Powerfully Simple

New applications incorporate social sensing, weather, and enhanced new product introduction forecasting Boston - Feb 15, 2017 - As part of its broader AI strategy, ToolsGroup announced today the availability of three new machine learning applications to improve demand forecasting and supply chain planning outcomes.
ToolsGroup / ToolsGroup

NHS Blood Supply Chain Case Study

Learn how NHS, one of the biggest healthcare organization in the world, optimized its blood supply chain by leveraging data intelligence.

How to Respond Automatically and Accurately to Changing Demand

The NHS (world's largest healthcare provider) has implemented a new supply chain planning system for managing England's blood supply that's frees up hospital costs. Telefonica O2 (mobile telecom provider) faced growing demand volatility and forecast accuracy problems in a dynamic market. Learn how both companies conquered their challenges.

Best-in-Class Demand Sensing for Best-in-Class Coffee Vendor

An interview with Chris Clowes, Supply Chain Manager of Costa Enterprises

Telecom Provider O2 Drives Up Forecast Accuracy and Retail Shelf Availability

When it came to planning and execution, O2 has migrated from a manual, supply-driven approach with multiple standalone systems, to one based on a centralized single model that enables its trading teams and suppliers to collaborate in a demand-driven process. Learn more here:resources.toolsgroup.com/en-o2-cs

Service Optimizer 99+ (SO99+) is ToolsGroup’s platform for delivering our “Powerfully Simple” supply chain planning solutions. Its uniqueness stems from being designed from the beginning based on a single model for an end-to-end planning process. It encompasses core supply chain functions such as demand forecasting, fulfillment and replenishment. But it also includes next-level functionality such as demand sensing, multi-echelon inventory optimization, and trade promotion and new product demand modeling.

Most “integrated” software suites have been assembled from independently acquired products built on disjointed models. Passing data from one module to the next contributes to bullwhip effect and often introduces scalability problems. Most importantly it creates shortcomings in the ability to precisely and correctly model the demand and supply chains.

At ToolsGroup, precise modeling is our forte. All of our solutions are built from the same detailed coherent model of demand and supply. Rather than thrown together and dressed up to look the same, our solutions were all born from the same DNA. The net result is an end-to-end process that minimizes the bullwhip effect, scales easily, and provides deep insight into demand signals, inventory behaviors and supply chain volatility.

Powerfully Simple Demand Forecasting

Businesses complexity is growing, driven by multi-channel marketing, the growing influence of demand shaping (media, promotions, NPI), and the impact of the internet on buyer behavior. Yet, surrounded by an explosion of valuable new data, most companies have no way to take advantage of it. They are still using approaches based on cumbersome old algorithms and aggregated sales histories.

These systems cannot easily identify trends and don’t solve the difficult problem of measuring the impact of external stimuli on baseline demand. They are producing disappointing results, significant forecast errors, and manually intensive processes leading to poor planner productivity.

The good news is that a data-driven approach using more intelligent software is readily available. And it has been shown to achieve big improvements not only in forecast accuracy, but more importantly in demand visibility and level of forecast detail, both which translate directly into improved service levels and inventory efficiencies, particularly for “long tail” items. You don’t just get a more accurate forecast, you get a better plan.

ToolsGroup’s innovation is to embed technology into its forecasting solution to solve problems that planners face every day. Rather than a “pick best” approach, ToolsGroup utilizes a better self-adaptive demand forecasting algorithm. Our demand modelling creates a reliable baseline. Machine learning can be employed to adjust the baseline by identifying the effect of stimuli and demand indicators at a detailed channel level. Our approach analyses all the relevant variables and the complex interactions among them in ahighly automated fashion.

Powerfully Simple Inventory Optimization

ToolsGroup’s inventory optimization is uniquely capable of managing slow movers andlong tail items and is also ideally suited for companies targeting high customer service levels.

Employing our Powerfully Simple approach, ToolsGroup’s inventory modelling is highly automatic with an extremely low cost of ownership, delivered either as a low cost cloud-based inventory target setting service or via hosted or on-site software. Yet it routinely generates million dollar ROIs via increased service levels, reduced stock-outs and improved inventory turns.

Unique statistical demand and inventory models reliably describe the relationship between inventory and customer service, identifying the right inventory mix to truly deliver customer service targets. And rather than assign the same "one size fits all" service level target to all SKUs in a group, Service Level Optimization assigns different service targets to each individual SKU-Location, further driving inventory savings while still achieving the same overall service level mix.

ToolsGroup’s Multi-Echelon Inventory Optimization (MEIO) performs an intelligent simultaneous global optimization over a large assortment of SKU-Locations, considering other SKUs up and down the supply chain. It is highly effective at finding the most globally efficient balance between upstream and downstream inventory.

In the manufacturing supply chain, ToolsGroup handles multi-level Bill-of-Material (BOM)conversions back to raw materials while considering production resource capacities. Upstream production “pushes” based on forecasts. Downstream assembly is “pulled” by market demand. Adequate levels of materials, subassemblies and finished goods are positioned to assemble on demand within acceptable service-time. This trade-off establishes an ideal decoupling point for each product and solves the inventory staging problem using postponement strategy.

Sales and Operations Planning (S&OP)

ToolsGroup’s Powerfully Simple technology enables an entirely new approach to S&OP by injecting a higher level of automation and intelligence into your planning process.

With the grunt work out of the way, your team is free to focus on what they do best – fine-tuning the plans with their market and business knowledge. Consensus decisions are based on highly automatic statistical analysis of massive amounts of data. You get a smarter planning process that is not subjective, but objective.

ToolsGroup’s S&OP solution reliably translates aggregate models to an operational plan at the item level. The model supports the tactical and financial plan, but is also capable of supporting supply chain execution. The modelling environment is coherent from top to bottom and at every level in between because the aggregated S&OP model and operational model are continuously synchronized through perfect mapping.

By modeling demand and supply chain uncertainty and simulating the impact of inventory on customer service, the cross-functional team can define desired target customer service levels by product segment and market segment. The software then translates these service level goals into an optimal inventory mix that absorbs the upstream and downstream supply chain fluctuations.

This allows the team to make fact-based collaboration trade-offs in the planning process. They can understand and manage the interdependency between target service level and inventory investment in determining the best way to achieve their business objectives. Planning is focused on a common strategic objective function such as margin attainment. And most important, due to the robust and precise single model, the resulting time-phased capacity constrained plan can be executed reliably.