bakke, R. Hellbery,‘The challenges

sudden emergence in the release queue of large amounts of items, affecting individual work centers. When items are identified at this late stage the delivery-dates of affected customer orders might be impossible to save through subcontracting. Firstly, the time consumed by the company itself, searching for subcontractors with available capacity, generating technical specifications and documents, handling and transport etc. may exceed the time left. Secondly, the short delivery times demanded increase the risk of late deliveries from subcontractors. (7) Loss of capacity due to earl?>start. Due to long lead times and the wish to utilize non-bottlenecks, many work orders are started early. Because of the early start important changes in requirements routinely occur after completion of the items. This led to a measured loss of capacity within the range of 2-5% in the studied firms. There are a number of reasons for an early start: l By low utilization many firms make an early release of non-critical orders. This may happen because demand is low, or because material or NC-programs etc. fail for urgent orders making them unavailable for manufacturing. This practice is usually defended with reference to the capacity loss incurred by idle resources. l Another reason to start production early is long lead times. The companies in our study plan to make one or two operations per week per order. With the possibility of 50-70 operations for some items the planned lead times may reach 6-15 months, in spite of a total processing time allowing a completion within l-2 weeks. l The release of larger lots covering the supposed net requirements for individual items for longer time horizons implies an early start for at least a part of the lot. l Poor understanding of important scheduling parameters frequently leads to an early start. Typically the companies increase the planned lead-times when experiencing delays, not realizing that the real reasons are failure to adjust capacities and not the leadtime parameters. Consequently the early start does not improve due date observance,

of capacity planning

251

rather other problems are aggravated (e.g. increased inventory and loss of capacity). The reasons for starting early may seem plausible, but in the end the result is almost invariably a loss of valuable capacity: l New customer wishes, changes in construction etc. are more liable to occur after completion (or partly completion) of items if production is started early. l Changes in delivery dates are more liable to occur after completion (or partial completion) of items if production is started early. l The early release of orders to utilize nonbottlenecks is liable to increase competition for capacity in real bottlenecks. The risk that valuable bottleneck capacity is used for non-critical items increases. The lesson to be learned is: For long term bottleneck resources an early start might be justified to save capacity if more critical orders are not available, otherwise an early start should be avoided.

3. Suggestions for improving traditional approaches Many of the problems described in Section 2.2 are recognized in earlier literature on the topic. However, there are still a number of open questions about how to deal with them both theoretically and practically. Some tentative answers to these questions are given in the following two sections. Section 3.1 describes through a case study how the problems mentioned in 2.2 can be handled. Based on the case studies, in Section 3.2 a general theoretical framework for analysing and improving capacity planning is formulated. 3.1. Improving

capacity planning: a case study

This section describes the capacity planning of a manufacturer trying to overcome the weaknesses described in 2.2 (company B, Table 1). The capacity planning concept was implemented during a joint project between one of the authors and the company.

Company B develops and produces machinery for production of paper. It employs 690 people and has a yearly turnover of approximately 160 mill. DM. Due to long lead times company B seldom makes offers with less than 34-month delivery time. 6-8 months might be needed if the customer wants special features demanding a lot of engineering. The lead times of production and assembly are of considerable significance for all customer orders. A number of items have a BOM with many levels, requiring 5&70 manufacturing operations. The larger machines have approximately 400&5000 components to be assembled. Customers carefully plan the installation of new machinery. Due to their relative capital intensive facilities they want to install new equipment when activity is low. This causes an over proportional share of the customer orders to be delivered in summer, around Christmas and in the Easter week. If delivery is late the customer might refuse to receive the order because the cost of installation is perceived too high at this point of time, or the producer might have to pay a considerable penalty. Consequently due-dates are of critical importance in this market. The methodology developed for company B can be characterized as a sophisticated version of the capacity planning alternatives 3-4 presented in Section 2.1. A sophisticated version as important features are implemented that extend these alternatives. e.g. sophisticated scheduling and simulation techniques, a more complete integration of different planning horizons. etc. The description thematically follows the structure of Section 2.2. (I j Keeping the MPS up to date The MPS consists of orders with different status. Not surprisingly the uncertainty increases as the horizon is prolonged. The next 46 months the program is almost exclusively composed of confirmed orders. Within the 7-12-month interval the program consists of a mix of confirmed orders, orders under negotiation and prognosed orders. The whole MPS is also available in detailed form as work orders. For not confirmed customer orders, and orders

where necessary BOM updates are not completed, standard BOMs and their corresponding standard operation time data are utilized to generate so called “simulated” work orders. The planning process is not principally different from the practice of most companies of this type. Once a week representatives from sales, construction and production meet to discuss the medium to long-term production program. However, compared with the other case studies the frequency of update is very high, as the plan is never more than 24 hours in backlog in relation to the most recent information. Important is also the continuous conversion of “simulated” work orders into real work orders when their end product counter-parts are confirmed and BOMs, routings, etc., available. This conversion process is mandatory to secure that the overall load at detailed level is correct. (17) The integratiorl qf’difj$v-ent plunning hoviz0n.s into orw sj’stenl. The problems of separated planning horizons were described in Section 2.2. To alleviate these difficulties company B integrated the different planning levels into one system. The middleand long-term capacity planning are integrated with the short-term planning. Independent of their current status all customer orders are uniformly expressed at work order level in this system. The system uses data from the MRP system, but works independently as a stand alone system. The database used by the system is produced through the following steps. (1) The MPS, the BOM, routing and standard time information for every operation of the individual items delivers the basic information processed in the system. The MPS consists of customer orders characterized by different degrees of uncertainty: (a) Open work orders available in the MRP-system (including WIP). These orders are based on confirmed customer orders having a low degree of uncertainty. Routings. calculated standard time, etc., are known for all work orders as they have passed through all pre-production departments. These orders can be loaded directly into the system. They usually include a

N.A. Bakke, R. HellbergiThe

24-month horizon and all open orders in backlog. (b) Confirmed customer orders hitherto not transformed into work orders. They have a higher uncertainty as a number of changes in the BOM may occur. They are transformed into simulated work orders by making a complete breakdown of a standard BOM and utilizing corresponding routings and standard operation times. (c) Non-confirmed orders, e.g. where negotiations are taking place with the customer or prognosed orders are expressed at a detailed level through the same procedures as in (b). If the order is regarded highly uncertain the resulting load might be discounted by a percentage given by sales. (2) All work-orders are directly attached to one end-product. The lot-sizes are identical to the requirements of the actual item given by the relevant BOM. Consequently the load is exclusively composed of requirements directly attached to customer orders. (3) Items produced for a spare-parts inventory are basically treated as the customer orders. They are included in the load either as real work orders or as simulated work orders based on some kind of prognosis. (The inventory is held as part of the standard customer contract to secure immediate repair by machine break down by the customers.) (4) The consequences of scrap and late ECOs for the load are taken into consideration through the use of statistical data from the past. This resulting quantity of expected extra work is added to the load of not released orders as a percentage (ca. 56%). (5) All “simulated” orders are deleted when real work orders can be generated, eliminating the possibility of double booking of orders. (6) The system includes a description of the factory (work centers, capacity, etc.). This description is continuously modified to increase reliability of the simulations. Through this process a heterogeneous production program, existing in different systems

challenges

of capacity planrung

253

modelled at different levels of detail, is transformed to a homogeneous and uniform program in one system. Regardless of the status of the orders in the MPS and their position in the planning horizon, in this system all orders are principally alike. The system thereby bridges the traditional gap between the MPS (and the classical MRP modules) and shop-floor control systems.Using this data base the system can be used to, e.g.: l Identify bottlenecks and non-bottlenecks on work-center level 8-12 months into the future. l Simulate the probable lead time and duedate deviation for the complete production program displaying the results for both individual work orders and complete customer orders. l Simulate the consequences of increased capacity at bottlenecks, changes in delivery dates and changes in the production program. l The scheduling technology is flow oriented, meaning that the computed lead-times are results of the actual mix of the load (the system does not use fixed lead times for the simulations). l The system can administer three different plan-alternatives concurrently. The alternatives can be analysed individually and compared with each other. l These properties enable the system to make a detailed capacity planning for a horizon stretching from tomorrow and 8-12 months into the future. Information generated with this system is a main input to the weekly discussions between the involved departments (sales, construction, production). This enables production to make detailed decisions at an early stage if considerable overload or under utilization are likely to emerge. On the other hand the available information allows sales to influence the customers to place their orders at a favourable point of time from a capacity point of view, and to evaluate with high precision the consequences of chosen priorities. The practising of this methodology is illustrated in Figs. 4-6 displaying a CNC-turning work-center. The target curve displayed in figure 4 represents the cumulated load

254

N.A. Bakke.

R. Hrllberg/The

challenges

of capacity

planning

horizontal length of the curves. The average simulated WIP is approximately 1900 hours compared with the target of 190 hours. “Today” company B has a 9 day backlog, and it is likely to grow the next months to 3&40 days if no action is taken. Figure 6 displays the expected due date observance at the same work center. For the whole period an average due-date deviance of 25 days late is prognosed. The lines at the left side of the capacity curve indicate orders going to be late. Correspondingly the lines at the right side indicate that a number of orders are going to be completed too early. The length of the lines represents the size of the deviation to plan (measured in work days). Principally early completion should be regarded a waste of capacity. Yet, the early orders are accepted because orders with higher priority cannot be

caused by the MPS for a 7 month period (11.11.91-19.06.92). The distribution of the load in time is made through a conventional backward scheduling from delivery date. The curve has a cyclical form. Between 11.1 l-lo.02 and 01.05-19.06 the load is growing faster than the cumulated capacity. 11.02-30.04 the backlog is almost eliminated. At the end of the period there is a 2500 hour capacity deficit. Figure 5 shows the same information together with the results of a simulation displayed in the simulated curve. The simulation is made presupposing 3 shifts and a 5 day week at the work-center. The result is much longer lead times and higher WIPs than planned. The average simulated lead-times are approximately 28 days compared with 2.8 days in the original scheduling based on fixed lead times. The lead times are displayed in Fig. 5 as the

Backlog “today” = 508 std. hours WIP “today” = 150 std. hours Act. capacity = 8079 std. hours Target output = 10093 std. hours Target lead-time per operation = average 2. 8 days

12.88.

Fig. 4. Capacity

and estimated

16.89.

28.1&t

“Today”

89.12.

load. (Based on MPS-data.)

28.81.

24.82.

36.83.

84,fiS.

N.A. Bakke, R. Hellberg; The challenges

of capacq

planning

255

Backlog “today” = 508 std. hounr wp “today” = 150 Std.hours Act. capacity = 8079 std. hours Target output = 10093 std. hours Target lead-time per operation = avaage 2.8 days Simulated lead-time per operation = average 29 day8

GTT

n

I

5EFIfl

f_l

T

I

I

I

I

12.88.

16.89.

28.18.

1

09.12.

28.81.

24.82.

38.03.

84.85.

“Today”

Fig. 5. Simulated-

and target-output.

(Based on MPS-data.)

made available at this work-center due to other bottlenecks earlier in the flow. of such information The availability enables the company to identify problem areas and take action at an early stage, e.g. increase or decrease of capacity through the planning of overtime or short-time work. The capacity of the displayed turning-machine should if possible be increased through weekend shifts, and eventually subcontracting, in the critical period where the target curve is steep. Because of the long horizon there is plenty of time to inform and negotiate with unions and relevant subcontractors. Capacity of non bottlenecks should be reduced through planned holidays and education

programs in periods with low utilization. This should e.g. be done in situations similar to the one displayed in Fig. 7. The figure show that the milling machines can be utilized only 3&40% in the weeks 2-14. Before week 2 and after week 14, until the end of the planning horizon, a 9&100% utilization is possible. The bottleneck problem typically has a high importance also when the aggregated load is low. Also with an aggregated load of 5(r70% there are bottlenecks. In this situation the optimal solution usually is to combine overtime at bottlenecks with short time work at other resources. Curiously enough, the utilization of non-bottlenecks is increased through overtime

N.A. Bukke. R. Hellhery,

256

Thr ddrnyes

c!f‘capucq

pkminy

Backlog “today” = 508 std. hours WIP “today” = 150 std. hours Act. capacity = 8079 std. hours Target output = 10093 std. hours Target lead-time pet operation = average 2. 8 days Simulated lead-time per operation = average 29 days Average due date observance = 25 days jate

h

GTT

12.EB, 16.89. 28.18, 09,12. 26.81. 24.62. 38.83. 64.85.

Fig 6. Simulation

of expected

due date observance.

(Based on MPS-data.)

at bottlenecks thereby reducing the total need for capacity reductions [7]. Correspondingly the problem of idle resources is of importance even in a situation of general overload. If a company basically produces items only belonging to orders already sold, there is bound to be excess capacity in parts of the organization (due to the complexities of job-shop production and a broad an unstable mix of items to produce). The costs of idle resources might be considerable, also in periods with high demand. Typically only l&15% of the resources are in reality overloaded, even in very good periods. To reduce the costs of idle resources the reduction of capacity at non-bottlenecks should be made dynamically according to the simulated work

flow. A general reduction of capacity according to the average load does not work, because many long-term non-bottlenecks are likely to fluctuate between overload and idleness. (3) Customer-order (end-product) oriented cupncit?? planning and order release.

As described in Section 2.2. the studied companies are not able to make a synchronized delivery of parts to assembly. This is caused by the usual practice of separating work orders from customer orders. The link can typically be reestablished only through a time-consuming “pegging” process. However, the systems are neither able to generate lot-sizes nor schedules optimizing the customer-order synchronization. Consequently, available capacity is not invested in making items optimizing

N.A. Bukke.

R. Hellherg,i

The chnllenges

of capacity

plaming

257

Act. capacity = 6562 std. hours Target output = 4148 std. hours

Fig. 7. Fluctuating

load. (Based on MPS-data.)

cash-flow. To alleviate this problem company B has recently implemented a new scheduling and order release methodology. The methodology works according to the following main principles: Rank the customer orders according to priority, and pick the most urgent. to (2) Make a break down of net-requirements be manufactured. all items belonging to this (3) Schedule customer order, taking both the dependencies of the product structure and capacity constraints into consideration. (The lot-sizes are identical to the net requirements of the items.) (4) If the same item is required for two or more customer orders with close delivery dates they may be processed together. The lotsize decision is supported by parameters, e.g. process all identical items together if they have delivery dates within the same week, as long as the processing time does

iu

not exceed 8 hours at any resource. The work-order lot-sizes are variable until they are released for manufacturing (24 hours before start of production). Until this point of time the work-order lot-size might change due to MPS-changes. The link between work orders and customer orders is easily available in the system. (No time consuming “pegging” is required.) (5) If there are bottlenecks affecting individual work orders the decision maker is informed about the bottlenecks creating problems and the expected due-date deviation of both work orders and relevant customer order(s). If capacity cannot be changed the orders are postponed based on a simulation of the probable completion times - synchronizing all work orders linked to the same customer order. (6) Pick the next customer order on the list. With this methodology the individual work order releases are made according to the

requirements of the most important customer orders. This has led to an improved synchronization of delivery of items belonging to the same customer order to assembly. The number of not required items manufactured and early started items are also drastically reduced. (4) Cupacitly con trolhg. Without knowing the amount of capacity available it is impossible to identify bottlenecks and non-bottlenecks. Company B put much weight on a continual maintenance of the capacity parameters: For every work-center the actual and planned output measured in standard times is compared and deviations analyzed. This may lead to a calculated daily capacity of 20 hours for one resource and 10 hours for another, although both resources are activated 16 hours per day. For planning purposes the calculated times need not be correct, as long as a stable relationship between calculated and actual times can be established. (5 i CapacitJ* plaming fbr construction and other pre-production actioities. Capacity planning is established for all preproduction activities. The calculation of load is done using rougher methods compared with Due to higher flexibility manufacturing. in these departments (the key resources are not machinery), calcuusually manpower, lation errors can usually be corrected through overtime. The due-date observance of all preproduction activities is surveiled, and capacity increased if the average due-date deviation exceeds 5 work days late. (6) Reorganizing subcontracting. For the turning work center mentioned above the company chose to handle the problem through subcontracting. Earlier this decision would have been postponed because of the risk of subcontracting wrong items. Due to a 2-6-week planning horizon on work center level it was difficult for the company to decide what items to subcontract. Long-term projections were only available at aggregated level, and therefore not supportive about what items to subcontract. Consequently company B had experienced a lot of failures in the past, experiencing that a few weeks after a main subcontracting decision the supposed bottlenecks

went idle and concurrently some supposed non-bottlenecks were characterized by a dramatic overload. Such experiences had made company B reluctant to do much subcontracting before the need for capacity was beyond all doubt. That is when it is too late to realize an acceptable due date observance. By increasing the detailed planning horizon the company was able to alleviate these problems, as the danger of being mislead by short time fluctuations were eliminated. The company now decides subcontracting on the basis of long time simulations of load at work center level. The work center displayed in Figs. 4-6 show strong fluctuations, but in the period as a whole the work center has a considerable capacity deficit. The long-term capacity deficit justifies massive subcontracting of parts to be processed at this work center. For the work center displayed in Fig. 7 the decision is not to subcontract, in spite of the possibility of temporal bottleneck situations, as there is overcapacity in the period as a whole. The long horizon simultaneously reduces uncertainties of the company and its subcontractors. Through long term-agreements and data-technical integration of planning systems company B is able to directly place work-orders by the most important subcontractors. Further negotiations are only necessary when the subcontractor also experiences bottleneck problems. (7) The elimination of early start. Company B has realized that early start of manufacturing normally leads to loss of capacity. Firstly, rules are defined to reduce the problem of early start: l No item is allowed to be started more than 20 days early, and then only to secure utilization of bottleneck resources. l Reduce the number of items with an early start. 95% of all items should start with a deviation less than 5 days. Secondly, to move the start of manufacturing closer to delivery, a lead-time reduction project has been launched.The following results are realized: l The average manufacturing lead-time is reduced from 28 to 12 days (approximately 10

N.A. Bakke,

R. Hellberg!

The challenges

of capacity

259

planning

c

MPS uncertainties: - Composition and time uncertainties Capacity uncertainties - qualifications of manpower not known

fioad

uncertainties

heduling methodology uncertainties: Inability to simulate (compute) accurate work ow at workcenter level Connection between individual work-orders d customer-orders is weak J IPre-production

uncertainties:

Capacity loss - idle bottleneck resources - production of wrong items

I

Cumulated un&ty concernin 18 time-phased load and capacity needed at work center level

Fig. 8. Capacity

planning

and cumulated

uncertainty

days of the reduction is realized through better planning of capacity and load). An over proportional lead-time reduction for the parts with multi-level BOMs is realized. No part is allowed to reach a planned manufacturing lead time longer than 30 days. The average assembly lead time is reduced from 7-8 weeks to 34 weeks. The improved capacity planning contributed to the shorter lead-times. However, reaching this also implied a series of other projects which are not the theme of this paper e.g. WIP-reductions, higher process reliability, reduced scrap, shorter set-up times, etc. 3.1. A conceptual framework,fbr improving capacity planning We think studies can

1

analysing and

the experiences from the casebe placed in a more general

conceptual framework. The main concepts of this framework are “cumulated uncertainties”, “flexibility”, “complexity” and “adjustment capability”. These concepts represent important variables that should be taken into consideration in projects attempting to increase the quality of capacity planning. They are of profound significance for companies trying to change production strategy, responding directly to market fluctuations without unnecessary buffers. Cumulated uncertainties of capacity planniny. The short-comings discussed in Section 2.2 are all contributing to a cumulated uncertainty concerning what resources to activate at what time. The cumulated uncertainty can be computed as the product of the individual uncertainties (Fig. 8). The described improvements of company B can all be traced back to efforts to reduce individual uncertainties. The cumulated

260

N.A. Bakke.

R. Hrllhergi

The challenges

character of the uncertainties as a product of individual uncertainties indicates a need for an integrated approach to capacity planning. An integrated approach means that reliable plans depend on high performance in all areas. A high uncertainty in one or two areas may render the whole plan valueless, in spite of outstanding performance in other areas. Increased flexibility. The cumulated uncertainties of capacity planning influences the flexibility requirements placed on labour and machinery. With a high level of cumulated uncertainty the companies have to establish considerable short-time flexibility to handle unpredicted problems. Sources of such flexibility are e.g. over-capacity of both manpower and machinery, multi-skilled manpower, flexible work-times, reservation of capacity by subcontractors, etc. The development and maintenance of flexibility may negatively influence important cost-drivers. These costs might be reduced if the cumulated uncertainties of capacity planning are reduced’. Investments in increased quality of capacity planning can therefore be considered partly substitutable for investments in flexibility. Complexity reduction. The complexity of the planning environment profoundly influences the pressure placed on the capacity planning systems. Reducing the complexity of the planning environment has many of the same effects as sophistication of planning methodology (cf. Ref. [8], and the general JIT-debate). The complexity of the planning environment can be reduced in a number of ways, e.g.: 0 Reduce the own-manufactuiing by permanent outsourcing (simpler internal planning environment) investments in CNC and FMS 0 Through technology the number of operations per work order can be reduced

in other sources of flexibility can simultaneously contribute directly to lower costs and increased quahty of capacity planning, e.g. man-power flexibility (broad skills and flexible working-times) might contribute to both higher productivity and machine utilization. ‘Investments

of capacity

pluwung

Through segmentation of the production according to group technological principles the work flow is easier to predict. l Through standardization and optimization of product construction the number of items might be reduced Adjustment capabilit 1:. The three ways ofincreasing responsiveness towards unstable environments are partly substitutable. However, for firms of the kind studied in this paper the high level of cumulated uncertainty makes it necessary to invest in all three areas.Improvements in these areas all contribute to the firms’ “adjustment capability”, that is their capability to handle fluctuating capacity requirements. In this paper the main emphasis is placed on showing how uncertainties can be reduced through improved capacity planning, but in practice most firms should work parallelly in all three areas. The emphasis to be placed on the different areas can be decided only after analysis of the individual case. l

4. Conclusions This paper has concentrated on the methodological failures of traditional methods of capacity planning utilized by producers of customized complex products. In this last section the more general significance of the discussed themes is put to the forefront. Firstly the general significance of the suggested methodological improvements is evaluated.Secondly, the themes of the paper are related to broader theoretical issues within logistics and organizational theory. 4.1. The general releliance oJ’ the suggested methodological itnprovetnen fs The above discussion has pictured capacity planning as a complex activity with wider methodological and organizational ramifications. A main conclusion is that successful capacity planning presupposes systematic efforts to reduce different kinds of planning

N.A. Bukkr, R. Hellhery/The

uncertainties. Because the overall result to a large extent is determined by the product of these uncertainties, an integrated approach to capacity planning is required. Outstanding but isolated solutions of parts of the problem complex have a limited effect (e.g. only concentrating on scheduling algorithms). Another conclusion is that most of the suggested improvements imply investments in increased information processing capability. An outstanding capacity planning seems to presuppose the ability to rapidly collect and process large amounts of information. The measured improvements in case study B seem to justify the efforts of an integrated approach, but the question of its broader relevance remains. The paper concentrates on companies where capacity planning is both very important and very difficult. Some of the problems are non-existent or relatively easy to handle for companies with simpler and more standardized products and more stable markets. However, studies including a variety of industrial branches show that capacity planning represents a problem also for many firms having a simpler task to manage than the firms referred to in this paper (cf. Ref. [9]). The extent of the problems in industry and the potential gain in competitiveness by overcoming them justify more intensive efforts in this area. Many of the problems described in Section 2 are relatively well known, as are some of the suggested remedies. However, it is our view that some of the problems discussed, as well as the problem in its totality, are not very well recognized in the standard literature on the topic. Our study of a number of MRP-systems shows the same conceptual deficits. To indicate where the main deficits are located we will attach them to a short summary of the main methodological recommendations suggested in Section 3. (1) A high update ,frequency of the MPS both in aggregated and detailed jbrm orders) is of main importance.

(as work

The importance of a reliable MPS is widely acknowledged in the relevant literature. However, the empirical material indicates that only a minority of the firms have reliable MPS’. The

chullerqes

qf’cupacity

planning

261

poor quality of MPS maintenance seems to have both organizational and technical causes. Methodologically it is important that the MPS is simultaneously maintained at an aggregated level (end-products), and a detailed level (work orders). The detailed information is required for subsequent analyses necessary for significant capacity decisions.’ (2) The integration of diJf2rent planning horizons of a This tion sible

in one system, enabling the computation detailed load for longer time periods. enables an early and correct identi$caof capacity problems, which is not posin most companies today.

Most of the literature recommends the opposite of this. The planning process is typically described as hierarchical, with a limited need to expand the dis-aggregated horizon. The following passage from Schonberger is typical: “There is no sense in asking the computers to run CRP-projections for all workcenters, and for 52-weeks into the future, because the real problems are in the near future and likely only in certain work centers” [lo, p. 3861. There are two main weaknesses of this point of view. Firstly, the suggestion that it is only necessary to concentrate on a small number of problem work-centers.According to our view all work centers are important, as an optimal capacity planning tries to reduce the costs of both bottlenecks and idleness. Additionally the problem areas are changing dynamically, making it necessary to pay attention to all work-centers. Secondly, the implication that 9A side-effect of a high update frequency is the destabilization of the MPS. This could have negative consequences for e.g. the sequence of work orders and the release of supplier orders. Consequently, the informattons of the middle to long term horizon should not be utilized for actual release of work orders and supplier orders. This horizon indicate at a detailed level (work center and items level) the cumulated capacity reqmred and volume of specified materials needed in a 2-10 months perspective, where the time-changes of individual demands have a minor influence on the cumulated requirements. Operational turbulences are avotded as the actual release of work orders and supplier orders are made in the short horizon, close to delivery of the customer order.

the length of the planning horizon is of limited importance. On work-center level our simulations show that there is considerable fluctuations in load in a 8-12-month period. The same work-centers might be characterized by both overload and idleness in this horizon. Knowledge of this kind is of major importance in deciding what kind of capacity adjustments to implement.Ignorance of this problematic seems to be a main deficit of most MRP systems. Shop-floor control systems are in most cases unable to compensate for failures at earlier stages. Therefore, the increased use of shopfloor control systems in industry are not likely to increase performance significantly. The implementation of such systems is not able to bridge the gap between the aggregated informations of the MRP (MPS) system and the detailed but short horizon of the Shop-floor control system. Company C had e.g. implemented a shop floor control system in 1990 without significant positive effects.Because the problems frequently are to server, when they at a late stage become transparent. such systems cannot contribute to large improvements. We therefore think that most of the shop-floor control systems installed these days will turn out to be bad investments. However, a cautionary note is required as it must be admitted that the suggested alternative is not an universal solution. Due to product and market characteristics not all firms are able to establish an MPS with the required length, accuracy and reliability. Although, the significance of such restrictions should not be exaggerated as the main problem of the studied firms is not that a longer detailed horizon is principally impossible, but rather a mediocre information processing ability. As case study B shows, the required raw data are to a large extent available and can be transformed to a decision relevant form through increased information processing capabilities. (3) The scheduling technologJ1 should predict lead-times and material ,jlo\v, and he able hzdle complex product structures.

to

The critique of scheduling based on fixed lead times are well established (e.g. Refs.

[2, 111). However, most available software still uses traditional methods. A fairly new accomplishment to our knowledge is the simulative scheduling of complex product structures (cf. synchronised simulation of 1000 work orders belonging to one end product). (4) Systematic maintenance of plannilig parameters, capacity.

especially

data

about

w&able

Methodologically, this point should be obvious. The problem is that less than 10% of the studied companies seem to reach an acceptable accuracy. (5) Capacit_v plumzing qf ull activities necessaq to complete customer orders, including construction and other pre-production activities.

This is a neglected theme in most of the standard literature. This neglect is reflected in the companies where these activities frequently are characterized by a total absence of capacity planning. Especially as these activities have increased their relative importance compared to manufacturing there is a need to integrate them in capacity planning systems. (6) Start of manufhcturing ut a point of’ time close capucitJ’.

to deliver)> to customer

~ to suve

The many merits of a late start is generally well recognized today, e.g. in the JIT-literature. However. in the kind of company studied in this paper we observed an ignorance concerning the capacity consequences of an early start, which is seldom given attention in the literature on the topic. Mostly. an early start is vindicated by the wish to save capacity, however. the result is in most cases the opposite. (7) The integration qf‘ subcontractors in the plannhg

process.

This is a well known strategy in industries characterized by JIT-production, e.g. the automotive-industry. In the industries studied in this paper such arrangements are very seldom. This has probably a number of reasons, but one main reason seems to be the inability to give suppliers and subcontractors information of appropriate detail for a middle- to longterm planning horizon. We think that some of the suggestions in this paper show how this deficit can be overcome.

N.A. Bakle.

4.2.

Broader

organi-_ation

R. Hellberg/

theoretical

perspectives

of’ capacity

planning

The chal/er~ges qf capacity

OH the

The effort to produce directly to order without building inventories and simultaneously reduce delivery times, dramatically exposes the technical core of the companies to the uncertainties of the environment. This can be labelled as a conscious move counterdirected to the traditional strategy of protecting the technical core as a means of increasing rationality in the basic activities of the firm. Rationality in this view implies an intended “decoupling” of the technical core both from customers and suppliers. Today one actively works in the opposite direction trying to “integrate” both customers and suppliers directly into the technical core, (cf. Ref. [12]). The increased emphasis placed on capacity planning can be regarded as a way of coping with the resulting increase of uncertainty. Exposing the technical core to the environment means that the quantity of information to be processed

increases and the time span available for the processing decreases. Logically one way to cope with this situation is to increase the information processing ability of the organization. This represents a recognized causal relationship both in organizational theory and logistics (cf. Refs. [13-l 51). As the above discussion clearly demonstrates, capacity planning requires a highly sophisticated information processing ability. Most of the deficits of the traditional methods were directly connected to poor processing of information basically available (e.g. the inability to express a known production program at a detailed level for a longer time horizon) and deficits of information collecting (e.g. about capacity of own manufacturing resources and load/capacity of pre-production departments). By increasing the information processing ability of the capacity planning, the response time is reduced and the quality of the decisions are increased. This have a direct influence on important competitive parameters.

Costs (Revenue lost) 1 caused by failures of capacity planning

4-l output

-b

lnformatlon

* Idle resources - incr. fixed and variable costs l

263

planning

Customers ‘I‘ ,, d : :;:t . ;_ , : :ra”:‘y

- Knowledge about own capacity

Bottleneck resourced - penalty by late delivery - loss of liquidity - lossof revenue

4

* Wrong use of resources - incr. Inventory carrying cost!

b

Inverse relationship between capacity planning performance and costs (revenue lost;

processing ability and ftexibillty of resources

- Planning frequency - Length of planning horizon - Planning detail - Planning penetration

- Planning logic

- loss of revenue

- Late agreement wlth

- Labour skills

subcontractors and suppllers - higher prices - Quallty deficlls - late delivery

Fig. 9. Capacity

planning

- Labour working time - machinery/technology - subcontractors

performance

as function

of information

processmg

ability and flexibility

of resources.

264

N.A. Bakke. R. Hrl/hrrg,‘Tlw

However, improving the capacity planning alone is not enough to secure performance under these open organizational conditions. The flexibility of the resources to be planned represent constraints influencing the complexity as well as the success of the capacity planning. By increasing flexibility and reliability of the resources the pressure on the planning systems is reduced, as the amount of informations to be processed is reduced and the reliability of the planning basis increases. The realization of organizational simplicity as emphasized by the JIT-philosophy also reduces the amount of information to be processed (cf. Ref. [8]). The capacity planning performance understood as “adjustment capability” (Section 3.2.) may then be expressed as a function of the capacity planning information processing ability, the organizational simplicity, the flexibility and reliability of the planned resources. Figure 9 illustrates the basic relationships between capacity planning performance and some significant competitive parameters. The output accuracy, that is the delivery of a customer order in due time within the calculated costs, is increased parallelly with increased capacity planning performance.‘” Correspondingly the subcontractors/suppliers will realize the same improvements of capacity planning if their information bases are improved. Consequently the increased capacity planning performance indirectly reduces the input uncertainties when the generated information is made available to the subcontractors/suppliers. The overall costs and the amount of lost revenues stand in an inverse relationship to the performance of capacity planning. The costs and the

“This is true given that capacity planning costs are not increasing dramatically. Actually the opposite development is observed in the detailed study (company B). Due to the better transparency and the ability to make decisions at an earlier stage the costs of rechedeling an d other short-time adjustments of the plan is drastically reduced. Also the operating costs of realizing the production program is significantly reduced as the orders are flowing through manufacturing and assembly with less disturbances (e.g. number of expediters was reduced from 8 to none in company B).

chalkwyrs

qf‘cupucity

quantity capacity

plumtiny

of lost revenues decrease when the planning performance increases.

References 111 Stalk, G. and Hout, T.M., 1990. Competing Against Time, Free Press, New York. PI Goldratt, E. and Fox. R.E., 1986. The Race, North Review Press. Ferc31 Wiendahl, H.P., 1987. Belastungsorientierte tigungssteuerung, Carl Hanser Verlag. c41 Plossl, G.W., 1983. Production and Inventory Control ~ Applications. Prentice-Hall, Englewood Cliffs. I51 Plossl. G.W . 1985. Production and Inventory Control - Principles and Techniques, 2nd Ed. PrenticeHall, Englewood Cliffs. C63Vollman. T.E.. Berry, W.L. and Whybark, D.C., 1988. Manufacturmg Planning and Control Systems, second edition Business One Irwin, Homewood-Illinois. r71 Bakke. N.A and Nyhuis. F.. 1992. Planungsfaktoren Mensch und Arbeit ~ was tun wenn Kurzarbeit angesagt ist??, 5 Deutscher Betriebsratetag, 29130 April 1992, Berlin. R.. 1982. Japanese Manufacturing PI Schonberger, Techniques. Nine hidden lessons of simplicity. Free Press. New York. c91 Bakke. N.A and Nyhuis. F., 1992. The use of operations-oriented controlling systems in complex production environments, Prod. Planning Control, 3(l): 999110. R.. 1985. Operations Management, Cl01 Schonberger, Business Publications, Plano, Texas. G. and Orthmann, Cl11 Pritsker. A.B. Schmidt-Weimar, L.. 1991. Management der Liegezeiten durch zeitdynamische Simulation. CIM Management 6,‘1991, Oldenbourg Verlag. Munich. J.D., 1967. Organizations in Action. Cl21 Thompson, McGraw-Hill, New York. Cl31 Galbraith, J.. 1973. Designing Complex Organizations, Addison Wesley. Reading. MA. Design, Addison Cl41 Galbraith, J., 1977. Organization Wesley, Reading. MA. [I51 Stock. J.R and Lambert. D.M.. 1987. Strategic Logistics Management. 2nd Ed., Irwin, Homewood, IL. Cl61 Krepchin. I.P., 1985. From Planning to Shopfloor, Computers Take Charge. Modern Materials Handling, No. 12. Oct. 1985. [I71 FAST users manual, 1992. Gesellschaft fur Technologie Transfer, Hannover. C181March, J.G. and Simon, H.A., 1985. Orgamzations, Free Press. [I91 Cyert, R.M. and March, J.G., 1963. A Behavioural Theory of the Firm, Free Press