Energy and manufacturing;National accounts and business cycles
osi, Index of orders in manufacturing, new orders, order reserve, domestic market, export marketBusiness cycles , Manufacturing, mining and quarrying , National accounts and business cycles, Energy and manufacturing

Index of orders in manufacturing


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Key figures

9 %

reduction in total stocks compared with the same period in previous year

Index of orders. 2005=100
Change in per centIndex of value
2nd quarter 2017 / 1st quarter 20172nd quarter 2017 / 2nd quarter 20162nd quarter 2017
New orders received:
Manufacturing working on new orders-6.75.7107.3
Domestic market-
Export market0.31.8118.7
Chemical and pharmaceutical products4.63.2163.8
Basic metals-5.96.6117.2
Machinery and equipment13.139.0114.7
Ships, boats and oil platforms-
Stock of orders:
Manufacturing working on new orders-2.9-9.0138.2
Domestic market-0.71.8122.3
Export market-4.7-16.5155.3
Chemical and pharmaceutical products2.56.5114.6
Basic metals-10.80.3119.8
Machinery and equipment-0.3-26.0190.7
Ships, boats and oil platforms0.2-0.2124.7

See more tables on this subject

Table 1 
Index of orders. New orders received, by division and market. Unadjusted series. 2005=100

Index of orders. New orders received, by division and market. Unadjusted series. 2005=1001
Indices of valueChange in per cent
2nd quarter 20163rd quarter 20164th quarter 20161st quarter 20172nd quarter 20172nd quarter 2017 / 2nd quarter 20162nd quarter 2017 / 1st quarter 2017
1The levels of aggregation refers to the Standard Industrial Classification (SIC2007). See NOS D383 for further details.
Manufacturing working on orders101.5101.1107.0115.0107.35.7-6.7
Domestic market86.193.390.7111.595.711.1-14.2
Export market116.6108.8123.2118.4118.71.80.3
Textiles and wearing apparel174.6159.5178.6177.8174.80.1-1.7
Domestic market207.8181.8198.5181.4176.6-15.0-2.6
Export market83.598.2123.6167.2169.0102.51.1
Paper and paper products59.258.464.063.766.412.24.2
Domestic market49.145.354.757.250.52.7-11.7
Export marked62.863.267.466.172.315.09.4
Chemical and pharmaceutical products158.7144.1144.7156.6163.83.24.6
Domestic marked148.0138.7143.0166.7155.75.2-6.6
Export marked162.5146.1145.4153.0166.62.58.9
Basic chemicals133.1116.0114.1131.3132.6-0.41.0
Domestic marked147.9142.4143.4179.5149.10.8-16.9
Export marked129.0108.8106.2118.2128.0-0.88.3
Basic metals109.994.0156.2124.6117.26.6-5.9
Domestic marked79.073.675.794.086.910.0-7.6
Export marked113.796.6166.0128.3120.96.4-5.8
Non-ferrous metals112.797.1173.8125.7111.8-0.8-11.1
Domestic marked74.284.572.189.986.216.2-4.1
Export marked116.098.1182.4128.7113.9-1.8-11.5
Fabricated metal products129.2120.1127.4140.4121.0-6.3-13.8
Domestic marked120.1102.8126.2133.3116.6-2.9-12.5
Export marked161.6181.9131.9165.3136.8-15.4-17.2
Computer and electrical equipment162.3109.6124.9123.2115.4-28.9-6.3
Domestic marked119.888.5100.2114.4103.1-13.9-9.9
Export marked199.4127.9146.3130.9126.0-36.8-3.7
Machinery and equipment82.572.172.9101.4114.739.013.1
Domestic marked81.173.985.289.7115.141.928.3
Export marked83.570.763.4110.4114.336.93.5
Ships, boats and oil platforms37.189.551.579.553.143.2-33.2
Domestic marked33.675.048.882.652.054.8-37.0
Export marked54.2161.065.063.758.47.7-8.3
Transport equipment n.e.c136.1121.7158.0150.7144.86.4-3.9
Domestic marked105.586.1111.9100.397.4-7.6-2.9
Export marked159.5148.9193.2189.3181.013.5-4.4
Repair, installation of machinery107.3105.3113.4125.0119.111.0-4.7
Domestic marked145.0140.5141.3174.2154.26.3-11.5
Export marked43.545.966.241.959.837.642.7

Table 2 
Index of orders. Stock of orders, by division and market. Unadjusted series. 2005=100

Index of orders. Stock of orders, by division and market. Unadjusted series. 2005=1001
Indices of valueChange in per cent
2nd quarter 20163rd quarter 20164th quarter 20161st quarter 20172nd quarter 20172nd quarter 2017 / 2nd quarter 20162nd quarter 2017 / 1st quarter 2017
1The levels of aggregation refers to the Standard Industrial Classification (SIC2007). See NOS D383 for further details.
Manufacturing working on orders151.9146.4141.3142.4138.2-9.0-2.9
Domestic market120.2122.5115.1123.2122.31.8-0.7
Export market186.0172.1169.5162.9155.3-16.5-4.7
Textiles and wearing apparel302.4245.7236.4243.8272.3-10.011.7
Domestic market365.4270.1251.2240.9267.0-26.910.8
Export market145.2183.8198.4249.6283.695.213.6
Paper and paper products50.249.153.960.267.634.912.3
Domestic market25.521.118.924.
Export market57.857.764.771.279.938.412.2
Chemical and pharmaceutical products107.6101.3103.1111.8114.66.52.5
Domestic market84.375.699.6104.476.4-9.5-26.8
Export market110.2104.2103.4112.6118.97.95.6
Basic chemicals60.456.758.571.069.915.7-1.5
Domestic market53.739.746.258.742.3-21.3-27.9
Export market61.258.759.972.473.119.41.0
Basic metals119.4109.0137.5134.3119.80.3-10.8
Domestic market28.733.435.135.032.513.1-7.1
Export market136.7123.3156.9153.2136.4-0.2-11.0
Non-ferrous metals165.7151.1191.4185.8164.3-0.8-11.6
Domestic market21.327.126.925.022.76.8-9.2
Export market197.3178.3227.5221.0195.4-1.0-11.6
Fabricated metal products217.3205.1190.2188.4182.6-16.0-3.1
Domestic market126.0114.9113.2125.4113.5-9.9-9.5
Export market425.2410.3365.4331.8339.8-20.12.4
Computer and electrical equipment246.6241.5241.5247.5230.8-6.4-6.7
Domestic market227.0227.3219.6230.2214.4-5.6-6.9
Export market261.4252.3258.1260.6243.3-6.9-6.6
Machinery and equipment257.5215.3191.5191.3190.7-26.0-0.3
Domestic market175.1156.2141.6158.4175.20.010.6
Export market293.7241.3213.4205.8197.5-32.8-4.0
Ships, boats and oil platforms124.9135.7123.4124.5124.7-0.20.2
Domestic market103.7113.8104.1111.3113.49.31.9
Export market201.4215.1192.9172.1165.3-17.9-4.0
Transport equipment n.e.c88.593.391.793.990.32.0-3.8
Domestic market48.749.148.249.845.5-6.6-8.6
Export market99.7105.6103.8106.2102.93.2-3.1
Repair, installation of machinery103.598.395.697.491.5-11.6-6.1
Domestic market141.3140.7136.8143.8136.9-3.1-4.8
Export market45.433.032.426.221.7-52.2-17.2

About the statistics

The Index of orders in manufacturing indicates the development in new orders and stocks of orders for Norwegian industry. This includes new orders received in the reporting quarter and the extent to which the total stocks are completed during the reporting quarter.


Definitions of the main concepts and variables

Local unit (establishment) : An enterprise or part of an enterprise that is located in one particular place and can be identified geographically.

Enterprise : The smallest combination of legal units that is an organisational unit producing goods or services and that benefits from a certain degree of autonomy in decision making.

Altinn : The Reporting portal for delivery of figures electronically to Statistics Norway. 

NACE : Standard for industrial classification used by EUROSTAT. It is based on the UN's international standard for industrial classification, ISIC Rev. 3.

Standard Industrial Classification (SIC) The standard is primarily a statistical standard. It forms the basis for classifying units according to main activity in the Central Register of Establishments and Enterprises (CRE). The use of common standards is essential in enabling the comparison and analysis of statistical data at national/international level and over time. The standard is identical to NACE. However, a fifth figure (subclass) is added to the standard to create a national Norwegian level.

Imputation : An estimated value for a missing observation.

Processing level : The most detailed level of the statistics.

Seasonal adjusted figures : Time series for which calendar and seasonal effects have been removed. X12-ARIMA is used to calculate these figures.

Unadjusted figures : Raw data figures with primary information from the respondent.

Order : Order refers to a customer's request to the producer for future deliveries.

Elementary index : A formula where the estimated value of a variable is divided by the average annual value for the same variable for a previous (base) year - e.g. 2005..

Domestic : This refers to all orders from customers in Norway. The export market includes all other customers.

New orders received : The value of new orders received during the period excluding taxes. Orders from group enterprises in the same industry are not included. Packaging and transportation costs are included if they are included on the invoice.

Executed orders : The value of orders and sales of goods and services during the period - either by production or by sale from stock.

Stock of orders : The value of all orders in stock not delivered at the end of the period. The stock of orders is divided into the domestic market and the export market.

Standard classifications

The survey is classified according to the Standard Industrial Classification 2007 (SIC2007). This is a Norwegian adaptation of NACE Rev. 2. SIC2007 forms the basis for coding units according to principal activity in the Central Register of Establishments and Enterprises. The use of common standards is essential in enabling comparison and analysis of statistical data at national/international level and over time.

Administrative information

Name and topic

Name: Index of orders in manufacturing
Topic: Energy and manufacturing

Next release

Responsible division

Division for Manufacturing and R&D statistics

Regional level

National level only.

Frequency and timeliness


International reporting

Not relevant


Non-revised and revised micro data are stored in accordance with Statistics Norway's guidelines for storing of computer files.


Background and purpose

Statistics on new orders play an important role in the system for short-term statistics that monitor the economy. The Statistics on new orders are a leading indicator for the changes in production in the short and medium term.

The survey measures in current prices the value of new orders received during the period and the stock of orders at the end of the period.

The statistics were first published in 1975. In 1996, a major revision was conducted in which the number of units was increased from around 380 to around 750 units.

As from Q1 2009, all results will refer to SIC2007 (See explanation under Definitions). The historical series have been recalculated according to this version of SIC, and results dating back to 1995 are available in the Statbank database. Historical series based on SIC2002 are still available, but they will not be updated. The survey is wholly financed by government appropriations.

Users and applications

The results are used in internal controls in other economic trend surveys. Other users include financial and analytical institutions and, to some extent, public institutions (the Ministry of Finance and Norges Bank among others).

Equal treatment of users

No external users have access to the statistics and analyses before they are published and accessible simultaneously for all users on ssb.no at 08 am. Prior to this, a minimum of three months' advance notice is given inthe Statistics Release Calendar.

Coherence with other statistics

The order statistics are a leading indicator of future production and turnover in the manufacturing industry and one of several indicators that monitor the performance of the economy. The correlation with the Index of production and Statistics on turnover is utilised for control purposes.

Legal authority

The Statistics Act of 16 June 1989, §§2-1, 2-2 and 2-3.

EEA reference

Not relevant



The population covers all establishments except sole proprietors in the industries textiles and wearing apparel (13-14), paper and paper products (17), chemical and pharmaceutical products (20-21), basic metals (24), fabricated metal products (25), computer and electrical equipment (26-27), machinery and equipment (28), ships boats and oil platforms (301), transport equipment n.e.c (29,30(-301), repair, installation of machinery (33), see Standard Industrial Classification 2007 (SIC2007). The population is defined by the Central Register of Establishments and Enterprises, and establishment is the observation unit in the survey. (See Definitions for a complete definition of establishment and enterprise.)

Data sources and sampling

The survey uses investment data collected by questionnaires from the units included in the sample, in addition to information from the Central Register of Establishments and Enterprises.

The sample includes about 750 establishments. The sample includes all establishments with 100 employees or more, or establishments with a turnover of at least 10 per cent of the publishing level. The remaining units are drawn based on stratification and optimal allocation proportional to the size of the unit measured by the number of employees. The sample does not include establishments with ten employees or fewer.

Collection of data, editing and estimations

The survey is based on data collected by questionnaire in Altinn. More than 99 per cent of the respondents prefer to use the Internet. Contact are informed that a new period of the survey is avaliable in Altinn through a text Message.

The establishment's local office normally fills in the questionnaire, but in some cases the head office reports data for several units. Establishments that fail to report in time receive a reminder approximately 5 days after the deadline. A new deadline of seven to nine days is given, depending of which quarter, and a compulsory fine if they do not return the questionnaire within fourteen days after the new deadline.

The data are automatically checked for duplicates and errors in totals. The figures are edited on the basis of a revision programme (for example errors regarding reporting in NOK million or large deviations from previous reported figures). Where there are considerable deviations, the establishment is contacted. In case of extreme deviations, further revisions are carried out.

The sample data are inflated to population level using a ratio estimator. The ratio estimator uses turnover figures from the Manufacturing statistics, structural data as auxiliary variables.

Time series sometimes contain significant seasonal variation that makes it difficult to interpret the results from one period to another. In the survey, seasonally adjusted figures and trend figures are calculated with X12-ARIMA for the manufacturing industry.

Seasonal adjustment

For seasonal adjustment, more details are available in About seasonal adjustment


Confidential micro data : According to § 2-4 of the Statistics Act , collected data are subject to secrecy and are to be kept or destroyed in a secure manner. Any use of the data must be in accordance with the rules set out by the Data Inspectorate.

Time series that are not to be published : The publication of data is subject to the provisions of § 2-6 of the Statistics Act . The main rule is that data should not be published if they can be traced back to the respondent, i.e. figures for which less than three respondents make up the foundation for a cell in the table, figures where one respondent represents more than 90 per cent of the total value or figures where two respondents represent at least 95 per cent of the total value.

Unpublished data : Revised data that are not published are subject to secrecy. This implies that they are unavailable to users without distinct approval. Such agreements only apply to internal users.

Comparability over time and space

As from January 2009, SIC2002 is replaced by SIC 2007. Users must ensure that they use results based on the same version of SIC when making comparisons over time.Historical series based on SIC2002 remain available in the Statbank database for the period 1988 to 2008. To get an overview of possible changes in industrial groupings, see the Correspondence Table SN2007, SN2002 .

Accuracy and reliability

Sources of error and uncertainty

Measurement errors are caused by the questionnaire or the respondents internal system for obtaining the data. Examples are ambiguous questions, misunderstood questions or erroneous data from the respondents. In the Statistics on new orders, errors in reported figures may originate from misunderstandings of the concept of orders or the definition of the main variables used in the survey. Unambiguous guidelines and definitions are therefore emphasised. The use of incorrect units of measurement may occur since the figures should be reported in NOK million. This type of error will become evident during the revision of the data. The introduction of Altinn has contributed to reduce such errors, as data from electronic questionnaires are loaded directly into the system.

Errors of non-response refer to errors that either occur due to missing questionnaires or blank boxes in the questionnaire.

The response rate after the deadline has expired is around 95 per cent. Critical units, i.e. units that have a considerable impact on the results on a detailed level aggregation (2-digit NACE), are contacted by mail and telephone. Calculations of the effect of missing units have been carried out, but no skewness has been encountered. Missing questionnaires are mainly imputed automatically, based on previous reported figures (cold-deck method). Large units are imputed automatically using rates of change at processing level and the reported figures from the enterprise in the previous quarter (type of hot-deck). An imputed value is not imputed in the following quarter.

Boxes that are left blank (partial non-response) are imputed manually.

Sampling errors refer to uncertainty that occur in sample surveys as opposed to full counts. The sample variance equals the expected deviation between a sample survey and a full count. In the Statistics on new orders the sample represents 15 per cent of the population that covers about 80 per cent of the turnover in the population. In order to ensure a high degree of relevance at the lowest cost possible, great effort is put into including all large units in the population in the sample.

Calculations of the sampling errors for the survey have been conducted. This is limited to an interest variable: "total new orders received". In the survey the variation coefficient according to SIC2007 is calculated to be:

  • 3rd quarter of 2014: 0,9 per cent

The given percentages represent the total.

Establishments that close down may be a source of skewness if the proportion of closing downs in the sample deviates from the population. The Statistics on new orders are mainly based on a fixed sample (panel). Periodic updates of the sample ensure that the sample is in accordance with the population.

Coverage errors refer to errors in registers that define the population, in this case the Central Register of Establishments and Enterprises. As a result of such errors, units may be incorrectly included in or excluded from the population. Other problems are related to delays in the update of the registers and units that are incorrectly classified. From experience a limited share of the population units are incorrectly classified. This is usually due to misleading or insufficient information at a certain time. Calculations of the size and significance of such errors have not been carried out. However, such errors are not considered to be greater than for other quantitative short-term statistics.

Modelling errors are mainly related to problems with seasonal adjustment of time series. Such problems are caused by deviation from the conditions that form the basis for the model used. Typical problems are related to movable public holidays such as Christmas and Easter. However, such problems are considered greater for surveys published on a monthly basis. X12-ARIMA generates a number of indicators that are used to evaluate the quality of the seasonal adjustment. These indicators have identified a stable seasonal pattern.


Data is revised two quarters back in time as a routine. This is due to changes in previous data and late deliveries.

About seasonal adjustment

General information on seasonal adjustment

Monthly and quarterly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.

For more information on seasonal adjustment: metadata on methods: seasonal adjustment

Why seasonally adjust these statistics?

The main of seasonal adjustment is to remove changes that are due to seasonal or calendar influences to produce a clearer picture of the underlying behaviour.

Seasonally adjusted series

The overall index and groups according to the structure of SIC 2007 are published in the new orders received in Norway ( see Table 1 ).


Pre-treatment routines/schemes

Pre-treatment is an adjustment for variations caused by calendar effects and outliers.

  • Running an automatic pre-treatment of the raw data based on standard options in the seasonal adjustment tools.

Calendar adjustment

Calendar adjustment involves adjusting for the effects of working days/trading days and for moving holidays. Working days/trading days are adjustment for both the number of working days/trading days and for that the composition of days can vary from one month to another.

  • To perform calendar adjustments on all series showing significant and plausible calendar effects within a statistically robust approach, such as regression or RegARIMA (a regression model with an ARIMA structure for the residuals). The regression variables for the calendar adjustment are adapted to reflect the working days, public holidays and so forth specific to Norway.

Methods for trading/working day adjustment

  • RegARIMA correction – in this case, the effect of trading days is estimated in a RegArima framework. The effect of trading days can be estimated by using a correction for the length of the month or leap year, regressing the series on the number of working days, etc. In this case, the residuals will have an ARIMA structure.

Comments : A few series is not adjusted for the number of working days.

Correction for moving holidays

  • Automatic correction. If performed by X-12-ARIMA, automatic correction of raw data will be based on US holidays.

Comments : Some series is not adjusted for moving holdidays.

National and EU/euro area calendars

  • Use of default calendars. The default in X-12-ARIMA is the US calendar.

Treatment of outliers

Outliers, or extreme values, are abnormal values of the series.

  • No preliminary treatment of outliers.

Model selection

Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.

  • Automatic model selection by established routines in the seasonal adjustment tool.

Decomposition scheme

The decomposition scheme specifies how the various components – basically trend-cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.

  • Manual decomposition scheme selection after graphical inspection of the series.

Comments : Automatic decomposition is used for some series.

Seasonal adjustment

Choice of seasonal adjustment approach

  • X-12-ARIMA

Consistency between raw and seasonally adjusted data

In some series, consistency between raw and seasonally adjusted series is imposed.

  • Do not apply any constraint.

Consistency between aggregate/definition of seasonally adjusted data

In some series, consistency between seasonally adjusted totals and the aggregate is imposed .For some series there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.

  • Do not apply any constraint.

Consistency between aggregate/definition of seasonally adjusted data

In some series, consistency between seasonally adjusted totals and the aggregate is imposed .For some series there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.

  • Do not apply any constraint.

Direct versus indirect approach

Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.

  • Direct approach where the raw data are aggregated and the aggregates and components are then directly seasonally adjusted using the same approach and software. Any discrepancies across the aggregation structure are not removed.

Horizon for estimating the model and the correction factors

When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.

  • Only part of the time series is used to estimate the correction factors and the model.

Audit procedures

General revision policy

Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.

  • Seasonally adjusted data are revised in accordance with a well-defined and publicly available revision policy and release calendar.

Concurrent versus current adjustment

  • The model, filters, outliers and regression parameters are re-identified and re-estimated continuously as new or revised data become available.

Comments : The trend filter stays permanent

Horizon for published revisions

  • The period of revisions is defined according to the characteristic features of the series based on information from the seasonal adjustment tool.

Quality of seasonal adjustment

Evaluation of seasonally adjustment data

  • Evaluation of quality based only on graphical inspection and descriptive statistics.

Quality measures for seasonal adjustment

  • For most of the series, a selected set of diagnostics and graphical facilities for bulk treatment of data is used.

Table of quality measurement for this statistics

For more information on the quality indicator in the table see: metadata on methods: seasonal adjustment

Special cases

Seasonal adjustment of short time series

  • All series are sufficiently long to perform an optimal seasonal adjustment.

Treatment of problematic series

  • All problematic series are treated in a special way.

Comments : Some series is not always corrected for either Easter and working days.

Posting procedures

Data availability

  • Raw and trend adjusted data are available.

Press releases

  • In addition to raw data, at least one of the following series is released: pre-treated, seasonally adjusted, seasonally plus working day adjusted, trend-cycle series.

Relevant documentation