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6 Ways To Transform Performance Management To Deliver What Employees Actually Need

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Performance management has room to improve. According to Gartner research, 52% of chief human resource officers (CHROs) believe they are not rewarding the right behavior in employees, and only 32% of HR business partners believe performance management delivers what employees need to perform.

Because of this, in the last five years, 74% of organizations have significantly changed their performance management processes. “Companies are implementing a variety of new practices, from linking pay to project performance to eliminating performance reviews entirely,” says Benjamin Loring, Research Director at Gartner. “The real unlock, however, is making performance management useful to both managers and employees with this six-part roadmap.”

Six ways to fix performance management

Improve performance management by considering the amount of conversations you have, the lens through which you look at performance, and the style of feedback you provide.

Ongoing conversations

No. 1: Encourage ongoing manager-employee feedback throughout the year

Create a mutual understanding of what type of feedback employees need to be successful and enable them to own and schedule feedback conversations by educating them on the types and frequency of dialogue that can occur.

No. 2: Promote discussions beyond individual contexts

To enact this, promote team goal-setting. Encourage team members to reflect and develop their individual goals for teams to review for alignment, impact, relevance and overlap. Similarly, create a space for employees to provide feedback to managers to reinforce employee agency and power in feedback conversations.

Forward-looking reviews

No. 3: Develop a framework for assessing future performance

Assess employees’ development readiness — their capacity, ability and willingness to take on professional development at a given point in time — not just performance, and align coaching conversations, and support to their true needs. This may require evolving how you evaluate growth and reframing the value of the process, while also navigating ambiguous situations and meeting organizational needs.

No. 4: Encourage managers to communicate actions needed for future success

Help managers provide feedback on what skills their employees need for the future, in addition to reflecting on their past accomplishments. Increasing transparency of skills across a team encourages cohesiveness, coaching and on-the-job development.

Peer feedback

No. 5: Gather feedback from co-workers on how employees help fellow team members

A huge part of performance management is feedback from colleagues. Guide managers on how to identify sources of feedback based on who has knowledge of an employee’s work, rather than limiting feedback to the employee’s formal relationships. Peer assessments are a good way to hold employees accountable for demonstrating critical behaviors and get a more comprehensive understanding of their contributions. Just be sure to develop evaluation guidelines that focus on outcomes.

No. 6: Foster an environment of feedback

Encourage employees to recognize their peers’ contributions to create comfort and confidence regarding feedback exchanges. Create a simple approach to seeking and requesting feedback and frequent prompts to focus managers on recognizing and reinforcing good behaviors throughout the year.

 

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Why decision intelligence is essential for overcoming supply chain constraints

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The current supply chain disruption is one of the many types of crises the marketplace has faced over the years. Even before COVID-induced challenges had cargo ships anchored off of ports across the globe and store shelves barely stocked, supply chain leaders have been in a race to keep up with changing consumer demands, a shifting competitive landscape, and technological advances.

Yet, as the development, reach and success of businesses has become highly dependent on tightly linked supply chains, the structure of those connections has become increasingly fragile and intricately connected.

Over the last two years, an unprecedented supply chain crisis has unfolded. With networks spanning multiple continents, global supply chains have broken down. From COVID-19 and the war in Ukraine to a sideways freighter that blocked the Suez Canal for a week and a growing list of environmental disasters, the upheaval has created a new benchmark for business-as-usual. A survey from the UK Office for National Statistics showed that 40% of businesses in the wholesale and retail trade industry reported global supply chain disruptions at the end of the first quarter this year.

This disruption is closely tied to a failure of foresight and planning built into supply chain systems.

Asking the right questions

Many companies tackling supply chain disruption see themselves as “data-driven,” when in fact, most are not. A Gartner report shows that less than half of organizations have actively started to build a roadmap for supply chain digitization transformation, despite it being a key priority for most leaders. Another survey showed only two-thirds of supply chain organizations felt the strategy and execution of their supply chains were well aligned.

Business intelligence (BI) and analysis tools were the promised future, where business users could easily access and transform huge volumes of corporate-wide data to predict business outcomes and future demand. However, the reality is that traditional BI solutions and ERP systems are static and can only provide a snapshot of the present or past.

Decision intelligence rests on prescriptive analytics

Such foresight comes from adding a prescriptive analytics layer to a firm’s supply chain management. This layer answers the question “what should happen” and becomes the basis for generating decisions, not just insights. This approach elevates the level of analytic inquiry, using machine learning and optimization models to propose a course of action based on data, analytics and business models.

Ultimately, this can dramatically transform how companies manage the flow of goods throughout their supply chains because it resolves the question how to proceed to achieve the targeted outcome.

Decision intelligence and the future of the supply chain

Taking a new approach to supply chains relies on a new vision for data in an organization. Data is the engine of growth and the source of intelligence that will allow businesses to get a grip on their supply chains.

This means drawing on data from a wider variety of sources than ever before. Businesses need more actionable, real-time data from across their supply chains. They need to quickly and securely access multiple data sources across on-premises data centers and multiple clouds. To plan for future shocks, businesses need to learn from this historic moment and feed this information into predictive and prescriptive analytics modeling.

A new tomorrow

Supply chain management solutions based on decision intelligence and real-time prescriptive analytics models are potent instruments in the fight against the supply chain crisis. Such systems can improve overall processes throughout the enterprise and build resilience into demand forecasts. They can reduce costs associated with overstocking, inventory stockouts, and product obsolescence — even in the face of widespread crises.

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Resilience is the New Name of the Game in Supply Chain

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Resilience is the New Name of the Game in Supply Chain

Resilience is the New Name of the Game in Supply Chain

For the majority of Supply Chain’s history, this evolution has been driven by a knack for finding efficiency. Companies have leveraged digital tools, and evolving skills, to collect vast data about product or raw materials sourcing, transportation, logistics, and manufacturing. They’ve hired strategic Supply Chain professionals who can turn this data into actionable intelligence, and redesign the supplier, production, and transportation network to get products to market quicker and cheaper. They use advanced ERP software and S&OP strategy to match supply with demand, and turn over inventory faster and faster. “Just-in-time” production has become a hallmark of today’s Supply Chains.

Case in point: research firm Gartner includes the speed of inventory turns as a key metric in its annual Top 25 List recognizing companies for their excellence in Supply Chain.

Now, the top Supply Chain professionals are those who can find those efficiencies, while providing a strong customer experience that safeguards the company’s brand. It’s been a long evolution, and it’s made the field more ascendant within companies than it’s ever been, with a bigger seat at the C-suite table. Risk mitigation, innovation through supplier collaboration, and increased sustainability have also driven Supply Chain’s strategic value – but they’ve taken a back seat to efficiency.

Then came COVID-19.

As we’ve also written about recently, the COVID-19 pandemic has caused almost-unprecedented disruptions to a majority of companies’ Supply Chains – as many as 72%, according to a recent Supply Chain Canada survey.

We’re four months into the pandemic, and it appears that these disruptions have spurred another evolution:

More than ever, companies are focusing on Supply Chain resilience

All around the Supply Chain world, professionals are shifting their focus to make sure that they can withstand supplier disruptions, not only due to COVID-19, but to future emerging issues as well.

In our recent interview with Procurement Guru Jill Button about the particular Supply Chain challenges of the moment, she highlighted this shift, saying: “People are beginning to understand the risks and fragility of a Supply Chain and not having a sound Procurement practice. I think, as a field, we need to step up and embrace this moment.” In March, at the outset of the pandemic, industry thought leader Bob Ferrari wrote about how, in a world of supplier disruption, companies might shift from a just-in-time inventory model that maximizes efficiency, to one that prioritizes a diverse supplier base to maximize resilience.

Top consulting firms are taking notice too, in their own advice to corporate leaders: Bain, Deloitte, McKinsie, and Baker McKenzie, and others have released white papers in recent days on the importance of Supply Chain resiliency and risk mitigation in this new era.

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Reefknot Investments launches $50 million fund to invest in logistics and supply chain startups

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Reefknot Investments launches $50 million fund to invest in logistics and supply chain startups

Reefknot Investments launches $50 million fund to invest in logistics and supply chain startups

Reefknot Investments, a joint venture between Temasek, Singapore’s sovereign fund, and global logistics company Kuehne + Nagel, announced today the launch of a $50 million fund for logistics and supply chain startups. The firm is based in Singapore, but will look for companies around the world that are raising their Series A or B rounds.

Managing director Marc Dragon tells TechCrunch that Reefknot will serve as a strategic investor in its portfolio companies, providing them with connections to partners that include EDBI, SGInnovate, Atlantic Bridge, Vertex Ventures, PSA unBoXed, Unilever Foundry and NUS Enterprise, in addition to Temasek and Kuehne + Nagel .

Dragon, a veteran of the supply chain and logistics industry, says Reefknot plans to invest in about six to eight startups. It is especially interested in companies that are using AI or deep mind tech, digital logistics and trade finance to solve problems that range from analyzing supply chain data and making forecasts to managing the risk of financing trade transactions. Data from Gartner shows that about half of global supply chain companies will use AI, advanced analytics or the Internet of Things in their operations by 2023.

“There is a high level of expectation from vendors that because of technology, there will be new methods to do analytics and planning, and greater visibility in terms of information and product, materials and goods flowing throughout the supply chain,” says Dragon.

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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 Network Optimization Technology is Ripe for Disruption

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Something struck me after spending a few days in Phoenix at Gartner’s Supply Chain Executive Conference.

Supply Chain Network Optimization is key to running an efficient and profitable operation today.

But while the market has changed, network optimization hasn’t actually advanced much since the 1990s. Yes, there are lots more features and a big increase in computing power. Yet, network optimization is still just a richer version of the 90’s experience.

Analyzing the Software Market

Network optimization software has become a big business that’s experienced exponential growth. There has been a strong adoption of boxed solutions that are feature rich with many bells and whistles.

What I heard at the Gartner conference is growing frustration with these large packages that have become cumbersome to use, too difficult for the average supply chain expert, lack flexibility and have high price tags. Sound familiar?

So what is the alternative? First, we need to go back to the original purpose. Supply Chain teams shouldn’t be overly focused on technology. Instead, they should have their eyes set on the desired outcome.

Supply Chain teams want a supply chain network that runs in an optimized fashion, with signals that indicate when and where to invest in future infrastructure. The network optimization tool should just be a means to an end.

So why hasn’t it become easier and cheaper to have an optimized network? Why are companies investing more and more in this focused discipline?

Read more at Supply Chain Network Optimization Technology is Ripe for Disruption

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Magic Quadrant for Business Intelligence and Analytics Platforms

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Magic Quadrant for Business Intelligence and Analytics Platforms

Magic Quadrant for Business Intelligence and Analytics Platforms

 Magic Quadrant for Business Intelligence and Analytics Platforms

Data discovery capabilities are dominating new purchasing  requirements, even for larger deployments, as alternatives to traditional BI tools. But “governed data discovery” — the ability to meet the dual demands of enterprise IT and business users — remains a challenge unmet by any one vendor.

The BI and analytics platform market is in the middle of an accelerated transformation from BI systems used primarily for measurement and reporting to those that also support analysis, prediction, forecasting and optimization. Because of the growing importance of advanced analytics for descriptive, prescriptive and predictive modeling, forecasting, simulation and optimization (see “Extend Your Portfolio of Analytics Capabilities”) in the BI and information management applications and infrastructure that companies are building — often with different buyers driving purchasing and different vendors offering solutions — this year Gartner has also published a Magic Quadrant exclusively on predictive and prescriptive analytics platforms (see Note 1). Vendors offering both sets of capabilities are featured in both Magic Quadrants.

For this Magic Quadrant, Gartner defines BI and analytics as a software platform that delivers 17 capabilities across three categories: information delivery, analysis and integration.

As a result of the market dynamics discussed above, the capability definitions in this year’s Magic Quadrant have been modified with the following additions and subtractions to reflect our current view of critical capabilities for BI and analytics platforms.
Capabilities dropped:

  1. Scorecard: Most companies do not implement true scorecard/strategy maps using BI platforms — they implement dashboards. Also, most BI vendors report limited sales activity for their scorecard products. Scorecards are primarily delivered by corporate performance management (CPM) vendors (see “Strategic CPM as a Driver for Organizational Performance Management”). Therefore, we have included scorecards as a type of dashboard, rather than as a separate category.
  2. Predictive Analytics: covered in the new “Magic Quadrant for Advanced Analytics Platforms.”
  3. Prescriptive Analytics: covered in the new “Magic Quadrant for Advanced Analytics Platforms.”

Capabilities added:

  1. Geospatial and location intelligence (see the Analysis section)
  2. Embedded advanced analytics (see the Analysis section)
  3. Business user data mashup and modeling (see the Integration section)
  4. Embeddable analytics (see the Integration section)
  5. Support for big data sources (see the Integration section)

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Revolution Analytics named a Visionary in the Gartner Magic Quadrant for Advanced Analytics Platforms

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Revolution Analytics named a Visionary in the Gartner Magic Quadrant for Advanced Analytics Platforms

The entire team at Revolution Analytics is very proud to announce that Gartner has named Revolution Analytics a Visionary in the inaugural Gartner Magic Quadrant for Advanced Analytics Platforms, published February 19, 2014. The report evaluated 16 vendors through a series of stringent criteria related to the ability to execute and completeness of vision.

Revolution Analytics is positioned the furthest for Completeness of Vision and Ability to Execute in the Visionaries Quadrant. We believe this is a validation of the leading-edge innovations of the open-source R community, and that of our own Revolution R Enterprise development team who continues to complement R with scalability, performance, and enterprise readiness. Here’s what CEO Dave Rich has to say:

“It’s such a pivotal moment for data scientists and the growing open-source R community that Gartner has embarked on its first ever Magic Quadrant for Advanced Analytics Platforms. Gartner estimates advanced analytics to be a $2 billion market that spans a broad array of industries globally, and ‘Gartner predicts business intelligence and analytics will remain top focus for CIOs Through 2017.’ We believe that this new Magic Quadrant puts a spotlight on big data as the great analytics disruptor and we feel highlights the need for solutions like Revolution Analytics’ that are built upon a flexible, open platform, and designed for today’s Big Data Big Analytics challenges.” — Dave Rich, CEO, Revolution Analytics

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