Tag Archives: planning

Strategic financial management for women is highly effective

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Strategic financial management for women is highly effective

Strategic financial management for women is highly effective

A look at the many ways in which women’s financial positions and needs can differ from those of men, and how women can strategically plan their finances to protect their financial futures.

The financial planning needs of women are in many ways unique – and with the shape and pace of their career trajectories being somewhat different from men’s, so too should their financial management strategies.

In this article, we explore the many ways in which women’s financial positions and needs can differ from those of men, and how women can strategically plan their finances to protect their financial futures.

Child bearers and family careers

The interruption of women’s careers as a result of childbirth and child-rearing can have long-term financial implications for women. Besides the actual loss of earnings during maternity leave and child-rearing years, it is important to factor in the knock-on financial effects.

The longer-term impact of not having pay parity

Although gender pay parity is improving, the process is a slow one and on average women still earn less than men do. Again, the effect of earning a lower income permeates across every aspect of a women’s portfolio: less group risk cover, lower investment contributions, reduced bonuses, commissions or incentives, a weaker position to negotiate from, less access to credit and financing, a weaker capacity for wealth building, and a lower net asset value over time – exacerbated, of course, by the fact that women generally live longer than men and therefore need to save for a longer – potentially more expensive – retirement.

The associated risks of living longer

According to the US Census Bureau, in 2017 the life expectancy for men was 76.1 years while that of women was 81.1 years, and it is anticipated that the gap in longevity will continue to grow. The longevity risk faced by women has a number of key implications for their financial planning which should be addressed sooner rather than later.

Wealth creation challenges of the stay-at-home spouse

Women who choose to stay at home to raise children face an enormous challenge when it comes to generating wealth. Without an income and the associated tax benefits, investing is something that many stay-at-home mothers fail to do which places them in a precarious financial position if the relationship comes to an end.

Challenges facing single mothers

The challenges that many single mothers face can have far-reaching effects on their ability to generate income and build wealth, particularly when it comes to securing maintenance and pursuing payment from non-payers.

Differing investment style

Generally speaking, women’s investment style differs from men’s, and this is often not supported by the products or advice available in the marketplace. Research shows that women are more likely to seek advice and stick to it, have a more goals-based approach to investing, and – being time-poor – require efficiency in terms of communication and administration.

Post-pandemic planning

The work-from-home regulations during the pandemic placed a massive child caring burden on many women which, in turn, impacted their ability to generate an income and save for the future. In response to the pandemic, however, many women have subsequently demonstrated an increased interest in investing, become more involved in the management of the household’s finances, and are more open to engaging in financial discussions with their partners and children.

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How Visual Data Can Improve Performance Management in Business

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How Visual Data Can Improve Performance Management in Business

Nowadays, employees like to be recognized for the work they do. They want to know that the work they put in is valued. One way of doing this is to give them a detailed performance review and performance appraisal.

But how do you know if your employees are truly performing at their best?

Workplace performance is a very subjective thing to measure for any employee. The usual way of doing this is through an annual performance appraisal. Although this is a decent way to measure performance, it can often lead to situations where employees try to game the system or are afraid to speak the truth. There are many different tools for measuring workplace performance now and one of them is to utilize visual data for performance management.

This blog will look at some of the ways you can use visual data to improve performance management in your Business.

What is performance management?

Performance management is the continuous process of setting objectives, assessing progress, and providing ongoing coaching and feedback to ensure that employees are meeting their goals and career interests. The primary goal of performance management is to promote and improve employee effectiveness.

Performance management can be used to:

  1. Align employee goals with those of the organization
  2. Increase employee engagement
  3. Improve workforce productivity
  4. Encourage ongoing development
  5. Support goal attainment

Performance management is not just annual performance reviews. It includes planning work and setting expectations, continually monitoring performance, developing the capacity to perform, periodically rating performance in a summary fashion, and rewarding good performance.

The primary purpose of PM is to help employees understand how they contribute to organizational success through their individual roles and responsibilities.

Performance management is a stage-by-stage process for managing performance and improving employee performance. It includes the following stages:

  1. Setting clear expectations
  2. Monitoring progress against those expectations
  3. Providing regular feedback
  4. Celebrating successes and addressing failures
  5. Rewarding great performance

The performance management process has four stages:

  1. Planning: This stage involves setting objectives that are aligned with your company’s goals, helping employees understand their role in achieving those objectives, and ensuring everyone is focused on the right priorities.
  2. Tracking: Employees need to know how they’re getting along. In this phase, managers should provide regular coaching and feedback on progress toward defined objectives.
  3. Developing: When an employee needs support in meeting their objectives, the development phase kicks in. In this phase, managers work with employees to identify opportunities for learning or skill development or offer training programs or other
  4. Current trends in performance management: Current trends in performance management are changing this approach. They’re moving away from traditional methods and towards more continuous feedback loops that focus on encouraging employee development throughout the year.

Read more at How Visual Data Can Improve Performance Management in Business

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AI This. Not So Fast.

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AI This. Not So Fast.

AI This. Not So Fast.

Grounded during this pandemic, and unable to interact with clients in person, I try to write 3,000 words a day. Morning after morning, fueled by good black coffee, I type away. I share insights, based on research, for the supply chain leader. I write for this blog, craft reports from research for our newsletters, create blogs for Linkedin, and build articles for Forbes. I am also developing a framework for my new book. Stay tuned.

Frequently, when I post about an issue, a well-intending consultant or an aggressive business development executive will tout the evolution of the autonomous supply chain as the answer. The comment is usually something like, “Implement RPA or AI to solve this problem.” Or, “If you need an answer, implement my solution.” When this happens, I sigh. This type of response is just not helpful. Everyone tries to be a cool kid with over-zealous comments on posts, but unfortunately, there is no truth in advertising in the supply chain market. (If the industry were grounded in truth in advertising repercussions, there would be far fewer signs in the airports from consultants and technology providers.)

Background

The autonomous supply chain is a vision, but it is not today’s reality. I find in my Supply Chains to Admire research that 96% of companies (when compared to their peer groups) are unable to drive improvement while delivering higher performance year-over-year on a balanced scorecard of growth, inventory turns, operating margin and Return on Invested Capital (ROIC). I define supply chain excellence as year-over-year performance better than the peer group on this balanced scorecard. Ecolab, L’Oreal, and TJX are exceptions. They did it. Each company ranks in the 4% of companies beating their peer groups.

Shifts in Technology

Data science and cloud-based delivery offer promise, but supply chain planning is morphing slowly. …and at the edges. No technology company is attacking supply chain planning at the center.

Let’s celebrate that over the last two years, there were four significant acquisitions by traditional supply chain planning providers to deepen analytics capabilities:

  1. 07/2018 JDA purchases Blue Yonder (Purchase price confidential.)
  2. 11/2017 Logility acquires Halo for 9.3 M$
  3. 10/2019 Llamasoft merges with Opex (Amount not disclosed.)
  4. 06/2020 Kinaxis buys Rubikloud for 60M$

Examining The Current State

The analyst mindset is to track software evolution by taxonomy where like solutions are grouped, named and tracked. Supply chain planning is a subset of the decision support technology taxonomy. Other forms of decision support include revenue management, trade promotion management, cost-to-serve, and network design. Now in its fifth decade of evolution, supply chain planning is starting to change. The shifts are happening slowly at the edges. I am celebrating, but my hope is to drive seismic changes from the center. What we have now is not good enough, and I have my fingers crossed that COVID-19 will drive a significant and positive shift by highlighting the deficiencies.

Read more at AI This. Not So Fast.

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How To Improve Supply Chains With Machine Learning: 10 Proven Ways

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Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionizing supply chain management include:

  1. Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems.
  2. The wide variation in data sets generated from the Internet of Things (IoT) sensors, telematics, intelligent transport systems, and traffic data have the potential to deliver the most value to improving supply chains by using machine learning.
  3. Machine learning shows the potential to reduce logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings.
  4. Reducing forecast errors up to 50% is achievable using machine learning-based techniques.
  5. DHL Research is finding that machine learning enables logistics and supply chain operations to optimize capacity utilization, improve customer experience, reduce risk, and create new business models.
  6. Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today.
  7. Reducing risk and the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point across supply chains today.
  8. Machine learning is making rapid gains in end-to-end supply chain visibility possible, providing predictive and prescriptive insights that are helping companies react faster than before.
  9. Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains.
  10. Capitalizing on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilization and reducing operating costs.

Read more at How To Improve Supply Chains With Machine Learning: 10 Proven Ways

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Supply Chain Planning Systems Become Increasingly Intelligent

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Machine learning is hot. Solution providers in supply chain planning (SCP) tell me customers want to know how these technologies will be used in future SCP solutions. But machine learning is just one form of intelligence that can be embedded in SCP applications. The growing intelligence of these solutions ranges from better integration frameworks all the way up to fully automated planning.

Better Integration Frameworks

Integration frameworks allow data from multiple sources and networks to be pulled into planning solutions much more easily. Logility’s Karin Bursa, an executive vice president, points out that “many companies have multiple ERP systems.” She sees faster integration with better certainty and master data management, as a key differentiator for Logility. The master data logic understands the range of data that is appropriate for a particular field and can track and highlight when inappropriate data gets entered. Logility’s solution also uses net change logic. In other words, their system only looks at data elements that have been updated or changed. This makes same day or inter-day data updates more efficient.

Robust Role-based Views

This is not a new area of investment; it has been going on for several years. Many suppliers have invested in easier to use interfaces, particularly excel style interfaces. These interfaces have workflows that allow planners to tackle the most important planning problems in order of importance. Demand planners may want to view forecasts in units by week at ship to locations. Financial planners may want to see monthly views of revenues by business unit. Many suppliers offer integrated business planning (IBP) modules, sometimes called supply chain control towers or cockpits, that allow for a variety of views by the wide variety of actors in a corporation involved in balancing supply with demand in ways that maximize the company’s strategic objectives. Those objectives might differ by product or customer and can include things like profit maximization, achieving revenue targets, gaining market share, and other things as well.

Bigger, Better Solves

There are always new problems to solve. Omnichannel is the best current example of that. Manhattan Associate’s Scott Fenwick, director of product strategy, points out that when a new flow is supported, like order online but pick-up-in store, inventory allocation decisions need to change. But picking up that shift in the demand signal can be difficult. They are using machine learning to help solve this true demand problem.

Read more at Supply Chain Planning Systems Become Increasingly Intelligent

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Why You Don’t Need Perfect Data to Begin Implementing Sales & Operations Planning

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Royal Boon Edam a global market leader in entry solutions, was looking to shift towards a combined business model of “made to stock” and “assembly to order” – where modules which could be placed into a configuration requested by the customer would be ready for production, this meant the company needed a different logistics approach to fulfilling these orders.

An interview with Boon Edam’s Aron Waas

Implementing Sales and Operations Planning (S&OP) has many benefits.

To truly leverage it to improve business performance and predictability, you need to embark on a change management process and you need the right technology to self-enable your team.

Often, teams think they also need plenty of clean and accurate data to do it right.

But starting small can pay off. We spoke with Aron Waas, Global Supply Chain Director at Royal Boon Edam International to hear about his company’s experience.

Hello Aron, can you tell me more about Boon Edam and your role as Global Supply Chain Director?

Boon Edam is a private, family-owned company that is over 140 years old. We are a manufacturer of premium entry systems, such as revolving doors and security access gates.

We have 3 factories, one in the USA, one in China and one in the Netherlands (in the city of Edam). We have over 20 sales subsidiaries and, at this stage, 3 different Distribution & Support Centers.

These centers (or D&SCs) support our sales subsidiaries with all their inquiries, service requests and the delivery of products and services.

I am part of the global management team, responsible for everything that has to do with supply chain management. The directors of our D&SCs report directly to me.

You are currently using AIMMS to enable your S&OP process. What was the driver to look for S&OP technology and how did you do things before?

We have worldwide demand for all kinds of products and services and as I mentioned before, we have 3 different factories. We were trying to optimize the workload between these factories to have our manufacturing be as efficient as possible.

We had a financial reporting tool and based on the financial forecasting of our different sales subsidiaries, we made a forecast for products and services which was translated into a monthly demand plan and a capacity plan. This process was based on a lot of assumptions.

Read more at Why You Don’t Need Perfect Data to Begin Implementing Sales & Operations Planning

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Top 20 Supply Chain Management Software Suppliers 2017

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The market for supply chain management (SCM) software, maintenance and services continued its growth in 2016, generating more than $11.1 billion, a 9% increase over 2015 revenues, according to the research firm Gartner.

That total includes applications for supply chain execution (SCE), supply chain planning (SCP) and procurement software. Since the market’s 2% decline in 2009, the market has posted double-digit growth in four of the past six years, according to Gartner. The SCM market is expected to exceed $13 billion in total software revenue by the end of 2017 and exceed $19 billion by 2021, Gartner forecasts, with software as a service (SaaS) enabling new growth opportunities.

“It continues to be a good year for the supply chain overall,” says Chad Eschinger, managing vice president of Gartner. “The Cloud-based segment grew 20%, which is consistent with what we’ve seen in recent years.”

The push for Cloud capabilities also fueled some of the acquisition activity over the last year. Eschinger cites examples such as Infor’s acquisition of GT Nexus, Kewill’s acquisition of LeanLogistics, Oracle’s acquisitions of LogFire and NetSuite, and E2open’s acquisitions of Terra Technology and, more recently, Steelwedge.

“Broadly speaking, we’re seeing cyclical consolidation,” Eschinger says. “For some companies it’s a land grab, for others it’s an effort to add functional and technical underpinnings to go to the Cloud or provide a fuller complement of Cloud capabilities.”

Suite vendors are increasingly inclined to offer end-to-end solutions, Eschinger says, tying in customer relationship management capabilities, replenishment, network design, clienteling and more. In addition to supply chain efficiency, these solutions are also aimed at improving and standardizing the consumer’s experience.

“The Amazon effect continues to wreak havoc in retail and for manufacturers selling direct-to-consumer,” Eschinger says. “Everyone wants real-time visibility into inventory, so data and the associated analytics continue to be front and center for most organizations.”

Read more at Top 20 Supply Chain Management Software Suppliers 2017

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Transforming Integrated Planning & Supply Chain Processes with Augmented Intelligence Capabilities

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In conjunction with the announcement, o9 released an eBook titled, “Who Gets the Cheese?”

Aptly named after one of the greatest business books of all time (Who Moved My Cheese?), this resource details one of o9’s systems for optimally allocating resources across initiatives and brands at consumer goods companies.

Founded by executives, practitioners and technologists that have led supply chain innovations for nearly three decades, the o9 team has been quietly developing a game-changing Augmented Intelligence (AI) platform for transforming Integrated Planning and Supply Chain processes.

The team has deployed the AI platform with select clients, including:

  1. Bridgestone Tires
  2. Asian Paints
  3. Restoration Hardware
  4. Party City
  5. Del Monte
  6. Aditya Birla Group
  7. Caterpillar
  8. Ainsworth Pet Foods

Speaking on behalf of o9 Solutions, Co-founder and CEO Chakri Gottemukkala said, “While executives we work with hear the buzz around technologies for data sensing, analytics, high performance computing, artificial intelligence and automation, they are also living the reality of slow and siloed planning and decision making because the enterprise operates primarily on spreadsheets, email and PowerPoint.”

Read more at Transforming Integrated Planning & Supply Chain Processes with Augmented Intelligence Capabilities

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Commentary: Managing risk in the global supply chain

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The World Economic Forum defines global risk as an uncertain event that, if it occurs, can cause significant negative impact for several countries or industries within the next 10 years.
Global supply chains create both opportunity and risk. Some of the macro issues we face both in day-to-day operations and future planning include cybersecurity, terrorism, climate change, economic instability, and political discord.
More specific to executives who manage global supply chains, risk is more apparent, and on a micro-basis potentially more consequential in the short term, in areas such as but not limited to reducing spend, leveraging sourcing options, creating sustainability, political and currency instability, government regulations in the U.S. and abroad, trade compliance management, free trade agreements, energy costs, and what the incoming Trump administration will mean for global trade.
Since the recession in 2008-2009, we have witnessed a serious uptick in companies worldwide reviewing their operational exposure and then creating risk strategies in managing these vulnerabilities. Risk exposure can negatively impact margin, profits, growth strategies, operational stability and personnel maintenance.
For companies operating in global supply chains the risks are vast, convoluted and often unanticipated. As a result, we tend to be unprepared for the impacts.

Read more at Commentary: Managing risk in the global supply chain

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Supply Chain & Big Data ÷ Analytics = Innovation

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Google the term “advanced analytics” and you get back nearly 23 million results in less than a second.

Clearly, the use of advanced analytics is one of the hottest topics in the business press these days and is certainly top of mind among supply chain managers.

Yet, not everyone is in agreement as to just what the term means or how to deploy advanced analytics to maximum advantage.

At HP, the Strategic Planning and Modeling team has been utilizing advanced operational analytics for some 30 years to solve business problems requiring innovative approaches.

Over that time, the team has developed significant supply chain innovations such as postponement and award winning approaches to product design and product portfolio management.

Based on conversations we have with colleagues, business partners and customers at HP, three questions come up regularly – all of which this article will seek to address.

  1. What is the difference between advanced and commodity analytics?
  2. How do I drive innovation with advanced analytics?
  3. How do I set up an advanced analytics team and get started using it in my supply chain?

Advanced analytics vs. commodity analytics

So, what exactly is the difference between advanced analytics and commodity analytics? According to Bill Franks, author of “Taming The Big Data Tidal Wave,” the aim of commodity analytics is “to improve over where you’d end up without any model at all, a commodity modeling process stops when something good enough is found.”

Another definition of commodity analytics is “that which can be done with commonly available tools without any specialized knowledge of data analytics.”

The vast majority of what is being done in Excel spreadsheets throughout the analytics realm is commodity analytics.

Read more at Supply Chain & Big Data ÷ Analytics = Innovation

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