Supply Chain
5 ways AI can benefit demand forecasting and inventory planning
What do the stock market and weather forecasting have in common? A clue: Stocks are valued based on trend projections, whereas a particular location’s weather change is determined based on prevailing atmospheric conditions and meteorological data. If you guessed ‘forecast’ by now, congratulations!
Planning and execution are the two main pillars of supply chain management (SCM). Demand planning kicks off the planning side of SCM. For businesses, it answers the crucial questions of WHAT, WHEN, WHERE, WHY, and HOW people will demand goods/services.
Demand planning creates a strategy with inputs from historical sales, inventory and marketing data, to meet anticipated demand. The plan feeds into subsequent supply chain processing steps, material requirements planning (MRP) and product planning.
Accurate demand forecasting reduces the risk of overstocking or under-producing, while realistic sales goals enhance the efficiency of supply management execution.
Organisations face challenges in demand forecasting due to an extensive product range and vast consumer base. Therefore, they require business intelligence tools equipped with precise demand forecasting capabilities.
AI for demand forecasting
As you can see, data and analysis form the basis of accurate forecasting. Artificial intelligence (AI) is frequently mentioned while discussing data because of its unmatched data processing and prediction powers.
Let us examine the five benefits that AI tools can offer in the context of demand forecasting.
Data consolidation and organising
Let us take an example of demand forecasting for the coming quarter. The preparatory phase involves gathering data scattered throughout the ERP system and organising it. With the massive volume of information businesses generate these days, preparation will take several weeks if done manually. By this time, the data will be outdated and projection numbers won't account for recent developments.
With AI, not only can data from all relevant departments (purchasing, sales) be consolidated with the push of a button, but they can also be formatted appropriately for analysis and demand forecasting.
Automate replenishment planning
Let us see an example where the current inventory level of each product is assessed. How fast does each product move? How long does it take to replenish each product? The basis for all significant supply chain decisions depends on accurate and real-time answers to these questions.
Does it seem daunting? Wait, the challenge has just begun. If you add to that unpredictable supplier lead times, your inventory planning is likely to be thrown off course. Not if you rely on AI, though.
AI tools can automatically update ordering plans to accommodate supply and demand restrictions. Therefore, your replenishment plan can always be current as well as realistic.
Quick response to demand fluctuations
If the Covid-19 pandemic has taught us anything, it is that anything can change anytime. Demand for certain products increased suddenly. Businesses that adapted to fluctuations survived.
How do you create effective ordering plans for upcoming products without historical sales data?
AI can use automatic demand analysis to quickly respond to such ad hoc demands and enable proactive planning. In terms of profitability and consumer satisfaction, this offers a significant advantage over rivals.
Add clarity to supply chain decisions
Historical sales information is the basis for demand forecasting. But does flash clearance sales data represent typical demand? No. These are outliers that must be omitted from demand planning considerations. AI-based systems detect them effortlessly, launch efficient countermeasures and help businesses make the right decisions.
Minimise too-much & too-little stock scenarios
The key to effective inventory planning is placing orders for precisely the right amount of merchandise to satisfy client demand. With AI-based tools continuously evaluating supply and demand in real-time, supply chain managers may easily implement an optimum purchasing plan.
Conclusion
“We need an early warning system, because 10% or 20% plus-or-minus could have a major impact on the major decisions we need to make” — Mark Schoolcraft, CFO, Midwest Industrial Supply
Demand forecasting is a crucial process for businesses that enables planners to make informed decisions across the organisation and plan inventory levels appropriately. This ensures that warehouse space is utilised optimally, and orders for fast-moving products are easy to fulfil. Furthermore, it allows businesses to make intelligent decisions regarding budgeting, resource allocation, pricing, and staffing, which ultimately leads to increased profitability and customer satisfaction while decreasing overhead costs.
When businesses implement proper demand forecasting, they can gain valuable insights that help them position themselves better to meet customer needs. In fact, studies show that an increase in forecast accuracy can result in at least a 15% improvement in shareholder value.
As businesses expand their operations, AI-powered demand forecasting tools become increasingly necessary to ensure long-term growth.
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