A formula for accurate demand planning at store level

(Hint: it doesn’t start with “=”)

Technology is the biggest enabler of people in the digital age. Yet arguably, it’s also the biggest threat to the modern day workforce. When it comes to using technology to enhance demand planning in business, harnessing digital tools in a way that empowers people is the key to getting the balance right. So while there’s no substitute for the strategic human mind to help you plan your inventory levels, there are some tasks that in this day and age, should always be handled by technology.

Here’s a three-step pathway to a more efficient (and effective) way of doing things.

Step 1: Consider the limitations of a person with a spreadsheet

If you consider a large-scale retail business operating in various centres around the world, offering online purchasing and possibly third-party distribution channels - things get complex pretty quickly. With hundreds of SKUs, across multiple sites and various sales channels, maintaining a birdseye view of activity is hard. Add to that the challenges of applying data at a granular level, to forecast and plan accurately across an ever-more complex landscape, and you have a near impossible task. Expecting people to handle this kind of role armed with nothing more than Excel, is not only futile, but it deprives them of the chance to focus on something far more worthwhile - devising strategies to meet market demand and reduce commercial waste.

Step 2: Automate the admin

At the very least, automating store-level demand planning with an effective inventory management system is a must. When you remove the laborious admin associated with capturing and recording data from the equation, people are empowered to do what only people can do. Think laterally and strategically about business challenges and how to mitigate them.

Step 3: Bring in the big guns - AI, ML and Big Data

Once inventory management systems have been automated across a complex commercial landscape, the next step in accurate store level demand forecasting is to harness the power of cutting-edge technology. Artificial Intelligence and Machine Learning can help you make centralised decisions about your physical stores, on a case-by-case basis. Insights and predictions can be drawn from location-specific data sets and presented in an easily-digestible, actionable way. Being able to combine broadstroke business statistics (captured by an automated system), general market information and localised data makes it easier to make more accurate forecasts. Demand Strategists can then adjust stock requirements before items leave the warehouse, saving on both freight costs and unnecessary carbon emissions.

QU - a tool for the times

QU is an AI-powered SaaS demand forecasting tool that has been developed to measure, analyse and predict product performance in real time. Using the latest AI technology along with proprietary ML algorithms and innovative design, QU draws on millions of data points from hundreds of sources. It presents powerful insights in an easily-digestible and customisable dashboard that not only predicts buying behaviour, but adjusts forecasts of future sales right down to individual product level (SKU).

QU is a tool that is designed to empower people, giving them the insight and control they need to do better business.

So if your business is ready to start freeing up resource and firing up profitability…

Inject the smarts into your new product planning

Consumers now have more choice than ever before - of what to buy, and where to buy it. The explosion of e-commerce has driven product and competitor proliferation to all-new levels, so what worked before (i.e. analysing previous product performance) no longer serves as an accurate indicator of which new product ideas are worthy of investment.

And that’s before you even get to developing new products in emerging categories, where no previous data exists.

So as a switched-on business, how do you build new product use cases that avoid a warehouse of excess stock, or a glut of unhappy customers who missed out?

Business-generated data

Businesses generate huge amounts of valuable data every day, and the importance of this should not be overlooked.

It’s imperative that you capture and record pertinent information, wherever possible, for example:

• Historical sales data of similar products in your business
• Current sales data of similar competitor products

You can then begin to make predictions, based on the likely impact of:

• Marketing activity (promotions, offers, advertisements, competitions etc)
• Any unique properties and/or propositions
• Launch timing (e.g. Christmas, Summer etc.)

By combining this hard data with educated predictions, you can start to form a picture of what might happen in the initial three months after launch. But with today’s developments in technology, what was once the final step in the new product demand forecasting journey, is now just the beginning.

Innovating in the age of AI

Not-so-fun fact: around 80% of new products fail. But imagine if it was possible to take some of the guesswork out of the equation, so you could stop backing the wrong horse before the race even started. With AI (Artificial Intelligence), ML (Machine Learning) and Big Data in the mix - this is now a reality. Demand forecasting systems that are driven by these technologies have the power to draw on historical performance-based data, and combine this with huge data sets from well-considered, trusted sources across the internet. They can then deliver real-time insights and predictions that can help you to eliminate some of the uncertainties around new product launches.

The right mix of technology acts as a crystal ball, making it easier for businesses to put their time, attention and investment in the right place, at the right time.

With these tools behind strategically-skilled people, it’s now possible for businesses to do what was previously impossible. Meet 100% of demand with zero waste.

QU - a tool for the times

QU is an AI-powered SaaS demand forecasting tool developed to measure, analyse and predict product performance in real-time. Hosted on and supported by AWS, QU uses the latest AI technology, proprietary ML algorithms, and innovative design; QU draws on millions of data points from hundreds of sources. It presents powerful insights in an easily-digestible and customisable dashboard that predicts buying behavior and adjusts forecasts of future sales to individual product levels (SKU).

QU is a tool that is designed to empower people, giving them the insight and control they need to do better business.

So if your business is ready to start freeing up resource and firing up profitability…

Forecast. 

To predict or estimate a future event or trend.

Let’s start with a forecast we’re all very familiar with as an example - the weather. Once upon a time, forecasts were based on historical data captured around certain dates. Then, with the invention of telegraph networks, weather conditions could be observed and shared across larger geographic regions to predict changes. But these days, Meteorologists have the power to see what’s going on all over the world and with the help of computer modelling, can make ultra-precise predictions in real-time.

Demand Forecasting is no different. The science and technology in this space has moved on in leaps and bounds. Today’s tools have the power to give Demand Planners access to data-driven, actionable and explainable insight. These advancements are making the need to input reams of historical data into Excel all but redundant. Also drawing to a close are the days of formatting and maintaining epicly complex spreadsheets - which means more resource freed up to focus on strategy, and better continuity throughout staff changeovers.

Consider a new way to forecast demand

It’s been said that there are 6 types of Demand Forecasting:

1. Passive Demand Forecasting
   Using past sales data to predict the future
2. Active Demand Forecasting
   Building in market research, marketing campaigns and expansion plans.
3. Short-term projections
   Looking at just the next three to 12 months to manage your supply chain.
4. Long-term projections
   Making projections one to four years into the future, taking into account historical data and future goals.
5. External macro forecasting
   Incorporating trends in the broader economy and predicting how those trends will affect your goals.
6. Internal business forecasting
   Factoring in your internal capacity to meet demand.
   
   

However, there’s a new methodology to add to the list that can do all of the above, and more.

AI, ML & Big Data-based forecasting

This next-generation method of predicting demand pulls on innovations in technology to enhance business operations and balance supply with demand. Not only can smart forecasting tools automatically pull in information from across your technology stack, they can scour carefully-curated data sets from all over the web and correlate findings with your business stats. This consolidation of internal and external trends, macro events, seemingly irrelevant anomalies and your business’s historical data is a powerful mix. Add the capability to constantly refine and learn and deliver real-time, actionable insights (thanks to AI / ML) and you have a Demand Forecasting tool that can reduce waste and seriously impact your bottom line.

All it needs is a skilled strategist at the helm.

Eyes front

Until recently, businesses spent a lot of time looking over their shoulders, with no choice but to use solely historical data in their demand predictions. The consequences were often dire. For example, in 1974 US Electric Utilities had planned to double their generation capacity by the mid-1980s. This was based on historical sales trends and a forecast of 7% annual growth in demand. The reality was that from 1975 to 1985, growth crawled along at just 2%, meaning the extra plants that were being built were not required. This is just one example of how relying solely on historical data can be a dangerous business.

The not-so-basics of Demand Planning

At its simplest, effective Demand Planning means reducing the gap between held inventory and actual sales. It’s about meeting demand in the most efficient way possible to help retail organisations avoid stock-outs at one end of the scale, and wastage at the other. Yet this vital role is often drowned out by louder voices in other areas of the business. (Yes, we’re looking at you, Marketing).

When businesses DO decide to elevate Demand Planning, the inclination is usually to focus on getting back to basics. But the problem with “basics” is that they are just that - rudimentary, and limited in their capability to help you move forward with increased insight and accuracy.

Stats Vs Data

Traditionally, organisations have looked to sales figures, along with consumer buying patterns and seasonal cycles, to help them make demand forecasts. This approach is always based on information that has already been generated and is retrospective in nature. Once upon a time, it was all that was available and alongside long periods of market stability, it helped businesses make more accurate forecasts.

Enter an explosion in e-commerce activity. Suddenly, consumers were leading the charge on how they researched, sourced and bought items. Next, unforeseen events disrupted the norm and put a fire under the trend away from bricks and mortar shopping. This customer-led volatility has put many businesses on the back foot, and those who continue to fall back on outdated forecasting methodology to fix the issue are never going to catch up or even better, get ahead of the curve.

The good news is that this online activity provides enormous amounts of invaluable data, not just about what consumers are doing, but about what their intentions are. By correlating their activity with Big Data, we can see how changes in everything from the weather to building regulations and national sports team performance affects their purchasing habits. Apply this to your Demand Planning and suddenly, you have a system that can not only analyse information, but accurately predict activity.

Putting the right technology in the right hands

The best way to empower a Demand Planner is to give them the right tools. An accurate forecast can give any business a serious competitive advantage. But step one is aligning business units behind a solid consensus plan. The outcome will be reduced inventory holdings AND increased stock availability.

Bringing AI and ML to SCM, CRM and ERP

Many businesses and platforms are beginning to understand the value of integrated business operations across SCM, CRM and ERP. Some big technology providers are even crawling towards making it happen. But all of them are missing a trick.

The real gold lies in being able to extract data from all of your business systems and correlate it with millions of data points from carefully-selected sources across the internet, across a consistent timeline. When you enrich your business data with these powerful, predictive insight-loaded data sets, you can build a much clearer picture of the circumstances and anomalies that will affect your customers’ future buying behaviour.

More data in = more accuracy out

When it comes to Demand Forecasting, the more information your systems have to base predictions upon, the better. Let’s be honest, human-powered systems (like the humble spreadsheet, for example) can only ever handle a limited amount of data. There’s also a big problem with continuity, with one person’s spreadsheet wizadry becoming the next person’s nightmare. 

Next-generation digital systems, that have been designed to empower humans, can deal with and ingest vast data sets at speed, in real-time and store / present insights in a consistent (yet customisable) format. With powerful and innovative tools in their technology arsenal, businesses can identify peaks and troughs in sales figures and attribute them to specific circumstances - without burning through hundreds of admin hours. Add to that a layer of Artificial Intelligence that can learn from these data patterns and make predictions based on real-time information, and you have a system that can help you balance your demand and supply perfectly.

Empowering humans, not replacing them

Imagine a planning meeting where the biggest influence on decision-making was not how loud or determined the people around the table were, but how much information they had to back up their argument. With a data-powered solution on their side, Demand Planners can take their capability and outcomes to all-new levels of success, driving organisational change away from never-ending cycles of promotional activity towards a more accurate, streamlined and profitable way of doing business. This is not only good for the bottom line, it’s good for the planet as companies shift away from excess production and consumption towards more needs-based, sustainable models.

QU - a demand planning tool for the times

QU is an AI-powered SaaS demand forecasting tool that has been developed to measure, analyse and predict product performance in real-time. Using the latest AI technology along with proprietary ML algorithms and innovative technology design, QU draws on millions of data points from hundreds of sources. It presents powerful insights in an easily-digestible and customisable dashboard that not only predicts buying behaviour, but adjusts forecasts of future sales right down to individual product level (SKU).

QU is a tool that is designed to empower human beings, giving them the insight and control they need to do better business.

So if your business is ready to start freeing up resource and firing up profitability…

Knowledge is power

Supply chains - from the very first step of a product or service development, through sourcing, manufacture, logistics and sale - are a treasure trove of invaluable information. Centralising your processes via a robust, optimised SCM (Supply Chain Management) system is step one in transforming this data into profitability.

The next step is harnessing the data generated across your supply chain (and beyond) to ensure that your consensus plan reflects true market demand and is able to respond to fluctuations that may occur due to unforeseen events.

Achieving supply chain visibility

End-to-end transparency, analysis and efficiency is widely accepted to be driven by these three best practice pillars:

• Real time access to data
• The ability to quickly adapt and respond to new information
• Intelligent systems that automate order flows

With these elements of supply chain analysis in place, businesses undoubtedly position themselves to understand their existing supply chain via the information it generates. However, there’s another step in this process that’s often overlooked as many companies don’t know it exists. It can extend an organisation’s visibility beyond what is happening now, to what’s around the corner and how that might impact the flow of products throughout the supply chain.

Supply chain analysis and Big Data

The world of commerce is undergoing what some describe as a “data-driven revolution”. Online operations, which have been adopted by most businesses through necessity, mean that data is being generated and collected at an unprecedented rate - every day. However, most Demand Planners and Supply Chain Managers are not data scientists, and without the right tools at their fingertips, the secrets and insights of these huge data sets remain hidden.

The right power in the right hands

There is undoubtedly fear surrounding the rise of Artificial Intelligence as people understandably question the threat it poses to their roles in life. But Machine Learning, combined with Big Data and some clever technology design, can empower professionals to do their jobs better. A clear and customisable dashboard that presents insights in an easily-digestible way, while predicting and forecasting demand in real time (not just according to business data, but millions of data points from hundreds of sources across the internet) can be extremely powerful in the right hands.

Technology can drive more informed, accurate decision making capability across a supply chain, which means greater efficiencies to companies - and better value for consumers. A fully-integrated, intelligent digital system can also deal with far greater complexity than a human / spreadsheet combination, freeing people up to focus on more profitable endeavours than data entry and formula application.

Once a business has achieved full supply chain visibility, can perform end-to-end analysis and is able to quickly adapt and respond to anomalies that occur both inside and outside of the chain, it should start working towards integrating its ERM and CRM with its SCM.

Why Optimised SCM matters to literally everyone

When it comes to effective Supply Chain Management (SCM), few businesses fully recognise the true competitive edge it can deliver. In the past, the process of micro-managing the flow of goods that turn raw materials into customer-ready products meant slow logistics and increased costs. But today, the reality is that an optimised supply chain benefits everyone - from raw material suppliers, to manufacturers, distributors, consumers and isolated communities in the middle of nowhere.

• Suppliers lower their costs
• Distributors operate more efficiently
• Consumers get better value
• Retailers secure increased market share
• The whole world benefits from greater transparency, reduced waste and less freight

Supply Chain Management in the Data Age

As businesses operating in the 21st Century, we have all come to understand first-hand the increase in competition and volatility of markets. The good news is that while increased turbulence is certainly a sign of the times, the tools are now available for organisations to chart a steady path through choppy waters.

Today, huge amounts of data is generated every day and with the right technology at our fingertips, we can predict and prepare for anomalies. What Big Data means for retail businesses is the opportunity to embed a more dynamic SCM process that uses real-time data insights to predict, and respond to, market turbulence.

AI, ML and Supply Chain Management

Artificial Intelligence and Machine Learning is powered by Big Data - which is a winning combination when it comes to truly agile Supply Chain Management. Because AI and ML is an “always-on”, automated process of optimisation that is constantly refining and improving business processes, choosing a system that draws on all of these technologies is a smart move.

An optimised SCM process that’s powered by Big Data, AI and ML can:

• Generate forecasts that refresh in real-time
• Integrate historical and new data with ease
• Predict changes in activity based on hard data
• Turn data into valuable insights across a supply chain
• Improve forecast and demand planning accuracy

Effective SCM and sustainability

Old-school SCM practices would see organisations throughout a supply chain weathering the volatility of current markets by carrying excess stock in order to ensure operational continuity. However, this bloated approach is not only costly, it’s a recipe for unnecessary waste via spoilage or obsolescence.

A more collaborative, centralised approach empowers businesses to analyse and understand their entire supply chain, ensuring maximum efficiency at every point. Organisations can then provide clear, confident and transparent Impact Statements to their customers, boosting their sustainability credentials and positioning themselves competitively for tighter environmental regulation.

Predictive, reactive SCM ultimately means less production, less waste and less freight. As a business, if you can guarantee your customers more sustainable processes across your supply chain, you’re giving yourself a point of difference for them to buy into while also making a meaningful impact on our planet’s future.

To dig deeper, read our article.

Has truly optimised replenishment been possible until now?

The word “Optimisation” has been buzzing around boardrooms for many years now. Choosing to constantly measure performance, learn from findings and refine accordingly is a wise move and this process has been adopted by most, if not all, high-performing businesses. These organisations are likely to not just survive, but thrive in challenging times.

But the question is this - are these processes (usually based on historical data generated by their business operations) truly “optimised”? Or is AI, ML and Big Data bringing true optimisation to the table, by allowing organisations to react to market anomalies before they even happen?

Old school optimisation

It’s widely known that an optimised system can help move businesses away from more blunt force instruments of planning like rigid minimum / maximum stock levels. Regularly testing and adjusting replenishment processes gives Demand Planners the ability to create or change supply orders, executing these changes automatically or as a one-off.

The benefits of doing this include:

• Minimised lost sales
• Reduced risk of obsolescence/spoilage of goods due to inventory excess
• Reduction in waste levels
• Increased planning efficiency and visibility of stock requirements
• Streamlined inventory and related costs
• Optimal stock movement and storage throughout the supply chain

But the reality is that a system that’s built on retrospective information can only achieve so much.

Optimisation that PREDICTS as well as measures / adjusts

Let’s consider the two main elements of optimised replenishment - CPFR (Collaborative Planning, Forecasting and Replenishment) and Time Phased Planning - and consider how AI and ML can enhance these processes.

1: CPFR

At its core, Collaborative planning, forecasting and replenishment means that knowledge and information is shared throughout the supply chain to eliminate some of the uncertainty associated with supply and demand. Initially developed for Walmart, this process boosts operational efficiency by using demand forecasting based on data points captured throughout the supply chain and even from the customer, to ensure stock levels meet (but don’t exceed) demand.

CPFR is a process of continuous improvement and will only be effective if it’s systematically implemented. A truly optimised supply chain can help you deliver your goods accurately, in line with customer demand and as cost-effectively as possible.

This process - alongside Artificial Intelligence, Machine Learning and Big Data - is a powerful combination. While CPFR certainly predicts consumer behaviour based on the knowledge shared across a supply chain, incorporating wider consumer data allows businesses to forecast demand even more accurately. With all the right elements of an optimised replenishment system in place, they can meaningfully impact performance by increasing stock turn and maximising ROI across their organisation.

2: Time phased planning

This element of optimised replenishment is most relevant to retailers who manufacture products or goods, where raw materials are required to fulfil production requirements and in turn, customer demand. It is a Material Requirement Planning procedure that works to a predefined time frame. So if a vendor always delivers a material on a particular day of the week, you would plan this material according to its delivery cycle.

It works by assigning materials with an MPR date in the planning file, which re-sets after each planning run. By time-phasing material requirements, a good system can express future demand, supply and inventories by time period. The system will also delay the release of orders until they are needed, reducing storage costs and saving processing time.

Again, the more information you can feed into time phased planning, the better. A truly optimised system will draw on as much data as possible, providing accurate historical information as well as predictions that provide visibility of indicators. Big Data is a game-changer here. Add ML, AI and clever technology that allows people to override automation (because there are some business variables that can only be known by humans) and you have a process that can take replenishment optimisation to new levels.

QU - a replenishment planning tool for the times

QU is an AI powered SaaS demand forecasting tool that has been developed to measure, analyse and predict product performance in real time. Using the latest AI technology along with proprietary ML algorithms and innovative technology design, QU draws on millions of data points from hundreds of sources. It presents powerful insights in an easily-digestible and customisable dashboard that not only predicts buying behaviour, but adjusts forecasts of future sales right down to individual product level (SKU).

QU is a tool that is designed to empower human beings, giving them the insight and control they need to do better business.

So if your business is ready to start freeing up resource and firing up profitability…

Can your retail inventory management system see around corners?

Imagine if your retail inventory management could see into the future. While other businesses might still be using retrospective stock turn to help them get their inventory levels right, yours would be able to see what was around the corner and predict changes in consumer buying behaviour. Powerful stuff.

Well, the good news is, it’s possible to transform a retail inventory management system into something this powerful - today. Developments in Artificial Intelligence and Machine Learning along with massive data sets, mean innovative new tech tools that can help businesses develop an infallible inventory management system, to give them a truly competitive edge.

Why an excellent retail inventory management system matters

Accurate inventory management gives businesses the information they need to operate more profitably. A robust system means:

• Increased profit margins
• Streamlined stock levels
• Reduction in waste levels
• Reduced inventory costs
• Increased understanding of sales patterns
• Increased customer satisfaction (less out-of-stock items)
• Less spoilage and obsolescence
• Easy management of multiple sales channels (e.g. physical store and online)
• Well informed and planned growth
• A more efficient workforce

What does innovation in AI and ML mean for retail inventory management?

There are many apps and tools that can help Demand Planners manage inventory by automatically measuring and setting stock levels while ordering according to perceived sales needs. Some might even allow them to use the data generated from their system to forecast demand and adapt their inventory management according to learnings.

But these tools all have limitations. For example they work solely with data that businesses have generated in previous sales cycles. Today, innovation in AI and ML, along with huge data sets, mean that businesses can predict changes in consumer behaviour and adapt their inventory planning accordingly.

There is no one-size-fits-all in machine learning

Machine learning is a bespoke solution for retail businesses by its very definition. The algorithms and models that drive it are designed to respond to unique data sets and requirements. So while a manual process is more generalised and making relatively minor changes can be time-consuming, ML / AI technology will grow and adapt organically with your business. This constant automated improvement and adaptation makes for a more flexible solution and frees up resource that can be better utilised elsewhere.

Back to basics Vs embracing the future

The tendency for companies who are wanting to improve their overall performance is to go back to basics like a solid retail stock management system. In reality, the smartest thing they can do is move on from outdated ways of working and embrace innovation to streamline their processes and optimise their demand planning for the future.

Not only does this empower your people, it ensures that you are carrying more accurate levels of stock for consumer needs at any given time, which can only increase customer satisfaction, help you maximise ROI and drive profitability across your business.

A final word on retail stock management systems

While having a sound system in place will help to streamline processes, using AI and ML to truly optimise replenishment planning can help businesses prepare for what’s around the corner.

If you would like to find out more about truly optimised replenishment, have a read of our article.

Calculating minimum stock levels in the age of AI

You already know that defining your safety stock level, minimum stock level or buffer stock is a key cornerstone of effective supply and demand planning. And no doubt you also understand all the formulas and processes that have been used for decades to set an “accurate” target that will ensure operational continuity and reduce the world’s waste levels.

But let’s be honest, no matter how flashy and complex your Excel sheet, using it to calculate safety stock is, quite literally, backwards. That’s because this process relies on retrospective data already generated by your business to set buffer levels and ensure seamless operation through market anomalies.

Today, there is a smarter way to define your minimum stock level. Recent innovation in AI (Artificial Intelligence) and ML (Machine Learning) means that businesses can now look ahead and predict changes in consumer behaviour, rather than simply reacting to them. These leaps forward in AI and ML, along with the enormous data sets that are generated by consumers every day, mean that businesses can more confidently make operational decisions that could make or break their retail business performance.

Flexible safety stock levels across complex businesses

People and process will always be vital when it comes to getting your minimum stock levels right. But a great team backed up with solid technology will always provide a more robust solution. When it comes to managing complex inventories across multiple locations, people can only ever do so much. But harnessing the power of the latest digital technology allows your business to fine-tune minimum stock levels across multiple locations, SKUs or regions. This ensures accuracy on a site-by-site or product-by-product basis, driving the overall accuracy of your minimum stock levels business-wide.

Ultimately, carrying less stock across your business is always going to free up capital to invest in potentially more profitable areas of your business, thereby maximising your overall performance.

Know what you need to, before you need to

Accurate forecasting that’s based on large and unbiased data sets helps you not just react to market anomalies, but predict them. With this information, you can get ahead of the curve -  pivoting your product and distribution strategies to mitigate problems, before they arise.

The limitations of traditional safety stock level processes

Previously, safety stock levels were based around key information that was generated by your business.

1. How many units of an item are usually consumed within a given timeframe?
2. What is the length of time associated with order processing and delivery?

Armed with this information, there were two formulas you could use to set your safety stock level. Both centred around usage and lead times that could only be worked out after a sales cycle.

The problem with this way of defining your safety stock levels is that the information required to optimise your demand planning can only be generated in the process of doing business. Basing your decisions soley on historical data is inherently flawed, as learnings will be made too slowly to implement effective product or distribution strategy changes. And because consumer behaviour is always changing, you can never be sure that your new-found learnings will apply to future market circumstances.

The problem with rigid minimum stock levels

When you are very rigid about your minimum (and maximum) stock level, you can end up accumulating more inventory than you need. It is therefore important that you continue to optimise your planning as you go and take a more flexible approach to stock levels. Working in this way will help you to reduce any expenditure associated with holding unnecessary stock, increasing your inventory turn and freeing up warehouse space for range expansion or other strategic business goals. Along with being inflexible, a manual process sucks up excessive people-power and so opting for an innovative system that can help you accurately forecast trends and adapt your demand planning will free up your people to focus on more fruitful endeavors, like research and development.

Why having AI and ML on your team is good for business

Whether you are a Demand Planner or a CEO, demand forecasts based upon vast swathes of data put you in a powerful position when it comes to effective decision-making. It’s not just about measuring and predicting buying trends against broad stroke indicators like consumer confidence or interest rates. Today’s leading organisations are using smart data capture to gain insight into consumer buying behaviour that is influencing the future of their sales. Everything from weather data, search data or data from sources that are leading indicators (e.g. building consents, mortgage rate increases etc.) can now provide vital forecasting for businesses.

If you would like to ditch the spreadsheet and find out more about optimising your replenishment planning, we have put together a helpful guide.