Today’s manufacturing organizations operate in a dynamic environment characterized by increased complexity and uncertainty. The financial performance of manufacturers hinges on their ability to manage production costs efficiently, while rapidly adapting to constantly changing conditions, from demand fuctuations to delivery challenges.
Prescriptive analytics helps companies see where process improvements could have the biggest, most immediate impact on their bottom lines.
Predictive analytics helps companies understand the drivers behind customer buying patterns to anticipate which products customers want, how many they want and when. Prescriptive analytics, on the other hand, optimizes production planning, scheduling, inventory and supply chain logistics to meet business requirements. Through a combination of mathematical algorithms, machine learning and artificial intelligence, a prescriptive analytics solution can recommend the optimal action plan likely to drive specific business outcomes.
Prescriptive analytics techniques such as decision optimization can tackle highly complex problems ranging from hundreds to millions of constraints and variables that could never be analyzed manually. Decision optimization technology also makes it easier for non-technical users to factor in constraints, tradeoffs and multiple objectives, for faster, better answers to questions like:
How should we balance service levels with production constraints?
What maintenance should we do to avoid a predicted failure in a machine?
When should we reduce SKUs or introduce new products?
Which activities should be assigned to which workers and when?
How much overtime will be necessary in a specific period?
Maximizing decision value
The real payoff of decision optimization is the ability to make reliable business decisions that create value. Optimization techniques can drive huge efficiencies in dynamic manufacturing environments, helping manufacturers make decisions that improve performance and profits.
To maximize profit, manufacturers must be able to respond to market demand with optimal production cost, speed and flexibility. The problem is determining how much of each product to produce, when to produce it and in which location. Demand can fluctuate significantly due to seasonal trends and other factors. Factories have limited production resources, so the challenge lies in considering all constraints to maximize use of available equipment and personnel, minimize product costs and downtime, and deliver orders on time.
Many leading manufacturers rely on decision optimization solutions to find the best possible ways to make use of their facilities, employees and raw materials for increased profits. To boost manufacturing efficiency, Continental Tires uses a cutting-edge solution based on IBM Decision Optimization to optimize production across 20 plants. The solution eliminates bottlenecks and enables the best possible use of materials, staff and machine capacity.
Supply chain management
Manufacturers have a finite number of trucks, warehouses and drivers and a perhaps infinite number of potential delivery addresses. On any given day, they need to decide how many drivers and trucks to put on the road, but also need to keep the costs of equipment, fuel and drivers to a minimum while meeting delivery commitments.
FleetPride, North America’s largest distributor of truck and trailer parts in the independent heavy-duty aftermarket channel, is transforming its supply chain management with IBM Decision Optimization. The solution helps FleetPride work out optimal warehouse locations, minimize delivery time and costs across its network. That results in reduced labor costs and higher revenues.
With products becoming more complex and supply chains more extended, production scheduling is often highly complex. Production facilities may only have a few days each month to handle specific customer orders. Optimization considers not only asset use and production objectives in scheduling decisions, but also their impact on shift configurations, selection of regular versus overtime labor and other constraints.
Stocking the right products in the right quantities at the right locations requires precision. Decision optimization software helps firms manage their inventories to meet customer demand while reducing costs. It enables firms to compare multiple planning scenarios using what-if analysis and choose the best option, avoiding under- and overstocks and freeing up capital for reinvestment elsewhere.
In manufacturing, when an asset breaks down, every minute lost is costly. When equipment breaks down, it reduces profitability in a variety of ways, including production downtime, higher labor costs per unit, and added stress on employees and machines.
Decision optimization can recommend the best time and sequence for scheduling maintenance tasks in relation to production targets, downtime, inventory requirements and other interdependencies. The results can then be fed into companies’ enterprise resource planning, business intelligence, logistics and other enterprise systems to continuously reoptimize decisions as conditions change. This adds up to major time savings, increased agility and greater ROI.
Start turning predictions into decisions
Manufacturers ready to take the next step toward prescriptive analytics should focus on complex decisions where they have a lot of data. These should be in areas where they are already getting good results from descriptive and predictive analytics but need help turning those predictions into decisions.
When it comes to solving complex manufacturing challenges, decision optimization can help companies stay focused on their goals. IBM Decision Optimization offers all the capabilities manufacturing firms need to capitalize on the power of prescriptive decision making.