Gig Economy

Project Management in the Gig-Economy: Reports and Documentation

One of the keys to a successful field services business is the ability to produce highly informative, accurate, and comprehensive reports, guides, invoices, and other documentation that represents the work provided. The challenges are: 1) automating the production of a varying set of services reports and documents, and 2) producing all the documentation needed for all the associated stakeholders. Also, the complexity of the documentation can vary from simple weekly progress updates with a few line items, to detailed reports and/or design guides with hundreds of line items, including pictures, drawings, charts and graphs. The types of documents/reports produced and/or supported by a project management system includes:

narratives/project descriptions
progress reports
workflow descriptions
design/installation guides
performance statistics/graphs/diagrams
executive summaries

and other collections of information gathered or derived from the requested services. Given each project and associated stakeholder drives differing requirements and points-of-interest, customization of reports and documents is necessary to satisfy a diverse and growing clientele.

There are stakeholders who need information from the project management system. The stakeholders, and related documentation, commonly associated with field service projects are as follows (Note – most project types have similar stakeholders and related documentation):

Project Management Team – Progress reports are needed for tracking of project phases, milestones, spending, representative assignments, work-orders, inventory, inquiries, permits, sub-contractors, etc. Reports that provide statistics on service delivery performance across work-orders and projects, including pending, active and completed projects will be important as well.

Executive Team – Reports providing insights on scheduling/milestones actuals-vs-planned, spending actuals-vs-planned, projected activities, workforce allocation, project revenue/profit, and other performance metrics are needed to support decision making at the executive level. Reports on the performance of the project managers and representatives may also be of interest to the executive team.

Field Service Representatives – The project management system should provide each representative information into their respective work record and performance. It also can provide insight into pending and/or future work assignments.

Clients – Reports and documentation for a field service client often must include work-order completion and findings, design guides, list of repairs, cost of repairs, work-order response/completion time, and statistics showing weekly, quarterly and year-to-date performance. Potential clients and “Request for Quotes/Proposals” often require data on past performance and present capacities that should be available from the project management system.

Suppliers/Distributors – Most of the documentation and reports needed for suppliers is in support of tracking inventory changes, deliveries, orders, and future needs.

Marketing/Sales – The marketing and sales teams will need information to support interactions for present and potential clients. This information may include several projects/work orders in process, completed and pending, client list, location of project actives, and project performance in specific areas of service delivery.

The project management system must be able to generate automated reports using an interface that allows the project management team to select the content and format of a report. This will be key for reports that are periodic, e.g., weekly, monthly, quarterly, etc. Standardized report frameworks may also be created to support high level insights into operational effectiveness and identify potential issues and/or areas of improvement. While the project management system may be able to automate creation of most field service reports and documentation, it will need to also provide tools and facilities to create custom reports and/or guides that require the integration of information from outside data sources. For example, the creation and integration of a CAD drawing to show a floor layout and proposed object placement may need to be completed by a separate application, but the project management system will need to support the file type, placement, and conversion to the target report format. Finally, the project management system must provide APIs to allow stakeholders to import and export data and documentation from field service work.

In closing, efficient and user-friendly report and document creation will be necessary in a sustainable and effective project management system.

Project Management in the Gig-Economy: The Field Service Representative

A major element or capability of the target project management system is to provide a way to establish a viable and supportive working environment for the Field Service Representative. Enabling a culture of community, inclusion, empowerment, ownership, growth, opportunity, impact and accomplishment will be important to sustain a high performance and highly satisfied gig-workforce. To date few if any project management systems provide this capability.

Key features and functions of a project management system in support of a field service gig-workforce:

Open, honest, and constructive feedback and communications among representatives and between project managers and representatives.
Social recognition and community celebration of impactful ideas, project accomplishments/major milestones, and career milestones.
Allow representatives to establish a “collective voice” and build a sense of community with their peers.
Enables due process and input on decisions affecting representatives and employees.
Clearly defines tasks and any known risk associated with the execution of a work-order.
Provides details of the amount paid for completion of each task associated with a work-order, and the right for a representative to choose which work-orders and tasks they wish to support.
Provide access to training and certificate resources to advance the skills of the representatives.

One of the keys to establishing a sustainable, inviting, quality minded, and innovative culture in a gig-workforce is to enable and encourage the sharing of ideas among peers and with management on how best to improve efficiencies, complete a task, reduce cost, and/or make changes in processes, workflows, methodologies, and other company operations. Establishing a cultural norm can be difficult, especially with a highly distributed and dynamic workforce. But establishing the right culture and empowering the workforce will be necessary to sustain efficient operation of a field services business using a gig-workforce.

One of the emerging areas important for any interface between a business and its workforce is the provision for corporate social responsibility to the representatives, their families, and the environment. This capability can also be an important attribute for the client, allowing companies to express how their projects support society and the advancement of the community.

An effective project management system that support a gig-workforce must provide real-time access to job opportunities and allows representatives to express their interest, request they be assigned, and/or accept assignments to a particular project or work-order. The representative’s profile carried in the system will contain information on their skills, interest, and estimated amount of work they are seeking. The system should also provide an environment for representatives to document their skills, accomplishments, completed assignments, productivity, and other key measures of their capabilities and interest (often referred to as a ranking and reputation system). This documentation cannot only be leveraged by the field services company but helps the representative to easily accumulate and share their experiences and skills with other companies. The system will allow the capability of sharing representative’s information with clients, similar to a LinkedIn platform.

Finally, the project management system must provide a means for the representative to do long-range planning of their work commitments. It will give the representative access to a public calendar and a private calendar to help them schedule their commitments,

One goal of the system is to support the concept of the “economics of mutuality” where every entity in the supply chain receives a fair return on their investment of time and money, including clients, distributors, workers, manufacturers, and innovators. (e.g. greater transparency in the production networks). In support of this, the system must consider support of the five principles of “fair work” in the gig economy (created by the Fairwork Project convening at the International Labour Organization meeting in Geneva): fair pay, fair conditions, fair contracts, fair governance, and fair representation (https://fair.work/en/fw/principles/fairwork-principles-gig-work/ ). With support of these principles, the project management system will establish a viable and sustainable business environment for everyone involved.

Project Management in the Gig-Economy: The Dashboard

Given the complexity of receiving, assigning, tracking, reporting, invoicing and billing field service work using a blended workforce (gig, part-time, full-time, and other workers), an efficient and effective way is needed to selectively see and analyze the company’s projects, resources, assets, and open quotes for future work. A dashboard that gives management the ability to quickly make decisions, identify issues, and understand the effect of potential opportunities will be key. Operational elements like work-order analysis, worker productivity, contracted cost versus actual spending, resource availability, quality control and other important measures must be monitored in real-time to avoid negative impact on the company’s operations and business metrics. The project management system must be able to provide a way to visualize the status of the company’s projects, the workforce, pending/future work, resources and assets, as well as assess the issues and impact of things happening in society, the workforce, project locations, related areas of business, and government.

The integration, programmability, and automation of productivity analysis functions and reports is very important in a project management system for field service work using a gig-workforce. Any loss in time to assign, track, complete, and/or report on a project equates to increased cost, decreased profits, inefficient use of resources, and a loss in future opportunities. Also, the integration with data visualization tools like Microsoft Power BI, Tableau, Sisense, Chartio, Salesforce Einstein Analytics and SAP Analytics Cloud will allow management insight into key components of their business without reviewing raw numbers or complex reports.

We will call the ability to analyze and visualize a company’s business operations “business intelligence”. Wikipedia (https://en.wikipedia.org/wiki/Business_intelligence) defines business intelligence (BI) as follows: “comprises the strategies and technologies used by enterprises for the data analysis of business information.” BI technologies can take structured and unstructured data and provide insights into ongoing business operations. Most modern BI tools also provide data visualization capabilities. Data visualization is the ability to represent BI data in a human readable format. The ability to visualize data in a way that enables effective decision making is an important attribute of BI tools. Other functions provided by BI tools include reporting, analytics, data mining, business performance management, and predictive analytics. This list of functions and capabilities is applicable to the needs of our field services project management system.

We must recognize that every client and project is different. But field service projects, in fact projects in general, carry some common attributes. Therefore, some BI and visualization actions can be programmed to provide high-level operational insights, while others will be created for a specific project, client, or type of work. Management must establish what they would like to see in real-time at the corporate, client, project, project type, work-order and work-order type level. Decisions must be made on how management wishes to view the availability, workload, productivity, and attributes of the workforce. And the project management system must provide support for the different reports and views of interest, as well as making all possible elements programmable.

Now, while ultimate programmability of views and reports is valuable, the system must provide some standard reporting and visualization capabilities as part of its base functionality. From this base capability, the project management team can customize the system to deliver specific monitoring and decision-making insights.

The challenge for most project management systems, and their integration with BI tools, is dealing with the volume, variety and veracity of the data collected. Another challenge is handling real-time operations and concurrency in the data collection and processing. Finally, identification and notifying the user of anomalies and outliers in the data and/or resulting analysis will be important, helping the management team to avoid inaccurate and/or poor decisions.

Project Management in the Gig-Economy: Data Systems Architecture

As stated previously, the project management system architecture needed to support associative management of structured and unstructured data gathered in support of infrastructure projects must consist of three main parts: (1) data harvesting subsystem, (2) data storage subsystem, and (3) data analysis subsystem. There are many approaches one could consider in implementing these three subsystems and how they interface to each other. From our evaluation of present and future requirements for managing infrastructure projects using a gig-workforce, we have chosen a system architecture and related technologies to support the “data system” for the platform. The following provides a high-level description of the primary subsystem components to support the platform’s data system.

Key system technologies and/or structures targeted to support managing data and information include:

Open Data Format (ODF – http://www.opengroup.org/iot/odf/ ) – The ODF is specified using XML Schema. It defines a simple and extensible ontology for data storage that allows the creation of information structures that are similar to those of objects and properties in object-oriented programming. It is thereby generic enough for the representation of any object and information needed for information exchange in domains such as physical infrastructure.

Structured Query Language Relational Database (SQL – https://en.wikipedia.org/wiki/SQL ) – Primarily used for management, transaction processing, queries and analysis of structured data with tabular relations. Most of the data gathered while completing a workflow document or form will be stored in a relational database. Companies like IBM, Oracle, Microsoft, SAP, Teradata, and many others have SQL database environments. Often the best database software to use is driven by a company’s existing data and their data system. We will not make a specific recommendation at this time relative to which SQL database system would be best.

NoSQL (originally referring to “non-SQL” – https://en.wikipedia.org/wiki/NoSQL ) – A database structure which provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases (e.g. SQL). The data structure options or classifications for NoSQL databases include key–value pair, wide column, graph, or document. The proposed data structure for unstructured data is a “document” store given its ability to integrate well with SQL databases and associative queries. A document store assumes that documents encapsulate and encode data (or information) in some standard formats or encodings. Documents are addressed in the database via a unique key that represents that document. Another defining characteristic of a document-oriented database is an API or query language to retrieve documents based on their contents.

An example of a viable NoSQL database is MongoDB (https://www.mongodb.com/), a cross-platform document-oriented NoSQL database program. MongoDB uses JSON-like documents with optional schemas. It provides high performance, high availability, and easy scalability. MongoDB is developed by MongoDB Inc. and licensed under the Server Side Public License (SSPL).

Hadoop (https://en.wikipedia.org/wiki/Apache_Hadoop) – An open source big data framework from the Apache Software Foundation designed to handle huge amounts of data on clusters of servers. Hadoop is a general-purpose form of distributed processing that has a distributed file system (HDFS), a scheduler that coordinates application runtimes (YARN), and an algorithm that actually processes the data in parallel (e.g. MapReduce and Spark).

Apache Spark (https://en.wikipedia.org/wiki/Apache_Spark) – A data processing framework that can quickly perform processing tasks (e.g. machine learning) on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools. Spark has become the framework of choice when processing big data, overtaking the old MapReduce paradigm that brought Hadoop to prominence. The advantages of the Spark framework include speed and its APIs.

Our primary goal in this system architecture is to provide the necessary functionality, flexibility and scale in support of effective use and association across data types needed to support value creation in an infrastructure focused project management system. We believe the proposed structure will work.

Project Management in the Gig-Economy: Data Types and Management

A project management system designed to support a wide variety of infrastructure projects must also support a large variety of data and information types, formats, and methods of data egress/ingress. This includes all types of structured and unstructured data, as well as information created and/or provided by other platforms or systems. We will define “data” as content from a device in its original, unaltered form. Data is most often raw, unorganized facts gathered from an environment with a spatial, temporal and/or elemental component or association. Examples of data include:

stream of measurements from a tool;
video, audio, pictures, distances from a camera;
text gathered from a keyboard,
sensor readings,
distances from a laser scanner,
raw human observations, etc.

Information is data that has been processed, organized and/or presented in a useful and meaningful context. Information will always have some structure or associative properties, while data can be structured or unstructured. For example, the individual sale of items in a store is data, while the identification of the most popular and least popular items in the store is information. A project management system must be able to input and output all forms of data and information in support of the projects targeted by the business. The system must also be able to create and distribute the data and information needed to satisfy the client request, as well as provide the service company incremental insights to yield opportunities beyond the immediate client request.

The key is to create a system that enables the association of structured data with unstructured data, of which both have some temporal, spatial and/or elemental relationship. The challenge is to identify the associative elements that tie the data together, thus yielding insights and information key to making decisions in support of a target objective. The majority of the data collected will be unstructured, making most of a company’s data storage complex and difficult to analyze. But this unstructured data will most likely be the most valuable data to the company. Another challenge is identifying and understanding the characteristics associated with the collection, storage, and analysis of the unstructured data. These characteristics include variety, volume, velocity, veracity, and value.

Variety refers to unstructured data in different forms such as messages, social media conversations, videos, and photos
Volume refers to large amounts of data
Velocity refers to how fast the data is generated and how fast it needs to be analyzed
Veracity refers to the trustworthiness of data
Value refers to the worth of the data stored by different organizations

Project Management in the Gig-Economy: Work-Order Elements and Constraints

A project management system designed to leverage a gig-workforce to deliver real-time infrastructure services requires key work-order elements in a flexible workflow construct. The system must also support programmable constraints that will limit select actions, materials, and assignments for a given work-order or project. These constraints may be associated with a project and/or work-order type, or be unique for a given client, site, contractual component, or regulation. The project management system must simplify the creation of a “project definition document”, “work-order request” and subsequent “workflow descriptions” for each client request. The following is a high-level hierarchy of elements to support client request for delivery of real-time infrastructure services (the range provided in [ ] represent how many entries can be supported per client request):