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(영문) 애널리틱스 빅데이터 건설업 이용사례와 전략 및 분석에 대해 기술한 리포트 참고자료입니다.

저작시기 : 2021년 1월

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Analytics Strategy Memo
Introduction
eTEC Engineering & Constructi
on, or eTEC in short, is a construction company whose main service is building industrial facilities such as petrochemical plants, power plants, and factories. The company's strength is the price competitiveness compared to the engineering quality it provides, and its ability to implement turn-key service which means the company provides everything for its client, from designing and procurement to commissioning and mechanical completion of a facility. All the client has to do is simply "turning the key" to operate the facility.
Analysis
Currently, eTEC's analytic ma
turity is at the phase of descriptive and diagnostic analytics. The company does not develop a prediction model. eTEC simply sees accumulated internal data and assumes the future. Often times, analytical tasks would be given to employees who did not receive proper analytics education or training. Moreover, the software and tools used in analytical work are outdated and do not have sufficient computing power. Therefore, implementation of cutting edge analytics framework and methods, and integration of analytics professionals within the organization would be highly desirable in order to develop much more precise and effective prediction models and make data-driven decisions. Although all of the company's business units collect analyzable data, the key business functions that heavily utilize data are project management, construction management, procurement, sales, and planning and coordination (this unit proposes the direction of the organization and recommendations to decision makers). Each of these units stores the data in its distinct ERP system and/or in Excel files. The typical use of analytics in these units are as follows.
Project Management
The project management team,
or PM team in short, involves in overall components of a project: the management of schedule and the overall budget for the project (construction, labor, operation, miscellaneous costs, etc), serving as a communication channel between the company and a client, monitoring the progress of the project, and resolving any legal issues. PM team's analytics mostly involves in planning schedule and budget of the project which is mostly descriptive; the team gathers information and data obtained from a new contract with a client which specifies the requested design and specification for the construction, the completion date of the construction, and the total $ amount. And the team sends the data to engineering, construction management, and procurement where most of data processing and analysis will take place. The problem is that every other team has different data storage forms and has authority to manipulate the data. Therefore, the data PM team sent to other teams can become very different from the original data later on.
Construction Management
The construction management t
eam, or CM team in short, handles the actual work of and overall schedule of the construction. With the information from PM team, CM team develops much more detailed schedule for each phase of the construction such as leveling, setting up scaffold, building in mechanical equipment, piping, installing distributed control system for the facility, and commissioning. However, the schedule always ends up being delayed or incorrect, because the scheduler does not perform analytics, but rather takes empirical approach, relying on his or her past observations and experience in other projects.
Procurement
The procurement team is in ch
arge of purchasing equipment and materials requested by engineers, and logistics and inspection of the items. The procurement team put much effort in cost reduction by utilizing its internally accumulated procurement data such as prices and technical specifications for each broken-down components of purchased products, and contractual terms and conditions. The team uses these data to figure out "How much the product was in the past" and "How much its price would be negotiable," and determine the target purchasing price. Along with its gut feeling, the team performs such a descriptive analytics to gain vintage point in their negotiation meeting with suppliers before making a contract.
Other part of procurement tea
m's work is estimating procurement budgets, which was difficult because the materials and equipment used were mostly made to order in accordance with the technical designs proposed by the engineers and did not have standard price. Therefore at first, the budget estimations were significantly different from the actual costs. However, the team performed an analysis using the company's internal data on completed construction projects and found that each category of procurement (machine, piping, electricity, instrument and HVAC) usually takes up 30, 25, 10, 30 and 5 percent of the budget respectively. This pattern remained largely consistent in other ongoing and subsequent projects, and the findings proved invaluable as references for planning budgets and estimating costs in the early stage of new projects. Consequently, the team performed predictive analytics, but there are factors the team could improve such as building an automated model to estimate the budget for each equipment and material, not for each category of procurement.
Lastly, the procurement team
relies on its forwarders (B2B enterprises that deliver requested items for a company) to plan logistics route. The team has to spend much time contacting forwarders and finding which forwarder suggests better planning and lower cost. However, this logistics process can be automated with analytics and can be done by eTEC on its own. By doing so, the company can enjoy much higher cost savings from its logistics in the long run.
Sales
Sales prediction is crucial i
n every business. However, eTEC is not very confident with its sales prediction figures. The sales team's sales predictions are conducted by non-analytical professionals who did not receive relevant education or training. Moreover, the company mainly focuses on winning contracts and do not give much attention in the sales prediction. Hence, the sales team does not consider its prediction very important.
Furthermore, rather than usin
g state-of-the-art analytics techniques such as data mining, the company exercises its imperative authority on employees to gather potential sales information from any sources possible. This direction from the management level gives too much burden on the employees by giving additional task on top of their own work. The sales team needs advanced analytical methods and more employees to digest all of the work it should deal with and prevent giving burden to other teams.
Planning & Coordination
Having the vice president of
the company as the head of this unit, the planning & Coordination team, or PC team in short, is like a navigator of the company. It oversees all other key functions within the company, monitors their performance, and sends directions to them. Therefore, PC team can be called as "the core" decision maker. The main questions the PC team concerns about are "What is the status of each project," "What went wrong with projects that failed," "Why did they failed," and "What should we do in the future?" In order to figure out the answers to these questions, the PC team sends other functions instructions to preparing a report and data necessary for PC team's analysis. Each function's head will directly report to the PC team. With the reports, the PC team will make a presentation to all employees every quarter. The PC team may seem like it is doing well. However, the governance of data for each team's report is unstable. Because each team stores most of data in Microsoft Office files with different formats from the PC team's instructions for preparing data, each team has to process the data in accordance with the instruction. And data entry error may occur in this process. However, the PC team does not proofread the data because it is very time consuming to track down the errors for all reports.
The key business functions of
eTEC is utilizing some level of business intelligence and decision support in their work. The company's analytics environment somewhere between analytics 1.0 and 2.0 according to "Big Data @ Work" written by Thomas H. Davenport. Still, there are many difficulties and fallacies observed in their methods as described above. They are caused mainly by the company's unawareness of the framework and methods for predictive and prescriptive analytics. The validity, performance, productivity and decision-making of each team's work can be greatly enhanced with implementation of up-to-date tools, IT infrastructure, data governance, and training for analytics.    (이하 생략)

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