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How Can Data Science Enable Better Demand Forecasting and Planning For The Manufacturing Industry?
It’s a matter of concern for businesses to streamline operations, sales, finance, etc. to achieve better planning and demand forecasting. A heady mix of advanced analytics tools and machine learning can make it possible.
A Gartner study points demand volatility to be the topmost pain point that faces global business executives. Unlike internal factors, demand is not exactly in control of the executive’s hands. It is influenced by several real-time factors and audience sentiment, which can vary hugely.
For instance, novel situations like COVID-19 have upturned several previous market predictions and brought about a complete change in demand levels for several industries.
A basic knowledge framework can better assess and anticipate customer needs and upcoming market trends. Through proper demand forecasting, companies can effectively use their time and resources to further optimize most of their operations, including supply chain management, manufacturing, warehousing, pricing and shipping.
Jumping ahead with inaccurate forecasts can lead to severe financial losses. In contrast, accurate predictions can help you reduce costs, free up working capital, and fuels improvement and innovation.
Here is a brief overview of how data science can help you perform effective demand forecasting and optimize your manufacturing operations.
What Is Demand Forecasting
It is the process by which you get an estimate of possible future demand for a product or a service. It is often used synonymously with demand planning, which denotes the entire process, including the follow-up actions that use the estimated value.
In a traditional sense, demand forecasting is done using statistical methods. It uses software applications to create time-series forecasts on sales and trends. They predominantly fetch at least two years’ worth of historical data. These are fit for mid to long term planning for well-established products. However, it is essential to consider that statistical forecasting isn’t always accurate. What may have happened in the last two years may not necessarily reoccur.
That’s when you need to consider automated statistical forecasting. How does it help:
· Stable demand for well-established products
· Mid to long term planning
· Total demand prediction as opposed to sales of separate stock-keeping units (SKUs)
Demand forecasting leverage machine learning models to include volatile demand factors and accurate service data-driven predictions. These models can consist of structured and unstructured data viz. past financial and sales reports, inventory details, macroeconomic factors, social media inputs to optimize & plan the overall production.
IT research firm Gartner further says that companies that have accurate demand forecasting techniques tend to have 15% less inventory, 17% better at order fulfillment, have 35% shorter cash-to-cash cycle times, 60% higher profit markets, and enjoy better stock values than their peers.
Benefits Of Demand Forecasting
Predictive Sales Analytics
Machine learning algorithms can be applied in conjunction with historical data to help discover future sales trends and optimize your manufacturing strategies. It also enables you to meet the emerging trends in product demand & market conditions.
Supply Chain Efficiency
Demand forecasting in operations management helps forecast the number of sales most likely to occur — this aids in better production scheduling, warehousing, and shipping. You can plan for scheduled maintenance shutdowns and make the best use of labor and raw materials.
Having an adequate number of workers is essential to ensure the production process is carried on schedule and does not include over expenditure or under expenditure regarding employment management. Accurate demand forecasting allows you to plan your labor requirement & operations efficiently.
Demand sensing machine learning solutions allow you to capture real-time fluctuations in purchase behavior and improve your demand forecasts’ accuracy further. It extracts data from POS systems, warehouses, and other external sources like social media to analyze sales patterns and automatically adjust your short-term production plans.
Fault Prediction And Preventive Maintenance
Machinery and production equipment are all prone to wear downs, and if not correctly planned for maintenance, may experience failures. Employing machine learning to predict faults can help implement preventive care and ensure that the supply chain remains unaffected.
Demand forecast can help better manage and ensure you have enough money to pay off the monthly bills and labor costs. It allows you to make the best use of your cash reserves and negotiate loan terms in advance. It helps you work out efficient budgets.
As you can see, demand forecasting can help you optimize prices, sense demand, manage labor properly, and make predictive analytics. All these advantages will streamline your business operations and boost your efficiency. ML tools can certainly bridge the gap between anticipation and reality. Are you looking for incorporating these technological changes in this new year?
As the CTO of Rishabh Software, Srinivasa Challa (a.k.a CS) defines and drives the technology solution roadmap for global customers. CS is responsible for the seamless execution of the company’s technology strategy, development & cross-functional delivery. With 25+ years of experience, he is instrumental in creating CoEs across domains like Healthcare & Fintech. CS is a constant learner who loves to stay updated on technology, healthcare, digital transformation and customer experience design and more.
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