Will Generative AI tools like Chat GPT/GPT-4 replace Demand Forecasters and Demand Forecasting tools?

Given what Quantiful is famous for, this question has come up more than once around our office recently as thousands of new use cases deploying this tech come to light.  Forecasting demand has always been a critical aspect of business planning, and in the last few years, artificial intelligence (AI) has revolutionized forecasting.

 Tools like Quantiful’s QU have been at the leading edge of this revolution and are used by our customers to predict market demand with remarkable accuracy. However, the obvious question is, can Generative AI tools like Chat GPT/GPT-4 replace QU, or does it, in fact, further strengthen it by adding to its capability?

So we were fascinated by a recent article in a Supply Chain Movement newsletter quoting AB InBev’s use of Chat GPT/GPT-4 to improve their forecasting. See article here

It prompted us to explore the topic further.

In this blog post, we explore the capabilities and limitations of Generative AI tools like Chat GPT/GPT-4 for demand forecasting and consider three options. They are a replacement; they can play no role, or the combination of the two is most potent.

So let's look at the top four ways generative AI can help a Forecaster forecast better. We thought the best way to do this was to ask Chat GPT/GPT-4. Here is what it told us about its forecasting capabilities:

1. Uncover Hidden Patterns: One of the most significant advantages of using AI like Chat GPT/GPT-4 in demand forecasting is its ability to recognize patterns in large datasets that human analysts might overlook. This makes it extremely valuable for analyzing historical sales data and identifying trends that predict future demand.

2. Natural Language Processing: GPT-4 is trained on a diverse range of internet text and can be used to analyze qualitative, unstructured data, such as customer reviews and feedback, to identify shifts in consumer sentiment that could impact future demand.

3. Easy Integration: Chat GPT and similar tools with API access into their functionality can be integrated into third-party software tools relatively easily.

4. Providing Contextual Understanding: A significant advantage of Generative AI tools is their ability to understand the 'why' behind specific demand patterns. This can help businesses forecast the demand and understand it more deeply.

The next step was researching Chat GPT/GPT-4 limitations when forecasting demand.  Here we used several sources, and while opinions varied, there are probably at least two that were cited most often as significant limitations of generative AI as a standalone capability for demand forecasting:  

1/ Its inability to Interpret Raw Data: Data like graphs, images, and, most importantly, raw numerical data directly cannot be processed by Chat GPT/GPT-4 or similar tools. This limitation restricts its utility for demand forecasting as a starting point for this modeling is an analysis of raw transactional data like sales and inventory data.

2/ No Real-Time Learning: Generative AI tools don’t learn from their predictions or adapt to new events or trends in real time. The best-specialized forecasting tools use large external data sets for this purpose and can forecast multiple time series to improve accuracy. So except for the limited amount of data that can be included in a prompt, Generative AI tools cannot do this. Their training is static, based on the data it was initially trained on, and it cannot update or learn from new data or events.

So, in summary, where does this leave us:

In fact, at option three, the combination of Generative tools and a Specialised Forecasting tool is even more powerful than the two alone.

For example, the AI models in Specialised Forecasting tools are explicitly trained for Forecasting on structured data, and the good ones use multiple external data sets, all of which have signals in them, which will improve forecast accuracy. These data sets are often tailored to specific industry verticals.

However, the quality of data needs to be assessed and assured; garbage in and out still applies, and Planners need to have confidence in the data going in.

This is where Generative AI can be a powerful adjunct to Specialised forecasting tools; by using it to analyze the external data fed into them, more signals can be identified, which can then be extracted and fed into the models. In short, Generative AI can make the unstructured data structured and consumable by the scalable forecasting AI behind tools like QU.

Also, once a forecast has been generated, generative AI can then be used to summarise and provide clear text-based insight into the basis of the predictions in the forecast.

QU from Quantiful

QU is a specialized demand forecasting tool designed to onboard and scale rapidly in Enterprise Retail, Manufacturing, and Media. It uses customer sales and inventory data and multiple sets of custom external data such as search, social, and a host of others, amplified by Generative AI, to create highly accurate demand forecasts.

Contact us now to begin your evaluation.

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Top 10 Short-Term Replenishment Forecasting Challenges Retailers Face

Effective short-term replenishment forecasting is crucial in the complex and fast-paced retail landscape. It allows retailers to maintain optimal stock levels, minimize costs, and meet customer expectations.

However, retailers often need help with numerous challenges, so we asked Chat GBT to list the top ten short-term replenishment forecasting problems retailers frequently encounter. Based on this ranking and applying our experience working with major Retailers at Quantiful, we then offer some practical ideas on how to start solving them and how to evaluate any new AI tools you are considering as part of that solution.

1. Rapidly Changing Demand

Accurate forecasting is becoming more difficult as demand volatility continues to grow. Factors like seasonal trends, promotional activities, and sudden shifts in consumer behavior can cause rapid changes in demand, making accurate forecasting challenging.

Our advice: Start to consider tools that have two core capabilities. Firstly, automated alerting functionality can provide advanced notice of changes in demand in time for you to remediate them. Secondly, ensure the tools you are considering not only use your data for forecasting but also incorporate external data sets with advanced signals of those upcoming changes in demand patterns. The best tools use this data to improve forecast accuracy. More on this below.

2. Data Accuracy and Consistency

Traditional forecast algorithms rely heavily on historical sales data. Inaccuracies, inconsistencies, or gaps in this data can lead to incorrect forecasts, resulting in overstock or stock-out situations.

Our advice. Make sure any new tool you evaluate can automatically correct for historical data inconsistencies. For example, the best tools ensure an out-of-stock is not picked up by the forecasting algorithm as a lack of demand at that time, and a prediction of sales is automatically restored. This feature corrects the history and ensures a future forecast is consistent with actual demand.

3. SKU Location forecasting

Effective short-term replenishment becomes challenging where there is low demand, or particular SKUs have inherently low transaction activity across large SKU counts and complex distribution networks. Delays or inaccuracies can lead to misalignment between inventory levels and demand.

Our advice. It may seem obvious, but having tools that accurately build forecasts by SKU by Store is essential today. The trouble is if you have high SKU counts, a larger retail footprint or both, then most often, tools like Excel cannot handle the complexity and data loads. Again, ensure any alternative you are evaluating can provide that level of granularity in forecasting, not just at some clustered level like “all stores of the same size” but down to individual stores and their specific demand profile.

AI works by breaking a product down into its attributes and isolating which attributes most influence demand

4. Effect of Promotions

Promotional activities can significantly impact demand, often leading to sharp increases. However, accurately forecasting the impact of promotions can be difficult due to different promotional strategies' varying nature and effectiveness.

Our advice. The best consensus planning process combines product management intuition with the best AI forecasting tools, which illustrate the most influential drivers of demand in the past and future. Use specialised forecasting tools which use machine learning to understand the sensitivities of sales against your promotional activity. Then use these drivers of demand to scenario plan future promotional activity.

5. Supplier Reliability

Delays or inconsistencies in supplier deliveries can disrupt replenishment plans. Retailers must consider suppliers' reliability and lead times when forecasting short-term replenishment.

Our advice. The best AI tools deliver highly accurate forecasts over extended forecast horizons and have Administrator access-controlled UIs that can be shared securely with your vendor (or anyone who needs to see your forecast). This can be a read-only instance or an instance with controlled access provided to them to make forecast adjustments. These tools then allow comparisons of any variances they enter with your forecast, which can be surfaced in your consensus planning meetings.

6. Handling Product Variations

Products with many variants (like colour, size, and style) pose additional challenges. Each variant may have different demand patterns, requiring more granular forecasting.

Our advice. Much like the challenge introduced by complex distribution footprints, the best AI tools easily handle complexity at the portfolio level. As products have demand profiles, so do each product's attributes. Using machine learning, these tools can calculate size curves and colour and style variations to identify new product demand and an early indication of the end of life.

7. New Product Introductions

For new products without historical sales data, forecasting demand is particularly challenging. Retailers must estimate initial demand and subsequent sales patterns, often with limited information.

Our advice. This is where again, best-of-breed AI tools come into their own because of how they forecast. AI works by breaking a product down into its attributes and isolating which attributes most influence demand. So, if a new product shares any attributes with existing products, that will be used to generate a forecast. This means we can use predecessor identification on a many-to-many relationship of attributes. Further, if you use tools with external data, attributes from competitors or adjacent categories, to name two, it can be used to apply attributes extracted from new and emerging trends.

8. Cannibalization and Halo Effects

Sometimes, the sales of one product can impact another—this could be a cannibalization effect (where one product's sales decrease due to another) or a halo effect (where one product's sales increase due to another). Accounting for these effects in short-term replenishment forecasting can be complex.

Our advice. A significant limitation of emerging AI tools, even the largest, is their inability to forecast multiple time series simultaneously. Only the most advanced AI tools can calculate the relationship of demand across the whole portfolio. Ensure any tool you evaluate has this fundamental capability.

9. Returns and Damages

Returned or damaged goods can affect available inventory levels and must be factored into replenishment forecasts. However, predicting these rates accurately can be difficult.

Our advice. Inventory levels are treated like another data set and time series by the best AI tools and married to transactional sales. Variations from returns can then be accommodated to ensure forecasting considers “negative sales” in much the same way out-of-stocks underrepresent actual sales.

10. External Factors

External factors such as economic conditions, competitor activities, weather events, and more can influence demand and must be considered. However, the exact impact of these factors is often challenging to quantify.

Our advice. As mentioned under data challenges above, your new forecasting tool needs access to external data. It MUST also be able to forecast more than one time series of data, such as a critical external data set in addition to your transactional data time series, and integrate that forecast into a single forecast output. Even some of the biggest names in Software planning, like Microsoft D365, cannot do this. Make sure you ask and ensure any tool you evaluate can.

In conclusion, while short-term replenishment forecasting presents numerous challenges for retailers, it remains a crucial aspect of effective inventory management. By recognizing and addressing these challenges, retailers can improve their forecasting accuracy, optimize inventory levels, enhance customer satisfaction, and drive profitability. Emerging technologies like AI and machine learning offer promising solutions, helping retailers navigate these challenges with greater precision and agility.

QU from Quantiful has been designed to address many of these forecasting challenges, and if you would like to start an evaluation of QU with these requirements on your checklist, please get in touch.

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Get your SCM modelling right

Is something missing from your SCM model?

Not all retail businesses have the same type of supply chain. The path to market for a pair of shoes, for example, differs from that of a chocolate bar. So when it comes to ensuring your operations are running as efficiently as possible to meet the true demand of the market, it’s important to think about the nature of your supply chain.

The six types of supply chain

1: Continuous-flow models

This model refers to supply chains that work in high demand, stable situations with very little fluctuation. For example, a manufacturer that produces the same goods repeatedly might use the continuous flow model.

2: The fast-chain model

This is a flexible model used by manufacturers of trend-dependent products with short life cycles. So if a business changes their products frequently, they will need to shift them quickly before the end of a current trend.

3: The efficient chain model

This model is best for businesses that are operating in highly competitive markets, where end-to-end efficiency is the end goal.

4: Custom-configured models

These models are used during assembly and production, focusing on providing custom configurations and are a hybrid of the agile model and the continuous flow models.

5: The agile model

This model is a method of supply chain management that’s ideal for businesses that deal in speciality item orders. It’s a model that uses real-time data and updating information to scale up or down according to demand.

6: The flexible model

This is a model designed to give businesses the freedom to meet high demand peaks and manage long periods of low volume movement. It can be switched on and off easily to handle seasonal fluctuation in demand.

Aligning people behind a plan

Whichever type of supply chain a business operates, there needs to be holistic involvement from all departments, along with the mechanisms and processes to deal with anomalies as they occur. Building the right model into your S&OP (Sales and Operations Planning) is good business practice, but the problem with most supply chain models is that they are too slow to respond to volatility.

Whichever type of supply chain a business operates, there needs to be holistic involvement from all departments, along with the mechanisms and processes to deal with anomalies as they occur.

Consumer behaviour is becoming increasingly fickle and when supply chain models rely on historic data to achieve operational efficiency, they are never going to deliver. Even when organisations have moved towards fully Integrated Business Planning (IBP), they often don’t have the funds and capabilities to sustain the constant measuring and optimising.

Is the consumer missing from your team?

If a business has managed to get every department contributing towards and aligning behind a consensus plan, they have achieved something powerful. But the truth is that only those organisations that are also capturing the voice of the consumer are likely to succeed in accurately meeting true demand.

Choosing a flexible supply chain model while delivering real-time consumer insights and data to the right desktops, is a good way to ensure balance in your demand and supply planning. And meeting the actual needs of the market across manufacturing, freight and storage makes for tidier bottom lines, and ultimately, a cleaner planet.

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Power up your SCM with Big Data

Big Data. Ignore at your own risk.

In the beginning there was the Stone Age, then the Bronze Age and Iron Age. Skip a few millennia and here we are, in the Age of Big Data. As a business tool, the enormous amount of information generated by your business and beyond - along with the ability to mine it, capture it, store it and learn from it - is revolutionising the commercial landscape.

If you’re considering harnessing the power of Big Data in your supply chain planning, here are three things to consider.

1: The commercial landscape has changed forever

According to a recent Gartner survey, 76% of supply chain leaders report that their companies face more supply chain disruptions now than three years ago. Pandemics, macroeconomics, the explosion of e-commerce and global supply chain disruptions have all played a part. The resulting disruption to retail activity at some of the most profitable times of year has inevitably led to huge dents in bottom lines everywhere. It’s no wonder then, that businesses are now scrambling to find ways to mitigate this ever-increasing market volatility. They can no longer rely on the tried and tested cyclical patterns and systems of yesteryear and are looking to emerging technologies to help them stay competitive in a more chaotic commercial world.

Pandemics, macroeconomics, the explosion of e-commerce and global supply chain disruptions have all played a part

2: People can’t process data like a machine can

If your SCM strategy relies solely on historical business statistics, your Planners may well be able to interpret and analyse some of the information generated throughout your supply chain and use it to make predictions. But here’s the thing. They’re not Data Scientists and analogue, people-driven systems are inherently limited by the amount of information they can find and handle. A business that chooses to empower its people with Big Data, and the ability to analyse it, gives itself (and its Planners) a real competitive edge.

3: The right blend of technology is a super power

Digital systems that pull on new technologies are the key to harnessing the true power of Big Data in your supply chain strategy. The most powerful tool for Planners is a single, automated system that can pull large amounts of data from multiple sources, to marry business information with carefully curated data sets from all over the internet. A system that uses Artificial Intelligence and Machine Learning, allows organisations to consistently mine for insights, while constantly learning and optimising. No Data Science degree required. The best systems present these insights in an easy-to-navigate, actionable way, equipping Planners with everything they need to make more accurate demand forecasts. Planners can now have the power at their fingertips to drive business alignment behind a consensus plan, while playing an active role in streamlining processes, reducing freight and waste and increasing ROI.

Now there’s a promotion waiting to happen.

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Mitigate excess in your SCM Strategy

To buy-back or not to buy-back, that is the question

We’ve said it before, but forecasts are almost always wrong. What this means for many businesses is excess stock taking up valuable shelf or floor space. Whatever methods a business chooses to move this stock, the priority should always be to move it.

Buy-back is one way that retail organisations ensure excess stock isn’t weighing them down and preventing them from selling more profitable items. There are many ways businesses can build buy-back into their SCM processes, to help them clear poor performing stock to make way for more in-demand products. But unfortunately, there’s no way around the fact that each of these strategies has its draw-backs and will impact ROI.

Let’s take a look at some of the options for retailers of non-perishable items and the impact on CTC (Cost to Clear) of buy-back strategies.

Buy-back from up or down the supply chain

This is a buy-back model that’s built in at a contractual level with suppliers of either raw materials or manufactured finished products. On the surface, it sounds like a sensible strategy. But the drawback here is that no supplier will agree to the hassle and expense of buying back product without making it worth their own while. They will inevitably build the cost of shifting unsold stock into their contractual agreements. So while retailers might save themselves some hassle, they will pay for the privilege. The trick here is to measure the likely financial impact of holding and clearing excess stock against the cost of contractual buy-back agreements.

So while retailers might save themselves some hassle, they will pay for the privilege.

Pre-loved buy-back

This model is sometimes used as a marketing or brand initiative, with businesses buying back used items and onselling them to customers (à la Patagonia). It’s a worthy ethical initiative that undoubtedly reduces waste, but it’s unlikely to be profitable. For some brands, this clear disregard for profit is a way of telling the market that they value circular consumption above ROI and demonstrates that they’re prepared to walk their environmental talk. While the value of this may not be measurable in terms of immediate sales, it certainly builds integrity into the brand and in turn increases consumer loyalty and respect.

The smart alternative to buy-back

For organisations looking to streamline their supply chain and reduce waste, there is now another alternative that’s worth considering. Advancements in AI (Artificial Intelligence) and ML (Machine Learning), along with ever-growing collections of Big Data mean that businesses can remediate issues in their forecasts, before they impact profits. This eliminates the seasonal cycle of having to continually increase CTC and use excess stock as a loss leader for in-demand products. Instead, businesses are able to identify possible anomalies in advance, finetuning their supply chain management strategy and more accurately meet the true demand of the market. In this way, entire supply chains can become leaner, optimising their inventory levels while reducing freight-associated carbon emissions and manufacturing waste at every step.

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Shift from sales forecasting to demand planning

Top-down or bottom-up? How to approach forecasting in a data-driven world.

The role of any good Supply Chain Manager is to ensure regular reporting of the variances between top-down executive targets, and the bottom-up demand of the market. Effectively, this is the budgeting process, and achieving a balance between demand and supply is the best way to stay on target with financial projections.

This process has traditionally been managed by looking back at sales, and using historical information to forecast forward. But let’s be honest, adopting this approach usually means getting it wrong, because responding to historical data that was already built on a flawed model makes very little sense.

True demand vs projected demand

The term “true demand” refers to the amount of product an organisation could feasibly sell in an unconstrained market. If a business has a well-oiled supply chain that runs like clockwork, it should be able to flex and adapt according to demand. So all that’s left for that business to do is put its ear to the ground, find out what the market wants and provide it. The value of sales information in this situation is high. But historical sales information for companies with poor supply chain models is largely useless as all they tell an organisation is what they were able to sell with clunky processes and no visibility of wider consumer trends and macroeconomic shifts.

The term “true demand” refers to the amount of product an organisation could feasibly sell in an unconstrained market.

Minimising lost sales opportunity

The gap between what a business was able to sell and what the market would have bought is referred to as “lost sales opportunity”. Once businesses start thinking in terms of true demand, they are able to minimise this gap and start working towards satisfying the actual demands of the market, rather than hitting sales forecasts.

Digital tools that pull in information from thoughtfully compiled data sets all over the internet can help organisations to understand current demand for their product at any given time. And with technology like Artificial Intelligence and Machine Learning on board, these organisations can set seemingly unrelated data against sales performance, to observe where the impact can be attributed to external circumstances, and predict likely anomalies in demand forecasting.

This technology is the key to businesses unlocking their true sales potential and has the power to move them from sales forecasting, to demand planning. When organisations can more accurately measure the likely demand for their product, they are able to truly optimise all of their operations, across all departments.

And that’s a win for everyone.

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Take Excel to the next level

Spreadsheet, meet AI. AI, meet spreadsheet.

It’s no secret that Excel is the tool of choice for most supply and demand management systems. With its easy-to-navigate tabular interface and advanced filtering system, data can be easily selected and collated to make better supply chain and inventory management decisions. Excel is relatively unchallenged in terms of competitor functionality and is a standardised global tool that transcends the nuances of regional markets.

It’s used by businesses all over the world to evaluate data, make complex calculations, track inventory, plan demand, schedule logistics and much more.

So far, so good.

The problem arises with one key issue that strikes fear into the heart of organisations everywhere.

IP that walks away with staff

Continuity of information is one of the biggest issues facing Excel-dependent businesses today. After all, one person’s spreadsheet wizardry is another’s overly-complex nightmare. Personal preferences in set-up, formula application and formatting can lead to a total breakdown in information transfer that poses a real risk to supply chain management process continuity and efficiency.

Continuity of information is one of the biggest issues facing Excel-dependent businesses today

Another issue is that Excel is no longer well-equipped to deal with the complex, data-related challenges faced by modern businesses. Leaning heavily on spreadsheets for mission critical planning in the age of Big Data and AI limits how well a business can sharpen its competitive edge. By implementing more sophisticated, multi-faceted solutions, organisations can drive customer satisfaction, increase productivity and ensure their data is always accurate, relevant and up-to-date.

Integrating software and people

The beauty of today’s digital demand forecasting tools is that information from Excel can be easily ingested and converted into a more accessible format. This not only protects the integrity of the information, but facilitates the ability to marry it with wider data sets, enhancing and improving demand forecasting capability in the process.

Software that can draw-down, store and learn from a wide range of data sources is the ultimate consensus planning tool. When technology can analyse this data and present clear and actionable business insights that are optimised in real time, teams can unite behind a common goal to streamline stock levels, reduce waste and drive profitability.

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Enhance your demand pattern analysis

Understanding demand patterns in the Data Age

Demand pattern analysis is becoming increasingly valuable in business, as a way of predicting and preparing for future fluctuations in market demand. The problem is that the “best-practice” models that are still taught and employed today rely solely on historical patterns to make predictions.

In reality, looking backwards at the impact of previous seasons, promotional activity and events can only provide limited forecasting accuracy. The real gold lies in looking sideways, and even forwards.

Is there a storm brewing in your demand forecast?

Picture this. A business is in the final stages of development of its much anticipated new product. But somewhere, on a social media platform far away, negative reviews of a previous product are doing the rounds. It started with a single opinion in a comment thread and has grown to full-blown thought leadership with thousands of likes, follows and shares. This snowballing negativity will inevitably have an impact on new product sales, but more importantly, it can help the business mitigate any issues with its new product ahead of launch.

If the business knows about it, that is.

And it’s not just a social media storm that has the potential to disrupt demand forecasts. The actual weather can derail projections too. As can a planned launch by a competitor. Or a disruptive innovation that’s just launched in an overseas market. The price of oil. Changes in building regulations. Inflation. Analysis of historical demand patterns can never help a business mitigate the impact of circumstances like these.

The actual weather can derail projections too.

Technology that makes demand and supply planners look good

Without the ability to take a 360 degree look at what’s happening in the world, demand pattern analysis is only doing a small fraction of what it could. Technology that directs Artificial Intelligence and Machine Learning towards carefully curated data sets is not just powerful, it’s empowering.

The internet is a big place. A person alone could never mine it for meaningful information at a significant scale. But when the latest technology knows where to look it can measure, learn and predict, giving demand and supply planners the ability to make more informed, strategic and accurate recommendations based on hard data. This increased efficiency of forecasting makes for more streamlined businesses with less waste and greater profitability. And that’s good news for everyone.

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Optimise your SKU productivity

Not all SKUs were created equal

Many businesses are so focused on building revenue, their profit suffers as a result. Organisations worth their salt know that selling at all costs doesn’t make good business sense. A more sophisticated way to measure and drive success is ROI (Return On Investment). What is the business cost of selling a  line item (or SKU) and does it make commercial sense to invest in it in future? The faster the sale of a SKU and the higher the margin, the more profitable the item is to a business.

One size does not fit all

In order to truly optimise ROI, businesses need to measure product performance across their entire range, at an individual SKU level. By learning more about what’s working and why, organisations can start to optimise SKU productivity and increase ROI across the board. This is where Machine Learning combined with Big Data is invaluable. This power partnership can measure the impact of everything from the weather, to negative reviews to sports team performances on SKU performance. Able to handle massive amounts of information generated across multiple sales channels, smart forecasting tools can marry business statistics with data generated across the internet to learn more about SKU performance and empower planners to make more informed and insightful decisions about inventory levels.

This is where Machine Learning combined with Big Data is invaluable.

Balancing low inventory with high availability

The most profitable (and sustainable) way to do business is to maximise efficiency and minimise waste. However, the reality is that demand and supply planners are often stuck in the middle of contradictory business functions - Finance trying to keep costs down, Sales pushing to spend more to shift product and Retailers managing space restrictions. If the game plan is less about balancing departmental needs and more about adhering to a high-level strategy, demand and supply planners are in a far less problematic position. And if the strategy moves from selling at volume, to ensuring the volume of held stock has been set to meet the demand of the market - ROI will inevitably go through the roof.

Technology empowering people

Automated systems can take a big part of the battle out of the demand and supply planners’ hands, creating an agnostic and unbiased consensus plan that works in the best interests of the business as a whole. Able to ingest vast amounts of data across hundreds of SKUs, smart systems can consolidate this information and turn it into actionable insights.

With this power at their fingertips, supply and demand planners can confidently advise other departments of SKU business value, according to hard facts and clear strategy.

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Integrate demand and supply planning

Demand planning + supply planning = integrated business planning

If demand planning is forecasting customer demand, while supply planning is managing supply according to these forecasts, you’d be forgiven for thinking that these functions went hand-in-hand.

All too often though, demand planning and supply planning departments work to different agendas. One driven by ensuring sufficient stock to meet demand and the other by keeping costs down. But even when everyone is singing from the same songsheet (i.e. a strategic consensus plan) there are some things that can never be predicted.

All too often though, demand planning and supply planning departments work to different agenda

Unforeseen events and how to mitigate them

The events of Christmas 2021 were not on any retailer’s Santa List. Disrupted supply chains meant many orders that were due to land in November, were delayed until March. Nowhere is this issue being felt more keenly than in the technology sector, where product obsolescence poses a very real and present risk.

Integrated demand and supply planning gives organisations a better chance of managing exceptions to the demand forecast in a more timely and cost-effective way. Especially when it comes to shipping and logistics.

When both departments are working to a collaborative consensus plan, initiatives can be quickly deployed to mitigate excess, constrained or lumpy supply. For example, if companies are unexpectedly landed with excess stock, they may choose to lean on their supply chain to move stock where there is more demand. Or work together to ascertain the most efficient way to sell stock - optimising their CTC (Cost To Clear) as a team and impacting the business’s bottom line in the process.

A bird’s eye view

The best way to ensure demand planners and supply planners are working together towards a higher plan, is to make that plan easily accessible to all. This is where the right technology is key. An easily digestible dashboard that presents clear and actionable insights means that demand and supply planners can apply their strategic skills where it really matters. With the right people plugging in to the right technology, an integrated business plan can be adapted and optimised in real time, according to real circumstances.

Artificial Intelligence, Machine Learning and Big Data can both enhance forecasting capability and flag exceptions in a more timely way. This increased accuracy and foresight means that demand and supply planners can work in a more aligned way, towards a strategic, business-focused outcome.

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