Literature DB >> 30229129

Dataset for material logistics on construction sites.

Patience F Tunji-Olayeni1, Adedeji O Afolabi1, Emmanuel E Eshofonie2, Beatrice A Ayim1.   

Abstract

Data in this article describes logistics management on construction sites in Abuja, Nigeria. Data was elicited from 55 construction professionals comprising of Architects, Builders, Civil Engineers, Project Managers and Quantity Surveyors. The Data set in this study consists of responses on: factors affecting material purchase on construction sites, factors affecting accuracy of material delivery, challenges encountered during material delivery, benefits of material delivery on construction sites and methods of forecasting material demand on construction sites. This article provides insight into logistics management on construction sites in Nigeria and it can be a useful guide for similar research in other contexts.

Entities:  

Keywords:  Construction industry; Logistics management; Material management

Year:  2018        PMID: 30229129      PMCID: PMC6140358          DOI: 10.1016/j.dib.2018.08.194

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications table Value of the data The data provides insight into the significant factors affecting material purchase on construction sites From the data, factors affecting accuracy of material delivery on construction sites can be obtained The data presents critical factors to be considered in choosing a material handling equipment on construction sites. From the data, the challenges associated with material logistics on construction sites are identified. The data in this article can be modified for use in other context.

Data

Data for this article was solicited from construction professionals in Abuja, Nigeria. Fig. 1 presents the mean values of the factors affecting material purchase on construction sites. The factors are: material quality (4.87), material price (4.71), volume of order (4.15), reputation of manufacturer (4.15), waiting time (3.85), competence of purchasing officer (3.76) and sales discount (3.53).
Fig. 1

Factors affecting material purchase.

Factors affecting material purchase. Fig. 2 highlights the mean of the factors affecting accuracy of material delivery on construction sites. These factors include: failure from supplier (3.67), order error (3.58), use of uncommon materials (3.51), altering work sequence (3.47) and payment delay (3.04).
Fig. 2

Factors affecting accuracy of material delivery.

Factors affecting accuracy of material delivery. Fig. 3 provides the analysis of factors affecting material handling equipment including health and safety considerations (4.93), material quantity (4.45), equipment specification (4.38), equipment availability (4.24), equipment speed (4.16), cost of equipment (4.150) and building form (3.55).
Fig. 3

Factors affecting choice of material handling equipment.

Factors affecting choice of material handling equipment. Table 1 shows the challenges associated with material logistics on construction sites. The challenges are: transportation (4.45), inadequate storage (4.18), delay in material delivery (4.13), supply of low quality material (4.07), poor coordination (4.02), inability to forecast activity period (3.91), inaccuracies in material delivery (3.67) and increase in waiting time (3.62).
Table 1

Challenges associated with material logistics on construction sites.

ChallengeNMinimumMaximumMean
Transportation55254.45
Inadequate storage on site55254.18
Delay in material and component delivery55154.13
Supply of low quality material55154.07
Poor coordination among material planning team55154.02
Inability to forecast activity period with accuracy55153.91
Inaccuracies in material delivery55153.67
Increase waiting time between activities55153.62
Challenges associated with material logistics on construction sites. In Fig. 4, the benefits of material logistics are provided and include: saves construction time (4.93), saves construction cost (4.75), improves customer satisfaction (4.60), timely delivery of materials (4.56), reduce storage space (4.55), reduce waiting time (4.24) and reduce multi handling (4.02).
Fig. 4

Benefits of material logistics on construction sites.

Benefits of material logistics on construction sites. Fig. 5, shows the common methods of forecasting material demand in construction sites, which are: work progress (78%), process flow chart (15%), experience (5%) and logistics software (2%).
Fig. 5

Method of forecasting material demand on construction sites.

Method of forecasting material demand on construction sites.

Experimental design, materials and methods

The data in this article was generated by means of a cross sectional survey of construction professionals in Abuja, Nigeria. Previous researchers [1], [2], [3], [4], [5], [6], [7], [8] used similar approach to obtain empirical data from respondents. The questionnaire was adapted from similar previous studies [9], [10], [11], [12], [13], [14] and modified. The questionnaire was divided into 6 sections. Section A was used to obtain questions about material purchase on construction sites. Section B covered questions on material handling. Section C had questions that focused on accuracy of material delivery. Section D included questions about the problems encountered in logistics. Section E had questions which probed for the benefits of logistics management. Sections A to E were based on a 5-point likert scale type question with 1= not important, 2 = slightly important, 3 = not sure, 4 = important, 5 = very important. Section F was used to obtain demographic information about the respondents. Seventy questionnaires were distributed to Architects, Builders, Civil Engineers, Project Managers and Quantity Surveyors on a simple random sampling basis. Out of the 70 questionnaires distributed, 55 were returned and found suitable for analysis. Data was analyzed by means of Statistical Package for Social Sciences (SPSS) Version 22.
Subject areaConstruction
More specific subject areaMaterial Logistics
Type of dataTables and Figures
How data was acquiredField Survey
Data formatRaw
Experimental factorsRandom Sampling
Experimental featuresDescriptive statistics
Data source locationAbuja, Nigeria
Data accessibilityData is included
  3 in total

1.  Data exploration of social client relationship management (CRM 2.0) adoption in the Nigerian construction business.

Authors:  Rapheal A Ojelabi; Adedeji O Afolabi; Opeyemi O Oyeyipo; Patience F Tunji-Olayeni; Bukola A Adewale
Journal:  Data Brief       Date:  2018-04-17

2.  Survey dataset on occupational hazards on construction sites.

Authors:  Patience F Tunji-Olayeni; Adedeji O Afolabi; Obiora I Okpalamoka
Journal:  Data Brief       Date:  2018-04-13

3.  Statistical exploration of dataset examining key indicators influencing housing and urban infrastructure investments in megacities.

Authors:  Adedeji O Afolabi; Rapheal A Ojelabi; Adewale Bukola; Adedotun Akinola; Adesola Afolabi
Journal:  Data Brief       Date:  2018-05-02
  3 in total

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