How Demand Forecasting Can Boost Business Efficiency

The art of predicting the future is nearly as old as civilization itself. Oracles, fortune tellers and soothsayers of all kinds have long claimed to be able to see the course of future events, and they’ve often found a receptive audience in humans who want desperately to know what’s just around the corner.

These days, of course, the world has more scientific and data-based methods available. The business community, in particular, has embraced what has come to be called Big Data: the practice of taking the mountains of information that businesses accumulate and putting it to work. One of the most widely-used applications of Big Data is the practice of demand forecasting, in which analysts attempt to understand what customers will want next by understanding what they’ve wanted in the past.

What kinds of methods do businesses use to perform these calculations, and what goals do they typically want to accomplish? Perhaps most importantly, how can they improve their accuracy and make them a smart use of resources?


 
Basics of Demand Forecasting

Businesses of all kinds use demand forecasting to estimate future demand for their goods and services. A basic primer on today’s demand forecasting methods begins with the difference between each major type of forecasting:

  • Short-Term Demand Forecasting: Focuses on customer demand within several months to a year and how best to prepare ordering and shipping practices for seasonal demand cycles and approaching campaigns.
  • Mid- to Long-Term Demand Forecasting: Focuses on strategic deployment of resources to meet anticipated customer demand in the next one to five years.
  • Active Demand Forecasting: A more intensive technique used to evaluate how aggressive plans for expansion and scaling will interact with consumer demand.
  • Macro Demand Forecasting: The “big picture” demand forecasting technique that looks ahead at broader market conditions to help plan a business’s overarching strategies.

Beyond that, there are two broad categories of methods that each type of forecasting can employ. Many businesses use both, combining techniques to achieve better results:

  • Qualitative Forecasting: Uses methods that focus on opinion and consensus, such as market research and the Delphi method, to arrive at a reasoned conclusion.
  • Quantitative Forecasting: Uses statistical techniques and computer modeling to crunch the hard numbers and create a data-intensive picture of future demand.

Businesses use all kinds of tools and technologies to implement these methods, from relatively simple research surveys to complex data modeling tools. That’s certainly a big investment—so what, exactly, do these methods achieve?

Why Demand Forecasting Is Important

Most businesses consider demand forecasting to be critical for achieving a better deployment of resources. The individual goals that each business hopes to achieve through demand forecasting are as diverse as individual business plans, but there are some common themes:

  • Optimizing Inventory: In a business climate where warehouse space and fulfillment capacity are perpetually at a premium, it’s essential to ensure that valuable space and logistics resources aren’t being wasted on inventory that does not create value.
  • Improving Cash Flow: By the same token, it’s important that businesses keep their cash flow situation agile and not over-allocate resources to projects that aren’t aligned with market conditions.
  • Serving Customers Better: Whether it’s disposable consumer goods or high-end B2B services, every business wants to give its customers what they want. Accurate demand forecasting helps to improve customer satisfaction by ensuring that supply matches demand as closely as possible.

Obviously, there are some excellent reasons to implement demand forecasting—assuming that it works. All too often, though, businesses experience frustration with inaccurate forecasting. Thus, the relevant question becomes: How can forecasting be implemented in a way that provides solid ROI and improves efficiency, rather than simply prognosticating?


 
How to Boost Efficiency Through Better Demand Forecasting

Nearly every large- or medium-sized business today uses some form of demand forecasting, and thanks to the advent of affordable cloud-based ERP software, it’s more easily available than it’s ever been. However, to improve the accuracy and efficacy of demand forecasting, it’s important to implement some best practices used by the top forecasters in business. Although there are many ways to do demand forecasting right, these key tips can help a business get more out of their investment in the practice:

  • Remember that demand is not monolithic and try implementing a disaggregated model. Break down demand by demographics, product lines, regional differences and any other statistical segments that matter. Again, the powerful reporting features available in many distribution software products can help establish what these segments are and how they can be separated.
  • Focus resources not just on forecasting demand itself, but in determining the variability of demand forecasts. A forecast with a five percent margin of error and one with a 20 percent margin of error can both be useful, but building a business plan around a high-variability forecast is much more risky. Uncertainty will always be present, so a smart business will take steps to identify just how much uncertainty is present and allocate resources more or less flexibly based on that understanding.
  • Use automated systems to monitor how a demand forecast is matching up to actual expressed demand. Collaborate with data experts to identify breakpoints at which divergence will require revision of demand models and then set up alerts at those points. Many modern manufacturing ERP systems allow for sophisticated real-time modeling of demand that allows this kind of rolling analysis to take place.
  • Diversify forecasting methods. No one approach can capture every element of a system as complex as market supply and demand, so try out a variety of models and evaluate their accuracy using the previously discussed principles such as real-time monitoring and variability forecasting.


 
Planning for the future is a key part of any business, and demand forecasting is now an indispensable tool for making that planning useful and effective. So, although tools, techniques and goals may vary, one thing is clear: Establishing a forecasting system and evaluating its efficacy should be high-priority goals for a business that’s trying to grow, compete and innovate.

Stop Drowning In Data And Create An Optimisation Plan

StrategyDriven Organizational Performance Measures Article |Data Management|Stop Drowning In Data And Create An Optimisation Plan One thing is certain – Big data is big business. As the ways in which we can gather information have expanded almost infinitely, so the data we have stacks up and up. We’ve been promised the earth by understanding our customers better – enhanced profits, more repeat sales, higher average transaction values, loyal brand advocates. And while it’s true that data can deliver all of that, for most businesses, it doesn’t. Because data is a tool like any other, and when it’s misused or not used to its full potential, you’re not likely to see the results. Most businesses collect data without any clear idea of why they are collecting it, and their marketing strategy gets stifled under the sheer amount of available information. Instead of driving the data and mining it to find the relevant parts, it drives them. Learning how to effectively use data is highly individual to each company and their operations and KPIs, but there are some building blocks for good data hygiene and usage that work across all sectors and business types. So, how can you stop drowning in data and start using it to your advantage?

Closing The Feedback Loop

Often we believe that we should be coming up with a lot of colourful looking reports covered in pie charts and bar graphs that we can point to as concrete evidence of macro trends affecting our operations or changes in customer experience. But what do all those colourful reports actually show? Data in and of itself is literally just a bunch of numbers, and all the reporting you like isn’t going to make much of a difference to your bottom line. The most important output is actually the insights that only shrewd analysis can show, and this is the single most important function of the modern marketer. Seeing meanings, patterns and stories is the important part, not the raw data itself. Knowing what all these metrics mean for your business and what action should be taken is the only thing which makes data collection worthwhile.

Make Sure You Measure The Right Thing

The symbiosis between overarching business strategy and analytics can be a tough balance to get right, because both should feed off the other. What you measure should be dependent on what you want to optimise in line with the wider goals you have for your business. But equally, what your goals are should be at least partially dependant on the customer feedback that you amass through your data. Skew the balance too far one way or the other and it’s not going to work in your favour. Setting good metrics for your business is absolutely key to the success you’ll get. Look at things such as which channels drive the most conversions for your business, which landing pages on your website have the lowest conversion rates, what your average order value is in different segments of customers. Underpinning all of these metrics need to be two important things – a great CRM system which can allow you to use these insights to create dynamic marketing campaigns which really respond to individual customer preference and history, and a strict attention to data hygiene and legal practices. Ensure that you’re on the right side of the law when it comes to data collection and storage, and seek out advice from experienced professionals with a track record of legal matter management. The penalties and the damage to your professional reputation can be majorly severe if you get this wrong, so make it a matter of good practice.

Use Segmentation Effectively

Taking action on your data should all be driven by customer segmentation. Not only understanding your customers and their different backgrounds and preferences, but even allocating groups a persona to bring their journey to life and help you see how better to help them. Your knowledge of the goals set out in your business plan should guide which group of customers you look at first, but try to use the data you request to enhance your understanding of each group. This approach allows you to dig a lot deeper and come up with far more creative solutions.

Remember To Add Context

Data is never an island, and if you insist at looking at very narrow ranges of statistics in isolation, the picture that emerges is hopelessly skewed and will never give you an accurate base to work from. A better understanding of context can help you to make much more informed decisions. Make the connection between the figures you’re seeing and what they really mean for your business. Interpreting data badly can be very harmful to your operations and in many cases it would have been better not to collect it at all!

Pull Together Your Optimisation Plan

With the insights you have managed to gather, putting them into some form of actionable plan is the most important part. Six Sigma has a particularly useful concept which can be directly applied to using data insights in this way. The Define Measure Analyse Improve or DMAIC process can be very instrumental in shaping your approach. First, you define the problem that you are trying to solve, known as your hypothesis, set out your relevant stakeholders and the scope of your analysis. Then, you can measure the relevant data fields and use basic analysis to spot any anomalies. The third step is to analyse correlations and patterns within your data set using your visualisation skills to bring it to life. Improvement then corms from using these insights and coming up with a few options to explore. Finally, you control the change by using strategies like multivariate testing and monitoring KPIs to see the impact of what you’re doing. It’s then possible to make responsive adjustments in real time to ensure that your campaigns are fluid and provide a shifting technique to overcome any barriers and generate the best possible return on investment. With a little more careful planning the feeling of being overrun by statistics will be replaced by a focus on only the most relevant metrics to get you to where you need to be.

Designing an AI Strategy for Superhuman Experiences

StrategyDriven Organizational Performance Measures Article | Designing an AI Strategy for Superhuman Experiences | Artificial Intelligence | Superhuman InnovationThe most difficult question to answer when starting an Artificial Intelligence project is often to determine where to begin. The tendency is to jump straight into the technology without fully defining the problem or examining the market.

Before starting, define what problem needs to be solved and who needs the solution. It’s important to be very specific about your audience because these are the people who will actually purchase or use the product or service. What the end users need can be discovered using a variety of techniques, including market research, surveys and so on. Without defining the problem and the market, it’s likely the ROI will be weak and making sales will be difficult. Often, this is seen as technology for technology’s sake, or doing it just because it can be done. In other words, start with a business problem, an unused data set or survey the new AI techniques, which might identify a problem, a solution and a customer.

To operationalize an AI framework, use the concept of People, Processes, Data and Technology. With People, the concern is with building a team with the right skill set and organization. Processes deal with how the project is developed and the different methodologies available to achieve the goal. With Data, have a data strategy and focus on quality not quantity, as well as accessibility. Finally, Technology provides the software and hardware considerations on which to build the project. This approach can be molded and customized to fit the needs of any project. Just to be clear, this is a blueprint and is not intended as a straitjacket. Use the framework to enable progress, not to restrict your freedom of action.

If an organization is just starting with AI, which many are, change management strategy is very applicable. Change management helps build advocacy and a shared vision within organizations. The thing that many leaders understand is people implement change and that you can’t exclude people from the equation. Plans and processes are necessary but change often fails because the human side is not appropriately factored into the process.

For an AI project to be successful, somebody must ‘own’ it. This doesn’t imply that the project needs to be restrictively managed; rather, one or more senior stakeholders in the business must support the project, its goals and the team. And where the project sits depends on how your company is organized. No matter how a company is organized, the AI team must be embedded within the business and not siloed. If an AI team is isolated from the rest of the business, then their efficiency will be reduced, and they may not consider the needs of end users and stakeholders within the organization.

There also needs to be consideration of how data scientists and AI engineers work together. Are they working as one team or are there multiple teams? Do they work for the same organization? These and other questions must be addressed from the outset. First, you need to define the role of the data scientist. Are they a business or domain expert, statistics expert, programming expert, data technology expert or a visualization and communications expert?

To infuse AI into a company’s culture, communicate throughout the business to increase awareness and acceptance of AI, and build an understanding of the purpose, terms and options available. Your business can also provide educational opportunities to bring members of your organization in all areas of your business up to speed on the concepts. The team can be based out of IT, which would be IT-centric, integrated between data science and IT or a specialized group with team members from throughout the business.

Ultimately, start with the problem and work towards the solution with AI. AI is a profoundly powerful tool to get to that solution, yet there are many things to be considered; including the people who staff the projects and their skills, specialties and experience. However, choosing the right strategic AI framework will guide the project to success.


About the Author

StrategyDriven Expert Contributor | Chris DuffeyChris Duffey is author of Superhuman Innovation: Transforming Businesses with Artificial Intelligence, and the Head of Artificial Intelligence Innovation and Strategy at Adobe. Chris spearheads Adobe’s Creative Cloud strategic development innovation partnerships across the creative enterprise space.

For more information, please visit: https://www.koganpage.com/product/superhuman-innovation-9780749483838

Improving Your Data Security Following These Guidelines

For rhyming purposes and historical recall, it would be nice if it were the year 1964 when Gordon Moore discovered what came to be known as Moore’s Law. But it was 1965, and the rhyme is kaput. Still, the principle discovered lives on, and it is generally accepted today that technological capability in terms of computation doubles on itself about every eighteen months.

The trickle-down effect of this continual forward expansion can mean businesses must update tech systems every eighteen months to five years, depending on region, competition, forward development, and many other factors unique to a given operation. Generally, technological innovations pay for themselves through increased capability or competitive viability. There’s a balance, though, and you don’t want to be sidelined by tech that’s still buggy.

There’s an implication here that often goes without consideration, however. That is the cybercriminal element. As technology exponentially compounds on itself at eighteen-month intervals, cybercrime “startups” do their best to be on the cutting edge, as this allows them an advantage over targets.

With new technologies come new threats even as old issues are resolved. Cybercriminals work to be at the forefront of such tech frontiers to give themselves advantage. It’s absolutely integral that your business adopt, at least in terms of security, the latest available protocols. Following, several data security strategies and techniques will be explored to help you most effectively secure your business in a turbulent tech world.

StrategyDriven Organizational Performance Measures Article | Data Security| Improving Your Data Security Following These Guidelines1. Always Have Worst-Case Scenario Protocols Determined

Cloud computing is the friend of the data security professional today. Apps and server logs can be monitored to catch anomalous behavior and maintain reliable functionality.

Since cloud computing is managed by agencies who have competitive stakes in facilitating top-tier service, in addition to next-level cloud design apps and infrastructure solutions, the newest security protocols can be automatically applied.

This may depend on your cloud provider, or factors such as whether you’ve chosen hybrid, public, or private clouds. Whichever way you go, solutions like database mirroring can help operations on the web remain continuous while you troubleshoot a primary network.

You can also follow-through on the 3-2-1 rule of data protection. Basically, this rule is: three backups on at least two different kinds of media, with a backup being located off-site. Cloud computing can easily fill the niche of your off-site backup.

Lastly, ensure you’ve got a recovery protocol ironed out. Rebooting will be a process, and if you haven’t planned for it, downtime will likely be greater. In terms of recovery, you need not just backups, but restoration protocols.

StrategyDriven Organizational Performance Measures Article | Data Security| Improving Your Data Security Following These Guidelines2. Continuously Update Security Measures

Whether or not you use cloud computing for data security, it’s integral that you apply security patches as soon as they become available. Firewalls fall out of effectiveness given time, these must be updated at regular intervals. The same is true with antivirus protocols, anti-malware, or any other protective software solution. Make sure your data is safe by using the best security and data loss prevention tools there are. You can contact cybersecurity experts such as Alpine Security to learn more about those.

Oftentimes it takes a cybercriminal targeting an organization for security solutions to be determined. You’ve got to stay on the cutting edge of these things, or you’re a sitting duck. Perhaps initially, your business may be less likely to be targeted; but cybercriminals extort all economic targets if they can get away with it.

3. Facilitate Recurring Staff Security Training

It’s important that you “upgrade” your staff just as you update your tech systems. At intervals, they need to be appraised of new cybercriminal threats and hacker strategies. Consider the social engineering hack, as an example. This is when someone doesn’t use any software to break into an organization, they simply use human nature and clever posturing.

Such hacks are often attached to ransomware. Sometimes clever trickery can produce financial theft right from the hands of unsuspecting businesses when hackers get access to personal information, forge an email requisitioning funds, then steal indiscriminately. Your staff need to know what sort of cons are out there, and how to avoid being undermined by them. New ones develop all the time, so keep them educated.

StrategyDrivenOrganizational Performance Measures Article | Data Security| Improving Your Data Security Following These Guidelines4. Don’t Overlook Password Management

Passwords are some of your most important assets. You need to reset them regularly, and they need to be non-intuitive. Avoid mnemonic devices, avoid names you’re familiar with. What you need to do is initiate password management which automatically updates passwords and can provide new one’s when necessary. Working with MSPs can be integral in lining out the best password management; it will depend on your operation.

Covering All Your Bases

There’s no way to anticipate all disasters. You need to have backup and recovery protocols determined. You can defray some instances of cybercriminal intrusion through up-to-date tech security measures, but you must also provide your employees with the training necessary to protect against other means of intrusion. Finally, make sure passwords are properly managed. Such an approach to data security is comprehensive and can help you maintain optimum sustainability.

Using Big Data in the Classroom

StrategyDriven Organisational Performance Article | Big Data | Using Big Data in the ClassroomBusinesses the world over are leaping into the use of big data. The analysis of the vast amounts of consumer data is helping business to create more effective marketing strategies and streamline their business process. However, it’s not just businesses that are using this valuable tool. Big data is starting to make its presence felt in the classroom, and the results are proving to be worth it. As the next big step in the transformation of the modern classroom, technology is at the forefront, and big data could be the key to improving the education levels of a whole new generation. Here’s how.

Better Results

The real-time analysis of the performance of each individual student is now possible through data tracking. This enables teachers and educational centers to have a more accurate view of how well a student is performing. Traditionally, student performance has been judged according to the results of an exam or test, but this is not always effective. By highlighting the strengths and weaknesses of each student, it is now possible to create more effective and beneficial learning schedules, and even allow for better group work when complementary skill sets are combined. This can help improve a child’s learning skills, and create a more effective learning curve for every student in the classroom.

Reduce Drop Outs

There are an estimated 1.2 million high school dropouts in America every year. This is a huge issue for educators, and can have dramatic long-term effects on the future of every one of those children. Data analysis could be the key to tackling this issue. Modern software is able to use predictive analysis of data in order to create education programs that suit each student, and with college retention software it is possible to identify those students most at risk of dropping out. Many of the reasons for high school or college drop out can be tackled if they are identified early, which is why tech-savvy educational facilities are integrating these software models into their classroom management.

Education Customization

Blended learning, where students use a combination of online and offline resources, allows students to have much greater control over what they learn. For those with clear advantages in some areas, this allows them to tailor their lessons to their skill set. This is now possible even in large classes, with teachers able to oversee what students are doing in real time. By allowing students to work at their own pace and in areas that interest them, teachers are better able to tailor their offline lessons to those students that need extra help. This can not only help with student engagement, but it also means that those students who excel are not being held back by their peers. For those students that are slower to learn, customized lesson plans can help to keep them on a level playing field.

Data is being used by corporations and businesses in a wide variety of ways. As the full impact and potential of big data continues to expand, the classroom could end up being the most important user of data analysis, and the education of the next generation looks set to benefit from those changes.