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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Compare SCM Tools

Not all Supply Chain Management tools are created equal

These days, if you’re not looking to enhance your business processes with Big Data, Artificial Intelligence (AI) and Machine Learning (ML), you’re not doing your job.

While rapid digitisation of almost every business operation has brought various SCM tools to market, not all of them are fully leveraging the latest innovations in technology. There are plenty of products that are designed to help you manage your SCM process digitally and can even go as far as making rudimentary forecasts, but a tool that can use AI and ML to make accurate predictions based on huge quantities of hard data, is the key to streamlining operations, minimising waste, maximising resources and boosting profits.

QU is an AI powered SaaS demand forecasting tool that has been developed to measure, analyse and predict product performance in real time

Take a look at this SCM tool comparison table for an at-a-glance understanding of what each tool can do.

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…

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Move from demand forecasting to demand sensing

Demand forecasting is always wrong.


If demand forecasting was a precise science, we would be out of business. Organisations would apply their tried and tested formulas, and would emerge from their endeavours armed with 100% accurate predictions to take into their next phase of demand planning. Their wash-ups would show an exact correlation between their assumptions and commercial reality. Nothing would ever run out of stock and the world would have a far less alarming waste problem.

Sound familiar? Anyone…?

We know that contemporary markets are more volatile than ever before. Predicting demand according to what has gone before gives us some idea of what we might expect, with actual sales then providing metrics to test those assumptions against. But by that point, it’s too late. When businesses over or under order according to inaccurate forecasts, the results can be catastrophic for their bottom line.

Many wrongs often make a right

High error demand forecasts are actually very useful, it’s just a question of being able to learn from them on the fly. Historical business data and seasonal marketing activity can help businesses set some assumptions, but changes in the market along with macro and local events and supply chain disruptions can all impact consumer purchasing behaviour. With the right technology in place, businesses can see how these volatilities affect their assumptions in real time, identifying exceptions before they make firm decisions and place orders.

With the right technology in place, businesses can see how these volatilities affect their assumptions in real time

Demand sensing - good enough for Ikea

With recent developments in Machine Learning, businesses can make timely, accurate predictions about changes in consumer demand. Clever algorithms can automatically recognise patterns, navigate complex relationships in enormous data sets and flag the possibility of deviation from forecasts.

Retail giants like Ikea are realising the benefits of enhancing their demand planning with AI-driven forecasting, developing a proprietary system to give themselves a competitive edge. With 450 Ikea stores and e-commerce across 54 markets, even the slightest error margin can create significant commercial disruption. This is why the company has invested heavily in more intelligent “demand sensing” systems that can react and respond to anomalies in real time, rather than having to solve issues after they have occurred. The company’s lower error margins have reduced its carbon emissions (due to decreased movement of excess stock), lowered waste levels associated with obsolescence, raised profits and increased customer satisfaction.

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…

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