Data Mining Vs. Process Mining

Data Mining Vs. Process Mining

Data mining is a part of Business Intelligence (BI) that seeks to understand relationships and patterns in large datasets found in big data. Big data is a term referring to massive databases of both structured and unstructured static information that can be exploited for business intelligence needs. This data can have the potential for improving business operations. The data is mined from such sources as emails, data storage, phones, applications, and databases. This data is mined, processed, and analyzed. Companies gain insights from these harvested data.

Process mining seeks to understand real-time procedure steps to detect inefficiencies or make improvements in the accomplishing of a business task. Process mining is the analyzing and monitoring of business processes. Data is gathered through, or mined from, corporate information systems which displays the actual process. It does this by capturing a time-stamp and an event log of each of the process steps. The process mining is accomplished by using strong algorithms combined with advanced data transformation enabling the discovery and improvement of the business processes.

Similarities

There are similarities between data mining and process mining. Both are a subset of business intelligence (BI) and both access large volumes of data to achieve information for action. Both use algorithms to obtain hidden patterns and relationships within the data.

Differences

Data Mining Finds Static Data

Data mining is static and used by corporations to analyze big datasets to predict business patterns. The data analyzed is harvested from static datasets such as databases, which are available records. It looks for things like what group of consumers will buy what product, or where does a marketing effort have the greatest impact. Data mining has no concern with business processes.

Process Mining Finds Dynamic Data

Process mining is dynamic and gathers needed information from created actions. It can be from real-time events provided through a live feed. It looks for steps that are inefficient or time-consuming to control and improve those steps. It reveals a true, end-to-end process.

Data Mining Looks At Arbitrary Data

Data mining obtains information from what happens to be available. Data mining arbitrarily gains information from large databases without targeting a specific inquiry.

Process Mining Looks At Real-Time Data

Process mining targets a specific question about a process. Process mining gets current activity.

Data Mining Looks At Results

Data mining can only look at the results of available data. It cannot answer how those data came to be.

Process Mining Looks At Causes

Process mining can see the cause of actions.

Data Mining Analyzes Patterns

Mainstream patterns are analyzed by data mining. Exceptions to those mainstream patterns are not considered for analysis.

Process Mining Sees Exceptions

But exceptions and irregularities can be very useful for the process mining technique. They could provide clues to what is not working well and what needs improvement.

Conclusion

Both data mining and process mining serve important purposes in the realm of business intelligence (BI). They are necessary for successful, efficient business operations. Data mining provides the source of market knowledge for companies to make smart decisions. The analyzed results are applied in various industries such as retail, journalism, and scientific research. But process mining provides the knowledge of operations that help companies improve and function smartly.

Say It Plain With Plain Language

Say It Plain With Plain Language

What Is Plain Language?

Communicating in an understandable language is fundamental for meaningful human interaction. When a person does not understand a foreign language spoken to him, there remains a darkness about the intentions of that person speaking.

This same problem can exist when the same language is spoken between two people but not in a clear, understandable way. The meaning can be missed just as well. It can be argued that if the federal government, or anyone, insisted on unclear, complex statements when communicating, then they are being dishonest. They are not being honorable in the sense of being transparent with their citizens. This does not encourage trust. Trust is fundamental in both leadership and a contractual relationship.

The US government’s Plain Writing Act, established in 2010, mandates that government agencies use contract language that is plain and understandable to the average person. It was motivated by the conviction that the citizens of the US deserve to understand what their government is saying, although the Act does not cover regulations. It requires federal agencies to compose “clear government communication that the public can understand and use.”

Some Principles For Plain Language

· Use One Idea per Sentence Long, complex sentences with connected ideas and supporting clauses are hard to follow. People won’t absorb the main point because of the distractions of subordinate ideas. Instead, break up complex thoughts into easily expressed short sentence units that contribute to the main point. Readers will more effectively track your thinking.

· Avoid Long sentences Shorter sentences promote successful communication. A single idea conveyed in one short sentence appears and sticks to the reader’s mind more easily. Readers will see the idea better than reading longer sentences carrying a train of related notions.

Parts of a complex idea are more easily grasped when presented individually. The reader can then build up the simple ideas to the complex idea structure more successfully.

· Use an Active Voice Using an active voice easily points out the actor in a sentence. Passive voice sentences can seem evasive regarding significant actors in a contract agreement, for example.

Business Use Of Plain Language

Not only does the federal government need to use plain language for its public communications but private enterprises should too. Commercial corporations should be clear when drafting contracts to procure business. Whether these contracts are between business to business, or doing business with the federal government, the contractual language should be plain and understandable. Without such clarity in the language, both sides stand to lose something important. Promises might fall short of desired requirements. Expected obligations might not be met. And there will be violated the trust and damaged business prospects.

Advanced software technologies using AI and NLP can help companies to know whether their contract language is plain enough for fair dealings. It can demonstrate whether the contractual language is readable or not. The Content Analytics Platform (CAP), developed by Scion Analytics, can provide readability scores for any size document, regardless of its complexity.

RPA vs. Hyper-automation

RPA vs. Hyper-automation

Robotic Process Automation (RPA) is using software for the automating of a repetitive task. Hyper-automation adds the more advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), to RPA. These can automate many activities to work in an orchestrated method.

Robotic Process Automation (RPA) utilizes software technologies like Machine Learning (ML) to perform routine tasks in a company’s department. ML is a part of Artificial Intelligence (AI). ML is the ability of computers to learn by using algorithms, which are like digital flowcharts using rules. ML gathers insights from raw data or detects patterns without the computers being specifically pre-programmed towards those goals.

Some describe RPA as a non-invasive technology used to automate a routine task or repetitive process by using robots to emulate human actions.

Two Types Of RPA

· Assisted RPA, where bots are deployed in a desktop PC with the employee providing minimal interactions, and the bot performing the more complex and repetitive processes.

· Unassisted RPA, where a centralized server manages scheduled bots for designated workflows.

Hyper-automation conjointly uses several advanced software technologies such as AI, RPA, Intelligent Process Automation (IPA), and decision management systems. These combine to improvise an orchestrated use of cognitive functioning that goes beyond simple individual process automation. The result is automatic decision-making, predictive insights, and recommendations for specific tasks to be performed using automation. Hyper-automation provides the advanced benefit of employing digital workers that perform human-like interactions and responses. These interactive bots can also be asked questions pertaining to their assigned functions. The questions can also be complex due to the employment of NLP.

RPA Vs. Hyper-Automation Similarities

Use Advanced Software Technologies

The primary function of automating a process that formerly was done by manual labor. Whether it is simple, individualized bots performing perfunctory tasks, or an array of bots managed by a centralized server, the goal is to automate and improve a process free of human error and fatigue.

Improve Performance Efficiency And Save Time And Costs

The investment in intelligent automation technologies will only be a huge gain and value-add for any organization.

Differences

Some differences between RPA and hyper-automation are:

· RPA is a repetitive robotic process that does not require human-like judgment. Hyper-automation uses more advanced Artificial Intelligence (AI) systems to manage several RPA applications.

· RPA is an individual business operation. Hyper-automation is a cross-functional collaboration encompassing multiple robotic processes as an orchestrated infrastructure. Hyper-automation goes beyond individual bots by using intelligent automation software to scale multiple automation capabilities across an organization.

Conclusion

RPA can improve an organization’s productivity. Hyper-automation will streamline all the individual automated processes and can provide intelligent orchestration of various tasks. Hyper-automation extends the legacy applications of business process automation, surpassing individual processes. It is the next step to digitally advance the efficiency of an organization’s business operations.

Hyper-automation can enable quick accomplishment of complex work that typically relies on knowledgeable input from employees. Digital workers employed for intelligent automation will enhance the functions of personnel.

Posted in rpa
Color Team Review

Color Team Review

What Is A Color Team Review?

A Color Team Review is an industry-standard practice in the world of business proposals. It is a planned, structured, disciplined process to bring a contract response to a request for proposal (RFP) toward a winning completion. The process involves using several teams designated by colors. There are Blue, Pink, Red, Green, Gold, and White Teams. These color team reviews are composed of selected experts and proposal managers. They work collaboratively on the proposal response to achieve certain planned milestones in the development of the complex contract proposal. The developed proposal will be submitted to either a federal government or a commercial enterprise. When done well, the color team reviews ensure a cost-effective and comprehensive solution to meet a business need with a high probability of winning (Pwin). This effort at developing a winning proposal is called the capture process. The color team reviews are vital steps in its growth and maturity.

Benefits Of Color Team Review

There are key benefits of using the practice of color team reviews in an RFP response. These benefits arise due to the nature of the color team review discipline. They are teams pre-determined by well-defined roles and focused goals. These teams are planned in a logical, sequential order. There are increasing maturity goals in the development stages of the proposal. One color team must satisfy its objectives before the proposal is released to the color team next in line. These are like gates where the status of the working proposal will not be advanced unless completed. The focused concerns of each color team must be accomplished in a satisfactory way. Then it is determined to be forwarded to the next color team for its review. Some key benefits are:

· The color team review objectives are well-defined. The teams can give focused attention to the details required. No detail impacting full proposal compliance should be overlooked before it is moved to the next color team.

· Color team reviews are concerned with customer satisfaction and requirement compliance. They help improve the quality of the capture process by ensuring the detailed responses and addresses of the RFP concerns are fully met.

· It is a good practice for ensuring the quality of the proposal by identifying gaps or inadequacies. The teams will evaluate the presence or absence of discriminators, win themes, and full compliance to customer requirements.

· With close collaboration, a compelling, winning, final proposal product can be ensured.

Steps In A Color Team Review

Blue Team

Ensures the initial preparations. The initial proposal framework is developed. The sections are outlined. The initial graphics and charts are added. Key persons and resumes are added. Writers are assigned. Gaps are identified.

Pink Team

Narrative and details are added. Content is supplied. Emphasis is on the quality of the content rather than the quality of the form and style. Gaps are key action items.

Red Team

All the corrections, additions, and changes should be accomplished by the red team.

Green Team

The review of the pricing of the contract proposal is done by the green team.

Gold Team

The gold team ensures the readiness of the content, style, graphics, and formatting of the proposal. The document should be completely ready for submission. All sections are completed. All information is provided and fully compliant.

White Team

This team will mainly review the final product visually, with a focus on compliance.

Conclusion

Color team reviews help improve the competitive quality of contract proposals in a logical, sequential, focused, and structured manner. Vendors who subject their responses to RFPs through the channel of expert color

team reviews ensure the thorough readiness of a winning proposal. The discipline is an accepted business practice that drives successful contract proposals.

RPA And Process Mining

RPA And Process Mining

Robotic Process Automation (RPA) uses software technologies to perform routine tasks in a business setting. RPA is a related technology to Artificial Intelligence (AI) but not the same. RPA is a robotic tool that mimics human interaction by doing common work. It performs workflow operations that do not require decision-making and judgments. Process mining is the analyzing and monitoring of business processes. Data is gathered through, or mined from, corporate information systems which displays the actual process. It does this by capturing a time-stamp and an event log of each of the process steps. The process mining is accomplished by using strong algorithms combined with advanced data transformation enabling the discovery and improvement of the business processes.

What Is Process Mining?

Process mining is the collecting, analyzing, and monitoring of business processes in the form of event logs. It is a group of techniques combining data science and process management. Event logs provide the records needed to analyze the operational processes. The goal is to gain insights for action and improvements. It is a way to show the real activities of people, machines, and companies.

Process Mining Benefits

· Fast assessments of the processes of the entire enterprise, with high scale. With just the push of a button, minimal human effort is needed.

· Factual analyses of the processes, which are complete and precise.

· Pinpoint inefficient bottlenecks and deviations that prove costly and time-consuming, allowing the company to re-engineer those processes.

· Constantly monitor the processes and measure the improvements.

· Easier compliance with the availability of a full audit trail.

· Any industry can benefit from the process mining technique.

· The process mining technique can be used to analyze almost any business function area where there are customer transactions.

Process Mining Examples

· Accounts Payable Process mining can benefit an organization’s accounts payable department where it processes invoices. By using Machine Learning (ML), processing mining software can automatically extract invoice data, bypassing templates. Invoice costs go down and accuracy improves.

· Accounts Receivable In the collections procedure, customers that are more likely to pay and have invoice value are automatically prioritized. ML can then make cash forecasts based on the data from source systems.

· Procurements Optimize purchase requisition procedure with process mining software by improving purchase requisitions to automatically detect and correct issues such as discrepancies without human intervention.

· Order Processing Process mining software can help automatically detect and resolve order processing problems such as errors and blocks, credit checks that are unnecessary, or price and quantity variations.

Conclusion

Every enterprise has numerous processes for its business, whether using people or machines. The technique of process mining takes an X-Ray of the status and quality of a company’s actual business procedures. Using this information, an organization can see where steps in a procedure need improvement. These problem steps are unnecessary, or bottlenecked, or hindered, or error-prone, or require too much human intervention to be cost-effective and timely. With software tools empowered with ML, processes can be vastly improved, automatically, with touchless corrections

Posted in rpa
Federal Government Contract Management Software

Federal Government Contract Management Software

What Is Federal Contract Management Software?

An essential part of every business transaction is the creation, negotiation, and management of the contract, otherwise known as the contract lifecycle management (CLM). These steps are true for any size company wishing to procure business. But these contract processes are tedious, time-consuming, and prone to errors when done manually. This causes inefficiency and diminishes revenue. If few contracts are won, less money is made.

What is needed is a better, faster, more accurate data content management process. This is available with contract management software tools that are empowered with Artificial Intelligence (AI) such as Natural Language Processing (NLP) and Machine Learning (ML).

Artificial Intelligence (AI) is an advanced level of digital automation where software mimics human intelligence and judgment. Computers are trained to process human-like traits such as learning, problem-solving, and decision making. Ordinary IT automation digitally performs perfunctory, thoughtless operations, but AI does automated decision-making and thoughtful tasks.

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI). It performs analyzing, understanding, and then generating human-like language for interfacing. The software-generated language can be produced in both written and spoken contexts. NLP enables computers to comprehend human language and to respond with human language.

Machine Learning (ML) is a part of artificial intelligence (AI). ML is the ability of computers to learn by using algorithms, which are like digital flowcharts using rules. ML gathers insights from raw data or detects patterns without the computers being specifically pre-programmed towards those goals. ML can be either unsupervised without human intervention or supervised with human intervention. ML is a fast-growing technology being driven by advancements in computing technologies.

With AI-enhanced tools, you can easily find:

· key words

· requirement statements

· conditions

· legal phrases

· clauses

· vendor employee qualifications

· other legal texts.

Adequately addressing these data contents are critical for full contractual compliance. The items addressed by the proposal team can be ranked for readability and cross-referenced with the RFP, ensuring compliance.

The Content Analytics Platform (CAP), developed by Scion Analytics, is a suite of applications that can vastly improve any vendor’s contract lifecycle management (CLM):

· Compliance Matrix Quickly generates a starter compliance matrix using Shipley Associates’ Excel spreadsheet template. Shred a federal government RFP using the Legal parser and keyword group “Required” to help identify requirements.

· Compliance Matrix/ Analysis Results Go to the Analysis Results panel to collaborate with your team. Split, combine, annotate notes, and edit individual sections. Export multiple reports, and quickly search the RFP for specific keywords.

· Compliance Matrix/ Sentence Level Granularly shred an RFP into individual sentences.

· Compliance Matrix/ Select Keywords Generate a compliance matrix and select the keyword group that matters most to you.

· Compliance Matrix/ X-Ref Go to cross-reference matrices in the Matrix Builder.

· Bid/ No Bid Assessment the Bid/No Bid Assessment (BNBA) provides a matrix for decisive assessment of the individual requirements for bidding on a contract.

Federal Contract Management Software Applications

When it comes to responding efficiently to contract RFPs, using AI-empowered software is critical for saving resources and time. The ContentAnalytics Platform (CAP), developed by Scion Analytics, is valuable for enhancing RFP responses and ensuring proposal compliance. There are several key applications in the CAP to help with these:

Compliance Matrix

The gold standard for contract requirements compliance matrices are those developed by Shipley Associates. The Compliance Matrix Dynamic Application can quickly generate a starter compliance matrix using a Shipley Associate’s’ Excel spreadsheet template. The RFP can be parsed into a structured output using the Legal parser selection and the keyword group “Required” to help identify requirements. The Compliance Matrix generates the government RFP document fully restructured for easy sequential addressing. Each header section is listed with page numbers. Requirement statements are displayed in separate paragraphs. Keywords are highlighted in red for easy accountability by the proposal team members.

Compliance Matrix/Analysis Results

The Analysis Results feature of the Compliance Matrix Dynamic Application generates a panel where you can collaborate with the team members. You can split, combine, annotate notes, and edit individual sections. You can export multiple reports, and quickly search the RFP for specific keywords.

Compliance Matrix/ Sentence Level

The Sentence Level feature of the Compliance Matrix Dynamic Application can parse the RFP down to individual sentences in a structured manner.

Compliance Matrix/ Select Keywords

The Select Keywords feature of the Compliance Matrix Dynamic Application can generate a requirement compliance matrix with a selected keyword group to highlight what matters the most to you. No critical term will be overlooked. There are provided keyword groups, or you can build your own keyword group.

Compliance Matrix/ X-Ref

The X-Ref feature of the Compliance Matrix Dynamic Application has a separate Matrix Builder window. This application can allow you to generate two structured matrices to cross-reference the RFP and proposal to ensure full compliance.

Bid/ No Bid Assessment

The Bid/No Bid Assessment (BNBA) feature provides a structured matrix template allowing you to do decisive assessments of the individual requirements for bidding on a contract.Each important department, such as Legal, IT, QA, Security, etc., is color coded and the keywords are highlighted in red for easy accountability.

Federal Contract Management Software Capabilities

The Cap offers many tools for advanced contract data management that can be leveraged for quick and intelligent contract lifecycle management (CLM). Some of these capabilities are:

· Any size RFP document can be quickly analyzed and parsed into a structured format that is more accessible and transparent for all proposal team members. The result is represented within an Excel template matrix.

· Quickly analyze the RFP document and comprehend the significance of each paragraph in a structured format. AI can classify the clauses based on their contents.

· Mark out significant clauses by highlighting the importance of the information they contain.

· Identify requirements by highlighting key words and requirement statements.

· Check the readability ranking of the language of the document.

· Assess the language by pre-determining the weight of important terms.

· Compare RFPs with proposals using the cross-reference feature.

· Quickly generate a Bid/No Bid Assessment matrix facilitating each highlighted item’s bid or no bid evaluation.

· Quickly generate a RACI matrix or select your own responsibility assignment matrix (RAM).

· Quickly generate a risk assessment matrix using the Risk keyword library.

· Quickly generate a Color Team Review template.

· Quickly find Federal Acquisition Regulations (FARs) in RFPs.

Federal Contract Management Software Benefits

The US Government is Tech-Savvy

A major benefit for employing AI-enhanced contract automation software is preparedness for the federal government’s tech-savvy requirements. The US government agencies always keep up with the latest technological advances. They are aware of software improvements and adopt those advantages in their operations. Because the government agencies are tech-savvy, they will have more regard for those contractors who also improve their business operations with the latest technologies. Contracts with government agencies require a high level of accounting accuracy and fiscal management. And with the level of today’s technology, government agencies prefer to award contracts to enterprises that have automated contract management to ensure this accuracy and facilitate more-efficient management. With a vendor employing automation, a government agency can rely on better timeliness, accuracy, fiscal responsibility, and transparency. It is almost a necessity to use contract automation software to be competitive when doing business with the federal government.

Compliance Security

The federal, state, and local government agencies require complex and rigorous contract obligations for their project needs. A high level of accountability and fiscal management is demanded from vendors. The government agency will prefer the contractor who uses software automation when preparing and presenting their contract proposals. This is because the agency knows the vendor that uses contract automation software will already prove careful to meet the rigorous requirements stipulated in the RFP. They will be careful to present the most compliant and most cost-effective proposal to meet the need of the government agency. Because of the advantages of contract automation, the vendor can confidently submit bids with a high probability of win (Pwin). Software technologies such as the CAP can quickly analyze an RFP and present the data content in a structured matrix. The structured data template output will enable a high level of requirement compliance.

Risk Reduction

The converse benefit of better compliance through contract management software is risk reduction. When the data content of an RFP is automatically and thoroughly restructured into a clear, manageable template, errors and non-compliances are reduced. By using AI tools, possible risks can be pinpointed by intelligently analyzing the text of contracts and rating the language. The overall performance of the proposal team can be significantly improved.

Conclusion

The significant technological advantages of the digital age have transformed how federal government contracting is done. Software technologies enhanced with AI now automate the rigorous process of RFP analysis with amazing speed and ease. Data content management is now simplified and streamlined. An AI-enhanced software platform such as the CAP can systematically analyze complex federal government contracts. It can extract, organize, and classify important pieces of information, presenting the data in clear, accessible chunks for effective contract management. The enhanced oversight afforded by contract automation is crucial for a competitive edge in today’s technologically advanced federal government

NLP vs NLU vs NLG

NLP vs NLU vs NLG

Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) are closely related topics but are distinct from one another. These are related because they deal with the human language. These are all natural language topics because they are addressing how humans naturally speak with one another rather than how a computer may speak to another computer. All three components play a part just as they do in the human experience of conversation. In our normal conversation, we not only speak words in a certain way, but we also try to understand other persons’ words as we listen to them. Further, when we answer, we are generating speech for our response.

What Is NLP?

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI). Besides AI, it is a product of disciplines such as computer science, linguistics, and data science. These are used to enable computers to understand human language as written or spoken. NLP especially uses the technologies of Machine Learning (ML) and deep learning to be enabled to accomplish such things as language translations or answering questions.

NLP processes the unstructured data of human language, spoken or written, by restructuring it into digital data more easily understandable to intelligent computer programs. This is done by detecting named entities and word patterns and looking at the root forms of words.

What Is NLU?

Natural Language Understanding (NLU) is a subset of NLP. It is employed by NLP to perform the part of understanding the language that NLP is processing. For example, our general thinking is like the NLP part, and our understanding of what we hear or read is like the NLU part of the process.

Without getting too deep, this NLU part does an analysis of semantics and syntaxes of text and speech. It tries to discern the meaning of what is received. Analyzing the syntax of a sentence helps us understand the grammatical structure: what is being said. Think of the phrase, “Are you asking me or telling me?” Analyzing the semantics of a sentence helps us understand the intended sense: what is being implied. Think of the phrase, “What are you implying by that question?”

What Is NLG?

Natural Language Generation (NLG) is another subset of NLP. It does the function of providing output language in response to the input language. It does the answering in written language understandable to humans. This response can also be converted to spoken words by using text-to-speech services. Originally, the NLG component simply performed a method of filling in blanks according to a template in response to key phrases or questions. But NLG has evolved into a more dynamic process by using such services as recurrent neural networks, transformers, and hidden Markov chains. A hidden Markov model is a statistical method to guess what will be said based on some other known parameters.

Conclusion

NLP and the companion components of NLU and NLG are a group of significant Artificial Intelligence (AI) software technologies. They have revolutionized the way digital technology serves human intelligence. These technologies enable computers to interact with people in a much more human-like way. They make the services that computers provide much more accessible for the greatest number of average users possible.

RFP Response Automation

RFP Response Automation

RFP Response Automation

RFP response automation deals with AI-enabled software improving the efficiency of a proposal team’s response to Requests for Proposal (RFP). In both federal government and commercial business contracting, RFPs are published needs or projects. The responses offering to supply the needs or fulfill the projects by potential contractors are the proposals. Automated RFP response software uses artificial intelligence (AI) such as Natural Language Processing (NLP) and Machine Learning (ML) technologies. AI is an advanced level of digital automation where software mimics human intelligence and judgment. Machine Learning (ML) is a part of Artificial Intelligence (AI). ML is the ability of computers to learn by using algorithms, which are like digital flowcharts using rules.

What Is RFP Response Automation?

Traditional responses to RFPs were manually done by proposal teams. These responses were costly and time-consuming. Mistakes were bound to happen. RFP response automation uses AI software technologies to instantly shred an RFP. It rearranges the entire content to be presented in clear segments with keywords highlighted. These itemized segments can then be addressed automatically by matching Subject Matter Experts’ (SME) saved responses to standard questions. The stated requirements are automatically presented as structured data according to RFP section and page. They are easily scannable and thoroughly addressed.

How Do You Automate An RFP Response?

When it comes to responding efficiently to contract RFPs, using AI-empowered software is critical for saving resources and time. The Proposal And Contract Suite, developed by Scion Analytics, is valuable for enhancing RFP responses and ensuring proposal compliance. There are several applications in the CAP to help with these.

Proposal And Contract Suite Capabilities

Compliance Matrix

Quickly generate a starter Compliance Matrix using an Excel Shipley Associates template. Shred a federal government RFP using the Legal parser and keyword group “Required” to help identify requirements.

Analysis Results

Go to the Analysis Results panel to collaborate with your team. Split, combine, annotate notes, and edit individual sections. Export multiple reports, and quickly search the RFP for specific keywords.

Sentence Level

Granularly shred an RFP into individual sentences.

Select Keywords

Generate a compliance matrix and select the keyword group that matters most to you.

X-Ref

Go to cross-reference matrices in the Matrix Builder.

Bid/No Bid Assessment

The Bid/No Bid Assessment (BNBA) provides a decisive assessment of the individual requirements for bidding on a contract.

Benefits Of RFP Automation

Optimize Productivity And Save Time

The significant advantage of automating RFP responses is in the time savings and better collaboration among the proposal team members. The knowledge barrier is siloed departments. Automating RFP responses with the right software technology can centralize the document contents for all members. This barrier is eliminated. Team members can then concentrate on higher-value proposal components.

Consistency And Compliance

All proposal team members can have access to the centralized data of the compliance matrices produced by automating software.

Data Capture And Analytics

The right software platform empowered by AI technologies can produce a structured data matrix that presents all the requirements, keywords, acronyms, and questions to be addressed by the entire proposal team and SMEs.

Conclusion

Many business experts believe data and analytics are the future of successful industries. Going digital with data is a no brainer for any organization. Using a software platform empowered by AI that automates digital data management is a strong start toward success.

Proposal Review Guide

Proposal Review Guide

How To Review A Proposal

Proposals are the responses that commercial contractors offer to either federal government or commercial business requests for a business that needs to be met. These formal solicitations are published in Requests for Proposals (RFPs). The proposals need to be reviewed by proposal reviewers before final submission. The most important thing that the reviewer looks for is the full compliance of the company proposal to meet the need published by the contracting enterprise. Only by full compliance can a proposal be a winning proposal. A no-compliance in the proposal can mean being thrown out of consideration by the requesting organization. Some steps can be taken to properly review a proposal to ensure full compliance and assess the probabilities of win (Pwin) for bidding.

Proposal Review Steps

· Read the entire Proposal The entire proposal, including all the clauses, terms, conditions, should be read and understood. Bid protests from competing contractors can be made against the failure to address each item stated.

· Understand the Customer After reading the RFP to understand the requirements, take time to understand the requesting agency to gain any insight about them and the project. Take time to review any information provided by the proposal team about the procurement.

· Review the Draft Review the Proposal draft to assess the responses laid out. See where further logical segments can be made.

· Skim Before Reviewing Make a general skim over the entire proposal draft. Take notice of any misplaced answers to requirements instead of backtracking. Then review the proposal draft thoroughly making any changes necessary.

· Stick to the RFP Not only attractive pricing but compliance with every item is most important for contract capture. This includes the evaluation criteria. Continually reference the RFP to keep in mind all the qualities requested by the agency.

Things to Look for in Reviews

· RFP Compliance Vigilance Always evaluate how compliant is the proposal draft against the requirements.

· Adequate Capabilities Does the proposal draft reflect the capabilities, tools, and personnel needed to accomplish the request?

· A Persuasive Response Does the proposal draft provide compelling reasons to select this bid over competitors. Are the concerns fully addressed?

· Note Inconsistencies Are there inadequate responses to items of concern? Is there consistency in the proposal?

· Cultivate Improvements Make notes where additional information could strengthen the proposal draft.

· Give Helpful Comments Instead of merely noting an insufficient segment, make comments that guide how to respond to a requirement.

· Practice Evaluation With an established scale, such as a color scale, provide an assessment of each requirement.

· Summary Summarize the strengths and weaknesses of the proposal.

Deliverables There should be three deliverables:

· The summary of the strengths and weaknesses of the proposal

· The proposal’s rating against criteria

· An annotated copy of the proposal

Mistakes To Avoid During Proposal Review

· Don’t just read the important sections of C, L, and M. Read the entire RFP.

· Don’t fail to skim or scan the entire proposal, getting a sense of the layout, before tackling separate items. Concerns may have been addressed already but in the wrong location.

· Don’t fail to gain an understanding of how the criteria will be evaluated by the customer. Be sure to address the qualities asked for in all sections of the response.

· When commenting on insufficient responses from the proposal team, don’t just say so but show how they can be responded to.

Proposal Review Tools

The Proposal And Contract Suite, developed by Scion Analytics, has several applications to help with proposal development:

Compliance Matrix

Quickly generates a starter Compliance Matrix using an Excel Shipley Associates template. Shred a federal government RFP using the Legal parser and keyword group “Required” to help identify requirements.

Analysis Results

Go to the Analysis Results panel to collaborate with your team. Split, combine, annotate notes, and edit individual sections. Export multiple reports, and quickly search the RFP for specific keywords.

Sentence Level

Granularly shred an RFP into individual sentences.

Select Keywords

Generate a compliance matrix and select the keyword group that matters most to you.

X-Ref

Go to cross-reference matrices in the Matrix Builder.

Bid/ No Bid Assessment

The Bid/No Bid Assessment (BNBA) provides a decisive assessment of the individual requirements for bidding on a contract.

Conclusion

The important work of thorough proposal review is a major factor in submitting a bid with a high probability of win (Pwin). A best practice is to use AI-empowered software tools, like the CAP, to analyze and restructure all the data content quickly and thoroughly. These tools can be a catalyst for an efficient proposal review procedure.

Benefits And Challenges Of Enterprise Resource Planning

Benefits And Challenges Of Enterprise Resource Planning

The benefits of implementing Enterprise Resource Planning (ERP) software in an organization’s business operations outweigh the challenges. ERP is a suite of software technologies designed to automate routine business tasks, unify interactions between departments, simplify business processes, and centralize data access. Both on-premise and cloud-based ERP systems are available. ERP systems can be a Software as a Service (SaaS). This is a suite of cloud-based applications with a centralized database.

Benefits Of Enterprise Resource Planning

Smart Business Operations

Although Implementing an ERP system in an organization can be costly at first, it will produce significant advantages with the improvements of procedural efficiency and cost savings. It will unify disparate business operations and centralize data access that will make the business smarter, increasing both revenue and employee satisfaction.

Total Data Accessibility

Centralizing the business data will make it completely visible and easily accessible by all departments and the senior management.

Easy Department Transactions

ERP systems are a single source of data easily generating reports and data analytics. Departments can easily review data and compare reports without the office barriers of transmitted spreadsheets and email communication.

Modular Capabilities

Because of its modular design, ERP systems can be implemented according to business needs. A company can deploy only what components work best while omitting the remainder.

Reduced Operational Costs

When properly implemented, ERP systems can eliminate the routine business operations and repetitive manual functions. By doing so, ERP systems can reduce operational costs by 23% and overall administrative costs by 22%. Those are significant savings over time.

Improved Customer Satisfaction

A Priority of any business should be to gain and retain customers. With the efficiency of ERP centralized databases and streamlined processes, employees are freed up to give better attention to the customers. In the end, customer satisfaction is ensured.

Easy Collaboration

With the key advantage of centralized databases, coworkers can easily collaborate. The silos of disparate departments are eliminated. ERP provides on-demand access to the entire organization’s data and real-time updates and reports.

Optimum Operations

Best practices guide the development of most ERP systems. Combined further with a company’s own best practices, smart, standardized, and consistent business results will follow.

Scalability

Successful companies need to be scalable to keep up with growth. Growth can be an increased customer base, new products and services, or new procedures. Having the right ERP system in place accommodates a smooth scalability.

Challenges Of Enterprise Resource Planning

Expensive Purchase Costs

Small to medium businesses especially will find the price and the licensing high. Other solutions might be to obtain a cloud ERP system as a SaaS, or an open source ERP software.

Deployment And Maintenance Costs

Additionally, there will an additional a cost for manpower to help deploy the ERP system and to help maintain the system.

Inept Customization

ERP systems are customizable but complex. Without adequate expertise, time, and money, there will be usage deficiencies. Either not all capabilities are leveraged, or the system is not fully deployed. Best practices results will suffer.

Conclusion

Both small and large businesses can gain significant time and costs savings with the improved efficiencies deriving from the right ERP system. Both on-prem and cloud-based ERP solutions can prove effective. There are challenges, and the risks should be weighed. But the main in-house objectives of eliminating manual routine tasks, improving communication between departments, and centralizing the data store will be catalysts for growth and revenue increase.