N183-143
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TITLE: Machine Learning to Enhance Navy Service Desk
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TECHNOLOGY AREA(S):
Information Systems
ACQUISITION PROGRAM: PMW 250
DS-CRM Navy 311
OBJECTIVE: Develop a
capability that improves upon current Information Technology (IT) help desk and
customer relations, specifically in parsing and analyzing help desk
communications (text and speech), reports, and logs. The resulting data set
will be used to employ technical enhancements to the IT support and customer
relations management space.
DESCRIPTION: Navy 311 is a
Customer Relationship Management (CRM) component of the Navy�s Distance Support
(DS) capability, managed by Program Executive Office Enterprise Information
Systems (PEO-EIS). Navy 311 is a single point of customer service entry into
the Naval ashore infrastructure and network of fleet support providers. Through
Navy 311, the Fleet, Sailors, military families, and civilians can get
on-demand information assistance for non-emergency, non-tactical issues. This
gateway to comprehensive support can assist with issues including but not
limited to: systems and equipment (e.g., hull, mechanical and electrical,
weapon systems, information technology, technical data), quality of life (e.g.,
medical and chaplain care), personnel (e.g., career, manpower, training), supply
and logistics (e.g., requisition follow-ups ordnance, food service, household
goods), and installations and facilities (e.g., environmental, public works,
community support). Navy 311 supports various communication mediums that
include phone, email, chat, and mobile texts.
The desired capability shall have the following characteristics:
� Ability to search by meaning and context of desk communications (text and
speech), reports, and logs to include:
��� - Considering user search, email, chat, and browsing history to guide and
enhance current search results
��� - Promoting search results that other users selected when entering similar
search criteria to the current user�s query�
��� - Promoting search results for emergent, common issues when several other
users� enter similar search queries, predicting instantaneous recently-arisen,
widespread trends in user searches�
� Ability to communicate to the customer base via bots (e-mail or chat) to
include:
��� - Automatically responding to end-user�s emails or chats describing their
problems with the most likely solutions to the resulting tickets, closing the
ticket, when appropriate, without human involvement
��� - Automatically linking the user to a Frequently Asked Questions (FAQ) page
that is known to address the issue the systems has determined the user is
encountering
��� - Automatically identifying, retrieving, and pre-populating forms the user
will need to submit to address the issue at hand
� Ability to predict future workloads and resource utilization, allowing
routine IT support tasks to be enhanced by automation, supplementing (not
replacing) service desk agents. This includes:
��� - Pre-populate emails and other communication forms, from helpdesk
personnel to end-users, with the text and other content that has successfully
addressed the same issue in the past
��� - Search IT service logs and content to determine the most likely cause(s)
of the user�s issue and display the historically-known fixes (with
supplementary information) to the helpdesk personnel�who then selects the
appropriate option
� Ability to automate routing and workflow of new issues to the appropriate
personnel or electronic resource to meet end-user expressed needs, based on an
understanding of the meaning and context of the issue, past successes (with
similar issues), and the availability of different support resources
� Ability to predict future IT service trends by predicting the demand for
new/existing IT services, or the future levels of IT support personnel needed,
entailing:
��� - Predicting upcoming spikes in resource utilization that will require
extended labor demands, expanded electronic resources utilization, etc.�
��� - Predictive analytics employed to predict future levels of customer
satisfaction based on the past impact of various contributory variables
��� - Crawling the web for upcoming Security Technical Implementation Guides
(STIG) or STIGs that will likely impact operations
The desired capability should also be built to improve upon its own activities,
improving over time. Once built, the capability should be able to process data
to train itself, making improvements that will be tested and either kept or
discarded by the system automatically. The system should also be capable of
tuning by expert system operators, and then tested and deployed.
Current state-of-the-art technologies that can address this capability include
machine-learning cloud services. However, these current services only provide
general tools to begin an approach that address the above requirements. These
tools only analyze stored state data sets within online searchable databases
and the product is an online response which cannot be consumed as a service by
Navy 311. A specific implementation is desired.
PHASE I: Complete a
feasibility study describing a novel design for an analytics capability capable
of performing tasks specified above with an environment to be proposed by the
Small Business Concern (SBC). Include simple proof-of-concept prototypes and
research-backed mockups.
The Government will provide a subset of communications (text and speech),
reports, and audit logs related to Navy IT support help desk and customer
relations that the SBC�s capability can process to implement the desired
characteristics above.
Develop a Phase II plan describing the costs and technical effort required to
implement the design described in the study. The plan should include visual
depictions of the products� features and general user experience.
PHASE II: Based on the
results of the Phase I effort, develop a pilot implementation of the
capability�s desired characteristics as mentioned above, integrating in a
limited fashion with Navy 311.
The Government will provide a subset of communications (text and speech),
reports, and audit logs related to Navy IT support help desk and customer
relations that the SBC�s pilot implementation can process to implement the
desired capability�s characteristics. The Government will also provide data
necessary for the SBC to integrate its capability with Navy 311 in the limited
fashion agreed to in the Phase II contract.
The SBC will present a plan to build the final product and integrate its use in
the wider Navy IT community (100+ service desks serving a wide array of IT
capabilities including: pay, logistics, records management, recruiting,
retirement, healthcare, etc.). The plan should include projections of reduction
in cost and lost time by employing these solutions in support of Navy 311
activities.
The following performance attributes will be assessed during the Phase II
effort:
� Fidelity of cost projection to pilot implementation
� Fidelity of schedule projection to pilot implementation
� Effectiveness of software in enhancing capabilities from a help desk and
end-user perspective to include:
��� - Average time to assign ticket (NAVY 311 Support Center KPP)
��� - Time to assign ticket to final actor (i.e. resolver)�
��� - Average time to resolution (NAVY 311 Support Center KPP)
��� - % of requests with mandatory fields blank (Metrics KPP)�
��� - % of requests with invalid entries in fields (Metrics KPP)
��� - Number of reassigned tickets
PHASE III DUAL USE
APPLICATIONS: Complete necessary engineering, system integration, packaging,
and testing to field the capability into Navy 311. Support Navy test and evaluation
activities during software transition to production and software effectiveness
after deployment is complete. Following testing and validation, the end product
is expected to produce results outperforming the current Government agency
business processes and ad hoc methods in use today.
The capability described in this SBIR topic could have private sector
commercial potential for any IT business that needs to improve upon IT service
support capabilities.
REFERENCES:
1. Mann, Stephen. �5 Use
Cases for AI on the IT Service Desk�. SunView Software, Inc. 07/10/2017.�
https://www.sunviewsoftware.com/blog/5-use-cases-for-ai-on-the-it-service-desk
2. Ragupathi, Ashwin R.
�Machine Learning and ITSM: Helping Help Desks�. DevOps. 07/24/2017.�
https://devops.com/machine-learning-itsm-helping-help-desks/
3. Fletcher, Colin and Lord,
Katherine. �Apply Machine Learning and Big Data at the IT Service Desk to
Support the Digital Workplace�. Gartner, Inc. 02/29/2016.
https://www.gartner.com/doc/3232017/apply-machine-learning-big-data
4. Navy 311 Systems
Engineering Team.� �Navy 311 Systems Engineering Plan (SEP)�.�� v3.2 January
17, 2017 (uploaded to SITIS)
5. PMW 250 Logistics.�
�Life-Cycle Sustainment Plan (LCSP)�.� v1.1 October 6, 2016 (uploaded to SITIS)
KEYWORDS: Machine Learning;
ML; Artificial Intelligence; AI; Natural Language Processing; NLP; IT Support;
Help Desk; Service Desk; Call Center; Customer Relationship Management; CRM