Jun 30, 20203 min
Updated: Apr 3, 2023
Predictive analytics is gradually becoming an integral part of every organization. Sales and marketing has been most receptive the past few years, but now manufacturers are catching up as they see enormous opportunities to increase productivity and profitability using artificial intelligence.
Fundamentally, there are three main areas where manufacturers are benefiting from predictive analytics.
Using Advanced Demand Forecasting techniques and predictive analytics to create sales forecasts, both short- and long-term, is far more accurate than any other forecasting tools ever to exist. Forecasts can be done for each and every client and each and every product, or product category, every day if needed. In fact, companies like Walmart are doing 500 million forecasts every week! That is a forecast for every product in every store, along with various combinations and product categories.
The benefit for manufactures is obviously superior accuracy and understanding of what the future brings. They get information automatically and faster than ever before which allows them to prepare for what’s coming, instead of being reactive in a panic-mode, trying to fix what came unexpectedly. Additionally, the benefits are tremendous for procurement, production planning, and inventory management:
Procurement: By implementing superior advanced demand forecasting, manufacturers can know with high accuracy how much raw material to buy and when. It allows them to plan procurement and aim for periods where raw material is cheaper, and negotiate cheaper shipments. Obviously, things tend to become more expensive when done last minute with high urgency.
Production planning: How much should we produce? When should we produce? How many factory workers should we have? Demand forecasting allows manufacturers to accurately plan production ahead to avoid peaks and downtime by spreading the production evenly according to the demand forecast. This reduces cost in production during unexpected peaks when plants need to urgently hire temporary workers, work extra shifts or overtime.
Inventory management: How much inventory should we keep, and to which warehouses should we ship the products? Advanced demand forecasting significantly decreases inventory cost as organizations are able to plan inventory with superior accuracy and keep it at minimum, distributed to the right locations at the right time, all according to the demand forecasts.
Demand forecasting alone is reducing significant cost for manufacturers already and organizations are starting to see the real value of data. But another area is the production plant itself. Every Plant Manager’s nightmare is to see the production stop due to machine failure. When a machine breaks down the whole production line stops, and with every minute that passes the production cost increases.
By implementing Predictive/Preventive Maintenance, factories can predict which machine will break down and when. In fact, they can predict what specific part in what specific machine will fail, why it will fail, and how the maintenance team should respond. Unsupervised machine learning tools such as clustering and artificial neural networks are applied to historical data to monitor machinery in factories and use past data to predict future breakdowns. This allows maintenance teams to significantly reduce machine downtime and reduce costly quality defects with improved efficiency across the production process.
The different case studies show that manufacturers are reducing unplanned downtime by 15-30%, and those companies that have the largest room for improvement are seeing machine breakdown reduced by 70-75%. This can have a serious impact on the production cost and, hence, the profitability of the product produced. Companies with low margins, such as food producers, are flocking towards the implementation of Predictive/Preventive Maintenance with immediate impact on the bottom line.
Another lesser known area within manufacturing is Predictive Quality Management. By applying algorithms to past quality- and laboratory data organizations can detect emerging quality issues early enough and avoid costly call backs, lost production, and brand damage. Even if most quality issues can be traced back to decisions, or mistakes, early in the process they are usually not discovered until much later. Using machine learning techniques that use past laboratory/quality tests to predict potential defects allow manufacturers to step in early in the process and to prevent further costs from potential quality defects. These methods have originally been mastered in the pharmaceutical industry, but have since spread into all industries with food producers reporting serious benefits.
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Although many organizations have implemented predictive analytics into their manufacturing, there are still many that have tremendous potential for improvement. Obviously, those companies who do not adapt to change will start lagging behind those who do. As wars change, so do the weapons. Predictive analytics is just a new type of weapon in the bloody battlefield of business, and if everyone else has it but not you, then your days are numbered.