Author: Andrew Kohter

The contemporary supply chain landscape faces a multitude of challenges. Disruptions like labour shortages and geopolitical tensions create obstacles in delivering products efficiently. Accurate demand planning in the supply chain is crucial in this environment, allowing businesses to optimise inventory levels, prevent stock-outs, and adapt to market fluctuations. Predictive analytics takes this a step further by leveraging data and advanced algorithms to generate more precise forecasts.

Traditional vs. Predictive Demand Planning in the Supply Chain:

• Data Sources: Traditional methods rely primarily on historical sales data and basic statistical models. Predictive analytics incorporates a broader range of data sources, including:

• Forecast Accuracy & Horizon: By analysing a richer data set, predictive analytics generates more nuanced forecasts with greater accuracy. Additionally, it can extend the forecasting horizon, enabling businesses to plan further into the future.

• Risk Management & Opportunity Identification: Predictive analytics can uncover hidden trends and potential disruptions in the data that traditional methods might miss. This allows for proactive risk mitigation and the identification of new sales opportunities.

• Scenario Planning & Decision-Making: Predictive models can be used to simulate various scenarios, such as the impact of a marketing campaign or competitor actions on inventory needs. This data-driven approach empowers businesses to make informed decisions about resource allocation, production planning, and inventory management.

• Automation & Efficiency: Predictive analytics can automate repetitive tasks associated with demand planning, freeing up human planners to focus on strategic initiatives.

The Benefits of Implementing Demand Planning in the Supply Chain:

• Enhanced Forecast Accuracy: Improved forecasts lead to better inventory management, reduced stock-outs, and increased customer satisfaction.

• Proactive Risk Management: Early identification of potential disruptions allows for the implementation of contingency plans, minimising negative impacts.

• Data-Driven Decision Making: Businesses can make strategic choices based on a deeper understanding of customer behavior and market trends.

• Improved Supply Chain Efficiency: Streamlined inventory management and proactive risk mitigation lead to a more efficient and resilient supply chain.

Conclusion:

Predictive analytics is not a silver bullet, but a powerful tool that can significantly improve demand planning accuracy and supply chain efficiency. By leveraging a wider range of data sources and sophisticated algorithms, businesses can gain a deeper understanding of customer behavior and market dynamics. This empowers them to make data-driven decisions, optimise operations, and gain a competitive edge in today’s challenging supply chain environment.

Introduction

In a fast-paced and ever-evolving world, accurate demand forecasting is crucial for success. Without it, companies risk stockout, overstocking, inefficient allocation of resources, and ineffective marketing strategies. Traditional methods of demand forecasting, while effective to some extent, are often plagued by inaccuracies and inefficiencies. Enter Demand Forecasting with Predictive Machine Learning (ML), a technology that is revolutionising the way businesses predict and manage demand.

In this blog, we’ll explore how predictive ML models improve demand forecasting and its potential to drive better decision-making in inventory management, and marketing campaigns.

The Challenges of Traditional Forecasting

Traditional demand forecasting methods often rely on historical data and statistical models. While these methods have been useful, they have limitations:

• Lack of Real-Time Adaptability: Traditional methods may struggle to adapt to rapidly changing market conditions, making them less effective in dynamic industries. In today’s fast-paced business environment, market conditions can shift in the blink of an eye. Traditional forecasting techniques, rooted in historical data, often lack the agility to respond to these real-time and rapid changes.

• Complex Interactions: Demand is influenced by a myriad of variables, including economic trends, consumer behavior, competitive landscapes, and more. Traditional forecasting models often oversimplify or fail to consider the complex interplay between these factors, resulting in less accurate predictions.

• Data Volume and Complexity: Traditional methods are less capable of handling vast amounts of data and complex patterns, which are increasingly prevalent in today’s business landscape. The digital age has ushered in an era of big data, where an abundance of information is available for analysis. Traditional forecasting techniques fall short when dealing with the sheer volume and complexity of this data, leading to suboptimal predictions.

• Lack of External Data: Traditional methods typically rely heavily on internal historical data. They often lack the capability to integrate external data sources, such as social trends, weather conditions, national sentiment, and economic indicators, which can be instrumental in refining demand forecasts. External data sources can provide critical context and insight into changing consumer preferences and market dynamics.

Predictive machine learning (ML) overcomes these limitations by incorporating external data sources, adapting to real-time changes, and identifying intricate patterns that might escape traditional forecasting methods. This makes predictive ML a powerful tool for businesses seeking to enhance their demand forecasting accuracy in the face of an ever-evolving market landscape.

How Predictive ML Enhances Demand Forecasting

Demand Forecasting with Predictive Machine Learning leverages advanced algorithms, vast datasets, and computing power to improve demand forecasting in the following ways:

• Data Integration: ML can incorporate diverse data sources, such as social trends, weather conditions, and economic indicators, into demand forecasting models. This allows businesses to gain a more holistic understanding of factors affecting demand beyond their internal sphere of influence.

• Real-time Analysis: ML models continuously analyse data in real-time, enabling businesses to react swiftly to changes in demand patterns and market conditions.

• Pattern Recognition: ML excels at recognising complex patterns and correlations in data that may go unnoticed by traditional methods. This means businesses can make more accurate predictions.

• Forecast Accuracy: By providing more accurate demand forecasts, predictive ML helps businesses reduce excess inventory and minimise stockout. This, in turn, reduces carrying costs and boosts customer satisfaction. Using ML to drive your demand forecasting can see up to a 20% increase in accuracy. Leading on average to a 10% increase in revenue. 

• Scenario Analysis: ML can simulate different scenarios, helping businesses make informed decisions about inventory levels, pricing strategies, and production schedules before actually committing the resources to the change.

• External Data Integration: Predictive ML has the capacity to bring in relevant external data sources, enriching the forecasting process. These data sources may include social media sentiment, economic indicators, and even competitor activities. This external data provides valuable context, enabling businesses to align their strategies with real-world events and consumer sentiments, ultimately leading to more precise forecasts.

• Personalised Marketing: ML segments customers into micro-markets and tailor marketing campaigns to specific customer groups. This results in more effective marketing efforts and improved customer engagement. This allows businesses to better understand where to find their customers, what to offer them, when to offer it, and how to talk to them. 

Conclusion:

The inclusion of external data in predictive ML models opens up a world of possibilities for businesses, allowing them to tap into real-time trends and market dynamics that can significantly impact demand. As a result, predictive ML not only provides more accurate forecasts but also equips businesses with the knowledge to proactively adapt to changing conditions and stay one step ahead of the competition.

Contact Us to find out how Demand Forecasting with Predictive Machine Learning can help you!

A recent family trip to a fairground gave me a different perspective on the challenges of retail and hospitality professionals dealing with customers: where all is never as it seems.  

In the ‘fun house’ I was staring at the special mirrors where no matter the viewing angle, what was in front of me had little resemblance to what my mind expected to see. Staring back was something that looked vaguely recognisable, yet when I acted in a familiar way, it behaved in an unexpected manner.  

Remove the flashing lights and over-sugared kids, and this experience struck me as a great analogy of what it’s like for retail and hospitality professionals trying to understand and meet customers’ ever-changing demands. The unexpected shifting of sentiment and expectations, coupled with the fact that the only thing that’s certain is uncertainty, means it’s very difficult to make good business-critical decisions with confidence. 

Until now, retail and hospitality professionals’ decision making has been supported by business intelligence and retrospective data analytics capabilities. What’s lacking from these capabilities are clear, undistorted, short and long-term future views and knowing what to do about them. 

Many of these solutions are constrained as they rely on historic data and insights describing something that has already happened. Why is this no longer good enough? It’s because of ever-changing customer expectations and a two-year gap in historical data due to Covid. It’s why you can no longer afford to look back to move forward.  

Internal, rear-facing insight isn’t enough, you must examine how customers are behaving in the wider world, or why they’re not shopping, eating or staying with you. Brands must start utilising much broader external data sources to understand the impacts of social economic and environmental changes have on customer behaviour in general. Those brands that do, will better understand the value of their actions, compared to the results of inaction.  

With ad spend increasing, the competitor landscape so high and margins so thin; not being certain of your decisions can be very expensive and you’ll miss big opportunities.   

Moving forward with clarity and certainty  

The good news is a relatively new, sophisticated capability, called prescriptive analytics promises to solve these challenges by looking into the future and then recommending the most profitable course of action.  

We’re talking about prescriptive analytics as a managed service blending descriptive, diagnostic and predictive insights, with cutting-edge artificial intelligence, machine learning, automation, genuine data science and in-sector consultancy expertise. Everything is custom built, with each step creating prescription models precisely choreographed to meet an individual organisation’s needs.  

This involves enhancing internal data with much wider external insights including global & local trends: weather, travel, localised demand spikes, and more.  Using this high-quality data, data scientists build and optimise prescription models which identify previously elusive, connected, patterns to deliver the most accurate foresight fuelled prescriptions.  

Data scientists also continually find new insights to keep models relevant, while learning from the data so they keep delivering value. By uniquely aggregating data from a wider range of external sector sources, models are further enriched to provide greater accuracy and depth to foresight.  

Ultimately, this means the prescription models keep getting better – so retail and hospitality professionals keep making the most profitable business decisions.  

Use case – demand forecasting 

Prescriptive analytics can help better forecast demand. Every retail and hospitality professional understands the importance of having the right product, in the right place, at the right time.  But how do they make profitable decisions through the lens of regional demand? 

It means digging deeper than just price, as customers’ expectations are also driven by availability, experience, and ethical considerations. It’s important for retail and hospitality professionals to ask the right questions. What you do? Who are your customers? Do you have stores? Where are they located?   

This baseline information is then enriched with global data including how stores, hotels or restaurants are affected by seasonality, bank holidays, days of the week and more. Individual stores hotels or restaurants are isolated and modelled independently: answering how customers in these areas are behaving. Is it an area of growth? Will it be impacted by reduced disposable income?  

Next up, more dynamic effects are considered, such as weather, tourism, travel disruptions, proximity to transport and event hubs. All this information is combined with advanced models to reveal what regional demand could look like.  

Imagine a scenario where you could utilise external data that tells you the volume of people expected to attend an event near your venue or store. How about understanding the demographics of those attending and the types of products and services they would be interested in. Then imagine layering further insight on what’s driving this increased footfall past your door: such as local traffic disruptions, or weather conditions at this specific time and day.  

Equipped with this foresight, means more profitable localised decisions can be made on staffing, inventory, promotions, and pricing. 

Expect these gains   

There’s so much value that can be generated with prescriptive analytics as a service, how about achieving these outcomes for starters: 

• Accurately forecast future demand 
• Enhance customer experiences 
• Boost sales and profits 
• Increase satisfaction and loyalty  

Expect a return on investment in months not years. McKinsey’s research asserts that prescriptive analytics is poised to continue to deliver strong return on investment and become an increasingly important tool for businesses. 

Final thoughts 

Whatever your size, whatever your uncertainty, Predyktable delivers fully managed prescriptive analytics as a service. We generate actionable foresight faster to address your specific needs, without complexity and compromise. We’d love to hear how we can help your retail or hospitality brand make more profitable decisions. 

Sentiment analysis has never really gone away, but it’s certainly seen a strong resurgence as social media has grown to become a core channel for so many brands and consumers the world over. Before social media, where exactly did we garner data for sentiment analysis?

Sentiment Analysis and the birth of the internet

Believe it or not, SA has been around since the 1950s, when it was primarily used on paper documents. Over the decades, it’s closely followed the channels and communities in which we express ourselves. By the birth of the internet, the use of SA adjusted to include the early channels of the social web, such as forum posts and online articles.

Today, it’s difficult to comprehend just how many sources and data points can be included in SA. But this works in our favour for several reasons.

1. The more data we can gather, the more accurate we can be in our reporting
2. The more channels and sources we can monitor, the more broad and diverse our data
3. The more choice we have, the more we can customise our requirements

Putting the customer front and centre

Sentiment analysis is one of the most valuable exercises in making your brand or organisation more customer centric. It’s a direct line to the collective voice of the consumer, whether they’re bought from you, plan to buy from you, and even if they aren’t planning to buy from you.

We must remember that positive, neutral and negative data are all good data. Brand strategy isn’t just built on why people want your product or service, but also why they might not want it too. And it’s important to use that data to shape your marketing and ultimately, your own voice.

Is all this data too much data?

It’s a very valid question. Can we have too much of a good thing? In our view, the current SA landscape is a wild, wild west. With new platforms springing up left, right and centre (notable new additional in the last several years include sites such as Glassdoor and Polywork) and entirely new ways of sharing content can completely disrupt a channel (we’re looking at you, TikTok), it’s tough to keep up, and it’s even tougher to sift through so much data.

That’s where Predyktable helps. Traditional SA methods can be time-consuming, confusing and sometimes inaccurate due to undetected anomalies. Our model accounts for all of this and takes the hard work and frustration out of SA. In fact, we go a step further, taking SA way beyond social media, a point at which many traditional SA businesses stop and send you their invoice. 

A method to the madness

The concept of monitoring such a broad range of sources, including blogs, reviews, call centre logs and even search engine terms, might be a little daunting. All of that unstructured data from so many different kinds of people using so many different channels. 

This is where our data visualisation tool comes in, breaking down broad and complex models into easy understandable data, which most importantly, is actionable. 

This quite literally gives you a clearer picture of pain points, frustrations, experiences and more. And with the increasing power of not only our tools, but the social channels that your customers are using, we’re able to segment by demographics such as region, gender, age and even lifestyle.

Imagine the future

Our sentiment analysis is different, and we’re incredibly proud of what we’ve built and will continue to build upon and improve. Imagine being able to understand your brand’s reputation across such a broad range of sources. Imagine being able to spot potential areas of growth and investment, and to be able to act upon them now rather than later.

And perhaps most importantly in this day and age, imagine being able to spot negative sentiment and being able to deal with it there and then, long before it spirals out of control and causes damage to your brand. (To be clear, we’re not talking about something malicious like covering up bad reviews, we’re talking about an interactive and agile approach to your brand strategy, and making positive improvements to your product or service as a result of negative feedback.)

It’s also important to consider how SA can open you up to more modern ways of marketing. For instance, imagine being able to identify key social media influences to champion your brand. In the age of influencer marketing, this isn’t a channel to be ignored. Our platform is so powerful that it will even ensure the influencers that you’re seeing within your data have been fully verified for reach and engagement, ensuring you work with the right people.

A voice of reason

In a noisy, crowded world of challenging reviews, increasing customer expectations and online channels where opinions fly overhead like rockets, it’s never been more important to harness the power of sentiment analysis. What’s difficult is finding the right people and platform to cut through the noise and make sense of it all.

Thankfully, that’s us. If you’re looking for a technology service that has its ear to the ground and will go the distance with you, let’s talk soon.