This Bulletin, which has been published as part of the Nordic Forest Work Study Council (NSR) project on forestry operations research methods, deals with methods for planning and analysis of time studies in the field of forestry operations. There are two main types of study: comparative studies and relationship studies. The chief problem in comparative studies is that of avoiding the influence of extraneous, disruptive factors on the results; in relationship studies, the chief problem is that of mastering the multiplicity of factors influencing forestry operations. The strategies for overcoming the main problem in comparative studies involve keeping the factors constant, replication, and measuring and standardization. Equivalent strategies for relationship studies involve splitting the operations into work elements, each of which is influenced only by a limited number of factors, and controlling the variation in the influential factors. A number of different models are available for analysis of comparative studies, although, unfortunately, there is no expedient technique for getting to grips in practice with the human factor or operator effect. In consequence, it is seldom possible to demonstrate differences between different machines and methods in a scientifically satisfactory way. There is a wealth of analysis techniques available for relationship studies. Some of the concepts dealt with include two-parameter functions, S-shaped functions, additive and multiplicative models, metric and coded variables, and dummy variables. The analysis technique used is often determined by whether the influencing factor is an expression of the extent of the work or of work-impeding circumstances. The customary technique for fitting a model to a set of data is the least squares method. There are two versions of this, depending on whether the model is linear or not. Many simple, rational models of relationships in the field of forestry operations are nonlinear, which means that they cannot be fitted using conventional regression programs. A number of methods for fitting such models by simple means are discussed. The analysis of study data often involves the major task of collating the primary data to make processsing possible. The work may involve the merging of different sets of data, refining the data, restructuring, recoding, transformation, and plausibility checks. Finally, a number of fields calling for continued research is identified, viz. - Compilation of a list of models for forestry operations. - Compilation of experience of the magnitude of the residual variation, to determine the number of replications for future studies. - Methods for normalizing productivity and using pulse-frequency recordings, etc. - Simple practical methods for fitting nonlinear models. - Package solutions for some conventional types of study.
356 (Die Arbeit beeinflussende Verhältnisse (Wetter, Gelände, Bestand, Holzeigenschaften usw.)) 305 (Arbeitsablauf und Leistung. Lohnberechnung auf der Grundlage von Leistungsmessungen (Zeit- und Leistungstafeln, Arbeitsbewertung)) 308 (Arbeitsplanung (Organisation) [Vom Standpunkt der Forstverwaltung siehe 684])
