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As customers increasingly demand rapid and reliable delivery, optimizing this final leg of transportation becomes essential for businesses aiming to enhance customer satisfaction and operational efficiency. Key Benefits of Last-Mile Delivery Optimization: Reduction in operational costs and fuel consumption.
Global supply chains have been tested repeatedly by a series of disruptive events, including the COVID-19 pandemic, U.S.-China In response, many organizations have shifted toward decentralized and regionalized supply chain models, distributing production and sourcing across multiple regions.
It allows operations to remain competitive even in unpredictable market conditions and supports a variety of business models and client needs. This approach protects the investment while enabling warehouses to adapt to shifting market trends and business models. Moreover, flexibility enables geographic expansion.
Three months into 2025, we have seen a barrage of on-again, off-again tariffs that have supply chain and logistics teams reeling, as they must rethink everything from next weeks shipping route to their foundational network models. The Ukraine-Russia conflict is ongoing. Tensions flare in the Middle East without warning. billion to $23.07
Executives at Blue Yonder refer to this as a “cliff event.” To avoid a cliff event, Blue Yonder has proceeded by turning its supply chain applications into applications that are part traditional software code and part microservices. Blue Yonder, for example, has created a microservice for transportation optimization.
For example, with a data gateway, a supply planner gains accelerated access to customer orders, inventory levels, and transportation schedules, all in one place, to increase the user experience of making the right choice to identify inefficiencies and make better, more informed decisions.
Datacenter Hardware: The demand for powerful computing to train ever larger and more accurate AI models is insatiable. AWS , Google , and Microsoft are also investing heavily in custom AI chips to reduce their dependence on NVIDIA and optimize performance and cost. Google is also reportedly working on its own Arm-based chips.
For example, with a data gateway, a supply planner gains accelerated access to customer orders, inventory levels, and transportation schedules, all in one place, to increase the user experience of making the right choice to identify inefficiencies and make better, more informed decisions.
Before a potential customer buys an autonomous mobile robot solution, Locus Robotics often uses different types of simulation to determine the type of robots needed and the number needed to optimize productivity at a warehouse. DES allows the modeling of complex warehouse operations at various levels of detail.
For example, signs that a company is moving in the right direction, talent-wise, might include: The deployment of staff in new roles, absent the traditional supply-chain-centric titles and instead, hybridizing across data-science and logistics skill sets. However, they can struggle to adjust to new challenges and volatile demand fluctuations.
Optimization is a ubiquitous term in the supply chain and logistics industry. We all talk about how we need to optimize our operations. In practice, however, relatively few companies are using optimization technology, particularly in transportation. Why is transportation optimization key today? Types of optimization.
Optimize Inventory Management Inventory often represents one of the largest expenses in a supply chain. By leveraging predictive analytics and a just-in-time (JIT) inventory model, you can maintain optimal stock levels, which reduces storage costs and cuts down on waste from unsold items.
Digital twins are emerging as digital transformation accelerators for supply chain and logistics organizations seeking enterprise-level visibility, real-time scenario modeling, and operational agility under disruption. These are not static dashboards or simple visualizationstheyre living, data-rich models of real-world operations.
What Celanese has accomplished is the single best example ARC is aware of employing agentic AI and copilots at scale. The occurrence of any of these events disrupts the global supply chain and can deeply impact profitability. One event could create so much churn, Mr. Al Syed explained. Celanese is an exception.
During COVID, this more agile and resilient model allowed the firm to grow their market share. An iGPU (integrated graphic processing unit) is a current example. As an example, if we have congested lanes, the system will automatically flag that we have a potential risk of delay based. Factories serve local markets.
The first product of this partnership is TacticalOps, a Planning & Optimization solution for Food Manufacturers. I spoke with Luis Pinto, Partner at UniSoma, to understand the need for new planning and optimization solutions in the global food supply chain. I’ve seen the attitude towards optimization evolving yes.
Inventory Control Techniques that use Stock Optimization Best Practices. So we thought we’d focus on the lesser known topic of ‘stock optimization’ – this is an inventory control technique that’s becoming more popular with inventory managers to improve the efficiency of their supply chain. What is stock optimization?
The system can detect a deviation from a forecast, for example, and yet understand if the deviation is in an allowable range and that an alert does not have to be generated. However, unexpected events do happen. For example, a large customer may place a large, unforeseen order that becomes visible at 9:00 a.m.
If you have been through this process at least once, you already have a good idea of what supply chain design is about: optimization. When most people hear the word “optimization,” they immediately think about minimizing costs. But optimization is much more than that! Let’s continue with this analogy.
ML looks into historical data (for example, transit time statistics of carriers) and data from impactful external factors (such as port congestion, weather or holidays) and uses this information to develop more accurate transit time estimates. The model learns continuously and can adapt to changing conditions in the network.
For example, signs that a company is moving in the right direction, talent-wise, might include: The deployment of staff in new roles, absent the traditional supply-chain-centric titles and instead, hybridizing across data-science and logistics skill sets. Bringing Production Closer to the Customer. Investing in Supply Chain?
In recent months, I ‘ve been active in several events in the region and I’ve noticed a changing trend. Companies are increasingly eager to hear about optimization and advanced analytics. There are several areas where companies are eager to apply optimization. Companies setting an example. Areas of i nterest
12,000 SAP customers and partners attended the event, and another 15,000 watched remotely. The RISE with SAP offering includes an AI-powered cloud ERP that’s managed and optimized by SAP. When SAP refers to AI, it refers to generative AI based on large language models or AI based on machine learning.
3 min read Supply chain optimization is crucial for businesses to enhance efficiency, reduce costs, and improve customer satisfaction. Here are some real-life examples of successful supply chain optimization across various industries. Sustainability and resource management are also critical concerns.
New import tariffs being introduced across the globe, extreme weather events, Brexit… The list goes on and on. For example, we don’t know where the next tsunami or hurricane will hit. Let’s consider some examples. What happens if one of my manufacturing plants goes down due to an unforeseen weather event?
Organizations must take the following steps to bring departments together to create truly resilient and sustainable supply chains: Leverage external data to sense market shifts Look to external causal factors and forecasting models to identify market shifts. By identifying these gaps, you can create sourcing events to close them.
The theory is that as more and more devices throughout the supply chain and manufacturing process become part of the ‘Internet of Things,’ they will produce an incredibly rich data stream that will send signals in real-time to trigger a wide variety of events. Digital Twin Model Builders. PlanIQ includes Amazon Forecast.
Further, while artificial intelligence helps solve certain types of problems, Jay Muelhoefer – the chief marketing officer at Kinaxis pointed out – optimization and heuristics work better for other types of planning problems. Lead times, for example, are a critical form of master data for planning purposes.
Industrial data scientists’ core mission is to build more comprehensive, performant and sustainable AI/ML models that are fit-for-purpose, domain-specific and address focused, real-world use cases. Hybrid modelling combines the first principle knowledge with experience and new insights from data.
Agility can also reflect a company’s ability to effectively deal with unexpected constraints caused by strikes, earthquakes, political strife, and a variety of other events. ARC defines supply chain planning (SCP) products as including supply planning, demand planning/inventory optimization, and network planning.
Supply chain executives must evolve from cost and service as the key objectives for optimal demand-supply balancing towards the “quadfecta” of cost, service, resiliency, and sustainability. The bullwhip effect is one example of this disruptive effect, when small changes in demand cause huge demand spikes downstream.
3PLs may have to reinvent their business model in these cases, if they want to continue serving such customers, possibly becoming the Uber of their logistics sector for needs ranging from massive bulk transport down to individual, end-customer deliveries. Cloud computing itself is a prime example.
As Josh Dritz, VP of Operations Technology and Automation at Messen Medical Surgical, pointed out, Geopolitical factors, extreme weather events, labor issues, and pandemics are just a few of the challenges that constantly threaten supply chains. Examples include: Labor Planning: Optimize workforce productivity based on real-time data.
Additionally, software vendors continuously invest in tuning the performance of their algorithms and models. For example, running a batch process that now takes 8 hours instead of 12 does not translate into supply chain agility. Supply chain management talent continues to be in short supply and attrition due to burn out is still high.
How the digital twin concept drives benefit By using advanced analytics and machine learning algorithms, digital twins can provide real-time insights and recommendations to optimize operations, reduce costs, and increase productivity. Physical change (i.e., changing the structure of the warehouse, modifying processes, etc.)
Supply chain policies and configurations can be evaluated and then optimized across the likely ranges of demand, supply, disruptions, and financial drivers – providing the best plans across strategic and tactical horizons. These events can range from minor supply disruption or canceled shipments to significant black swan events.
The first product of this partnership is TacticalOps, a Planning & Optimization solution for Food Manufacturers. I spoke with Luis Pinto, Partner at UniSoma, to understand the need for new planning and optimization solutions in the global food supply chain. I’ve seen the attitude towards optimization evolving yes.
A powerful order and inventory management system can track inventory levels in real time, help identify short and long-term trends, automate critical re-order points, optimize order size and cadence , generate customized reports and so, so much more. The amount of data available usually depends on the maturity of the product.
And agility is about reacting quickly and hopefully effectively to the actual disruptive event. But the event might not be disruptive at all if you had planned for it. But Mr. Delbar from OMP points out that our models are not integrated enough. Our SCP models do not understand the constraints of key suppliers or partners.
Demand forecasting should be tightly integrated to an inventory optimization application. Machine learning also makes it possible to make more granular forecasts – for example, instead of forecasting demand for the company’s products in the Eastern Region of the U.S., Demand models need to be continuously updated.
Here are some examples of such use cases. Building optionality in the supply chain through collaborative sourcing: Supply chain teams can proactively identify choke points within the existing network by leveraging emerging technologies such as digital twins and advanced analytics, and modeling their end-to-end supply chains.
The theory is that as more and more devices throughout the supply chain and manufacturing process become part of the ‘Internet of Things,’ they will produce an incredibly rich data stream that will send signals in real-time to trigger a wide variety of events. Digital Twin Model Builders. PlanIQ includes Amazon Forecast.
Led some supply chain planning supplier to create new digital twins – new supply chain models – that model this supply chain much deeper than it had been previously modeled. A more granular model means better planning – planning that more fully reflects the constraints that exist in these supply chains.
Maintaining all past and future promotional events in a database. Maintaining outlier events that have influenced demand patterns or supply availability . Modeling the impact of weather events. Modeling the impact of weather events. Automatic identifying and removing outlier events from the historical data.
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