Bridging the gap between theory and practice in production and inventory control

Engineering Costs and Production Economics, 6 ( 1982) 1 I 9- 129 Elsevier Scientific Publishing Company, Amst :rdam - Printed in The Netherlands

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BRIDGING THE GAP BETKERN THEORY AND PRACTICF, IN PRODUCTION AND INVRNTOHY CO:JTROL

tdwln L. Heard, D.R.A.

University of South Carolina Columbia, South Carolina 291tJ

USA

The existence of a substantial schism between the Jry and practice in production and inventory control 1s widely recogrlzed. Although forums for productive exchange exist, communication hetween r>ractitioners and academicians continues to be faulty because of the absence of a mutually acceptable comprehensive framework for discussion and analysis. A three- dlmens’onal conceptual model 1s advanced In an efrort to bridge the gap and facilitate the orderly evolu:ion of the area. One dimension of the model Is comprised of four Amportant conte- orary production and inventory control problems: capacity management, Inventory management, priority management and uncertainty management. Four major management functions make up the second dimension: targeting, planning, scheduling and controlling. The remaining dimension is the length of the plan2ing horizon and the nature of the activities 4thin that horizon. The generality and utility of the modeI are expected to enhance its acceptability to both groups. The model can be used to facilitate: 1) clarification and evaluation of past and future research; 2) identification of areas in need of further research; 3) clarification and evaluation of existing and future operational tools; 4) identification of areas in which further tool development Is needed; 5) development of integrated operating systems, and 6) communication between practitioners and academicians. The model Is used within the paper to identify and classify sixteen production and Inventory control problems. Several unusually signif icant research opportunities and directions are suggested and some needs for operational taol developmeat are identified.

1. INTRODUCTION The substantial divergence between con-

temporary theory and praccico In production and inventory control Is evident to all but

have emerged in the last decade 191 [III

[lb1 IlSl.

2. PRACTICEI’THEORY SCHISM the most maribund academicians and practl- tlonera. It 1s suggested here that the absence of a comprehensive framework for connnunlcat ion among and between proctl- tioners and academicians may well be re- spo..slble for the rather disorderly evolution of production and inventory control (PIG) theory and practice. A comprehensive PIC model is needed to facilitate the orderly evolution of theory and practice. This paper reports the current stages of an ongoing effort to develop such a model. The accomplishments described rely heavily on a number of significant, but limited, conceptually integrative contributions that

2.1 Evidence --.. A brief comparison of currelit practice

and hl!;toricnl research developments is suf f icieri: t?, establish that the PIC gap is not simpLy a question of diffusion time. Certain developments during the early sixties indicate a growing divergence in terms of philosophical .iirecticla during that period [3] [9] (141 [15j [I61 1171. A review of the PIC practice literature for the last several years lends further support to this contention (see back issues of Pro- duction and Inventory Management). But contemporary developments, including the growing academic interest in material

0 167-I 88X~82/0000-0000/$02.7s 0 1982 Elswier&ientific Publishing Cumpany

requirements planning and associated issues, are indicative of a substantial shifting of philosophical direction within the academic camp 111 I21 131 [61 191 [ISI 1191.

2.2 Reasans --- Communicatioh difficulties exacerbate

the d-ifferer?ic:es in philosophical direction between theoretician and practitioner. Before the era of scarce resources, it appears that many production managers simply did not recognize the existence of scheduling and inventory problems [ 101. On the other hand, PIC is only one of several areas with which an academician with a specialty in Production and Opera- tions Planagennent, Industrial Management, Industrial Engineering, etc. is expected to be familiar. It is also true that academicians have long been criticized for their preoccupation with more powerful tools for well-knowr ;roblems while more important issues go unaddressed. Communication failures are the inevitable resulr of such disparate b,ackgrounds and perceptions.

Upon entering the era of scarce resources, practitioners began to successfullyidentify scheduling and inventory problems and search for solutions. This search produced some fruitful collaboration between the two groups and some landmark accomplishments. The widespread adoption of economic order quantities and statistically derivecl order poinca is one such accomplishment. But becau-,e of the severity of the communication problG?m, as often as not, other attempted collahmorations led to disappointment, frus- tratior:. anger, re j ec ticon and avoidance. Practitioners went back to inventing their own tools on an ad hoc basis and academicians went back to the search for more powerful to,ols for less realistic but more familiar problems.

2.3 Research implications From the practitioner’s point of view,

much academic research has been essentially worthless. It is sometimes characterized by practitiloners as astonishingly repetitive research on trivial problems. Likewise,

PIG theory is often viewed as a collection of elegant models insufficiently rich enough in situational detail to be acceptable to man,agement. To the extent that thelse evaluations are correct, it is apparent th.at practitioners and academicians not only perceive the existence of different problems but also use drastically different criteria to evaluate research results.

It is the ‘contention of this author that the major reacon for the continuing schism between PIG practitioners and academicians

is the absence of a r,enerally accepted conceptual model. In :he absence of such a framework, practitioners and academicians still do not reC~JgniZe the existence and importance of tile same problems. Since there is no agreement on the importance of problems, objective or near otJective melsurcs of the utility of research contri- bunions are also missing. In the absence of a utility measure for research contributions, the existence of a problem, some- where, no matter how inconsecuential, has been used as a surrogate measure for problem importance and research contribution. Frag- mentation of research efforts along the paths of least resistance is the inevitable result.

2.4 Closure -- Regardless of the reasons for the absence

of a comprehensive PIC framework, one is clearly needed to lend structure. coherence and internal consistency to this rapidly changing field.

3. PREVIOUS CONTRIBUTIONS 3.1 Overview --

Although several major PIC research directions exist, the interrelationships between the problems being addressed do not appear to be well rj:cognized or understood. If an acceptable integrating SUperStnJctUre

has been advanced, it has clearly tint rctceived widespread exposure. A survey of tne PIC 1iteraLxre reveals that there have been four relevant deve opments.

3.2 KR model Disaggregation was defined by Krajewski

and Ritzman as the process of developing detailed plans from aggregate plans 1111. The taxonomy used to classify disaggregation problems in a manufacturing concern is labeled the KR model herein. The major contribution of the KR model to the objectives of this paper is its recognition of up to three level: of plans subordinate co the original aggregate production plan. The absence of explicit consideration of in- process inventory, finished goods inventory, capacity and uncertainty limits the utility of the KR model for the stated purpose of this paper. Criticism of the KR effort is probably unfair since if involved attempted definition of au entire class of disaggregation problems in service and manufacturing industries. In any event, the multi-level concept is a valuable portion of the model developed in this paper.

3.3 POW model -_-- The ideas and concepts that comprise the

POW model were contributed by George Plossl,

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Joe Orlicky and @liver Wight, all of whom are highly successful PIC trainer/con- sultants (141 [15] [16] (201. Wight says tha’_ “the job of production and inventory management is to generate plans rhst other people .an be held responsible for execut- ing” [20, p. 111. According to Wight, four basic functions must be performed to accomplish thir, objective: priority planning, capacity planning, priority control and capacity control.

Although this simple categorization represents a landmark contribution to the structuring of the PIG area, it still has substantial shortcomings. Certainly the POW model has facilitated tool evaluation and development, and basic education in PIC. But it Is too general to be of mucn assist- ance In structuring higllly specific PIC research contributions and guiding further PIC research efforts. The implicit treatment of inventories as a dependent variable is unacceptable to most PIC academicians. This is understandable in view of the customer service and aggregate production capacity imp1 lcations of various inventory levels in a make-to-stock environment. In fairness to Orlicky, it should be pointed out that he did include certain inventory planning and control activities in his version of the POW model 114, pp. 144-51.

The basic functions of the POW model described above are frequently depicted lr flowchart fashion in pursuit of greater clarity. In the author’s experience, the flowchart representation does clearly establish the nature of the logical relationship,? between the four basic ft,ictions. But it is ?ot ,at all clear how the four functions are or should bc related through time. Other trainer/consultant POW imitators have published flowchart depictions of PIC that attempt to improve on the original POW version. In general, these imitators offer’ improved treatment of the inventory <ssue but are no more successful with the timing issue than were the originators of the Pow madel.

3.4 APICS certification I- The zexcan Production and Inventory

Contra1 ( oclety Curricula dnd Certification Council t hose to structclre the cerelfica- tion exatinatlon along five topical lines. In 1980, the council revised the original format to reflect evolution within the area I81 l The five areas are master planning, inventory -management, materlals requirements planning, capacity management and production activity control. After atlempt- ing to develop a flowchart of the JIG process .in order to assign groups of functions to each examination,

the Council concluded that the area is evolving so fast that any flowchart model would be outdated before it could be dis- seminated. A subjective evaluation of the original five exam areas seems LO reTTen substantial confusion between technique, managerial functions and managerial issues. Thz revision appears to indicate an evolution toward issuts but with some concentra- tion on functions still present. In their present form, the five ex.rm areas simply provide a convenient way to list a wide variety of PIC topics. As a PIC frdmework, it is not adequate to structure highly specific past research contributions ror to guide future research.

3.5 EliI model In c;?‘ril’r stages of the work reported

here, the author developed a three- dimensional conceptual model of the PlC area 191. The dimensions are management f unct ic111, manngernent process control variables and planning horizon. The management functions are targeting, planning , scheduling and controlling and represent a further delineation of the planning and control functions of the POW model. The three lower level functions also correspond to the multiple levels of the KR model. Capacities, inventories and priorities are the specified process control variables thus incorporating Orlicky’s view of PIC problems [14, pp. 144-51.

4. THE EHII MODEL 4.1 Her itage_

The model presented here is a refinement and extension of EHI. Management function and planning horizon still represent two dimensions of the model. But the EHI description of the remaining uimension, control process variables, has been replaced by the less limiting but more descriptive term, situatfonal variablas.

4.2 Situational variables --___.-___ _ _---_I The EHII modeJ incorporates four situa-

tional variables--one uncontrollable and three controllable. The uncontrollable variable is uncertailtty and the control- lablcs ones are priorities, inventories and capacities. The model is depicted in Figure 1.

4.2.1 Priorities. This variable is included to represent the myriad of priority choices in the PIC environment. The choices

range from product mix decisions to dispatching and machine assignment decisions. All of the choice situations include common elements. There are multiple productive

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SITUIITIDNAL VARIABLES

v

TARGETING

PLANNING

I SCHEDULSNG

+-,

activities uhich compete for limited production facilities. Choices have to be nade in terms of the amount of limited resources to devote to particular production activities duri_ specific time intervals. -- -_1_1

4.2.2 Inventories. Inclusion of inven- :ories as a situation variable permits consideration of several different inventories. In-process inventory control has long been a major source of problems in PIC. At the finished product level, the balance sheet and operating expense consequences of various aggregate inventory levels are formidable. At the same time, the customer service implications of end-item inventories are too important to be ignored. In addition to those sonsiderations, individual part .samber inventory control is imperative for ongoing production operations. Th,z common element for inventory decisions is the focus on quantity or level.

4.2.3 Capacities. From a PIC standpoint, plant and equipment are usually considered to be fixed. This simpl?r means that there is an upper limit on productive capacity. Manpower staffing levels, work schedules, preventive maintenance schedules, etc. all have an impact on actual capacity and can be manipulated to provide desired rates for machines, work centers and the entlre plant. By the same token, production schedules can impose widely varying capacity requirements over time. The major problems are the matching oE productive facility capacities (rates) and production schedules.

4.2.4 Uncertainties. All PIC plans, schedules, etc. are executed in an uncertain environment. Demand is uncertain, job completion dates are uncertain, vendor deliveries are uncertair , etc Plans a.ld schedules must be continually vised

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because of the randomness ever-present in the environment. Much of management’s time and energy is spent attempting to acsom- modate or compensate for random effects. But many of the problems are repetitive and can be effectively analyzed. General solution techniques can be formalized and widely implemented. C1earl.J no so-called comprehensive PIC framework is complete without inclusion of uncertainty as an uncontrollable variable.

4.3 Management functions Targzing, planning, Ycheduling a id con-

t;oll;ng are the activities that comprise this dimension of the model. From a level standpoint, the targeting function cor- responds to the aggregate productlon plan that was assumed as an input to the three levels of disaggregation of the KH sodel. The other three functions have a or.e for one correspondence vgith the three disaggregation levels.

4.3.1 Targeting. This term is usec to describe that series of activities required to periodically modify capacity, inveutory and priority targets given known profit goals. It include:; aggregate demand fore- cdsting, product mix planning and aggregate production planning.

4.3.2 Planning. Given capacity, :oventory and priority targets, it is necessary to determine plans for their achievement and/or modification. Naturally this determination or modification must take place repetitively in view of the progress that has taken place since the last update.

4.3.3 Scheduling. Master productL>n schedulcflnished goods inventory plans and rough-cut capacity plans must be disag- gregated or decomposed into highly specific schedules to support plans for target achievement. And these schedules must be extended and revised frequently enough to maintain their integrity.

4.3.4 Coctrollin~. p_-. Tar %et ing , planning and scheduling in the absence of controlri is pointless. Performance against schedules must be measured, trackea, evaluated and needs for corrective action determined. Control is imposed through the appropriate response to those needs.

4.4 Planning horizon Each management function is presumed to

exist within the constraints imposed by the function at the next higher level. Kolling planning horizons are assumed for all

f unc t ions . Controlling has the shortest planning horizon and repeats itself several times during one scheduling cycle During this irterval, performance is controlled a&SinSt the current version of the schedule. Scheduling takes place next most frequently and has the next shortest planning horizon. Schedules ave designed to execute the plans as currently constituted. And plans are intended +o enable target achievement. Naturall:l targeting has the longest planning horizon. At first giance it appears that planning horizons correspond directly to the four management functions. This is the case for the length of the planning horizon. But the activities involved in the execution of each management function and the necessary level of specificity of these artivi- ties further segment the PIC problem.

4.5 Sub-function activities -- Each PIC management function can be

characterized in terms of one or more of four activities. The four activities are:

I - Incorporating new forefasts of distant demand.

E - Extending intermediate term plans. s- Scheduling adaptive responses for

the near-term. P - Programming corrective action for

the immediate future. The targeting function is comprised of all four activities. The planning function includes activities ESP, scheduling includes activities SP and controlling consists of activity P. The four activity descriptions are useful in understanding the relation- ships between the four management functions. The main reason for this is that the nearer- term activities are frequently overlooked when examining higher level functions. The tendency to become preoccupied with the. long range aspects of targeting and planning often obscures the existence and importance of the nearer term activities that are also part of the functions.

4.6 Specificity It is apparent that the level of speci-

f icity for- iower level mandgement functions is greater than the necessary level of specificity for higher level functions. This specific ity may be in terms of degree of aggregation of the time units, levels or rates involved in formal descriptions of individual PIC problems. But there are two important specificity issues. The level of specificity for the same activity category within a lower level management function is usually greater than it is for a higher level function. Likewise, the level of specificity for,a near-term activity within

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a function is often greater than the level of specificity for a more distant activity.

5. IiJTERPRE~ATION OF THE EHII MODEL 5.1 Overview _-- -

The management functions and situational variables of the EHII model can be used to iderntify sixteen distinct PIC problems. The sub-function activities allow further decomposition into a total of forty PIC problems. In actuality, forty problems represent an over-specification for the current state of PIC knowLedge. Conse- quently definition of *all forty problems will not be attempted herein. The sixteen major problems are defined as follows.

5.2 9acity management ______- Capacity management has only recently

emerged as a major PIC problem [3] [4] [5]

161 191. The fact chat these recent developments fit so readily into the EHII model is an indication of its utility for problem definition.

5.2.1 :J’argeting capacities. The major issues concern xhe specification of production capacity targets over the planning horizon. Given fixed plant and equipment, the problem is to determine work force size< , production rates, overtime plans, subcsqtracring needs, backorder policies, hiri,.g and firing plans, etc.

5.2.2 Planning capacities. Identification of capacity bottlenecks due to master production schedule imbalances ip the primary objective of capacity planning. The aim is, of course, to highlight such problems. Determination of the appropriate work force (capacity) allocations or master production schedule changes is a secondary objective.

5.2.3 $zheduling capacities. Capacity requirements determination involves the detailed projection of the aggregate work load for individual work centers over the appropriate time horizon. Its objectives include determination of the correct Lverage capacity for each Kork center over the tir.:e horizon and development of manpower staffing plans, work schedules and subcontracting schedules.

5.2.4 Controltling capacities. The major concern of capacity control is consistency between planned and achieved capacity work center by work center. The objectives are efficiency and schedule compliance. Inef- ficiency represents cost overruns and lost capacity implies schedule slippage.

5.3 ImTentorL manxement WI_ Literally re= of pages have been de-

voted to t:lis issue. Irut much of :t has not bee.1 (I I -Lcted to the productisn environ- mellt. . .~sh an environment, most of the pro!,1 ems involve dependent demand as opposed to the independent demand assumed in most of inventory theory.

5.3.1 Inve! Lory tasting. W!lat itvel of -- .I- aggregate inventory i&%tment is acceptable for currc’nt and anticipated sales and production volumes and is consistent with management’s customer service ,lolicy? When sl;ould the inventory level increase or dt crease? By how much? These are the major questions tl)aL ryllst be ansr*ercc: to establish inventory targets.

5.3.2 InvenLurv tr!.lnning. Ho&d sflculd the --_ _ __ _ __ _ -- aggregate inventory tzrgets he allncrted to individual product lines? End items? What impact will these allocations irave on the level of customer service for he individual end items? WhaL levels of cust ome: service are appropriate? How much safk!ty stock is required to provide that level of customer service? What is t’le correct r.eorder quantity and reorder point, et::.?

5.3.3 Inventory scheduls. Yhis par- ..-- _- titular functi,nal activity is primarily concerned with dependent item quantity determination. Given a master schedule, inventory status, open order irformation, and inventory plans, how many cf each part number must be produced over tt.e plan.ling horizon? How should the total requirements for each part number be supplied, i.e., what size orders should be placed? What lead times are appropriate? Is safety stock necessary, and if so, how much? 1s safety lead time desirable? Hov much?

5.3.4 Inventory control. This is not to --_- be confa with finished goods inventory control. The items in question are raw materials and in-process inventory. C+ ven open raw material orders and cu!:rent inventory status, is expediting or dl3-expediting ca.1 led for? Is vendor delivery performance acceptable? Can it he improved” Given current work center open order backlogs, how much work should be leased for each work center in the immtuiate future? What is the relationship between demonstrated and scheduled capacity at each \lork center?

5.4 Priority management Prioz:y as used here simply means the

relative emphasis given to competing activities over specific time periods.

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As such, it involves quest Sons o.‘ al_loca- tion and sequence.

5.4.1 Priority targeting. Almost ~11 manufacturing firms have multiple products or product line*.. Some face seasonal demands. The production of each product can be thought of as an activity competing for scarce capacity. In addition, each product uses differing amounts of the various capacities and contributes a unique amount to profit. The problem is the appropriate allocation of the various capacities to the competing products over speclffc t ime periods-- a straightforward definition of Lhe classical product mix problem.

5.4.2 Prioritylanni.. Given product -- mix targets, product ion plans in the form of master production schedules are necessary to drive dependent item demand projections. This involves damdnd management and determination of production plans ol’er sub-

stantial horizons. These plans must, of necessity in many instances, be based on demand projectlons as well as on more near11 certain standard labor and maclline requirements. As demand materializes, planned production in the form of open c-ders must be “pro,nised” for individual customers. It is these activities that comprise priority planning.

5.4.3 Priority scheduling. Orders for individual parts must be time-phased in a manner consistent with the master produc- t ion schedule. The real issue is the determination of when each work order must be completed to support the next higher level of production. Likewise, it is neces rry to establish target release dates cons-sc _ it with planned lead times and desire ’ completion dates.

5. :r iority control. Individual work 0 the shopzr should be prll. in a manner consistent wlth their des Jmpletion dates. Thr! basic issue is what order to process next when a machine or work center became9 available? Further, how can such priority choices be accomplished consistently over time? And to determine answers to these questions, what priority choice is consistent with the competing demands of all current and future open orders? How can the correct choices be communicated to the shop floor or how to insure that personnel make and/or implement cne correct choices.

5.5 _*...._ Uncertainty management Uncertainty is used here to represent

the combined impact of numerous random elements in a production situation. Uncer- tainty management is concerned with ways to compensate or correct for the unwanted consequences of those random phenomena.

5.5.1 Uncertainty targeting. Capacity, m-.- inventory and priority targets have to be established on the basis of dprrand projec- t ions. LJncertainty targeting involves the estimation of aggregate demand for specific time periods over the planning horizon. Estimates of demand and forecast variability are also essential for effective targeting. Other issues include predictive model validity, marketing intelligence sources and reLiability , etc.

5.5.2 Uncertainty plannix. Forecasts of -_~ indiviJu.rl product line, product snd end item demand is necessary for capacity, inventory and priority planning. Estimctes of demand variability and forecast error are again essential. Other concerns also exist. Do the individual item forecasts sum to the aggregate demand forecasts? How should discrepancies be resolved? How can subjective input be acr,tmaodated?

5.5.3 Uncertainty scheduling. Estimates are needed to schedule capacities, ir- ventories and priorities. Planned lead timts for part numbers, work centers, etc. must be estimated. Open order completion predictions need revision. Scrap and yield factors need to be tracked, updated and predicted. Utilization and efficiency predictions are also desirable to facilitate effective planning and scheduling. Likewise, vendor delivery and quantity performance needs to be measured, tracked and predicted.

5.5.4 Uncertainty_control. To facilitate --_ effective rontrol via dispatch lists, work order a;r;vals at work centers and work order completions at work centers must be predicted based on current shop status and known performance factors. To facilitate ongoing capacity control, efficiency and utilizatjon have to be measured, tracked and predicted for each machineiwork center.

5.6 Limitations Like al.1 other models, EHLI has its

limitations. So far, those limitations appear to be more apparent than real.

5.6.1 Generax. The major apparent ___ limitaticns of the model concern its generality. Practitioners within certain industries might tie11 find it unrepresenta- tibe. For example, managers in process

industries do not perceive the inventory and pri’arity scheduling problems as being very important. To a certain extent, they are correct. As compared to an assembly envi roninent , their in-process inventory and priority scheduling problems are of little consequence. But a difference in importance does not negate the existence of the problems. In other words, their argument is not with the validity of the EHII model but with the importance of certain of its characterizations.

5.6.2 Validity. __-_-. Consistency of the model with certain contemporary manufacturing control topics is also troubling at fir ,i glance. For example, it appears to provide no w.3~ of characterizing Group Technology or KanEan [l] [lg] . This is an appropriate reaction because EHII is not intenr’.ed to be used to classify techniques. Insttz?d it is designed to aid in problem categori- zat ion. Group Technology and Kanban, to a lesser extent I are techniques or systeas, not problems. Even so, the model is hclp- ful Ln identifying which problems those techniques attempt to address. But su :h a discussion will be left for a later paI)er.

6. ‘SZS OF THE EHI I MODEL 6.1 Overview

Improved communication among practi- ticners 3 researchers, software developers, consultrnts and educators is the major benefit of the model. This benefit flows from the model’s utility for PIC problem identification, definition, clarification and categorization. Because of that utilit!; 7 the EHII model can be used to classify and evaluate past and future research and existing and future opera- ticnal tools. It can also be used to identify areas in need of further research and toA> i development. Finally, it provides a guide for the orderly evolution of the PIG area.

6.2 Classification and Evaluation Ln the Fast, both research and opera-

tional tool. development efforts have been poorly focused. Research efforts have concentrated on a few well-defined problems with very Tittle attention devoted to interactions between them. Operational tool development Iprojects have focused on problems perceived to exist -in certain highly specific situations. ‘The result is concep toal chaos.

6.2.1 Research. The EHII model can Je used to classify and evaluate past and future research results. All existir.g and

future PIG research efforts can be sub- jected to two critical inquiries. What PTC problem categories is/was the re- searcher addressing? How important is (are) the problem(s) within the PIC area? Historically, these questions have been asked in very weak voices since not many people had sufficient grasp of tbc area to question effectively. The existence of the problem became sufficient evidence of its impcrtance in tne absence of more utili- tarian measures. Now the Eli11 mcdel can be used to more effectively evaluate - esearch effcrts on these and several othf dimensions.

Consider the well-known HMMS zggregate planning research project 1 lo] :

Frsblems addressed Uncertainty lanagement Capacity Management Inventory Management

Ilanagenent function

Targeting

Planning horizon Immediate through distance

Decision variable Constant thrrughout the specificity planning horizon

Problem importance Very

Implementability Difficult

Clearly this is not an evaluation of the HMNI methodology. But when viewed in the context of the Eli11 model, there is no doubt which problem was addressed and 110~ it was f ratted.

6.2.2 Operation&l tools. The same sort of ---- rigorous questioning ‘can be used to clas- sifbr and evaluate operational tools. Is the toal. designed to facilitate uncertaluty, priority, inventory or capacity rlanagement? Wha:: PIC management function(s) ..s it de- sig!red to assist? What planning horizon does the tool require? Does the tool recag- nizo and facilitate the differenl sub- function activities within that tlorizon? Is tthe level of specif Lcity of tJ\e decisian variables constant thi-oughout thu planning hor iron? If not, how are the decision v?ri- ablss treated? Does the tool recognize and facilitate or inhibit linkages tu the other management functions zlnd situational vari- ablles? The last question is ehtl.emely important for operational tool evaluatibn sinse the output from each higher, level’ management functior drives the next Lower level. Likewise, ‘situational variabl‘eo on

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TABLE I

PIC CROSS-REFERENCE TABLE

PROBLEM CATEGORY _-

Targeting Uncertainty Priority Inventory Capacity

Planning Uncertainty Priority Inventory Capacity

Scheduling Uncertainty Priority Inventory Capacity

Controlling Uncertainty Priority Inventory Capacity

ACADEMIC AREA OPERATIONAL TOOLS .--- -

Econometric Models Linear Programming Aggregate Planniug Aggregate Planning

Forecasting Software Math Programming Software Production Planning Manpower Planning

Time Series Methods Goal Programming Inventory Theory Goal Programming

Forecasting Software Master Production Scheduling Inventory Control Software Rough-cut Capacity Planning

Estimation ? ? ?

? MRP Software MRP,Software CRP Software

Estimation Job Shop Scheduling

? Work Measurement

? Dispatching Software O-&r Release Control I/O Control Software

RESEARCH OPPORTUNITI

Implementation Implementation Model Development Model Development

Implementation Model Development Implementation Model Development

Implementation Problem Formulation Problem Formulation Problem Formulation

Implementation Implementation Problem Formulation Implementation

the same functional level must frequently be addressed simultaneously as opposed to sequentially. See Table I for a pre- liminary classification of existing operational tools.

6.3 -_ Identification and Evolutios The EHII model facilitates identifica-

tion of needs for further research and operational 1.001 development. Adherence to its problem categorization can be a substantial aid to the orderly evolution of the PIC area.

that well-known academic mcdels are unacceptable. Aggregate planning is a good example. Further :nodel development, refinement and enrichment is needed to promote the acceptance of existing knowledge. Finally, some operational tools exist for problems that have not yet been formulated by academicians. The capacit: schedulinp problem is an excellent example. Clearly, problem definition and formulation is needed in such areas.

6.3.1 Research. -_~_~ In Table I, types ?f research opportunities are identified as implementation, model development and problem formulation. In general, imple- mentatiob. opportuulcies exist where operational software is available ard is based on knowledge within clearly identi- fiable acedemil: ar,as. The use of linear programming for priority targeting is an example. The software exists and tl-.e problem is recognized but has not been formulated and solution procedures packager; in a manner acceptable to large numbers of practitioners. In some areas, academic perceptions of the problem and practitioner perceptions are so different

6.3.2 Operational. I__ In some prublem categories, no operational tools exist. In others, the operational tools are not consistent with known research results. Areas in which further operational tool development is needed include inventory and capacity targeting, priority and capacity planning, uncertainty scheduling and controlling, and inventory and capacity controlling. There appears to be a need for additional software in all of these arcss as well as for priority targeting and capacity requirements planning.

Softw.lre is available from vendors and more is ander development. Homegrown software is in use and also under development at individual manufacturing facilities. The EHII model can be used to evaluate

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existing software and serve as a guide tq# future development. Evaluating a software pack,age using the EHII model involves three major steps: identifying the tools included in the package; analyzing each tool according to problems addrc ;sed, management functions, etc.; and identifying o,b\*inus c&ersight.s and weaknesses. This wculd be especially helpful for the evaluation of integrated PIC software systems.

. .

;:1 SUMYARY AND CONCLUSIONS

Review -- There is a subssantial &ap between PIC

theory and practice. It was suggested that. this gap is largely due to the absence of a compreh~ensive conceptual model of the I’IC field. Reasons for and implications of this absence were explored. Such a mod21 is clearly needed to lend structure’ coherence and internal consistency to this rapidly evolving area. Some previous contributions in this direction were analyzed and found to be inadequate. Valuable portions of these earlier works were inc,orporateo’ into the EHII model--a three dimensional characterization of the PIC ares. The three dimensions of the model are management function, situational variable and planning horizon. The three dimensions were independently analyzed in detail ss~d used to delineate sixtc,n separate PIC problems. Uses of the model for classification and evaluation of past and future research and existing and future operational tools were described and illustrated. Opportunities for further research and need.s for operational tool develoPrnent were identified.

7.2 Conclusion.. A PIG conceptual framework general

enough to be acceptable to both academicians and practitioners and chal- lenging enough to capture the imagination of both is needed to promote the orderly evolution of the area. The EHII model is an attempt to fill that need. Pre- liminary results indicate that it has a wide variety of potential applicalrions. The ultimate test of the EHII mod* 1 will be the degree of acceptance it aclhieves within the PIC arena.

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Bridging the gap between theory and practice in production and inventory control