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An S-curve is an essential project-management tool. However, it is difficult to adjust S-curve to deal with a force majeure event. The present study develops four valuable adjustment approaches, designed to achieve a compromise between the views of the client and contractor. These can be used to control projects after a force majeure event.

The present study develops four adjustment approaches, which can be used to achieve a compromise between the views of the client and those of the contractor when controlling projects after a force majeure. To determine the S-curves during a force majeure event, two approaches can be selected: BCWS (budgeted cost of scheduled work)-base approach, or BCWP (budgeted cost of work performed)-base approach. To determine the rest of S-curves after a force majeure event, two approaches can be considered: maintaining the original curve of the remaining BCWS, or allocating the original curve of the remaining BCWS. Based on the validation of three empirical cases, drawn from a professional project-management website, this study confirms the feasibility of four proposed empirical approaches and a selection procedure for S-curve adjustment.

The S-curve-adjustment approaches presented here can be used to deal with cases that are ahead of, on and behind schedule. Using the proposed approaches and selection procedure, contractors can easily revise S-curves and control projects more effectively. To deal with a force majeure event, such as COVID-19, they are strongly advised to adopt the approaches labeled SA-A1 (to adjust the S-curve based on the extension ratio multiplied by the difference in progress during the force majeure) and SA-B1 (to maintain the original curve of the remaining BCWS) for the A/E and E/F curves, respectively.

The proposed approaches can be used in cases of continuous construction during force majeure events. If construction work is totally suspended during such an event, it will be necessary to fine-tune the proposed approaches.

Previous studies have used case-oriented or mathematical-simulation approaches to forecast S-curves. The present study proposes simple approaches that allow the client and contractor to adjust the S-curve easily after a force majeure event. These approaches can be used to adjust work and project-completion targets within an extended duration. Selecting the right S-curve adjustment approach can help to control the remainder of the project, reducing the possibility of delay claims.

Several cash flow forecasting models have been developed for use by the contractor at the tendering stage. The majority of these models have been based on the standard S‐curve. The accuracy of these models depends on the accuracy of the standard S‐curve used. Many researchers have attempted to develop standard S‐curves using past projects. These projects were primarily classified in terms of broad‐based categories (e.g. commercial buildings). The results of past research have demonstrated that cumulative value/cost curves vary significantly. In the present paper, a more specific type of building was selected (food retail stores). Historical data for 20 projects was collected and analysed for the feasibility of developing a more accurate standard S‐curve. The results demonstrate that an accurate standard S‐curve was not achieved even when projects were further classified into more detailed groups (i.e. different sizes of superstores).

In smaller projects with limited resources, schedule updates are often not performed. In these situations, traditional delay analysis methods cannot be used as they all require updated schedules. The objective of this study is to develop a model that performs delay analysis by using only an as-planned schedule and the expense records kept on site.

This study starts out by developing an approach that estimates activity duration ranges in a network schedule by using as-planned and as-built s-curves. Monte Carlo simulation is performed to generate candidate as-built schedules using these activity duration ranges. If necessary, the duration ranges are refined by a follow-up procedure that systematically relaxes the ranges and develops new as-built schedules. The candidate schedule that has the closest s-curve to the actual s-curve is considered to be the most realistic as-built schedule. Finally, the as-planned vs. as-built delay analysis method is performed to determine which activity(ies) caused project delay. This process is automated using Matlab. A test case is used to demonstrate that the proposed automated method can work well.

The automated process developed in this study has the capability to develop activity duration ranges, perform Monte Carlo simulation, generate a large number of candidate as-built schedules, build s-curves for each of the candidate schedules and identify the most realistic one that has an s-curve that is closest to the actual as-built s-curve. The test case confirmed that the proposed automated system works well as it resulted in an as-built schedule that has an s-curve that is identical to the actual as-built s-curve. To develop an as-built schedule using this method is a reasonable way to make a case in or out of a court of law.

Practitioners specifying activity ranges to perform Monte Carlo simulation can be characterized as subjective and perhaps arbitrary. To minimize the effects of this limitation, this study proposes a method that determines duration ranges by comparing as-built and as-planned cash-flows, and then by systematically modifying the search space. Another limitation is the assumption that the precedence logic in the as-planned network remains the same throughout construction. Since updated schedules are not available in the scenario considered in this study, and since in small projects the logic relationships are fairly stable over the short project duration, the assumption of a stable logic throughout construction may be reasonable, but this issue needs to be explored further in future research.

Delays are common in construction projects regardless of the size of the project. The critical path method (CPM) schedules of many smaller projects, especially in developing countries, are not updated during construction. In case updated schedules are not available, the method presented in this paper represents an automated, practical and easy-to-use tool that allows parties to a contract to perform delay analysis with only an as-planned schedule and the expense logs kept on site.

Since an as-built schedule cannot be built without updated schedules, and since the absence of an as-built schedule precludes the use of any delay analysis method that is acceptable in courts of law, using the method presented in this paper may very well be the only solution to the problem.

The paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations.

The proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory.

The proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance.

The novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.

This paper addresses the effect of different procurement methods (traditional, design and build and management contracting) on project cash flow. Historical data relating to the periodic cost of 150 construction projects were collected. Four criteria were identified to classify the projects: type of project, size, company and type of procurement. The effect of these criteria on the S‐curves was then examined using ANOVA. Results confirmed that differently procured projects with different sizes produce different patterns. The company and type of project did not significantly affect the shape of the S‐curve. Standard S‐curves were then modelled for the three aforementioned procurement routes. These curves were used to calculate the relevant cash flows using a computer based cash flow forecasting model. A series of simulation tests were conducted to evaluate the extent of variation in cash flow, given different contract conditions. Results revealed that, in some cases, the variation in procurement routes has a significant effect on contract cash flows.

Impact of currency depreciation or devaluation on the trade balance is not instantaneous. Indeed, because of adjustment lags favorable effects of depreciation is only realized in the future. This short‐run dynamics of the trade balance is summarized by the S‐Curve phenomenon. The purpose of this paper is to test this phenomenon by using commodity level data between China and Germany.

The methodology is based on cross‐correlation function.

Out of 62 industries studied, only 22 support the S‐Curve or enjoy improvement in their trade balance in the future. The list included small and large industries as well as durable and non‐durable goods.

No study in the literature has considered commodity level trade between China and Germany.

Both the Product Life Cycle (PLC) and a new version, the Product Evolutionary Cycle (PEC), are more than just descriptive marketing tools based on historical sales data. Rather, they represent dynamic instruments to enable an entrepreneur‐or coporate manager‐to estimate market development. The S‐curve, the growth part of the PLC and the PEC, makes it possible for an entrepreneur to time capital requirements, labor force recruitment, promotional efforts, distribution channels, target markets, and pricing. In the case of new products, research can be utilized to construct an S‐curve in advance of an innovation's introduction in the market‐place. Such information is invaluable to an entrepreneur, since it serves as a guide to future market development.

Cash flow forecasting is an indispensable tool for construction companies, and is essential for the survival of any contractor at all stages of the work. A simple and fast technique of forecasting cash flow accurately is required, considering the short time available and the associated cost. Seeks to examine this issue.

The paper argues that instead of producing an S‐curve that is based on historical projects combined (state‐of‐the‐art is based on classifying projects into groups and producing a standard curve for each group simply by fitting one curve into the historical data), here the attempt is to produce an individual S‐curve for an individual project. A sample of data from 50 projects was collected and 20 criteria were identified to classify these projects. Using the most influential criteria, a multiple linear regression model was created to forecast the programme of works and hence the S‐curves. A further six projects were used to validate and test the model.

The results of the model developed in this paper were compared with previous models and evaluated. It is concluded that the model produced more accurate results than existing value and cost models.

The paper proposes an alternative and novel approach to the development of standard value and cost commitment S‐curves. This approach is based on a multiple linear regression model of the programmes of works.

Since the introduction of the concepts of the J‐ and S‐curves, many researchers have tried to verify their validity empirically. This paper aims to review the related papers and to offer direction for future research.

This is a review paper. As such, no method is employed here. Rather, the methodologies used by others to test the J‐ and S‐curves are explained and reviewed.

No new findings are offered since this is a review paper.

The J‐ and S‐curves show whether currency depreciation worsens the trade balance first before improving it. Since the majority of studies are country‐specific, policymakers could benefit by learning whether currency depreciation will be effective in improving the trade balance.

This is a literature review paper and its originality is in terms of collecting the literature together and presenting it in one single paper.

First, this study assesses firms’ efficiency of transforming intellectual capital (IC) components into firm performance. Second, this study examines (1) cubic S-curve relationship between board independence and IC efficiency and (2) how firm size moderates the cubic S-curve relationship.

This study employs a stochastic nonparametric envelopment of data (StoNED) framework to estimate IC efficiency, which is derived from the estimation process of transforming structural, relational and human capitals into accounting- and market-based performance indicators. This study conducts regression analyses on 1,104 firm-year observations of Taiwanese semiconductor firms over the period of 2011–2018.

StoNED results suggest that sample firms' IC efficiency can be relatively improved by approximately 80%. Regression results indicate that a cubic S-curve relationship between board independence and IC efficiency exists, and firm size moderates the nonlinear effects.

Overall, this study highlights the importance of examining the nonlinear effect of board independence on IC efficiency from the perspective of agency theory, and the moderating effect from firm size, which may suggest availability of resources from the resource-based view of the firm.

This study contributes to the literature through the innovative application of an efficiency-based tool for evaluating IC efficiency. The cubic S-curve relationship between board independence and IC efficiency also points to the policy concerning the appropriate number of independent directors on board.