ToolsGroup Demand Forecasting technology means planners don’t need to add data science and algorithmic knowledge to their long list of required skills. Behind its elegant simplicity is a powerful engine, capable of optimizing each item or your whole SKU portfolio against target service levels. Instead of planners having to pick the best algorithm for a given situation, we provide one adaptive algorithm. It automatically generates a reliable baseline forecast for every SKU, by location – from popular items that fly off the shelf each month to unpredictable slow-movers – even in complex multi-echelon supply chains.

Demand Sensing closes the gap between your plan and what’s actually happening in your supply chain. It automatically detects changes and patterns in downstream activities by analyzing replenishment orders, POS data and EDI transactions so you continue to hit target service levels in the face of demand volatility.
The Machine Learning Engine (MLE) further refines your baseline plan by analyzing and predicting the effect of short-term, external factors like weather and macro-economic trends. It can also incorporate data from Demand Shaping activities such as Promotions Planning and New Product Introductions.
Demand Sensing is proven to help our customers cut short-term forecast error by up to 50% and raise inventory performance by up to 20% while hitting or exceeding target service levels.

Fast-moving industries from fashion to consumer technology are continually introducing new products with short and hard-to-predict lifecycles. Thriving in these scenarios depends upon being able to strike the right balance between introducing new products to attract and retain consumers while still managing inventory costs. Pressured by sales and marketing, most companies err on the side of introducing a wide range of products and holding excessive safety stocks to avoid disappointing customers. This approach, however, can lead to customer service problems.

Promotions, advertising and other forms of “demand shaping” can be enormously expensive, costing more than 10-15% of gross revenues at fast moving consumer goods companies. Yet determining their actual impact or “lift” remains a daunting supply chain problem. Solving this problem is critical not only to baseline and lift forecasting but is also the key functionality required for an effective and integrated planning process between marketing, sales, account management, supply chain and finance.
To solve this problem, ToolsGroup’s Promotions Forecasting harnesses Machine Learning technology.

Behind ToolsGroup elegant simplicity is a powerful engine, capable of optimizing each item in your SKU portfolio to achieve target service levels. It doesn’t just independently calculate inventory targets for each item in isolation, but simultaneously optimizes your entire global assortment of SKU-Locations.

What’s more, our service-driven approach positions inventory across the network to efficiently meet high customer service level objectives. Instead of simply creating a “one size fits all” inventory mix, it identifies different service level targets for each individual SKU by location to achieve a blended service.

With today’s large SKU portfolios and fast replenishment cycles, it’s not unusual for companies selling anything from home goods to spare parts to face intermittent demand patterns with up to 80 percent of their items.

Our inventory optimization solution was engineered for this challenge. It analyzes a company’s entire SKU portfolio and recommends the optimal inventory level for each item to achieve the target service level. Most tools employ a top-down approach that assigns safety stock levels based on aggregate order volumes. ToolsGroup’s bottoms up, statistical approach provides much greater accuracy by factoring in demand signal details (such as order frequency) and supply variables (such as lead time variation).

To truly serve the end customer, the entire network, from raw materials suppliers and factories through distribution centers and wholesalers, needs the right stock. This is no easy task. Our MEIO solution proposes optimal inventory levels at each stage of the supply chain, simultaneously balancing across multiple echelons, locations and different Bill-of-Material (BOM) levels. It finds the most globally efficient balance between upstream and downstream stock while optimizing safety stocks and cycle stocks (optimal postponement). It also supports centralized demand planning, lowers costs across the supply chain and streamlines operations.

Different products consume available inventory at different rates, depending on the need that they address.

Dynamic replenishment addresses the challenge of maintaining optimal inventory targets required to offer target service levels, without incurring costs due to over-stocking. It secures that you still have the right product available to meet customer need, respecting supply constraints, calendars, demand characteristics, production constraints, lot sizes and other distribution parameters.

The solution offers visibility into future purchasing and distribution requirements, allowing further collaboration with suppliers using an detailed view on optimal inventory evolution.

The best – most accurate – forecasts come from pooling knowledge and insight from people in different roles functions across the business.

ToolsGroup Demand Collaboration Hub (DCH) facilitates collaborative demand planning by combining data from multiple sources and stakeholders (like suppliers and partners), allowing customer service goals to be synchronised with production and operations capacity. DCH is a web application that is available for the primary operating systems (Windows, MacOS, Linux), tablets and Android/iPhone mobile devices.

Our Demand Collaboration software platform is simple to use so even inexperienced or occasional users can collaborate and contribute to improve forecast planning accuracy. Evaluating and comparing multiple forecast inputs from multiple sources decreases forecast bias and improves demand forecast accuracy.

DCH supports collaboration between internal sources, including marketing and POS data, and external sources, for example partners, retailers, distributors or suppliers. Once Demand Forecasting has provided the baseline forecast, DCH users input their knowledge, data and changes. The machine learning capability within DCH also takes into account seasonal events, weather and other significant factors affecting supply and demand.

This end-to-end collaboration results in far more precise projections that minimise inter-departmental differences and facilitates the SCP or S&OP (Sales and Operations Planning) process, resulting in improved demand forecasting accuracy, fill rates and business efficiency.

Our Production Planning solution supports production and procurement planning, factoring in market demand and accommodating different approaches including Make to Stock, Make to Order and Assembly to Order. It addresses medium to long-term planning, offering insight into long-term demand feasibility.

The Master Production Schedule System links sales to logistics, taking demand forecasts into account, proposing which products to manufacture and stock levels. It assesses available production capacity and calculates and generates proposals for materials and components needed to fulfil the production plan.

The high-performance Finite Capacity Scheduling System generates a production schedule in seconds, even for very complex environments with huge amounts of data. It also takes into account production process constraints specific to different industrial sectors.

The system’s user-friendly and interactive graphical interface lets planners make changes to the production schedule with just a few mouse clicks. Precise modelling, allows planners to easily simulate and compare alternative ‘what-if’ production plan scenarios.

The Manufacturing Execution System brings visibility to all steps in the production cycle, supporting production schedule dispatching, data collection and resources monitoring, shop-floor control, lot tracking and traceability, quality control plans management, label printing, plant maintenance and KPI analysis.

MES collects shop-floor data through a range of devices (touch-screen panels, industrial terminals, Wi-Fi handheld units, tablets), direct machine connections (SCADA, PLC, digital signals, etc.) and integration with dedicated measuring and weighing systems.

Roadnet Transportation Suite (RTS) application package allows the organization to share planning data among logistic units via common platform with a uniform user interface and shared database. The software uses market-leading advance algorithms to optimize the organization supply chain.

The system includes Tactical daily dynamic distribution routes planning, Strategic planning of Sales Representatives territories, Optimal truck loading planning, Optimization in real-time with advanced wireless technologies and a Customer service focused module which provides reach and necessary information in real-time