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The Quality of Standard, Routine and Nonroutine Processes

Article  in  Organization Studies · February 2003

DOI: 10.1177/0170840603024002344

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The Quality of Standard, Routine andNonroutine ProcessesPaul Lillrank

Abstract

Efforts to increase organizational effectiveness using standardization and qualitytechniques have been successful in repetitive production and administrative processesbut less so when dealing with nonroutine processes typical of professionalorganizations. Routines are defined very broadly in organization theory as either mind-numbing repetition, repositories of knowledge, or effortful accomplishments. In thisarticle, processes are analysed as systems with distinct input assessment, algorithmsand output-generating action phases. These are structured differently depending onhow they are set up to deal with variation (deviations from explicit targets) and variety(distinct but functionally equivalent targets). Thus processes can be classified intothree types. Standard processes are set up to deal with a single variety using binarylogic. Routine processes can distinguish a limited amount of variety using fuzzy logic.Nonroutine processes are open systems in which unrestricted variety is interpretedand assigned meaning. The implications of these process types are discussed in termsof the identification of quality errors and defects, change and learning.

Keywords: standards, standardization, routine, routinization, nonroutine, defects,errors quality management, statistical process control

Introduction

Quality and productivity improvement through standardization and statisticalprocess control is a modern success story. Economically feasible methods forcontrolling the uniformity of output enabled the use of interchangeable parts,which, in turn, made possible industrial mass production, economies of scaleand improvements in wealth and welfare (Womack et al. 1990). Manyorganizations other than manufacturers have tried to apply these methods toimprove their operations. However, results have not always been favourable.The perceived quality of services still lags behind that of industrial products(Edvardsson et al. 2000). QM (quality management) in professionalorganizations frequently meets resistance that tends to reduce it to emptyformalism (Zbaracki 1998). Software quality remains in its infancy (Hoch et al. 2000).

The success of QM has been compared across nations (Rommel et al. 1996;Dahlgaard et al. 1998), but there are no broad studies comparing variousindustries or organization types. The QM implementation literature tends to

OrganizationStudies24(2): 215–233Copyright © 2003SAGE Publications(London, Thousand Oaks,CA & New Delhi)

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Paul LillrankDepartment ofIndustrialEngineering andManagement,HelsinkiUniversity ofTechnology,Finland

focus on certain types of organizations (Cole and Scott 2000). Perrow (1967)has classified organizations and their technologies by the number ofexceptions they have to handle, and by the degree to which a search for asolution to an exception is analysable. Organizations in which there are fewexceptions and problems are analysable Perrow calls routine. These aretypically mass manufacturers and high-volume service producers most ofwhose processes involve identical repetition of standardized tasks. Theopposite type, nonroutine organizations, handles a lot of exceptions that arenot analysable following predetermined schemes. Although the evidence isnot conclusive, it appears that most of the QM success cases are from routineorganizations (Easton and Jarrel 1998; Flynn et al. 1995) or from routine sub-processes within nonroutine organizations (Silvestro 2001). This should notbe surprising, since most of the technical (Wheeler and Chambers 1992; Mitra1998; Oakland 2000) and classical literatures (Shewhart 1931; Deming 1982;Ishikawa 1985; Juran 1992) of QM deal with repetitive processes.

The concept ‘routine’ has been used in organization theory to describe thestability that comes from repeating the currently best-known practices (Cyertand March 1993; Nelson and Winter 1982). It also occupies a place in thetheory of the firm, particularly the versions that see the firm as a bundle ofspecific knowledge or dynamic capabilities embodied in the firm’s routinesand competencies (Nonaka and Takeuchi 1995; Penrose 1995; Tsoukas 1996;Spender 1996; Fransman 1999, Eisenhardt and Martin 2000). Although theemphasis is on the function of routines, the literature is not very specific indefining what they are and how they work. The purpose of this article is toexplore the inner workings of routines and develop distinctive definitions ofstandard, routine and nonroutine processes.

Routines and Nonroutines in Organization Theory

Routines as Mindless Repetition

Human relations and contingency theories associate routines with the mind-numbing repetition that goes with mechanistic organizations, such as short-cycle assembly work or working a cash register at a supermarket. Mechanisticforms of management, such as centralization, formalization and directiveleadership, are assumed to increase the efficiency with which workers performunvarying and repetitive tasks (Burns and Stalker 1961). Such tasks are notperformed consciously as the result of cognitive, rational decisions. Ratherthey are performed as habitual responses to familiar situations (Weiss andIlgen 1985) or following standard operating procedures (Cyert and March1993). Routines are identical to the scripted or mindless behaviour that occurswhen there are event schema, categorizable stimulus clues, action rules,minimal required effort, an absence of unstructured subroutines and anabsence of interruptions and unmet expectations (March and Simon 1958).Routine work is primarily completing structured problems, tasks charac-terized by accuracy of detail, short-term horizons, predominantly internal

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information and a narrow scope (Pava 1983). It proceeds through linear andsequential conversion processes — a series of particular steps that yieldspredefined output (Pasmore 1988). Routine work is associated with a lack ofautonomy and fulfilment and thereby also with alienation and a low level ofpersonal control (Ross and Wright 1998). Routines are prevalent in situationswhere there are few exceptions and the search for responses to exceptionscan proceed analytically and logically (Perrow 1967). In their study of healthand welfare organizations, Hage and Aiken (1969) state that an organizationhas a routine workflow if its clients are stable and uniform and much is knownabout the particular process of treatment.

Routines as Repositories of Knowledge and Capabilities

The evolutionary approach to organizations, pioneered by Nelson and Winter(1982), sees routines as the nexus that brings together a number of constructs.Routines refer to the regular and predictable aspect of a firm’s behaviour. Toexplain the behaviour of a firm is to explain its routines. To model a firmmeans modelling its routines and how they change over time. Routines allowfirms to cope with complexity and uncertainty under bounded rationality.Firms differ by developing different routines, even in similar environmentsor circumstances. Routines are thereby a source of a firm’s distinctivenessand competitiveness. They need to be exercised regularly to keep them viable.Routinized behaviour provides a mechanism for mediating the interactionbetween market changes and firm responses, as well as technical changes andeconomic growth. Nelson and Winter (1982: 128) summarize their view: ‘Anorganization in routine operation can be imagined as a flow of messagescoming into the organization from the external environment and from clocksand calendars. Organization members interpret them as calling for theperformance of routines from their repertoires. The performance of eachroutine again creates a stream of messages to others.’

In the discussion around the resource-based view of the firm, ‘dynamiccapabilities’ have been seen as kinds of mega-routines. Winter (2000: 983)defines dynamic capabilities as high-level routines or collections of routines.Whereas routines can be of any size and significance, capabilities aresubstantial and reflect large chunks of activity. Whereas routines may beinvisible to managers, capabilities are necessarily known at least for theircontrol levers and intended effects. Eisenhardt and Martin (2000: 1106) makethe distinction between dynamic capabilities in stable industry structures andin high-velocity markets. Where markets are moderately dynamic, capabilitiesresemble routines. They are complicated, detailed, analytical processes thatrely on existing knowledge, linear execution and predictable outcomes. Inrapidly changing circumstances, capabilities are simple, experimental andunstable processes that rely on quickly created new knowledge and iterativeexecution to produce adaptive but unpredictable outcomes.

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Routines as Grammars

Another view is of routines as a set of possibilities that can be described asgrammars (Pentland and Rueter 1994). The grammatical approach attemptsto look at the inside of routines. Selecting and performing a routine is aneffortful accomplishment. It is not a single pattern but, rather, a set of possiblepatterns from which organizational members enact particular performancesthat are functionally similar but not necessarily the same. Routines can bedescribed by a grammar that explains the regular patterns in a variety ofbehaviours. In the same way as English grammar allows speakers to producea variety of sentences, an organizational routine allows members to pro-duce a variety of performances. Thus a routinized activity is not mindless orautomatic, but rather an effortful accomplishment within certain boundaries.Feldman (2000) takes a similar approach in discussing changes in routines.Routines evolve and change because they are not mindless but emergentaccomplishments. Change is more than choosing from a repertoire ofresponses; the repertoire itself and the rules governing choice can also change.To Giddens (1984), routines constitute the material grounding of the recursivenature of social life, the link between stability and continuous recreation ofsocial forms. For example, at the beginning of each school day teachers assessthe situation in terms of the mood of the class, the weather and individualrequests. They view these in relation to the constraints of the curriculum andselect one of several possible routines for managing the day in a way thatsimultaneously ensures the continuity of the schooling process and adaptationto the contingencies of the day.

Routines and Nonroutines

In contrast to mechanical models, organic forms of management includeparticipative decision-making, collaborative problem-solving and supportiveleadership. They are assumed to provide workers with the information andsupport needed to cope effectively with variability and uncertainty (Rowan et al. 1993; Lincoln and Costello 1996). Pava (1983) used the term ‘nonroutine’to describe typical tasks in professional organizations, such as hospitals, lawoffices or consultancies. Nonroutine work primarily involves managing semi-structured or unstructured problems. These tasks are based on plausible butgeneral information inputs, variable detail, extended and unfixed time horizons,internal and external data and diffuse or general scope. Whereas routine workis guided by procedures established in advance based on past experience,nonroutine work is adapted to information learned from the task as it unfolds(Cheng and Miller 1985).

The boundary between routines and nonroutines is not clear. Weiss andIlgen (1985) emphasize that routinization cannot be identified solely from therepetition of behaviour. An actor may be confronted with a familiar situationand after careful consideration chooses a response similar to those made inthe past. Such an outcome would not qualify as an example of a routine in spite of observable repetition. Pentland and Rueter (1994) discuss the

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observation that apparently nonroutine work may still display a high degreeof regularity. In their study of a software helpdesk they found that the apparentdisorder of observed tasks exhibited a rather high level of stability whenlooked at in terms of higher-level grammatical rules.

In the literature, behaviour described as routine ranges from mindlessrepetition of standard operating procedures to effortful accomplishments insocial structuration. A more precise definition is obviously needed; not onlyfor conceptual clarity, but to help analyse various types of processes and linkthem to established theoretical constructs and associated improvementmethodologies.

Process and Process Quality

As Pentland and Rueter (1994) have pointed out, the word ‘routine’ can be used as a noun to objectify a recognizable pattern of action. It can also beused as an adjective indicating a judgement about a variable property of a pattern of action. In the following, routines, as well as standards andnonroutines, are seen as attributes of processes. A process can be standard,routine or nonroutine, depending on how it is structured in relation to itsenvironment and resources.

Definition of Process

In the QM literature, basic assumptions are that all work is carried out inprocesses, all processes exhibit variability, and quality problems arise as resultof excessive variety (Deming 1994; Gitlow 2001). A process is defined as atransformation of a set of inputs, which can include actions, methods andoperations, into outputs that more or less conform to expectations and satisfycustomer needs (Oakland 1993). In this view, resources such as people,equipment and knowledge are regarded as inputs. The essential aspects of aprocess are, however, easier to grasp if resources are included in the process,so that input consists only of the streams coming from other processes in anorganization or from sources outside the process (Conti 1993: 40). A processcan thus be said to be a system that has resources as fixed assets, inputs asvariable assets and services as output. A process can be part of a larger systemand also include one or several layers of sub-processes. A typical industrialprocess is the order-to-delivery sequence, which includes sub-processes suchas order handling, scheduling, purchasing, manufacturing and delivery. A healthcare process may include steps such as diagnosis, treatment, recovery andrehabilitation. Thus the study of specific processes must start with a definitionof the unit of analysis and its relevant system environment (Jackson 2000).

The Assessment–Algorithm–Action (AAA) Sequence

The process transforming input into output involves three different activities.First, there is an input transducer or sensor for receiving and assessing input.

Lillrank: Standard, Routine and Nonroutine Processes 219

Second, there are conversion rules such as a simple switch, a network ofconnections, algorithms, grammars or heuristics that generate controlinformation based on the assessed input situation and available resources.Third, there is an output transducer or an effector to produce the output as instructed by the control information (Beer 1981: 30–31). This can beillustrated with an example. Flying a commercial aircraft on a regular routeis a process equipped with several types of fixed resources, such as theaircraft, the cockpit crew, airports and air traffic control. There are severaltypes of variable input such as fuel, payload, weather and traffic restrictions.Pilots are trained to assess input conditions in a structured way. They aresupposed to observe a given set of variables, be alerted if an input straysbeyond normal, classify the situation correctly and come up with andimplement actions to steer the plane safely (Morrow et al. 1994). Pilots’ skillsdepend on three things: first, how well they perceive and assess all the relevantinputs into the situation; second, how well they know what to do and derivean action plan from the assessment using available resources; and, third, howwell they perform the action itself. In sum, a process has three distinct phases:assessment of the situation, the use of algorithms, grammars or heuristics tocome to a decision that produces control information; and action based on theinformation. This can be called the assessment–algorithm–action (AAA)sequence. The quality of an AAA sequence can be analysed with severalconcepts: targets, tolerances, variation and variety.

Targets, Variation, Tolerances and Defects

Inputs and outputs can be given some acceptance criteria in relation topredetermined targets. For example, a process of grinding a batch of engineparts out of steel can be given an exact target value, say 12 mm, for a criticaldimension of the part. The grinding process will, most likely, not be able toproduce all required parts exactly to target. Even under controlled factoryconditions there is minuscule variation from one repetition to the next, bothin the input and in the resources of the production process, which togetherproduce variation in the output. A solution to this problem, devised alreadyin the mid-19th century, was to define tolerances for each target. A part canbe accepted if its dimension stays within a tolerance zone, say 12 mmplus/minus 0.02 mm. In a similar vein, the steel blanks that are the primaryinput to the grinding process can be given targets and tolerances in terms ofdimensions and metallurgical properties. In addition to continuous variablessuch as dimensions, variation can also be measured with attribute data.Assume that the parts are coated. The paint job can be examined forirregularities, which are counted. Tolerances in this case define the maximumnumber of irregularities acceptable for one part. Zones of tolerance define therequired quality of a process, be it manufacturing or customer service, in termsof the amount of variation that can be tolerated without serious disruption(Shewhart 1931; Berry and Parasuraman 1991).

If variation affects output so that it does not stay within tolerances, theoutput is defective. To the extent that targets represent the current best-known

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design or agreed requirements, variation is associated with a loss function(Taguchi 1986). Deviations lead to costs in terms of rejection or subsequentproblems. Estimates of loss can be used to determine technically andeconomically optimal tolerances.

Variation and Variety

The term ‘variation’ has a different meaning in purposeful, target-orientedprocesses and in evolutionary processes. In the evolutionary approach toorganizations, variation is defined as any departure from routine or tradition.For example, the sales force of a company encountering a new customersegment may try out several different approaches. Eventually a best practicemay emerge out of numerous trials and errors. Variations are the raw material from which selection extracts those that are most suitable. The higher the frequency of variation, the greater the opportunities for change(Aldrich 1999).

Evolutionary variation is, for practical purposes, identical to the conceptof variety as it is used in systems theory, managerial cybernetics and newproduct development. Variety is defined as the number of possible states asystem is capable of exhibiting (Ashby 1956), the number of distinguishableinput and output items (Beer 1981), or the number of functionally equivalentproduct options (Stalk and Hout 1990). In business, variety is a set of choicesbetween alternatives in designs, colours or flavours. If there is, say, only onetype of car available in a given price category, several buyers are forced to accept suboptimal choices. The supply system does not have the ‘requisitevariety’ (Ashby 1956) demanded by its environment. If customers can choosefrom a hundred alternatives, the probability of a good fit is higher. Sincecustomers tend to prefer products that fit their individual needs over thosethat do not, variety can be assigned a utility function.

Although the terms ‘variation’ and ‘variety’ are often confused and usedas synonyms (Axelrod and Cohen 1999: 32), they have fundamentallydifferent meanings. Variation describes a measurable deviation from an exante given target, such as the given dimension of ground machine parts. It isthereby subject to quantitative statistical analysis. Variety, in contrast, meansa set of different targets that are functionally equivalent, such as the coloursthat the parts can be painted. Further, variety should not be confused withdifferent products or performance levels that do not constitute real alternativesto a customer. If machine parts are given different kinds of heat treatment toachieve different levels of hardness for different uses, the question is no longerone of variety but one of different product performance types or categorieswith different functions. In this sense, functionally equivalent product optionsare analogous to variety within natural species, whereas product performanceis equal to differences between species.

In sum, variation is deviation from given targets and is thus an exhibitionof the imperfections of human endeavours. Variety represents different targetsthat offer different ways to fulfil the same need. It can be seen as an expressionof the diversity, adaptability and creativity of human behaviour.

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Repetition

If a process is repeated a thousand times with the same single-variety targetand AAA setting, output can be described with a statistical distribution ofvariation in relation to the target. Careful analysis of variation tells somethingabout the nature and capability of the process and provides a foundation forimprovement. This is the situation in mass production. If a process is run onlyonce, the output is obviously of a single variety. Variation shows up as onedata point per measured characteristic.

If a process is repeated but each time with different target and AAAsettings, the output cannot be described as a distribution of variation arounda target. The output is a group of different varieties. This is the situation inmany professional service organizations such as health care and consulting.Each customer and context is different, requiring different actions that are not easily measurable with the same yardstick. For example, in anoccupational health clinic dealing with minor ailments, one patient is happyto receive some painkillers and return to work, whereas the next one wantsthree days off work to nurse a backache. Targets cannot be set as directprocess outputs. Higher-order achievements, such as the satisfaction ofpatients or employers, must be used. If requirements are met each time withdifferent methods, best practices are hard to establish. The hard tools ofstatistical process control are most applicable to problems of variation andare not much help in dealing with problems of variety.

Defining Standards, Routines and Nonroutines

With the concepts of input, output, targets, tolerances, variation and variety,various types of AAA sequences can be defined as standard, routine andnonroutine processes, as summarized in Table 1. The concept of routines, asdefined in the literature reviewed above, is split here into standards androutines.

Standard Process

Let us assume we are running a standard process for making orange juice.The output is a single variety of juice. It has several quality characteristicssuch as colour, amount of pulp and sweetness that can be either measured orevaluated. Each is given a target with a tolerance defining the acceptable range

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Standard Routine Nonroutine

Acceptance criteria Single variety Bounded variety set Open input setAssessment Acceptance test Classification InterpretationConversion rules Switch, algorithm Algorithm, grammar, habit HeuristicsRepetition Identical Similar but not identical Non-repetitiveLogic Binary Fuzzy Interpretative

Table 1 Classification ofStandard, Routineand Nonroutineprocesses

of variation. Input is assessed with an acceptance test. The assessment phasefirst determines whether the input is of the expected variety. A simple binarylogic is applied. Only oranges are accepted. Everything else — such aspineapples, twigs or remnants of packaging material — is promptly rejected.If variety is accepted, the quality characteristics are measured to determinewhether they are within tolerances. If some but not all of the characteristicsare outside the tolerance limits, a decision rule is required to determinewhether the lot is to be accepted or rejected. The assessment phase thus createsa signal with two possible states: ‘go’ or ‘no-go’. The signal enters analgorithm, which generates control information. If the signal is ‘no-go’, theprocess stops; if it is ‘go’, the process continues and leads to predeterminedaction. Output is examined to determine that the right variety has beenproduced and that characteristics are within tolerance. The management ofsuch processes primarily concerns controlling the input and reducing variationinside the system by attending to the resources that the process employs.

A standard process can accept only certain inputs since the assessmentphase can identify only one type of variety, accept only variation withintolerance limits, and produce only two control states, ‘go’ or ‘no-go’. If adifferent kind of output variety, for example extra- sweet juice, is expected,the process must be redefined and set up anew. A standard process is identicalto what was above called mindless repetition following scripts. Variationinside a standard process may not be fully known and controllable, thereforestandard processes are rarely fully deterministic.

Routine Processes

A routine process accepts two or more input varieties and can produce two or more output varieties. However, the assessment cannot be reduced to simple binary logic — because it is either technically impossible oreconomically not feasible. Therefore the assessment must employ fuzzy logicor tacit knowledge (Nonaka and Takeuchi 1995). The variety, however, maynot be infinite, but fall within the bounded rationality (Simon 1997) that theprocess employs. Thus there can be a wide range of routines, from those withonly two states to those managing, say, two thousand, provided the processhas the capability of distinguishing between them. A routine process, bydefinition, always exhibits the requisite variety: the number of assessmentstates is equal to or larger than input states.

Let us assume that our orange juice process has a disturbingly largevariation in terms of colour. A lot of juice is rejected as either too green ortoo red. We may upgrade our process from standard to routine by definingthree different output varieties — cool, regular and warm — which are offeredto customers as different varieties of our brand. Since we cannot affordinstruments to measure colour with scientific accuracy, an experiencedemployee assesses the incoming batches of fruit. She classifies them intoproper categories or ‘pigeonholes’ (Mintzberg 1989) that each are connectedto a ‘repertoire of responses’ (Feldman 2000). Each pigeonhole has itsalgorithms or grammatical rules to guide action. We know that greenish

Lillrank: Standard, Routine and Nonroutine Processes 223

oranges tend to be harder than reddish ones; therefore the presses must beadjusted accordingly.

As our understanding of different varieties of oranges increases and our business can afford investments in equipment, we may again change ourprocess from a routine to a set of parallel standard processes with a commonpre-assessment. After the incoming oranges have been assessed and sortedinto three varieties, three separate standard processes can take over. This,however, can be done only if the assessment and algorithm can be describedexplicitly. Since orange juice is a low-margin business, we may be temptedto apply industrial effectiveness. The high-grade vineyard in our neighbour-hood, however, rejects all calls to explicate and standardize the assessmentof grapes and relies on traditional tacit knowledge.

Returning to the airline pilot example, the input set including all possiblecombinations of information provided by instruments and direct observationmay be considerable, but not infinitely large. Owing to the dangers involvedin air travel, great effort has been made to explicate virtually all relevantassessment classifications and algorithms. Once action is taken, for instanceengine speed is increased and flap position altered, output can be reasonablywell predicted. Occasionally, however, the input set and/or the action–outputconnection may expand beyond the bounds of rationality. Should that happen,the process slips into a nonroutine mode.

Nonroutine Processes

In a nonroutine process, the input variety set is larger than the boundedrationality or experience set employed by the process. It cannot be describedexhaustively ex ante. A nonroutine process, by definition, does not have the requisite variety as an initial condition. The input has more states than theassessment. A nonroutine process, however, can recognize such a situationand therefore does not attempt to reject input that does not fit into existingcategories. Input lacking a ready-made pigeonhole is interpreted and assigneda meaning in an attempt to develop new algorithms and actions. This mayrequire a search for new inputs and several iterations of trial and error.

Let us assume that our orange juice business runs into trouble because ofa bad harvest. To stay in business we call on our suppliers to deliver whateverthey can lay their hands on. A shipment of excellent tomatoes arrives. To turn them into juice, we have to develop some new knowledge about howtomatoes behave and initiate a search for a recipe including some spices asnew inputs. Some of our orange-based algorithms and grammatical rules may be applicable, but some new ones have to be created using higher-levelheuristics. The results may not be immediately acceptable, so we have toiterate the process and apply some double-loop learning (Argyris and Shön1978). Although the input side is more complex than before, our newnonroutine process still has a clear objective to produce and sell processedfruit. It is not limited to varieties of the same basic product, but can producedifferent product categories. In a nonroutine process, task accomplishment,broadly defined, is more important than pre-defined output. If good tomatoes

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keep coming and our juice sells well, the nonroutine situation will soonstabilize into a routine. Should, however, the supply situation remain unpre-dictable, we may have to accept nonroutine as a way of life. It would not bean easy life, because every shipment would require extensive negotiation ofmeaning, experimentation with algorithms and constant worry about whetherthe output would be acceptable to customers. A nonroutine process may slipinto chaos.

Chaotic Processes

A discussion of chaos theory and the art of managing at the edge of chaos(Brown and Eisenhardt 1998) is beyond the scope of this article. It is sufficientto say that chaos is different from nonroutine in that not just input but also expected output are vague. Objectives provide little guidance for task accomplishment. In a chaotic process there may be considerable activitybut no distinguishable targets. Our juice factory may end up in chaos ifmanagement cannot agree whether the objective should be to stick to juicesand develop a better supply structure, to diversify into fruit cocktails, or toabandon the business and adapt to the lousy supply conditions by starting apig farm.

Discussion

Standard, routine and nonroutine processes differ in how assessment,algorithms and actions are structured in terms of variation and variety. Thisconceptualization has some management implications (see Table 2).Standardization appears to be the ideal type for achieving control andeconomic efficiency. However, it is not feasible if environmental variationand variety are large and cannot be reduced. Moreover, complex processesare often combinations of sub-processes that may be standard, routine ornonroutine. Attempts to manage the whole as if it were of one single type willcreate obvious problems. Process improvement and management should beadapted to the type of process. The question of the downside of quality —defects and errors — takes on a different meaning depending on whether theseare related to the variation or the variety of processes.

Lillrank: Standard, Routine and Nonroutine Processes 225

Standard Routine Nonroutine

Downside Defect: a critical Error: a faulty classification Failure: situation is not performance variable is of input leads to selection interpreted properly and outside tolerance limits of wrong routine targets are not achieved

Upside Conformance to Requisite variety Task accomplishmentspecifications

Control tools Specifications, manuals, Guidelines, repertoires, Shared values, competences, automation checklists resources

Learning Single-loop adjustment, More and sharper categories, Double-loop learning, better reduction of variation fewer errors in categorization interpretative schemes

Table 2 Implications forManaging Standard,Routine andNonroutineprocesses

Control and Learning

Standard processes represent the highest level of control, predictability andeconomic effectiveness. Since input is known and output predetermined,specialized algorithms can be developed and resources restricted to those neededfor the tasks. A standard process can take advantage of high asset specificity,whereas routines and nonroutines always require some degree of slack. Afrequently repeated standard process can be subject to hypothetic-deductivereasoning, experimentation and before–after comparisons. Increasingly sophis-ticated algorithms can be created. Standardization often draws on natural sciencetheories and engineering techniques.

Although standardization has been successful in repetitive processes under controlled conditions, it has less relevance in managing processes thatare inherently vague. Many routine processes in service industries, such asteaching and nursing, operate with a limited number of pigeonholes. Eventhough the categories of acceptable input can be restricted (a surgery clinicaccepts only patients with surgically treatable problems), the AAA sequencecannot be explicated using binary logic. Routine processes necessarily involvejudgement based on fuzzy logic or tacit knowledge, and require the capabilityfor two or more alternative lines of action.

Nonroutine processes cannot be closed by restricting input variety to a fewcategories. Exceptions, such as changed conditions or new customer requests,are often valid concerns and may not be ignored. Process manuals cannotcover all contingencies. Even if it were technically possible, the sheer size ofthe manual would render it useless. Only in some high-risk environments,such as nuclear power plants or jet engine maintenance, would comprehensivemanuals be justified. Even there, as Perrow (1999) has illustrated, theapproach has its limits.

Nevertheless, processes such as emergency healthcare and fire rescueservices that are basically nonroutine may include several sub-processes thatcan be routinized or even standardized. In such cases, a successful line ofimprovement has been to seek out and routinize everything possible and drillfrequently to make routines as close to standards as possible. This will freeresources for the nonroutine command function (Roberts and Bea 2001). Eventhough a nonroutine system may be described as a swamp (Juran 1992) or a mess (Ackoff 1999), it may here and there be punctuated by islands ofroutines and rocks of standard operating procedures.

The difference between standards, routines and nonroutines is relative.They form a continuum, on which the difference between each type is therelation between input conditions and the capabilities of the AAA sequence.Thus processes can change from one type to another through experience,experimentation and learning. The orange juice example described aboveprogressed from simple standard to sophisticated routine to adaptive non-routine, finally regressing into chaos. It is also possible to start from the otherend. What appears as utter chaos on the first day in a job will within a weekstabilize to nonroutine, which a year later feels like mind-numbing routine.Benghozi (1990) has described how introducing various routines stabilized

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a new product development process in a telecommunications company. Aslong as innovative products were considered rare and their development anunusual event, development processes were run ad hoc. As the businessenvironment started to require the regular launching of innovative products,several sub-processes had to be repeated with increasing frequency and theneed for routinization became apparent. As situations repeatedly appear,patterns and similarities emerge and difficult ad hoc interpretations can beturned into classifications based on experience and documentation.

A routine process can, by definition, handle a large set of input variety but not exceptions from predefined categories. Feldman (2000) has asked how there can be such a thing as a routine that changes. Some assessmentcategories and repertoires of response may be employed more frequently thanothers and therefore allow more accumulation of experience and learning.Finer distinctions between input states can be detected and more appropriatelytuned algorithms explored. Infrequently activated categories may be forgottenand turn into exceptions. Thereby, learning within a routine process can beseen as the emergence of more finely grained assessment categories and thepruning of irrelevant or infrequent ones. The reengineering movement(Hammer and Champy 1993; Champy 1995; Hammer and Stanton 1995) has emphasized this kind of learning. A great deal of trouble in industrialprocesses is interpreted as arising from situations in which exceptions areindiscriminately handled by the same process that deals with standardproducts. Exceptions, such as additional features custom engineered to meetparticular customer requests, create nonroutine situations inside processesthat are not designed to handle them. The exceptions do not receive appro-priate attention while they simultaneously keep disturbing the standardprocess. Separating various types of processes and equipping them withappropriate resources is one of the main principles of reengineering.

In summary, if a process is identified as a standard, its further improvementshould focus on explicating the process as far as possible and reducingvariation in output that may come from variation in input, resources or theAAA sequence. If a process is identified as a routine, emphasis should be on developing an appropriate and comprehensive set of pigeonholes andclassification criteria. For nonroutine processes, the essential task is to developan awareness of the limits of knowledge and to improve capabilities forsearch, feedback and double-loop learning. Well-managed organizations witha lot of nonroutine processes are called ‘high-reliability organizations’(Perrow 1999) or ‘mindful organizations’ (Weick and Sutcliffe 2001). Theytypically are preoccupied with possible failure, keep the interpretative assess-ment phases open and are reluctant to simplify interpretations, that is, theyrefuse to routinize things that are not routinizable.

Defects and Errors

An essential difference between the three process types concerns their quality criteria. Quality can be defined through either its upside, fulfilling orexceeding requirements, or its downside, the losses and inconveniences

Lillrank: Standard, Routine and Nonroutine Processes 227

caused because things were not done correctly. Variation and variety playdifferent roles in producing unacceptable output — defects, errors andfailures.

In standard processes, a defect is defined as unacceptable variation ofoutput in relation to targets and tolerances. In routines, the focal issue isselection among variety. Things may have been done right but they were notthe right things to do. Such variety-related problems may be labelled errorsor mismatches (Argyris and Shön 1978). Finally, a nonroutine situation maybe confusing to the extent that no interpretation is reached and thereforetargets are not achieved at all. This distinction is crucial in determiningproduct liability. In healthcare, for example, a medication regime may be setup for a patient but the nursing staff do not follow the instructions for properdosage, thereby producing a measurable defect. An error may happen whenan inexperienced surgeon looks at a series of X-rays and classifies a tumouras malignant when in fact it is not, sending the hapless patient into aninappropriate pigeonhole. A failure occurs when a weak signal, such as mildstomach ache, is not interpreted as worthy of attention, a further search forclues is abandoned, and consequently appendicitis is not discovered until itevolves into a life-threatening abdominal infection.

Defects in standard processes are analysed using the methods of statisticalprocess control. The first task is to determine whether they occurred becauseof ‘specific’ or ‘common’ causes (Shewhart 1986; Gitlow 2001). A specificcause is external to the process. It may be a freak input that the assessmentprocedure was not able to detect and reject, or some other external disturbanceaffecting the resources of a process. It can typically be identified and elimi-nated or prevented from causing harm again by tightening assessment andclosing the process from its environment. A common cause, on the other hand,is internal to the process and associated with its resources and algorithms asthey are in their normal state. It is typically random but predictable.

The primary function of statistical process control is to identify commonand specific causes, since they require different types of corrective action.Our standard orange juice process produces a batch of output with adisturbingly high amount of pulp. A customer complains and we promise toinvestigate. Assume that the true cause was a specific batch of bad input. Theright thing to do is to look over the assessment criteria and procedures andclose the system from further incidents. Assume, on the other hand, that thetrue cause was internal. Our second-hand machinery simply had one of itsbad days and produced a rare but predictable statistical outlier. We need todecide whether we try to live with it by tightening output control and/ordevising a warranty system to appease angry customers, or we take the troubleand expense to improve the process permanently.

For routines and nonroutines three decision rules can be developed. First,variation-related defects must be separated from variety-related errors. Eventhough the main concern for our poor tumour patient is the misguideddiagnosis, the ensuing process may still be producing defects. In routineprocesses both defects and errors may be present. Thus a simple four-classanalysis may be developed: right things done right, right things done wrong,

228 Organization Studies 24(2)

wrong things done right, and wrong things done wrong. Second, it should bedetermined in which part of the AAA sequence problems originate. Assess-ment problems tend to be related to perception and experience. Algorithmproblems may be related to faulty or insufficient theories or models. Actionproblems may arise from poor resources or lack of motivation. Third, it must be determined whether the situation was routine or nonroutine. Did theprocess have sufficient pigeonholes for dealing with the situation or shouldnew ones have been created? Nonroutine input conditions should not bemistaken for routine, and vice versa. If a nonroutine condition is wronglytreated as routine, the situation is akin to rigid bureaucracy. Input is squeezedinto predefined categories and, thereby, not only are variety and associatedutility lost but action may turn out to be utterly improper. On the other hand,if a routine condition is thought to be nonroutine, a lot of unnecessary fuss iscreated when additional input is sought and iterative learning loops are runto no avail. Using the language of Brown and Eisenhardt (1998), these twoerror types can be called the bureaucracy trap and the chaos trap, respectively.The former reduces a complex situation to pre-programmed categories,whereas the latter lets all kinds of irrelevant input disturb a process thatessentially is rather simple.

Further Research

The classification of processes into standard, routine and nonroutine is aconceptual construct. Its contribution to organization theory is to separateroutines-as-mindless-repetition from routines-as-effortful-accomplishment,and make clear the difference between routines and nonroutines. It points to the difference between variation and variety and thereby delineates theapplicability of standardization and statistical tools for process improvement.The difference between standard and routine processes explicates the observedsplit between industry-based QM and service marketing approaches (Silvestro1998), as well as the distinction between quality as control and quality aslearning (Sitkin et al. 1994). Further, using the assessment–algorithm–actionscheme, the various process types and their associated problems can beanalysed and linked to established improvement methodologies.

A conceptual construct cannot be validated in terms of being true or nottrue, but it can be validated in terms of whether or not it is useful (Eisenhardt1989). Such a validation could be conceived as follows. Take an environmentwith several different types of tasks, for example an outpatient clinic.Analysing the various tasks performed, it might be possible to identify severalthat have the characteristics of standardized processes, such as performinglaboratory tests or entering appointments in a computerized schedulingsystem. For these, detailed instructions could be created and processperformance evaluated in relation to fixed targets. Tasks such as interviewingpatients and selecting treatment schemes for known diagnoses could belabelled as routines and managed accordingly. Nonroutines, such as makingdiagnoses in non-obvious cases or negotiating complex treatment regimeswith external parties, could be allowed the discretion of nonroutine expert

Lillrank: Standard, Routine and Nonroutine Processes 229

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The definition of standard, routine and nonroutine processes opens up someinteresting questions for further research. The emerging knowledge-basedtheory sees the firm as a repository of knowledge that resides in its routines(Nonaka and Teece 2001). However, as Spender (1996: 48) has pointed out,it is logically impossible to construct meaningful statements about knowledgeso long as we have only a homogeneous concept of knowledge unrelated toanything else. There must be a correspondence between knowledge andreality, or knowledge and something else. A further elaboration of this viewwould require analysis of what kind of knowledge is involved in assessment,algorithms and actions. Could it be said that assessment involves ‘knowwhat’, which may take a binary, fuzzy or interpretative logic depending onprocess type? In a similar vein, would algorithms involve ‘know how’, whichmay vary from deterministic to stochastic to enabling, depending on theprocess context? Finally, could action be associated with the capability, willor skill to act upon what is known?

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Paul Lillrank has been Professor of Quality Management at Helsinki University ofTechnology since 1994. He is also Head of the Department of Industrial Engineeringand Management. He received his PhD in Social and Political Sciences from HelsinkiUniversity in 1988, after studying Japanese language, economics and managementin Japan, 1982–1988, as a postgraduate student at Sophia University and as aresearcher at the Science University of Tokyo. He worked as a business analyst andconsultant with the Boston Consulting Group in Tokyo and Stockholm. From 1992to 1994 he was Affiliated Professor at the European Institute of Japanese Studies atthe Stockholm School of Economics. He has published in the areas of qualitymanagement, Japanese economy and management, technology transfer, corporateculture, and the impact of information technology. He has served as adviser,consultant and educator for several major companies, including Nokia, Finnair, ABB,TietoEnator, and Kesko.

Address: PL 9500, FIN-02015 TKK, Finland.E-mail: paul.lillrank@hut.fi

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Paul Lillrank

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