Predict the future & demands with AI
With the Covid-19 outbreak, a humanitarian crisis, the facets of the consumer market and demands have rapidly changed course and a seismic shift has been observed in the consumer product demands. Consumers worldwide have adapted to the new normal and this change has witnessed a drift from where the customer’s shop, what they buy, and how much?
The supply chain systems and retailers are keeping ashore with the newest and most powerful analytical tool in hand, machine learning, and artificial intelligence.
With the ever-changing, trends and demand graphs, it is merely impossible to predict demand and supply numbers with accuracy and precision. Yet, only the one that adapts to the newer territories and meets the demands win the race.
How can you predict future trends and demands for your business or plans? Equip yourself with the right tools and witness the magic in action. We are just about to unroll everything you need to know about demand forecasting with AI and machine learning.
What is Demand Forecasting?
A predictive analysis of the past data sets and records to predict or forecast the consumer demand. It helps to reduce inefficiencies and makes you ready at every time and for the ever-changing line of demands.
But why should you even forecast the demand and built a supply chain resonating with the demands?
Well, it gives your business a 360-degree turn, set up for success:
1. Improved accuracy: with demand forecasting, you can plan and keep the inventory in check with the requirements to the demands and catch up with the latest trends.
2. Better customer satisfaction: eliminating the out-of-stock or shipments delays due to lack of resources, demand forecasting helps you make smart decisions with the inventory stocks and keeping the consumers satisfied on the other end.
3. Efficient manpower: with the accurate and precise demand forecast, you can adjust and make effective choices with the manpower in hand.
4. Better financial decisions: increase the profit ratios, improve the efficacy of your budget plans, resource sourcing, and allocation for better results of your efforts and work with the blueprint of the future in your hand.
5. Increased supply chain efficiency: with the right variables and numbers in the equation, you can manage the supply chain smoothly to meet all the needs and tick all the boxes.
For years, analysts, data scientists, and marketing teams have tried to understand the previous trends from the data available, draw a sensible and feasible conclusion about the demands of the future using statistical tools and rigorous processes. While they cannot fully grasp the big data and understand the sweet of play of pattern, artificial intelligence and machine learning have taken the hard job.
Leave the number crunching of the big data sets to the algorithms that run numerous and endless loops to make sense of the pool of data to bring out the sweet pattern playing in between the numbers. With the numbers in saying, AI-powered forecasting can reduce errors in the calculations by 30% – 50% in the supply chain networks that is half the work done.
But how do AI and machine learning do it all?
What makes AI possible?
Adjusting the commercial levers, finding the right audience and niche for the products has never been easy. The mix and match of the numbers and budgets make all the difference. So how do you find the right combination?
1. Machine learning: with the offset of the new tool of machine learning, the future is on the brighter side. Machine learning truly enables the algorithms to work in real-time with the real internal and external factors such as weather, demographics, online reviews, and social media that matter the most. Making sense of the big data sets, understanding the patterns, and making accurate and reliable conclusions.
2. Neural nets: impersonating the cognitive and neural behavior of humans, neural nets attempts at creating patterns in the given data by understanding the relationships between the varying factors and variables. Neural nets consist of nodes that act as calculators to process inputs and pass on an output
3. Expert systems: developed on the “if-then” rules, expert systems try to acquire knowledge from the past available data and the present factors that affect the output. Making sense of what you got in hand and what you could do with it is what expert systems aim at answering.
4. Natural language processors: a whole set of algorithms and models to understand what you write and speak to make sense of it. The NLP aims at understanding the high-level language to derive meaningful results.
Why should the retailers opt for ML?
Keeping up with the trends and changing of demands from time to time topped with the external factors that always find a way to dismantle everything, you could be trapped in the loophole of meeting the ends of supply and demand, customer satisfaction, ROI margins, and much more. To disentangle the mess and make sense of the data, here are the reasons why should incorporate AI into your business plans right away :
1. Tackle the recurring demand patterns: fully understand and make sense of the pool of data you are swimming in and comprehend the pattern to meet the demands with efficacy and top quality.
2. Better profits margins: adjust the gears of the commercials, budget, and finances at the right time to make the best of the situation. A few changes here can create bigger impacts on the profit margins of your overall sales
3. Beat the unpredictability: keep the guessing work out of the business and take full advantage of the data that is available. Or train the models on the data sets and time frame of similar products to improve your stand.
4. Optimized labor management: when you know what you need and when, you can pre-plan the labor required, their working duration, and much more to make the best out of every resource.
5. Higher fill rates and less stock out: going out of stock could become a nightmare if you are not preplanned and well equipped. Getting your inventory right is the perfection we crave for.
6. Supply responsive plans: when you keep the if out of the box, you can create robust and responsive supply chain plans that adapt themselves to the new fads and trends to keep you on the move. Fit right in at the right with real-time analysis of machine learning.
With the right tools and the ability to adapt with time, one is always set up for success. An estimated impact of artificial intelligence and machine learning on the supply and manufacturing chain plans equals up to $1.2 Trillion and $2T.
Some common pitfalls
But this road up the hill comes with its pitfalls that one should be aware of and take into consideration when working with the tech.
1. Inconsistent, inefficient or biased data sets: algorithms works on data, the more data the better and the more of quality data the more of fruitful results from the predictions. When we have misinterpreted data algorithms cannot make accurate and reliable predictions and hence becoming ineffective.
2. Unskilled or subjective teams: the core of the decision-making process lies in the hands of teams and departments. When loopholes or discrepancies are filling the working sector of the organization, then any algorithm will fail to meet the business goals.
3. Inflexibility: when the departments or organization is reluctant to make changes and incorporate shifts from their previous business plans, the organization can find it hard to navigate its way to success. Demand forecasting requires flexibility and openness to change to make alterations in the existing plans to deliver the best results possible.
4. Incomplete data: when the whole data is not included in the analysis, this could lead to partial and ineffective predictions leading to poor decisions.
5. Mixing causation with correlation can lead to drawing misleading conclusions. Try to answer the question of why before making any changes.
Want to get it all right and jet-speed your business scale to newer levels just like:
1. Hyundai Motors reduced their delivery time by 20% and improved their inventory turns from 3 to 3.4
2. Reynolds eliminated the forecasting errors by just 2% and tossed off the burden of $1 million in inventory.
3. Unilever reduced the errors by 30 – 40% resulting in multi-million dollars inventory save
Take a step back to leap forward
Stepping into the new world of artificial intelligence and machine learning can be pretty daunting if not intimidating. We value your journey, expertise, and time, therefore put only your best foot forward with AI development services by Queppelin.
A unique and the right blend of technology, goals, and time. We keep the AI simple so you don’t have to take the weight on. Leave the number crunching and gizmos operations on us and see the magic in action. Witness real results in real-time that moves with you. Real-time analytic, real-time predictions, and forecasting. And this is not even the end, we are just beginning!
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