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Posts from the ‘ERP & Manufacturing’ Category

Feb 17

IT And Marketing Show Strongest Interest In Adopting Gen AI First

IT, Marketing Show Strongest Interest In Adopting Gen AI First

  • Currently, 16% of organizations have implemented generative AI in production, while 44% are piloting it for potential applications.
  • Interest in deploying generative AI for production applications saw a fivefold increase from the first to the fourth quarter of 2023.
  • Healthcare, manufacturing, and education are the three industries most actively pursuing generative AI adoption.
  • A majority of organizations, 63%, deem CRM data critical to their generative AI initiatives.

These and many other insights are from Dresner Advisory Services‘ recent Generative AI Report. The advisory firm surveyed its research community of over 8,000 organizations and vendors’ customer communities. The study is global in scope, with 50% of respondents from North America, 26% from EMEA, 19% from Asia/Pacific and 6% from Latin America. Dresner’s report stands out for its in-depth and nuanced analysis of gen AI adoption across global organizations.

News about generative AI has captivated technology leaders. Demand for gen AI-related news and insights dominates many organization leaders’ time. 29% are following gen AI news updates constantly, and 30% say they often check in and see what’s new in gen AI, 24% regularly check the news. Overall, 72% of analytics and business intelligence (BI) professionals have made gen AI news a priority. North American respondents are the most diligent with constantly checking gen AI news, reflecting the region having the highest production use of gen AI.

Key takeaways from the report include the following:

Professionals in IT and marketing report plans to be the first adopters of generative AI, with 44% of IT and 36% of marketing professionals saying adopting gen AI is a primary focus. Operations/ production, sales, and C-level executives also show significant interest in adopting gen AI early. Dresner’s report states that “finance and human resources least often indicate overall interest, exceeding a majority only when aggregating their primary, secondary, and tertiary responses.”

gen ai

63% of organizations consider CRM data as critical or very important to generative AI. Finance and accounting data is considered the next most important, followed by call center and supply chain data. Dresner’s analysis found that respondents least often expect generative AI to leverage workforce (HR) data. Organizations are wary of using HR data due to privacy concerns combined with the stringent standards and safeguards on data security and its use across regulated industries today.

gen ai

Gen AI adoption across organizations accelerated rapidly in 2023. From 1Q23 to 4Q23, production use increased nearly fivefold, experimenting increased by 70%, and planned use in 12 months increased by 157%. Dresner’s research results reflect a major shift in generative AI prioritization last year. The report’s authors contend that implementation activity and funding were primarily from autonomous, decentralized sources, not from C-level mandates or sponsors, as it occurred late in fiscal years and into annual budget cycles.

gen ai

Consumer services lead gen AI production levels at 43%. Technology, business services, and healthcare have the next three highest levels of gen AI production in use today. The education industry reports the highest experimentation rate at 67%, closely followed by healthcare at 62%, while government trails at 50%. The report notes that the government also reports the highest levels for planned use beyond 12 months and no planned use.

gen ai

40% of organizations consider it critical to achieve productivity and efficiency gains from gen AI. One in three (30%) say improving customer experience and personalization is the next most critical priority, followed by improved search quality and decision-making (26%).

gen ai

Data privacy concerns are considered critical to 46% of organizations pursuing gen AI initiatives today. Legal and regulatory compliance, the potential for unintended consequences, and ethics and bias concerns are also significant. Less than half of respondents—46% and 43%, respectively—consider costs and organizational policy important to generative AI adoption.

gen ai

 

May 3

FinancialForce’s Spring 2022 Release Defines the Future of FP&A In Services

Economic uncertainty sends shock waves throughout businesses, with service organizations seeing its brunt. The recent drastic drop-off in Netflix subscribers is a case in point. Services CFOs say there is an urgent need to track how well their overarching planning strategies linking finance and operations perform. However, getting the data to analyze has been challenging for even the largest services businesses.

As a result, CFOs need Financial Planning & Analysis (FP&A) integrated with operational planning applications to make it easier to track plan performance across all P&Ls and financials. FinancialForce’s decision to launch a fully-featured FP&A on their ERP Cloud platform shows they read the services market clearly and listen to their customers’ CFOs on what matters most.

CFOs Want To Know The Financial Impact Of Every Planning Decision

Even during economic stability, finance teams struggle to get operations planning teams the data they need to predict the financial outcomes of decisions. Line-of-business leaders look to finance to provide accurate, detailed information on the financial implications of every planning decision. By having FP&A use the same data accounting, reporting and planning have, CFOs, COOs, and their teams get greater visibility and control over every aspect of budgeting and forecasting.

One of FP&A’s greatest shortcomings in the past was relying only on siloed financial data alone with little visibility into operational planning. Financial teams need access to all available data across finance and operations to do their jobs well and create accurate forecasts. Getting FP&A right with any ERP platform needs to start with the goal of delivering integrated business planning. Sales management and their teams also need visibility into FP&A reporting and analysis to manage revenue. FinancialForce’s decades of experience on the Salesforce platform combined with the integration expertise Salesforces’ MuleSoft acquisition brought to the company four years ago will increase the probability of their FP&A solution gaining adoption.

Services companies’ CFOs are grappling with new economic uncertainties every week. As a result, they’re most interested in getting greater visibility and control over the planning process, including version control, more automated multi-planning options, and more real-time enterprise-wide collaboration, all on a single platform. FinancialForce’s DevOps and product management teams deserve credit for identifying these challenges and including them in their FP&A application delivered in the Spring 2022 release.

FinancialForce

FinancialForce’s long-awaited FP&A solution enables analysts to create multiple what-if scenarios using calculation rules and mass functions, create dynamic plans and stress-test assumptions, and better anticipate their return by area and investment.

The future of FP&A Is An Integrated Cloud

Service organizations are quicker to migrate to the cloud versus their product-based counterparts. That’s because procurement, order-to-cash, and supply chain management workflows tend to be less complex than product-based businesses. Services organizations also need financial management, procure-to-pay, and Professional Services Automation (PSA), all on the same platform to support operational planning with FP&A.

FinancialForce’s Multi-X functionality is expanded in the Spring 2022 release to simplify the consolidation of financial statements and meet the needs of multi-entity organizations. In the latest release, it’s possible to record taxes due from intercompany tax transactions, accelerating the intercompany process for taxation and reporting. The Spring 2022 release also streamlines the creation of multi-company sales invoices and simplifies consolidated financial statement preparation with consolidation group structure capabilities.

FinancialForce

Multi-X enables the recording and sharing across a multi-tier or multi-entity business.

New localization features that are essential to running a global business were added, including support for Switzerland, Denmark, Finland, and Austria, as well as enhanced business operations in Germany and Australia. In addition, multi-X supports multi-company invoicing support and advanced invoice consolidations for multi-revenue billing. Calculating and recording tax on intercompany transactions and enabling cash matching process across companies are also supported.

FP&A’s future is an integrated cloud, further validated by FinancialForce’s’ launch of ERP Cloud, Professional Services Cloud, and enhancements to its Customer Success solutions. “In today’s business environment, organizations must be able to respond to disruptions quickly while continuing to innovate and deliver tangible outcomes to their customers,” said Dan Brown, Chief Product and Strategy Officer at FinancialForce. “Our Spring 2022 release gives our customers a richer toolset to help pursue their primary goal, delivering exceptional customer outcomes while improving the customer experience across the opportunity-to-renewal journey.”

New Professional Services (PS) Cloud additions in the Spring 2022 release include customer-requested improvements to skills and resource management, services estimating, and project management capabilities. FinancialForce’s customers have also requested improved resource management to scale their efforts to train and retain their workforce. As a result, the Spring 2022 Release adds intelligent automation to the staffing process by enabling auto-assignment of resource requests that meet specific criteria and an expanded capability to model ideal staffing scenarios across a project, opportunity, or region. These enhancements improve PS Cloud’s resource optimization capabilities and enable resource managers to deploy ever larger and more complex teams efficiently and cost-effectively.

Conclusion

Services organizations are looking for cloud-based professional services ERP systems that deliver greater forecast accuracy, faster forecasting and budgeting, and improved accountability, visibility, and control. Integrated clouds are the future of FP&A for all these factors and the need all services organizations have to improve revenue and operations performance. In addition, given the growing economic uncertainty today, CFOs also want to increase better predictability and better risk management strategies while also supporting more collaboration. All these factors combined are defining the future of FP&A in an integrated cloud, which is what FinancialForce has been doing for decades on the Salesforce platform.

May 10

Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022

Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022
  • Worldwide end-user spending on public cloud services is forecast to grow 23.1% in 2021 to total $332.3 billion, up from $270 billion in 2020.
  • Garter predicts worldwide end-user spending on public cloud services will jump from $242.6B in 2019 to $692.1B in 2025, attaining a 16.1% Compound Annual Growth Rate (CAGR).
  • Spending on SaaS cloud services is predicted to reach $122.6B this year, growing to $145.3B next year, attaining 19.3% growth between 2021 and 2022.  

These and many other insights are from Gartner Forecasts Worldwide Public Cloud End-User Spending to Grow 23% in 2021.  The pandemic created the immediate need for virtual workforces and cloud resources to support them at scale, accelerating public cloud adoption in 2020 with momentum continuing this year. Containerization, virtualization, and edge computing have quickly become more mainstream and are driving additional cloud spending. Gartner notes that CIOs face continued pressures to scale infrastructure that supports moving complex workloads to the cloud and the demands of a hybrid workforce.

Key insights from Gartner’s latest forecast of public cloud end-user spending include the following:

  • 36% of all public cloud services revenue is from SaaS applications and services this year, projected to reach $122.6B with CRM being the dominant application category. Customer Experience and Relationship Management (CRM) is the largest SaaS segment, growing from $44.7B in 2019 to $99.7B in 2025, attaining a 12.14% CAGR. SaaS-based Enterprise Resource Planning (ERP) systems are the second most popular type of SaaS application, generating $15.7B in revenue in 2019. Gartner predicts SaaS-based ERP sales will reach $35.8B in 2025, attaining a CAGR of 12.42%.
  • Desktop as a Service (DaaS) is predicted to grow 67% in 2021, followed by Infrastructure-as-a-Service (IaaS) with a 38.5% jump in revenue. Platform-as-a-Service (PaaS) is the third-fastest growing area of public cloud services, projected to see a 28.3% jump in revenue this year. SaaS, the largest segment of public cloud spending at 36.9% this year, is forecast to grow 19.3% this year. The following graphic compares the growth rates of public cloud services between 2020 and 2021.  
  • In 2021, SaaS end-user spending will grow by $19.8B, creating a $122.6B market this year. IaaS end-user spending will increase by $22.7B, the largest revenue gain by a cloud service in 2021. PaaS will follow, with end-user spending increasing $13.1B this year. CIOs and the IT teams they lead are investing in public cloud infrastructure to better scale operations and support virtual teams. CIOs from financial services and manufacturing firms I’ve recently spoken with are accelerating cloud spending for three reasons. First, create a more virtual organization that can scale; second, extend the legacy systems’ data value by integrating their databases with new SaaS apps; and third, an urgent need to improve cloud cybersecurity.

Conclusion

CIOs and the organizations they serve are prioritizing cloud infrastructure investment to better support virtual workforces, supply chains, partners, and service partners. The CIOs I’ve spoken with also focus on getting the most value out of legacy systems by integrating them with cloud infrastructure and apps. As a result, cloud infrastructure investment starting with IaaS is projected to see end-user spending increase from $82B this year to $223B in 2025, growing 38.5% this year alone. End-user spending on Database Management Systems is projected to lead all categories of PaaS through 2025, increasing from $31.2B this year to $84.8B in 2025. The following graphic compares cloud services forecasts and growth rates:

May 3

Which ERP Systems Are Most Popular With Their Users In 2021?

Which ERP Systems Are Most Popular With Their Users In 2021?
  • Sage Intacct, Oracle ERP Cloud, and Microsoft Dynamics 365 ERP are the three highest-rated ERP systems by their users.
  • 86% of Unit4 ERP users say their CRM system is the best of all vendors in the study. The survey-wide satisfaction rating for CRM is 73%, accentuating Unit4 ERP’s leadership in this area.
  • 85% of Ramco ERP Suite users say their ERP systems’ analytics and reporting is the best of all 22 vendors evaluated.

These and many other insights are from SoftwareReview’s latest customer rankings published recently in their Enterprise Data Quadrant Report, Enterprise Resource Planning, April 2021. The report is based entirely on attitudinal data captured from verified owners of each ERP system reviewed. 1,179 customer reviews were completed, evaluating 22 vendors. SoftwareReviews is a division of the world-class IT research and consulting firm Info-Tech Research Group. Their business model is based on providing research to enterprise buyers on subscription, alleviating the need to be dependent on vendor revenue, which helps them stay impartial in their many customer satisfaction studies. Key insights from the study include the following:

  • Sage Intacct, Oracle ERP Cloud, Microsoft Dynamics 365 ERP, Acumatica Cloud ERP, Unit4 ERP and FinancialForce ERP are most popular with their users.  SoftwareReview found that these six ERP systems have the highest Net Emotional Footprint scores across all ERP vendors included in the study. The Net Emotional Footprint measures high-level user sentiment. It aggregates emotional response ratings across 25 questions, creating an indicator of overall user feeling toward the vendor and product. The following quadrant charts the results of the survey:
  • 80% of Acumatica Cloud ERP users say their system helps create more business value, leading all vendors on this attribute. How effective an ERP system is at adapting to support new business and revenue models while providing greater cost visibility is the essence of how they deliver business value. The category average for this attribute is 75%. Of the 22 vendors profiled, 12 have scores at the average level or above, indicating many ERP vendors are focusing on these areas to improve the business case of adopting their systems.
Which ERP Systems Are Most Popular With Their Users In 2021?
  • 86% of Sage Intacct ERP users say their system excels at ease of implementation, leading all vendors in the comparison by a wide margin. Implementing a new ERP system can be a costly and time-consuming process as it involves extensive training, change management, and integration. Ease of Implementation received a category score of 75% across the 22 vendors, indicating ERP vendors are doubling down investments to improve this area. Just 11 of the 22 ERP vendors scored above the category average.
Which ERP Systems Are Most Popular With Their Users In 2021?

Apr 28

LinkedIn Best Companies To Work For In 2021 Dominated By Tech

  • Four of LinkedIn’s top ten companies to grow your career in 2021 are tech leaders.
  • Amazon is the highest rated company, followed by Alphabet (2nd), IBM (6th), and Apple (8th).
  • 15 of the 50 top companies in the U.S. are in the tech industry, including Oracle, Salesforce, and SAP.

These and many other insights are from the LinkedIn Top Companies 2021: The 50 best workplaces to grow your career in the U.S. published today. All 50 companies are currently hiring and have over 300,000 jobs available right now. LinkedIn’s analysis of the best companies to grow your career spans 20 countries, including Australia, Brazil, Canada, China, France, Germany, India, Italy, Japan, Malaysia, Mexico, the Netherlands, the Philippines, Saudi Arabia, Singapore, Spain, Qatar, the UAE, and the U.K. 

LinkedIn is relying on a new methodology for the 2021 Top Companies Report. They’re basing the methodology has seven key pillars, each revealing an important element of career progression: the ability to advance, skills growth, company stability, external opportunity, company affinity, gender diversity, and educational background. LinkedIn provides an in-depth description of how they built their methodology here.

The 10 Best Companies To Grow Your Career In 2021

  1. Amazon – According to LinkedIn, Amazon has built an innovative remote-onboarding system, and it has more than 30,000 openings now. The fastest-growing skills in demand at Amazon include User Experience Design (UED), Digital Illustration, and Interaction Design. LinkedIn’s analysis shows the most in-demand jobs are Health And Safety Specialist, Station Operations Manager, Learning Manager.
  1. Alphabet, Inc – Planning to add at least 10,000 jobs in the U.S. alone and investing $7B in data centers and offices across 19 states, Alphabet grew revenue 47% last year, reaching $13B.  According to LinkedIn, the most in-demand jobs are Digital Specialist, Field Sales Specialist, and Business Systems Analyst.
  1. JPMorgan Chase & Co. – JPMorgan now offers 300 accredited skills and education programs to its workers, and the bank has been boosting wages for thousands of customer-facing roles to $16-$20 an hour. The most in-demand jobs include Market Specialist, Software Engineering Specialist, and Mortgage Underwriter.
  1. AT&T – 2020 was a tough year for AT&T, increasing the urgency the company has to grow its wireless and WarnerMedia businesses. Due to the pandemic, the company had to close hundreds of stores. Fortunately, AT&T was able to help the employees affected by the closures to find new jobs. The most in-demand jobs are Service Analyst, Trading Analyst, and Investment Specialist.
  1. Bank of America – Bank of America rose to the challenges of 2020, quickly redeploying almost 30,000 employees to assist in its role facilitating the government-backed Paycheck Protection Program. The most in-demand jobs are Trading Analyst, Investment Specialist, and Financial Management Analyst.
  1. IBM – More than one-third of IBMs revenue now comes from work related to cloud computing. The company’s Red Hat unit is a leading contributor to that growth, prizing skills such as Linux, Java, Python, and agile methodologies. IBM also is a leader in hiring autistic people through its Neurodiversity program. Most in-demand jobs include Back End Developer, Enterprise Account Executive, and Technical Writer.
  1. Deloitte –  Deloitte’s key activities span audit, assurance, tax, risk, and financial advisory work, as well as management consulting. It’s aiming to hire 19,000 people in the year ending May 29. Top recruiting priorities currently include cybersecurity, cloud computing, and analytics specialists.
  1. Apple – LinkedIn finds that Apple is committed to building an inclusive culture. Over half of its new hires in the U.S. represent historically underrepresented groups in tech — and the company claims to have achieved pay equity in every country where it operates—looking for an in? Apple has nearly 3,000 open jobs in the U.S. right now, ranging from its “genius” role at its retail stores to executive assistants and software engineers. 
  1. Walmart –  In February, the retail giant promised further raises to over 400,000 of its people and months later announced it would increase the share of its hourly store employees who work full-time to over 66% (up from 53% five years ago). Meanwhile, Walmart continues to think beyond the store as it ventures deeper into the e-commerce realm. Most in-demand jobs include Operational Specialist, Fulfillment Associate, and Replenishment Manager.
  1. EY – The accounting firm spent $450 million on employee training in 2020. And it is planning to hire over 15,000 people in the next year. With that much talent coming in, EY is focused on bringing in workers with diverse backgrounds, focusing on gender identity, race, and ethnicity, disability, LGBT+, and veterans. The most in-demand jobs include Strategy Director, Business Transformation Consultant, and Information Technology Consulting Manager.

Apr 24

FinancialForce’s Spring 2021 Release Shows Why Being Customer-Centric Pays

FinancialForce's Spring 2021 Release Shows Why Being Customer-Centric Pays

Bottom Line: Customer revenue lifecycles are the lifeblood of any services business, making FinancialForce’s Spring 2021 release timely given the services-first revenue renaissance happening today.

The essence of an excellent services business is that it can consistently create expectations clients trust and the business regularly exceeds. Orchestrating the best people for a given project at the right time, tracking costs, revenue, and margin across all services revenue, including those associated with a client’s assets, is very challenging. Customer revenue lifecycles are in the data, yet no one can get to them because they’re hidden across multiple systems that aren’t integrated. Knowing how efficient a services business is at turning customer engagement into cash is what everyone needs to know, but no one can find. The challenge is equally as daunting for long-established services providers and those rushing into new services businesses to redefine themselves in the hope of profits that are more consistent and fewer price wars.

How Much Is Customer Engagement Is Worth?

Services businesses face the paradox of exceeding client expectations with every engagement but not knowing if extra time, resources, and staff invested are paying off with more revenue and profit. FinancialForce’s Spring 2021 release looks to solve this problem. What galvanizes the ERP, PSA, and platform announcements is a fresh intensity on customer centricity, both for the services business adopting the Spring 2021 release and the customers it’s intended to serve.

Knowing if and by how much a given customer engagement and its revenue lifecycle generate cash, and its potential is one of the core focus areas of the Spring 2021 release. It’s badly needed as many services are flying blind today, overcommitting resources for little return and too often losing control of client engagement and paying the price in lost margin and profits. FinancialForce sees that pain and wants to alleviate it with better financial visibility on all aspects of customer services revenue. FinancialForce aims to provide customer-centric financial reporting down to the revenue stream and costing measure level.  

FinancialForce's Spring 2021 Release Shows Why Being Customer-Centric Pays
Knowing every customer’s impact on revenue and profitability from all revenue streams will make managing services engagements much more accurate, easier to manage, and more profitable. 

Key Takeaways From The Spring 2021 Release

Customer centricity seen through a financial lens is the cornerstone of FinancialForce’s latest release. One of the primary goals of this release is to update more applications to Salesforce Lightning to provide FinancialForce users with a more consistent user experience across all applications.  Salesforce has been doubling down for years on Lightning and its user experience technologies, with FinancialForce reaping the benefits for over a decade. FinancialForce is transitioning their core Professional Services Automation (PSA), Billing, Accounting & Finance and Procurement, Order and Inventory Management to Lightning in this release in response to their customers wanting a consistent user experience across the entire FinancialForce suite of applications.  The Spring 2021 release reflects how FinancialForce strives to provide a real-time understanding of customer lifetime value for their ERP and PSA customers.  

Additional key takeaways include the following:

  • FinancialForce sees reducing days to close as one of the highest priorities they need to address today. The majority of new feature announcements center on how the days to close cycles can be streamlined, especially across multi-company and multisite locations across geographic and currency-specific regions of the world. Multi-company currency revaluation will help FinancialForce customers who operate across multiple geographies that operate in different currencies and will be especially useful for those clients creating new global channels and considering foreign acquisitions. Further showing the high priority they are putting on reducing days to close, the Spring 2021 release also includes automated eliminations, multi-company period close for software closes, which are designed to temporarily close out a financial report and revenue schedules that can provide a future view in revenues – a key factor in knowing customer revenue lifecycles.
  • New features and a new Lightning interface for Accounting, Billing Central, and Inventory Management simplifies complex transactions for users. FinancialForce has one of the most customer-driven product management teams in enterprise software. The depth of features they have added to inventory management, transactional and reconciliation processes for accounting, drop-ship use cases, and enhancements for adding products to billing contracts show how much FinancialForce is listening to customers.
  • AI-enhanced financial reporting that works with any Einstein data set. FinancialForce leads the Salesforce partner ecosystem when it comes to integrating Tableau CRM (formerly known as Einstein Analytics) into its platform. Now thirteen releases in, FinancialForce’s Spring 2021 release reflects the intuitive, adaptive intelligence that the product management team aims to achieve by integrating Einstein into their financial reporting workflows. 
  • Professional Services Automation (PSA) Applications Including Resource Management, Project Management, and Time & Expense upgraded to Lightning.  Transitioning three of the core PSA applications to Lightning will help broaden adoption and make them easier to upsell and cross-sell across the FinancialForce customer base. It will also help existing customers using these applications get new employees up to speed faster on them, given how much more streamlined Lightning is as an interface compared to previous versions.
  • Intelligent Staffing solves the complex challenges resource managers face when assigning the best possible associates to a given project. Designed to filter and intelligently rank potential resources based on region, practice, group skill sets, and availability, Intelligent Staffing is designed to get resource managers as close to an ideal match as possible for a given project’s requirements. This is a much-welcomed new feature by FinancialForce customers who are large-scale services providers as they’re facing the challenges of assigning the right person to the right project at the right time to ensure project success.    
  • Integration of Salesforce AI’s Next Best Action (NBA) will raise the level of project expertise at scale across customers.  Part of the customer centricity focus in Spring 2021 is focused on providing customers with new technologies and applications to share expertise and knowledge at scale. Next Best Action provides prescriptive guidance for the project manager and will see heavy use in new associate onboarding across services businesses and achieve greater corporate-wide learning at scale. This is consistent with the focus in the Spring 2021 release on bringing greater space and speed to mid-size and larger services customers.

Conclusion

FinancialForce defines customer engagement and centricity from a financial standpoint in the Spring 2021 release. Too often, services businesses commit to large-scale projects without a clear idea of the customer revenue lifecycle. With FinancialForce, they can stop and ask if the level of customer engagement they’re committing to is worth it or not – and if it isn’t, what needs to be done. FinancialForce is doubling down on user experience and accelerating time-to-close, two areas their customers want innovation to and look to them to deliver. Look for FinancialForce to scale out with more MuleSoft and Tableau integration scenarios, all aimed at capitalizing on their expertise developing on the Salesforce platform. There’s a bigger challenge to customer engagement on the horizon, and that’s providing a real-time view of financials across all customers with all available data across a business, making MuleSoft integration key to FinancialForce’s future growth.

Mar 8

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

Bottom Line: Using AI to measure and predict revenue, costs, and margin across all Professional Services (PS) channels leads to greater accuracy in predicting payment risks, project overruns, and service forecasts, reducing revenue leakage in the process.

Professional Services’ Revenue Challenges Are Complex

Turning time into revenue and profits is one of the greatest challenges of running a Professional Services (PS) business. What makes it such a challenge is incomplete time tracking data and how quickly revenue leaks spring up, drain margins, and continue unnoticed for months. Examples of revenue leaks across a customers’ life cycles include the following:

  • Billing errors are caused by the booking and contract process not being in sync with each other leading to valuable time being wasted.
  • When products are bundled with services, there’s often confusion over recognizing each revenue source, when, and by which PS metric.
  • Inconsistent, inaccurate project cost estimates and actual activity lead to inaccurate forecasting, delaying the project close and the potential for bad debt write-offs and high Days Sales Outstanding (DSO).
  • Revenue leakage gains momentum and drains margins when the following happens:
    • Un-forecasted delays and timescale creep
    • Reduced utilization rates across each key resource required for the project to be completed
    • Invoice and billing errors that result in invoice disputes that turn into high DSOs & write-offs
    • Incorrect pricing versus the costs of sales & service often leads to customer churn.
    • Revenue leakage gains momentum as each of these factors further drains margin

Adding up all these examples and many more can easily add up to 20-30% of actual lost solution and services margin. In many ways, it’s like death by a thousand small cuts. The following graphic provides examples across the customer lifecycle:

How FinancialForce Is Using AI To Fight Revenue Leakage

Why Professional Services Are Especially Vulnerable To Revenue Leakage 

Selling projects and the promise of their outcomes in the future create a unique series of challenges for PS organizations when it comes to controlling revenue leakage. It often starts with inaccurately scoping a project too aggressively to win the deal, only to determine the complexity of tasks originally budgeted for will take 10 – 30% longer or more. Disconnects on project scope are unfortunately too common, turning small revenue leaks into major ones and the potential of long Days Sales Outstanding (DSO) on invoices. When revenue leaks get ingrained in a project’s structure, they continue to cascade into each subsequent phase, growing and costing more than expected.

The SPI 2021 Professional Services Maturity™ Benchmark Service published by Services Performance Insight, LLC in February of this year provides insights into the hidden costs and prevalence of revenue leakage. The following table illustrates how organizations with high levels of revenue leakage also perform badly against other key metrics, including client referencability. The more revenue leakage an organization experiences, the more billable utilization drops, on-time project deliveries become worse, and executive real-time visibility becomes poorer.

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

It’s noteworthy that FinancialForce is now on its 12th consecutive product release that includes Salesforce Einstein, and many customers, including Five9, are using AI to manage revenue leakage across their PS business. Throughout the pandemic, the FinancialForce DevOps, product management, and software quality teams have been a machine, creating rich new releases on schedule and with improved AI functionality based on Einstein. The 12th release includes prebuilt data models, lenses, dashboards, and reports.

Andy Campbell, Solution Evangelist at FinancialForce, says that “FinancialForce customers have access to best practices to minimize revenue leakage by scoping and selling the right product and services mix to allocating the optimal range and amount of services personnel and finally billing, collecting and recognizing the right amount of revenue for services provided.” Andy continued, saying that recent dashboards have been built for resource managers to automate demand and capacity planning and service revenue forecasting and assist financial analysts in managing deferred revenue and revenue leakage.

By successfully integrating Einstein into their ERP system for PS organizations, FinancialForce helps clients find new ways to reduce revenue leakage and preserve margin. Relying on AI-based insights for each phase of a PS engagement delivered a 20% increase in Customer Lifetime Value according to a FinancialForce customer. And by combining FinancialForce and Salesforce, customers see an increased bid:win ratio of 10% or more. The following graphic illustrates how combining the capabilities of Einstein’s AI platform with FinancialForce delivers results.

How FinancialForce Is Using AI To Fight Revenue Leakage

Conclusion

FinancialForce’s model building in Einstein is based on ten years of structured and unstructured data, aggregated and anonymized, then used for in-tuning AI models. FinancialForce says these models are used as starting points or templates for AI-based products and workflows, including predict to pay.  Salesforce has also done the same for its Sales Cloud Analytics and Service Cloud Analytics. In both cases, Salesforce and FinancialForce customers benefit from best practices and recommendations based on decades of data, which should be particularly interesting considering the “black swan” nature of 2020 data for most of their customers.

Jan 12

10 Ways AI Is Improving Cannabis Yields And Security

  • According to BDS Analytics, the Covid-19 pandemic drove retail sales up 35% above industry forecasts, accelerated by cannabis businesses being declared “essential” for medical purposes in virtually every U.S. legal market.
  • Fueled by strong consumer demand, annual legal (medical and adult-use) sales are projected to grow at a compound annual growth rate (CAGR) of 21%, to reach more than $41 billion by 2025 (from $13.2 billion in 2019), according to New Frontier Data.
  • BDS Analytics predicts that the U.S. Cannabis Industry will generate $20.8 billion in direct spending in 2021 and $39.6 billion in total economic contribution after factoring its indirect economic effects.

Bottom Line:  With an average yield per acre of $1.1 million, legal cannabis agriculture dwarfs all other crops in revenue potential while also providing the resources needed to fund AI-based monitoring to improve yields and security. 

Cannabis’ value per acre dwarfs all other crops being produced in North America today, prompting every commercial grower to consider how they can improve yields further while securing their crops on a 24/7, virtual basis. Recent studies by the USDA, The Rand Corporation, and the Marijuana Cultivators of Oregon find that at an average price of $1,948 per pound at Colorado prices, an acre of marijuana can yield more than $1.1 million per acre. The studies compared the most widely grown crops in the U.S., including corn, soybeans, oats, and wheat, which all yield less than $1,000 per harvested acre. The following graphic from New Frontier Data illustrates how profitable an acre of marijuana is to cultivate than other crops. 

10 Ways AI Is Improving Cannabis Yields And Security

Using AI to Protect & Grow a Cash Crop

AI and machine learning-based techniques based on real-time monitoring data are an integral part of today’s innovation in cannabis farm management.  Supervised machine learning algorithms capable of identifying patterns and sequences in imagery from thermal, infrared, and night vision cameras in real-time can help identify diseases affecting plants early. Identifying and alerting farm staff of a breach or break-in by an animal or person is possible using AI-based smart monitoring systems.

The more advanced a smart monitoring system is in its use of machine learning and real-time monitoring integration, the more effective it is in spotting anomalous activity.  Over time, the best AI-based remote monitoring and surveillance systems “learn” or begin to identify recurring patterns in data. Cannabis farms rely on AI and machine learning to identify which techniques for improving yield rates by specific fertilizer treatment produce the most flowers and overall yield per acre.

The following are ten ways AI is being used for improving cannabis yields and security:

  1. Monitoring real-time video feeds of remote cannabis fields using machine learning-based surveillance systems can identify a breach by an animal or human then send an alert immediately.  Given how valuable a single acre of cannabis is to a farm, knowing in real-time if there’s been an attempted breach or break-in can save thousands of dollars in potential crop damage and theft. Federated cannabis farms with multiple remote locations are starting to use AI and machine learning-based remote monitoring to secure their operations. Machine-learning based video surveillance systems can be programmed or trained over time to identify employees versus unknown people and easily spot animals attempting to break into a field.  The following image from Twenty20 Solutions illustrates how machine learning is used for identifying activity at a remote location:
  • Reducing the dependence on onsite security guards alone and gaining a 24/7, 365-day monitoring view of each grow and farm site. Instead of relying only on onsite security teams to monitor video feeds in real-time, cannabis growers turn to AI and machine learning-based surveillance to isolate the most anomalous or unexpected events given the pattern of previous activity on a site. Reducing the cost and insurance liability of having security teams on site is one of the most significant benefits of relying on a cloud-based remote monitoring system that can interpret and provide alerts based on real-time data.
  • AI-based surveillance monitoring systems can prepare activity reports in minutes for state and federal auditors, saving farmers and administrative staff thousands of hours a year getting the data together for audit teams.  Using machine learning and advanced video analytics, growers and their staff can prepare for state and federal audit reports in minutes instead of the many hours needed in the past.   
  • Helping to keep licensed cannabis growers in compliance by providing a 24/7, 90 day or longer video history of all activities at their farms keeps them in compliance with state regulatory requirements. Included in several states’ requirements are the specific requirements for video footage access, video archiving, access requirements, how cameras are placed, and how quickly video footage can be accessed. State regulatory agencies are initiating audits of licensed cannabis growing facilities in 2021. All states require video footage to be archived, yet 72% of cannabis operators fail to comply with security and surveillance requirements, according to a recent study by the Brightfield Group:
    • California regulations require that all video recordings from surveillance be saved 90 days or longer.
    • Washington requires all video recordings to be archived for a minimum of 45 days.
    • Oregon requires licensed cannabis growers to retain 24/7 video for 90 days with a minimum of 1.3mp per camera at 10fps. The exterior is 5fps.
  • Cannabis farms often experiment with new fertilizers and plant treatments on a pilot acre to see if they achieve the expected results, and machine learning-based analysis of video stream data helps track results.  Agricultural improvements in cannabis farming continue to accelerate as medical and leisure demand continues to grow exponentially. For example, a cannabis grower will often begin planting in the May/June timeframe to achieve a density of up to 4,000 plants per acre. Taking the real-time data stream infrared and thermal cameras of the acre will quickly tell growers how effective their new fertilizer and plan treatments are. Using the data from their monitoring system, the growers will expand the treatment to their entire farm, often over 40 to 50 acres in size.
  • Monitoring every access point to a facility with video surveillance 24/7 combined with sound recording can prove invaluable in stopping a break-in before it happens. Every entrance to a cannabis farm needs to be considered a primary threat vector if the farm will stay safe. Advanced remote monitoring and surveillance systems can provide video analytics that correlates sound, video, and status of infrared and thermal cameras, which together can help identify potential break-ins. And with real-time alerts, farm staff can take action immediately even if they aren’t onsite.
  • A few of the largest cannabis growing companies are experimenting with advanced video analytics combining infrared and thermal camera technologies to monitor insects and rodents’ impact on yield rates.  Real-time video feeds are being digitally analyzed using advanced video analytics techniques by the largest cannabis farms today to find out how effective pesticides, insect, and rodent deterrents are at protecting their cannabis crops.    
  • When a surveillance system is cloud-based, it is possible to access any farm or cannabis sites’ real-time video feeds, history of alerts, and advanced video analytics from any browser-based device at any time. Remote monitoring systems that are cloud-based often provide much greater flexibility in viewing, analyzing, and sharing monitoring data than their on-premise system counterparts. Any device with a browser can access the platform’s reporting features and know what is going on at a remote farm or cannabis production facility. 
  • AI-based remote monitoring systems can also identify potential safety hazards to workers and reduce workplace injuries and potential liability litigation. Using advanced pattern matching supported by supervised machine learning algorithms, cannabis growers can identify when workers in high-risk roles are at risk of getting hurt while on the job. All cannabis facilities in the U.S. continue to have the requirement of everyone wearing a face shield and masks for the site to stay in compliance with CDC guidelines. Remote monitoring systems can tell immediately which work teams need coaching to remain in compliance. 
  1. Define access privileges across a farm facility by the level of access every employee needs to do their job, which is especially useful for new hires. New hires often start in the field and don’t need access to the front offices or the accounting department, for example. One of the most challenging aspects of running a cannabis business is cash management. Using an AI-based surveillance and monitoring system integrated into the local security system and intelligent locks, employees are provided the level of access they need on the first day to be productive.

Jul 25

10 Ways AI Is Improving New Product Development

10 Ways AI Is Improving New Product Development

  • Startups’ ambitious AI-based new product development is driving AI-related investment with $16.5B raised in 2019, driven by 695 deals according to PwC/CB Insights MoneyTree Report, Q1 2020.
  • AI expertise is a skill product development teams are ramping up their recruitment efforts to find, with over 7,800 open positions on Monster, over 3,400 on LinkedIn and over 4,200 on Indeed as of today.
  • One in ten enterprises now uses ten or more AI applications, expanding the Total Available Market for new apps and related products, including chatbots, process optimization and fraud analysis, according to MMC Ventures.

From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace.

Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout. As AI and ML get more ingrained in new product development, the IoT platforms and ecosystems supporting smarter, more connected products need to make plans now how they’re going to keep up. Relying on technology alone, like many IoT platforms are today, isn’t going to be enough to keep up with the pace of change coming.   The following are 10 ways AI is improving new product development today:

  • 14% of enterprises who are the most advanced using AI and ML for new product development earn more than 30% of their revenues from fully digital products or services and lead their peers is successfully using nine key technologies and tools. PwC found that Digital Champions are significantly ahead in generating revenue from new products and services and more than a fifth of champions (29%) earn more than 30% of revenues from new products within two years of information. Digital Champions have high expectations for gaining greater benefits from personalization as well. The following graphic from Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.) compares Digital Champions’ success with AI and ML-based new product development tools versus their peers:

10 Ways AI Is Improving New Product Development

 

  • 61% of enterprises who are the most advanced using AI and ML (Digital Champions) use fully integrated Product Lifecycle Management (PLM) systems compared to just 12% of organizations not using AI/ML today (Digital Novices). Product Development teams the most advanced in their use of AL & ML achieve greater economies of scale, efficiency and speed gains across the three core areas of development shown below. Digital Champions concentrate on gaining time-to-market and speed advantages in the areas of Digital Prototyping, PLM, co-creation of new products with customers, Product Portfolio Management and Data Analytics and AI adoption:

10 Ways AI Is Improving New Product Development

  • AI is actively being used in the planning, implementation and fine-tuning of interlocking railway equipment product lines and systems.  Engineer-to-order product strategies introduce an exponential number of product, service and network options. Optimizing product configurations require an AI-based logic solver that can factor in all constraints and create a Knowledge Graph to guide deployment. Siemens’ approach to using AI to find the optimal configuration out of 1090 possible combinations provides insights into how AI can help with new product development on a large scale. Source: Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019.

10 Ways AI Is Improving New Product Development

  • Eliminating the roadblocks to getting new products launched starts with using AI to improve demand forecast accuracy. Honeywell is using AI to reduce energy costs and negative price variance by tracking and analyzing price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management getting solid returns across the new product development process. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

10 Ways AI Is Improving New Product Development

  • Relying on AI-based techniques to create and fine-tune propensity models that define product line extensions and add-on products that deliver the most profitable cross-sell and up-sell opportunities by product line, customer segment and persona. It’s common to find data-driven new product development and product management teams using propensity models to define the products and services with the highest probability of being purchased. Too often, propensity models are based on imported data, built-in Microsoft Excel, making their ongoing use time-consuming. AI is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model created in Microsoft Power BI.

10 Ways AI Is Improving New Product Development

  • AI is enabling the next generation of frameworks that reduce time-to-market while improving product quality and flexibility in meeting unique customization requirements on every customer order. AI is making it possible to synchronize better suppliers, engineering, DevOps, product management, marketing, pricing, sales and service to ensure a higher probability of a new product succeeding in the market. Leaders in this area include BMC’s Autonomous Digital Enterprise (ADE). BMC’s ADE framework shows the potential to deliver next-generation business models for growth-minded organizations looking to run and reinvent their businesses with AI/ML capabilities and deliver value with competitive differentiation enabled by agility, customer centricity and actionable insights. The ADE framework is capable of flexing and responding more quickly to customer requirements than competitive frameworks due to the following five factors: proven ability to deliver a transcendent customer experience; automated customer interactions and operations across distributed organizations; seeing enterprise DevOps as natural evolution of software DevOps; creating the foundation for a data-driven business that operates with a data mindset and analytical capabilities to enable new revenue streams; and a platform well-suited for adaptive cybersecurity. Taken together, BMC’s ADE framework is what the future of digitally-driven business frameworks look like that can scale to support AI-driven new product development. The following graphic compares the BMC ADE framework (left) and the eight factors driving digital product development as defined by PwC (right) through their extensive research. For more information on BMC’s ADE framework, please see BMC’s Autonomous Digital Enterprise site. For additional information on PwC’s research, please see the document Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.).

10 Ways AI Is Improving New Product Development

  • Using AI to analyze and provide recommendations on how product usability can be improved continuously. It’s common for DevOps, engineering and product management to run A/B tests and multivariate tests to identify the usability features, workflows and app & service responses customers prefer. Based on personal experience, one of the most challenging aspects of new product development is designing an effective, engaging and intuitive user experience that turns usability into a strength for the product. When AI techniques are part of the core new product development cycle, including usability, delivering enjoyable customer experiences, becomes possible. Instead of a new app, service, or device is a chore to use, AI can provide insights to make the experience intuitive and even fun.
  • Forecasting demand for new products, including the causal factors that most drive new sales is an area AI is being applied to today with strong results. From the pragmatic approaches of asking channel partners, indirect and direct sales teams, how many of a new product they will sell to using advanced statistical models, there is a wide variation in how companies forecast demand for a next-generation product. AI and ML are proving to be valuable at taking into account causal factors that influence demand yet had not been known of before.
  • Designing the next generation of Nissan vehicles using AI is streamlining new product development, trimming weeks off new vehicle development schedules. Nissan’s pilot program for using AI to fast-track new vehicle designs is called DriveSpark. It was launched in 2016 as an experimental program and has since proven valuable for accelerating new vehicle development while ensuring compliance and regulatory requirements are met. They’ve also used AI to extend the lifecycles of existing models as well. For more information, see the article, DriveSpark, “Nissan’s Idea: Let An Artificial Intelligence Design Our Cars,” September 2016.
  • Using generative design algorithms that rely on machine learning techniques to factor in design constraints and provide an optimized product design. Having constraint-optimizing logic within a CAD design environment helps GM attain the goal of rapid prototyping. Designers provide definitions of the functional requirements, materials, manufacturing methods and other constraints. In May 2018, General Motors adopted Autodesk generative design software to optimize for weight and other key product criteria essential for the parts being designed to succeed with additive manufacturing. The solution was recently tested with the prototyping of a seatbelt bracket part, which resulted in a single-piece design that is 40% lighter and 20% stronger than the original eight component design. Please see the Harvard Business School case analysis, Project Dreamcatcher: Can Generative Design Accelerate Additive Manufacturing? for additional information.

Additional reading:

2020 AI Predictions, Five ways to go from reality check to real-world payoff, PwC Consulting

Accenture, Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies (PDF, 20 pp., no opt-in)

AI Priorities February 2020 5 ways to go from reality check to real-world pay off, PwC, February, 2020 (PDF, 16 pp.)

Anderson, M. (2019). Machine learning in manufacturing. Automotive Design & Production, 131(4), 30-32.

Bruno, J. (2019). How the IIoT can change business models. Manufacturing Engineering, 163(1), 12.

Digital Factories 2020: Shaping The Future Of Manufacturing, PwC DE., 2017 (PDF, 48 pp.)

Digital Product Development 2025: Agile, Collaborative, AI Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.)

Enabling a digital and analytics transformation in heavy-industry manufacturing, McKinsey & Company, December 19, 2019

Global Digital Operations 2018 Survey, Strategy&, PwC, 2018

Governance and Management Economics, 7(2), 31-36.

Greenfield, D. (2019). Advice on scaling IIoT projects. ProFood World

Hayhoe, T., Podhorska, I., Siekelova, A., & Stehel, V. (2019). Sustainable manufacturing in industry 4.0: Cross-sector networks of multiple supply chains, cyber-physical production systems and AI-driven decision-making. Journal of Self-

Industry’s fast-mover advantage: Enterprise value from digital factories, McKinsey & Company, January 10, 2020

Kazuyuki, M. (2019). Digitalization of manufacturing process and open innovation: Survey results of small and medium-sized firms in japan. St. Louis: Federal Reserve Bank of St Louis.

‘Lighthouse’ manufacturers lead the way—can the rest of the world keep up?  McKinsey & Company, January 7, 2019

Machine Learning in Manufacturing – Present and Future Use-Cases, Emerj Artificial Intelligence Research, last updated May 20, 2019, published by Jon Walker

Machine learning, AI are most impactful supply chain technologies. (2019). Material Handling & Logistics

MAPI Foundation, The Manufacturing Evolution: How AI Will Transform Manufacturing & the Workforce of the Future by Robert D. Atkinson, Stephen Ezell, Information Technology and Innovation Foundation (PDF, 56 pp., opt-in)

Mapping heavy industry’s digital-manufacturing opportunities, McKinsey & Company, September 24, 2018

McKinsey, AI in production: A game changer for manufacturers with heavy assets, by Eleftherios Charalambous, Robert Feldmann, Gérard Richter and Christoph Schmitz

McKinsey, Digital Manufacturing – escaping pilot purgatory (PDF, 24 pp., no opt-in)

McKinsey, Driving Impact and Scale from Automation and AI, February 2019 (PDF, 100 pp., no opt-in).

McKinsey, ‘Lighthouse’ manufacturers, lead the way—can the rest of the world keep up?,by Enno de Boer, Helena Leurent and Adrian Widmer; January, 2019.

McKinsey, Manufacturing: Analytics unleashes productivity and profitability, by Valerio Dilda, Lapo Mori, Olivier Noterdaeme and Christoph Schmitz, March, 2019

McKinsey/Harvard Business Review, Most of AI’s business uses will be in two areas,

Morey, B. (2019). Manufacturing and AI: Promises and pitfalls. Manufacturing Engineering, 163(1), 10.

Preparing for the next normal via digital manufacturing’s scaling potential, McKinsey & Company, April 10, 2020

Reducing the barriers to entry in advanced analytics. (2019). Manufacturing.Net,

Scaling AI in Manufacturing Operations: A Practitioners Perspective, Capgemini, January, 2020

Seven ways real-time monitoring is driving smart manufacturing. (2019). Manufacturing.Net,

Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019

Smart Factories: Issues of Information Governance Manufacturing Policy Initiative School of Public and Environmental Affairs Indiana University, March 2019 (PDF, 68 pp., no opt-in)

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

Team predicts the useful life of batteries with data and AI. (2019, March 28). R & D.

The AI-powered enterprise: Unlocking the potential of AI at scale, Capgemini Research, July 2020

The Future of AI and Manufacturing, Microsoft, Greg Shaw (PDF, 73 pp., PDF, no opt-in).

The Rise of the AI-Powered Company in the Postcrisis World, Boston Consulting Group, April 2, 2020

Top 8 Data Science Use Cases in Manufacturing, ActiveWizards: A Machine Learning Company Igor Bobriakov, March 12, 2019

Walker, M. E. (2019). Armed with analytics: Manufacturing as a martial art. Industry Week

Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156.

Zulick, J. (2019). How machine learning is transforming industrial production. Machine Design

Nov 14

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5 Proven Ways Manufacturers Can Get Started With Analytics

5 Proven Ways Manufacturers Can Get Started With Analytics

Going into 2020, manufacturers are at an inflection point in their adoption of analytics and business intelligence (BI). Analytics applications and tools make it possible for them to gain greater insights from the massive amount of data they produce every day. And with manufacturing leading all industries on the planet when it comes to the amount of data generated from operations daily, the potential to improve shop floor productivity has never been more within reach for those adopting analytics and BI applications.

Analytics and BI Are High Priorities In Manufacturing Today

Increasing the yield rates and quality levels for each shop floor, machine and work center is a high priority for manufacturers today. Add to that the pressure to stay flexible and take on configure-to-order and engineer-to-order special products fulfilled through short-notice production runs and the need for more insight into how each phase of production can be improved. Gartner’s latest survey of heavy manufacturing CIOs in the 2019 CIO Agenda: Heavy Manufacturing, Industry Insights, by Dr. Marc Halpern. October 15, 2018 (Gartner subscription required) reflects the reality all manufacturers are dealing with today. I believe they’re in a tough situation with customers wanting short-notice production time while supply chains often needing to be redesigned to reduce or eliminate tariffs. They’re turning to analytics to gain the insights they need to take on these challenges and more. The graphic below is from Gartner’s latest survey of heavy manufacturing CIOs, it indicates the technology areas where heavy manufacturing CIOs’ organizations will be spending the largest amount of new or additional funding in 2019 as well as the technology areas where their organizations will be reducing funding by the highest amount in 2019 compared with 2018:

Knowing Which Problems To Solve With Analytics

Manufacturers getting the most value from analytics start with a solid business case first, based on a known problem they’ve been trying to solve either in their supply chains, production or fulfillment operations. The manufacturers I’ve worked with focus on how to get more orders produced in less time while gaining greater visibility across production operations. They’re all under pressure to stay in compliance with customers and regulatory reporting; in many cases needing to ship product quality data with each order and host over 60 to 70 audits a year from customers in their plants. Analytics is becoming popular because it automates the drudgery of reporting that would otherwise take IT team’s days or weeks to do manually.

As one CIO put it as we walked his shop floor, “we’re using analytics to do the heavy data crunching when we’re hosting customer audits so we can put our quality engineers to work raising the bar of product excellence instead of having them run reports for a week.” As we walked the shop floor he explained how dashboards are tailored to each role in manufacturing, and the flat-screen monitors provide real-time data on how five key areas of performance are doing. Like many other CIOs facing the challenge of improving production efficiency and quality, he’s relying on the five core metrics below in the initial roll-out of analytics across manufacturing operations, finance, accounting, supply chain management, procurement, and service:

  • Manufacturing Cycle Time – One of the most popular metrics in manufacturing, Cycle Time quantifies the amount of elapsed time from when an order is placed until the product is manufactured and entered into finished goods inventory. Cycle times vary by segment of the manufacturing industry, size of manufacturing operation, global location and relative stability of supply chains supporting operations. Real-time integration, applying Six Sigma to know process bottlenecks, and re-engineering systems to be more customer-focused improve this metrics’ performance. Cycle Time is a predictor of the future of manufacturing as this metric captures improvement made across systems and processes immediately.
  • Supplier Inbound Quality Levels – Measuring the dimensions of how effective a given supplier is at consistently meeting a high level of product quality and on-time delivery is valuable in orchestrating a stable supply chain. Inbound quality levels often vary from one shipment to the next, so it’s helpful to have Statistical Process Control (SPC) charts that quantify and show the trends of quality levels over time. Nearly all manufacturers are relying on Six Sigma programs to troubleshoot specific trouble spots and problem areas of suppliers who may have wide variations in product quality in a given period. This metric is often used for ranking which suppliers are the most valuable to a factory and production network as well.
  • Production Yield Rates By Product, Process, and Plant Location – Yield rates reflect how efficient a machine or entire process is in transforming raw materials into finished products. Manufacturers rely on automated and manually-based approaches to capture this metric, with the latest generation of industrial machinery capable of producing its yield rate levels over time. Process-related manufacturers rely on this metric to manage every production run they do. Microprocessors, semiconductors, and integrated circuit manufacturers are continually monitoring yield rates to determine how they are progressing against plans and goals. Greater real-time integration, improved quality management systems, and greater supply chain quality and compliance all have a positive impact on yield rates. It’s one of the key measures of production yield as it reflects how well-orchestrated entire production processes are.
  • Perfect Order Performance – Perfect order performance measures how effective a manufacturer is at delivering complete, accurate, damage-free orders to customers on time. The equation that defines the perfect order Index (POI) or perfect order performance is the (Percent of orders delivered on time) * (Percent of orders complete) * (Percent of orders damage free) * (Percent of orders with accurate documentation) * 100. The majority of manufacturers are attaining a perfect order performance level of 90% or higher, according to The American Productivity and Quality Center (APQC). The more complex the product lines, configuration options, including build-to-order, configure-to-order, and engineer-to-order, the more challenging it is to attain a high, perfect order level. Greater analytics and insights gained from real-time integration and monitoring help complex manufacturers attained higher perfect order levels over time.
  • Return Material Authorization (RMA) Rate as % Of Manufacturing – The purpose of this metric is to define the percentage of products shipped to customers that are returned due to defective parts or not otherwise meeting their requirements. RMAs are a good leading indicator of potential quality problems. RMAs are also a good measure of how well integrated PLM, ERP and CRM systems, resulting in fewer product errors.

Conclusion

The manufacturers succeeding with analytics start with a compelling business case, one that has an immediate impact on the operations of their organizations. CIOs are prioritizing analytics and BI to gain greater insights and visibility across every phase of manufacturing. They’re also adopting analytics and BI to reduce the reporting drudgery their engineering, IT, and manufacturing teams are faced with as part of regular customer audits. There are also a core set of metrics manufacturers rely on to manage their business, and the five mentioned here are where many begin.

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