Tag Archives: Management

Capacity Management in Review: From Basics to Real-Life Use Cases and Best Practices

Welcome back to Syncsort Summer School. Take advantage of this traditionally slower business season to catch up on new technologies or trends to help you perform your job better and justify your next raise! We’re picking up this series of blog posts where we left, starting with everything you need to know about Capacity Management.

Getting Started

If you’re just getting started, we’ve got the perfect primer for you! This article covers the basics of capacity management. You’ll also walk away with a better understanding of how it helps align IT resources with larger business goals.

Capacity Management is Good for Business

In the age of Big Data, you need to consider tools that can improve business efficiency. Capacity management helps business to achieve two main objectives:

  1. Ensure capacity to keep the organization working. Beyond capacity to store files, you want to make sure your company has enough processing power to meet business requirements. Check out this customer success story for more info.
  2. Save the company money. See how capacity management goes straight to the bottom line with this look of a real world case study.

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Mainframe Management

Your data center is a key area to consider capacity planning, if you haven’t already. Optimizing capacity utilization is just one of the suggestions we cover in our tips for maximizing data infrastructure ROI.

Capacity management can keep your mainframe cruising and bring you closer to a self-managing mainframe by helping to balance out the loss of mainframe skills as more and more mainframers head off to retirement.

Capacity management delivers a vast amount of information about IT resources and their utilization, including enabling machine learning programs to perform analytics in the background for the reporting of “Time to Live” until a resource is exhausted. The key to the performance of this analysis is the setting of thresholds, whether those thresholds are static or self-learned. (Read more on capacity management’s role in Machine Learning and AI)

Extra credit: Check out our on-demand webcast, “The Changing Landscape of Capacity Management for the Mainframe”

Keeping Your Cloud in Check

One of the benefits of cloud storage is the idea of endless space. Some assume managing capacity is no longer necessary because if you hit your limit, you just add in more capacity from this infinitely elastic cloud. But even with physical limitations removed, you’re likely still facing a financial ceiling.

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Cloud capacity management can help you balance your technical and financial needs to ensure you’re getting what you need and not paying more than you should.

Best Practices

Implementing or maturing a capacity management process takes executive buy-in, proper planning and the tools to make it possible – and it helps when you get to enjoy a significant return on investment from the process!

Download our eBook to discover what defines a mature capacity management process and key takeaways to become best in class.

Cross-Platform Capacity Management Software

Capacity management software, such as Syncsort’s Athene™, provides automation around key processes and requires little-to-no mainframe expertise to operate in a cost-effective manner.

As a cross-platform solution, Athene allows organizations to bring data into a centralized Capacity Management Information System (CMIS) from all components that comprise a service and provides a 360° view of those services in a single dashboard. It also has predictive analytics that give organizations insight into how the mainframe is performing today and will perform in the future, even with changes in the hardware or the workload.

The latest release of Athene Cloud and Athene provide exciting new features and capabilities to our clients and open those capabilities to organizations everywhere.

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Microsoft Tools that Increase Performance in Asset Management by Extending CRM

CRM Blog Microsoft Tools that Increase Performance in Asset Management by Extending CRM

Part 3 of a 3-Part Series: How Asset Management Firms are Redefining Sales and Services Using CRM and More

Most technology solutions can’t meet what asset management firms need to be more profitable, productive, and successful. “Horizontal” CRM solutions like Salesforce or point solution like SalesPage, you are probably missing opportunities. Microsoft Dynamics 365 the capabilities to meet these needs and more.

In this 3-part series, we show you how CRM as an operational platform goes far beyond traditional CRM. “Does Your System Measure Up? What Asset Management Firms Need in CRM Software”, outlined the must-have components of a CRM software solution for Asset Management. “5 Reasons Why Asset Managers Should Make Machine Learning Part of Their Daily Routine…and Why Their CRM Software Should Help,” dived into machine learning and predictive analytics. Part 3 discusses tools beyond CRM provided by Microsoft–tools which you likely already have–that will help further improve performance.


The right tools can mean the difference between a productive day and a frustrating one for an asset manager.

No doubt, your organization is already using some Microsoft applications such as Outlook Exchange, Word, and Excel. Is your CRM connected to these tools? It should be. Tracking email activities, appointments, phone calls, and tasks with your Office tools integrated directly with your CRM software will help you make the most of all your systems, saving time and resources.

If your CRM software doesn’t directly sync with your other Office systems, you can’t do things like inline editing with Excel online, use Excel templates to import data directly from CRM, or distribute an updatable Excel spreadsheet.

LinkedIn and Microsoft Dynamics 365/CRM

Since Microsoft owns LinkedIn, the integration between Microsoft Dynamics 365 and LinkedIn is very powerful. Records in your CRM, for both companies and individuals, can automatically be linked back to LinkedIn Sales Navigator. If a contact’s email address in CRM matches an email address in LinkedIn, you will automatically see that person’s information. Likewise, within LinkedIn Sales Navigator, if you want to track a new lead, it can be synchronized between LinkedIn and CRM. This benefit is available only with Dynamics 365/CRM

Other asset management tools and Microsoft Dynamics 365

Of course, you use Outlook, Excel, and Word. But Microsoft Office 365 has so much more to offer asset managers.  Because OneNote integrates with Dynamics 365, you can take notes at meetings and then easily distribute them or store them for later referral within your CRM. SharePoint and OneDrive will also integrate with your CRM at the individual record level to facilitate sharing and collaboration.

Working on a proposal response to an RFP and being able to collaborate directly within OneDrive and have that linked back to the RFP (which is posted inside CRM) is just one of many examples of how SharePoint integration within Dynamics can make an asset manager’s day much more productive.

Microsoft Teams allows you to pull data from your CRM, OneNote, OneDrive, SharePoint and other applications for sharing and collaboration. If you are using market data feeds about a particular opportunity or account, that data can appear in Microsoft Teams and become the viewpoint from which you can reference all the collaboration about a specific item within CRM.

Power BI, with its reporting and analytics, is critically important for asset managers. Consolidate your CRM data with data from other sources for a single, complete view.

Microsoft Flow is the way to integrate your CRM with other business-critical tools. Flow is an integration point to Dynamics, but also to the entire suite of Microsoft products and it extends connectivity to dozens of third-party applications that you may already have.

Power tools help you customize your systems without having to touch code

Azure Machine Learning has a visual tool that allows you to apply of algorithms and learning mechanisms for data to produce Web services that can be used within CRM to evaluate the likelihood of winning a specific RFP.

PowerApps lets you extend your data beyond CRM into more of an XRM model that creates custom user interfaces by drag and drop, no coding required, for a smartphone, PC, or tablet.  External data and CRM information can coexist using Microsoft’s Common Data Service. Gathering transactional data across the industry allows asset managers to see which firms are trading on your buying agreements, and where to focus your sales efforts.

If you’d like to explore more ways that Microsoft Dynamics 365 can help you leverage the best asset management tools, contact our financial services experts at AKA Enterprise Solutions.

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Released: Public Preview for SSAS and SSRS 2017+ Management Packs (

We are getting ready to release brand new management packs for SQL Server Analysis Services and SQL Server Reporting Services 2017. Please install and use this public preview and send us your feedback (sqlmpsfeedback@microsoft.com)! We appreciate the time and effort you spend on these previews which make the final product so much better.

Please download at:

Microsoft System Center Management Packs (Community Technology Preview) for SQL Server 2017+ Reporting Services and Analysis Services

Version Agnostic SSAS and SSRS MPs

As you might recall, we introduced version agnostic MPs starting with SQL Server 2017. We are now doing the same for SSAS and SSRS. We understand that with many SQL Server versions in market and with new server releases becoming more frequent, it is becoming harder to manage a separate MP for each server version. We are moving to version agnostic MPs to address this issue. This will be valid going forward. The new MPs will be named SSAS and SSRS 2017+. The ‘+’ in the name indicates that it will be used to monitor SQL Server AS and RS 2017 and the releases that come after that. Current in-market MPs (2008 through 2016) will not be changed and the 2017+ MP cannot be used to monitor older releases.

For more details, please refer to the user guides that can be downloaded along with the corresponding Management Packs.
We are looking forward to hearing your feedback at sqlmpsfeedback@microsoft.com.

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The State Of Risk Management In 2018

OECD’s latest Economic Outlook, released on May 30, observes:

“the global economy is experiencing stronger growth, driven by a rebound in trade, higher investment, and buoyant job creation, and supported by very accommodative monetary policy. The pace of global expansion over the 2018-19 period is expected to hover near 4% … However, the Outlook also underlines that significant risks posed by trade tensions, financial market vulnerabilities, and rising oil prices loom large.“

Deloitte’s first-quarter 2018 CFO Signals survey of 155 CFOs of large North American companies found similar results. The CFOs’ assessments of the major global economies “hit new survey highs in the latest survey … But even with blue skies and forecasts calling for more sunshine, finance chiefs should be prepared for challenges that could get in the way of executing their organizations’ growth strategies and capitalizing on today’s buoyant conditions.”

Deloitte also reports, “One way boards are enhancing their risk oversight practices is by clarifying and formally approving the organization’s risk appetite, the aggregate level of risk that management is willing to take in pursuit of its strategy. As a first step, boards must also sign off on management’s strategy. Directors realize it is their role to oversee both risk appetite and strategy, but conversations linking the two are usually informal, if they happen at all. Moreover, the board’s understanding of risks, especially nonfinancial risks, is often more intuitive than explicit.”

The ISO 31000:2018 Risk Management Guidelines, which updates the 2009 guidelines, also highlights the new emphasis on “leadership by top management and the integration of risk management, starting with the governance of the organization and emphasis on the iterative nature of risk management, noting that new experiences, knowledge, and analysis can lead to a revision of process elements, actions, and controls at each stage of the process.”

Addressing risk management

Risk management solutions are used by companies to link their opportunities and business objectives to their risks. They can provide end-to-end capabilities for risk identification, analysis, monitoring, and reporting. Top management can have up-to-date information on the latest risk information while the iterative processes of risk activities are carried out. This way, risks aren’t just reported but are mitigated effectively with policies, controls, and other actions at the earliest possible stage. CFOs, chief risk officers, and other stakeholders can have better assurance that risks are managed with automated monitoring of key risk indicators.

Three lines of defense solutions help businesses manage risks more effectively by making business processes, controls, and fraud risks more transparent and efficient. They can automate end-to-end risk management processes, while compliance teams can automate policy management, controls, monitoring, testing, and so on. Internal audit can provide assurances that the strategy and investments in talent, digital transformation, and growth areas are protected and well managed.

This article originally appeared on the SAP Analytics blog and is republished by permission.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | FacebookYouTube              

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Is Big Data Dangerous? 3 Common-Sense Data Management Guidelines

Data is a crucial driver of value for most businesses today. But when managed improperly, data can become more of a liability than a benefit. Keep reading for tips on creating a data management strategy that helps, rather than hurts, your company.

No matter which industry your business is in, it almost certainly relies on data to a significant extent. These days, you don’t have to be a big bank or an insurance company to live and breathe data.

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You could also be, for example, a fast-food restaurant that relies on data-driven systems for ordering supplies and paying employees. You could be a construction contractor who uses data to help price bids. You could be an ice cream parlor that collects and analyzes data to determine which flavors to offer.

In all of these examples, data plays a key role in keeping the business operating normally and profitably — assuming that the data is well managed. Poorly managed data can not only undercut your ability to drive meaningful insights, it can also create extra costs and legal risks that lead to threats to your business’s overall stability.

When that happens, your data ceases to be a boon for your business, and it becomes a danger instead.

Building a Healthy Data Management Strategy

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That is why crafting a data management strategy that controls for the risks associated with data at your business is essential for using data wisely.

A data management strategy is your overall plan for storing, transforming, integrating, analyzing and archiving the data that your business collects. It applies to all types of data: Machine data from IT infrastructure, manually collected data like customer records, sales transaction data and much more.

When designing a data management strategy, you’ll want to take the following factors into account in order to ensure that your strategy is efficient and appropriate for your business needs:

  1. Compliance. Increasingly, compliance frameworks like the GDPR are imposing government regulations on the way data must be stored, managed and secured. It’s crucial to identify which regulatory requirements apply to your company based on its location and industry and make sure that your data management strategy can meet those requirements.
  2. SLAs. Service Level Agreements, or SLAs, are contractual guarantees that mandate that you provide a certain level of availability for your applications or services. SLAs typically involve more than just data availability, but because data availability is an important part of overall availability, you want to ensure that your data management strategy allows you to meet any SLA guarantees that you provide. SLAs are especially important if your business is the type that has a lot of external contracts and customers; they may be less important if your applications and infrastructure are used only internally.
  3. Cost. The portion of your budget that you devote to data management will vary widely depending on factors such as the size of your infrastructure and whether you use commercial or open source data tools. In any case, however, keeping data management costs in check is important for protecting the overall financial health of your business. To control data management costs, consider questions such as how much it costs you to back up data, and how many backups you can afford without breaking the budget, for example. For another example, think about the time spent transforming data before it is usable, and whether decreasing that time using automated data transformation tools can help reduce your overall data management costs.

A data management strategy that is designed to address these needs will help to ensure that data continues to drive business value, rather than creating unnecessary business risks. This goal will only become more important as the amount of data your business collects and analyzes continues to grow.

Make sure to download our eBook, “The New Rules for Your Data Landscape“, and take a look at the rules that are transforming the relationship between business and IT.

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Data Management Rules for Analytics

With analytics taking a central role in most companies’ daily operations, managing the massive data streams organizations create is more important than ever. Effective business intelligence is the product of data that is scrubbed, properly stored, and easy to find. When your organization uses raw data without proper management procedures, your results suffer.

The first step towards creating better data for analytics starts with managing data the right way. Establishing clear protocols and following them can help streamline the analytics process, offer better insights, and simplify the process of handling data. You can start by implementing these five rules to manage your data more efficiently.

1. Establish Clear Analytics Goals Before Getting Started

As the amount of data produced by organizations daily grows exponentially, sorting through terabytes of information can become problematic and reduce the efficiency of analytics. Such large data sets require significantly longer times to scrub and properly organize. For companies that deal with multiple streams that exhibit heavy bandwidth, having a clear line of sight towards business and analytics goals can help reduce inflows and prioritize relevant data.

It’s important to establish clear objectives for data and create parameters that filter out data points that are irrelevant or unclear. This facilitates pre-screening datasets and makes scrubbing and sorting easier by reducing white noise. Additionally, you can focus even more on measuring specific KPIs to further filter out the right data from the stream.

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2. Simplify and Centralize Your Data Streams

Another problem analytics suites face is reconciling disparate data from multiple streams. Organizations have internal, third-party, customer, and other data that must be considered as part of a larger whole instead of viewed in isolation. Leaving data as-is can be damaging to insights, as different sources may use unique formats or different styles.

Before allowing multiple streams to connect to your data analytics software, your first step should be establishing a process to collect data more centrally and unify it. This centralization makes it easier to input data seamlessly into analytics tools, but also simplifies the methodology for users to find and manipulate data. Consider how to set up your data streams best to reduce the number of sources to eventually produce more unified sets.

3. Scrub Your Data Before Warehousing

The endless stream of data raises questions about quality and quantity. While having more information is preferable, data loses its usefulness when it’s surrounded by noise and irrelevant points. Unscrubbed data sets make it harder to uncover insights, properly manage databases, and access information later.

Before worrying about data warehousing and access, consider the processes in place to scrub data to produce clean sets. Create phases that ensure data relevance is considered while effectively filtering out data that is not pertinent. Additionally, make sure the process is as automated as possible to reduce wasted resources. Implementing functions such as data classification and pre-sorting can help expedite the cleaning process.

4. Establish Clear Data Governance Protocols

One of the biggest emerging issues facing data management is data governance. Because of the sensitive nature of many sources—consumer information, sensitive financial details, and so on—concerns about who has access to information are becoming a central topic in data management. Moreover, allowing free access to datasets and storage can lead to manipulation, mistakes, and deletions that could prove damaging.

It’s vital to establish clear and explicit rules about who can access data, when, and how. Creating tiered permission systems (read, read/write, admin) can help limit the exposure to mistakes and danger. Additionally, sorting data in ways that facilitate access to different groups can help manage data access better without the need to give free rein to all team members.

5. Create Dynamic Data Structures

Many times, storing data is reduced to a single database that limits how you can manipulate it. Static data structures are effective for holding data, but they are restrictive when it comes to analyzing and processing it. Instead, data managers should place a greater emphasis towards creating structures that encourage deeper analysis.

Dynamic data structures present a way to store real-time data that allows users to connect points better. Using three-dimensional databases, finding methods to reshape data rapidly, and creating more inter-connected data silos can help contribute to more agile business intelligence. Generate databases and structures that simplify accessing and interacting with data rather than isolating it.

The fields of data management and analytics are constantly evolving. For analytics teams, it’s vital to create infrastructures that are future-proofed and offer the best possible insights for users. By establishing best practices and following them as closely as possible, organizations can significantly enhance the quality of the insights their data produces.

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Released: Public Preview for SQL Server Management Packs Update ( and SSRS Management Pack Update (

We are getting ready to update the SQL Server and SQL Server Reporting Services Management Packs. Please install and use this public preview and send us your feedback (sqlmpsfeedback@microsoft.com)! We appreciate the time and effort you spend on these previews which make the final product so much better.

Please download at:

Microsoft System Center Management Packs (Community Technical Preview) for SQL Server

Microsoft System Center Management Pack (Community Technology Preview) for SQL Server 2017+

Microsoft System Center Management Packs (Community Technology Preview) for SQL Server 2008-2016 Reporting Services (Native Mode)

New SQL Server 2008-2012 MP Features and Fixes

  • Updated the “Max worker thread count” data source of the corresponding monitor and performance rule
  • Fixed issue: the “Transaction Log Free Space (%)” monitor does not work
  • Fixed issue: in some environments, DB Space workflows fail when a secondary database is non-readable

New SQL Server 2014-2016 MP Features and Fixes

  • Updated alert severity in some monitors
  • Updated the display strings
  • Updated the “Max worker thread count” data source of the corresponding monitor and performance rule
  • Fixed issue: the “Transaction Log Free Space (%)” monitor does not work

New SQL Server 2017+ MP Features and Fixes

  • Implemented an ability to monitor SQL Server Cluster instances locally; formerly, it was possible only in Agentless and Mixed modes
  • Added the SSIS monitoring
  • Added the “Exclude List” property in DB Engine Discovery in order to filter instances, which are not subject to monitoring
  • Added the “Exclude List” property in DB Discovery in order to filter databases, which are not subject to monitoring
  • Implemented a feature: both “Exclude List” properties support usage of the asterisk character to make the filter more flexible. E.g. “*temp” is used to exclude instances/databases ending with “temp” and having anything in the beginning.
  • Added the “Computers” view
  • Added the “ClusterName” property to the AG class and updated AG alerts in order to display the property within
  • Updated the “SP Compliance” monitor in order to support the Modern Servicing Model for SQL Server: the monitor will check build number instead of Service Pack number
  • Updated the “SPN Status” monitor so that it requires only a single SPN record when only TCP/IP is enabled and the instance is the default one
  • Updated the “Database Backup Status” monitor: it is disabled by default now
  • Updated the DB Space monitors so that their alert descriptions include the actual value of space available
  • Updated the “Configuration Security” section in the guide
  • Fixed issue: the “Database Health Policy” monitor ignores the “Critical” state (on Windows only)
  • Fixed issue: the “Alert severity” property of the “DB File Free Space Left” monitor has incorrect Default Value
  • Fixed issue: the “DB Filegroup Fx Container” rollup monitor has an alert parameter with a wrong value within
  • Fixed issue: “Resource Pool Memory consumption” monitor may not change its state to “Critical” for the “default” resource pool
  • Fixed issue: “Number of Samples” parameter of “Resource Pool Memory consumption” alert displays incorrect data
  • Fixed issue: missed image resources in the SQL Server 2017+ Core Library

New SSRS 2008-2016 MP Features and Fixes

  • Added support for cases when a connection string of the SSRS instance to the SSRS Database is not in the “MachineName\InstanceName” format; e.g. “<IP Address>,<Port Number>” or “(local)”, etc. Such connection strings are fully supported for default SQL Server instances hosting the SSRS Database. If the instance is named, workflows targeted at the SSRS Instance object work properly, but those targeted at the Deployment object cannot work, as there is no possibility to learn the FQDN of the server.
  • Updated the Deployment Seed discovery so that it does not check if the SQL Server instance hosting the SSRS Database is running

For more details, please refer to the user guides that can be downloaded along with the corresponding Management Packs.
We are looking forward to hearing your feedback at sqlmpsfeedback@microsoft.com.

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5 Reasons Why Effective Data Management Is Essential for User Experience

Delivering an excellent user experience is essential to attracting and retaining customers. And although data management may not be the first thing that comes to mind when you think about optimizing user experience, maybe it should be.

User experience — or just UX, as really trendy folks put it — has become something of a buzzword at the intersection of IT and business.

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That has happened with good reason. In a world where a quarter of mobile users abandon an app after just one use, and where the growth in value of design-focused companies has far outpaced the stock market in recent years, the importance of user experience for driving business value is clear. (On the negative side of things, keep in mind, too, that social media and comment systems make it very easy for a user who has a bad experience to trumpet his or her problems far and wide.)

Data and the User Experience

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When you think about how to deliver a positive user experience, things like user-focused software design and rigorous application testing probably first come to mind.

However, the way you manage and deliver data is crucial to providing a positive user experience, too. Consider the following points:

  1. Applications run on data

    Virtually all applications rely on data to deliver a meaningful user experience. And the best-designed application deployed on the fastest host infrastructure will still frustrate users if the data that it needs to operate is difficult to access because of data availability or integration problems. It doesn’t matter how well designed your user interface is if the data that users want to see through the interface takes too long to load or is difficult to interpret.

  2. Data helps you understand what users want

    Assessing user expectations through anecdotal information, such as online comments, can be one way to understand user desires. But a data-driven approach is another, arguably more effective strategy. By collecting and integrating information such as how long users use a particular feature in an application, or what they do right before they stop engaging, can help you to pinpoint what users want and expect in order to give it to them.

  3. Data personalizes the user experience

    Users like feeling that you treat them as individuals, especially if they engage with you digitally and therefore do not directly interact with any humans at your organization. One way to make users feel like you recognize their individuality is to use data to personalize their experience. This is what Netflix does by recommending shows that a user might want to see based on past viewings, for example. You need well-managed data to drive this type of personalization.

  4. User perception counts as much as actual experience

    Even if the way you manage and secure data is not directly related to the user experience you deliver, users are likely to form overall impressions of your business, and their experience with it, based on how you manage data. If they sense that your business does not take data security seriously, or if efficient data management appears to be an afterthought, users are likely to form negative impressions of your organization, no matter what their actual experience with it is.

  5. Users need data integration, too

    When we talk about data integration, the conversation tends to focus on how data integration can help your business to make sense of all of its data by analyzing it through a single pane of glass. But data integration matters for users, too. For example, if you are an online retailer, you don’t want to present your users with a bunch of confusing data sets about different elements of their shopping history. Instead, you want to aggregate data into a single place and provide visualizations that help them to interpret it. Maybe you use graphs to show how their purchases compare across different categories, for example. That’s a lot better than only allowing users to view each past sales record individually.

Make sure to download our eBook, “The New Rules for Your Data Landscape“, and take a look at the rules that are transforming the relationship between business and IT.

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D365 In Focus: 4 Misconceptions About Change Management [VIDEO]

D365 In Focus Change MGMT Misconceptions 800x600 300x225 D365 In Focus: 4 Misconceptions About Change Management [VIDEO]

At PowerObjects, we know that your organization’s people are your biggest asset. We want to make sure we bring them along on this Microsoft Dynamics 365 journey with us! As change managers, we focus on how your team is going to adapt to new processes and how to make your implementation smoother. In today’s Dynamics 365 In Focus video, Sara Jo discusses four common misconceptions we hear from prospects when talking about change management on a Dynamics 365 implementation!

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4 Key Benefits of Omnichannel Order Management for Retailers

Posted by Ian McCue, Senior Associate Content Manager

Efficiently and cost-effectively getting a shirt, hat and shoes delivered to a customer in Billings, Mont. when a retailer’s stock spans warehouses from Burlington, Vt. to Bellevue, Wash. and a physical store in Baltimore is no small feat. Yet, for even the smallest retailers, it’s imperative to balance customer expectations with profitability — whether the order is shipped from a warehouse, shipped from a store, drop shipped or picked up in store.

That’s why optimizing order management is a crucial piece of a successful omnichannel strategy. Order management is now an art form. Businesses devote endless hours to improving order management because it is so critical to maintaining margins and keeping customers happy.

The lion’s share of that burden falls on employees, who typically must comb through all the fulfillment options and choose the best one based on criteria like closest fulfillment location, lowest shipping costs, potential for splitting or bundling orders and maintaining safety stock. When handled manually, this quickly becomes a tedious, time-consuming process of looking through reports for actual and potential issues.

Automating that process and enabling employees to manage by exception can maximize opportunities and minimize mistakes to make fulfillment a strategic differentiator for your business.

Optimizing Fulfillment Across Channels

At its core, an order management system (OMS) will evaluate all channels and supply sources to find the best way to fulfill an order with intelligent and automated order sourcing and allocation.

In other words, an order management system should make complex fulfillment decisions much easier. For instance, if a complete order cannot be shipped from the closest warehouse or retail store, the system runs through the options of splitting shipments across warehouses by distance, splitting shipments without restrictions on fulfillment source or location and, if it makes sense, backordering to the closest warehouse. The beauty of an OMS is that it finds the best method for that specific order without an employee touching it.

An OMS can also resolve a problem when something goes wrong with an order. If sufficient inventory is not actually available at the location where it was supposed to be, the system automatically reroutes it, choosing the next best location and shipping it from there.

Benefits for Retailers

Increased customer satisfaction: Using the closest possible fulfillment centers will get packages to customers faster. And by ensuring ecommerce and in-store channels can be utilized for fulfillment, retailers have an easier time solving customers’ problems.

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Decreased labor costs: Reducing the amount of time and labor devoted to making sure the business is optimizing inventory movement will mean significant savings. Staff members previously devoted to order planning can spend time on more value-added tasks to generate additional revenue instead of routing orders. In addition, in-store staff can handle a greater portion of order fulfillment, reducing the load on warehouse employees and optimizing your labor force.

Decreased shipping costs: By optimizing fulfillment by closest location, items included in an order/available inventory and more, retailers can reduce shipping costs. The OMS will always find the cheapest way to get an order to a customer while still meeting their expectations.

Increased sales and margins: Offering shoppers additional delivery options will increase conversions. In-store pick-up, for example, is vital for someone who needs a product right away. Also, safety stock can be dramatically reduced – if not eliminated – with an order management system. Ensuring any order can be fulfilled regardless of channel means safety stock – which can become quite expensive – is no longer necessary. Retailers can keep inventory in retail stores while allowing all channels to sell.

Any omnichannel strategy simply isn’t possible without a functionally rich, reliable order management system. It will help you deliver exceptional customer experiences at a lower, sustainable cost.

Learn more about solutions NetSuite offers to help with your order management challenges.

Posted on Wed, May 9, 2018
by NetSuite filed under

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