Customer Stories

A Consumer Products Company

→ Like many of its competitors, ConsumerCo is seeking to move closer to its customers and achieve service level excellence without skyrocketing inventories.

ADI

→ ADI faced an ambitious project to transform its entire logistics network: stock centralization, fulfilment process optimization, inventory reduction and high service level delivery were some of the goals of the project. After using ToolsGroup’s solution, the company dramatically improved the performance of its supply chain.

Alessi

→ Alessi needed a planning process to guarantee product availability and consistently fill orders in a timely manner – a stock-mix optimization process. This was difficult because Alessi’s business has high demand variability and long lead times from overseas suppliers add supply variability.

Amara

→ Everyday Amara faces the challenges of MRO environments from products with intermittent demand and low inventory turnover to a complex network. It was seeking to reduce MRO inventory throughout the network and stock obsolescence while improving its customer service levels.

Amplifon

→ Amplifon operates in a highly competitive, diverse and fragmented market and manages a highly complex extended supply chain. Over time Amplifon lost full oversight over its inventory, particularly which products needed replenishing and the assortment to be distributed to stores. It needed to boost inventory efficiency and integrate distribution and sales processes.

Ansaldo Energia

→ Ansaldo Spare Parts Service team was faced with an increasingly competitive market and changing business environment. Their order-based production system wasn’t meeting their customer service and inventory efficiency needs. Their most pressing issue was to avoid late deliveries to clients in the face of high demand variability and unpredictable emergencies.

Aston Martin

→ The new demands posed by its international client base prompted Aston Martin’s board to raise targets for first time availability (FTA) by 2 percent without increasing inventory. For the first time, the board also wanted to achieve FTA parity across all three of its car categories: “Heritage (pre-1997),” “Recent Production (mid 90s forward, but no longer in production),” and “Current Production (today’s models)”.

Bellota

→ Bellota’s supply chain was challenged with an increase in new SKUs due to the extension of its portfolio. To help assess its business performance, the company decided to introduce a new KPI: availability rate. Now it needed a tool help it deliver on this objective.

Boise

→ Dale Groetsema, Supply Chain Leader for Boise, deals nearly every day with the difficult problem of how to manage inventory in a long supply chain, owning the inventory all the way from raw material (the tree) to finished product in the warehouse. Manufacturing takes place at the mills located near the source of raw material, while customers are located mostly in distant metropolitan areas. In between, most of the product is shipped via rail, since shipping bulky paper products by truck is often not an economical option. The average transit time is more than two weeks. “At any given time, much of our inventory can be just sitting in rail cars,” notes Dale.

BP Castrol

→ The BP Castrol team was faced with reactive supply chains caused by forecasts that were inaccurate, unreliable and incomplete. The forecast did not extend to all SKUs and calculations required intensive manual work. The supply chain was still widely order-driven and structured to be reactive, rather than proactive.

Cipla Medpro

→ Over a nine-year period of rapid growth, Cipla Medpro found it increasingly difficult to maintain forecasting accuracy as its needs outpaced its systems. Joseph Ludorf, Executive Director Supply Chain, was initially apprehensive about investing in new planning software due to the ‘grief cycle’ he had experienced in the past with IT projects that came with long, expensive implementations and unmet expectations.

Delphi Technologies

→ The supply chain management team was under increasing pressure to pull inventory out of the business and achieve high service levels with less safety stock. This needed to be achieved in a large scale environment. The business has 59,000 stocked SKUs, receives about 17,000 order lines per day, and has more than 700 suppliers.

Deoleo

→ Due its size and complexity, Deóleo Group needed a more strategic, global supply chain solution in order to obtain and improve its global logistics, financial and organizational goals.

Deroma

→ Deroma experienced an unsustainable inventory increase and lacked a tool for accurately measuring supply chain performance and enabling appropriate corrective actions. They face a business with strong seasonality and a catalog that is renewed every year, while factories are capacity constrained and must produce product throughout the year.

ECommerce Home Goods Retailer

→ This Internet retailer was growing fast and outgrowing its planning system. It was taking a team of three planners roughly 100 hours a week to create a forecast and replenishment orders. They were also dealing with a growing assortment of SKUs, each with its own seasonality, demand pattern and supplier lead time. The onerous planning process was overwhelming, and the team knew it was time to find a new planning system.

Eurofred

→ Eurofred was challenged with planning and optimizing its complex supply chain in order to improve customer service levels and manage stock levels.

Findus

→ Findus struggled to balance the conflicting demands of high availability and capital tied to inventory. Like many food manufacturers, Findus often favored service levels to meet the tough demands of their customers. This often meant holding artificially high safety stocks to ensure availability.

Franke

→ Franke’s tremendous growth resulted in a very complex supply chain network that was no longer running optimally. Franke’s Kitchen Systems and Water Systems divisions consisted of 42 locations (including 12 production facilities) with four echelons serving 146 markets and more than 125,000 SKUs (equating to 1.4 million SKUMarkets). Seventeen different ERP systems were in use. Data structures and planning processes varied. Franke rolled out SAP as the master database for all locations where SAP was the ERP system, but the system for demand forecasting and planning hadn’t changed nor were these processes integrated. This posed a huge problem given the complexity of Franke’s supply chain network.

Global Eyewear Co.

→ As the company’s brands, models, and distribution channels were expanding, the company had the challenge of intelligently managing inventory to maintain high service levels but avoid excess stock. In addition to managing phenomena like high seasonality and launch profiles they needed to position SKUs differently according to their market behavior.

Granarolo

→ Granarolo required optimized inventory management and complete visibility into demand, distribution and production in an environment characterized by short shelf life and strong promotional pressures.

Grupo Gallo

→ FMCG is a very dynamic sector due to promotional activity and large number of new product introductions. To support this environment, Grupo Gallo needed a reactive and adaptive supply chain.

Gruppo Giovannini

→ The electronics distribution business is highly competitive and Gruppo Giovannini needed to make optimal trade-offs between working capital and service levels for their large, ever-changing product range, and to manage an increasing number of long-tail demand items.

Hero

→ Hero Spain faced the challenge of achieving substantial and sustained improvements in its supply chain KPIs, product availability, average stock levels and forecast accuracy.

Höganäs

→ Due to factors such as environmental regulation, economic strength and consumer preferences, demand varied greatly within each country. Höganäs was replying on local sales forecasts from its team of 50 global salespeople. Its sales forecasting system had been developed organically over time, merging components of Excel, Cognos and QlikView. The system did not support supply chain processes beyond sales and marketing, such as inventory planning. The system also required manual labor and guessing from the sales team which resulted in too much stock in the global supply chain and inadmissibly high forecast errors. The management team decided to migrate into a faster and more reliable system with a goal of decreasing inventory levels by 10%.

Kronans Apotek

→ Kronans Apotek has approximately 27,000 items and around 4 million location/SKU combinations. Inventory management had become too decentralized and Kronans could not properly manage varying seasonal patterns, frequent product replacements and new pharmacy launches. The inventory levels were high and Kronans wanted to decrease them by 15% while increasing the service level by 1.5 %. At the same time, they wanted inventory optimization to be less time consuming and less centralized to give their staff more time to spend on delivering service and advice to customers.

Lennox

→ Lennox Residential Heating and Cooling faced the challenge of managing an ambitious North American distribution network enlargement while simultaneously transitioning to a hub-and-spoke model with 55 shipping and 161 selling locations.

Lubinski

→ Lubinski faced a challenging environment of managing more than 20K slow moving items in their warehouse. Planning was done with fixed and manual methods. Although having two full-time planners, stock levels was still increasing forcing write-offs due to obsolete items. Above all, implementing a new ERP system contributed to the instability of the supply chain.

Mitsubishi Electric

→ Mitsubishi Electric Europe needed to get inventory levels back under control in one of its spare parts businesses as obsolete stock was doubling nearly every two years. The company was seeking to reduce inventory while achieving the same or even higher service levels. The challenge was that its heating, cooling and ventilation systems business is inherently ‘hyper-seasonal’ and most system repairs are urgent, requiring many parts to be delivered within hours.

Mohawk

→ The fine paper industry is unrecognizable from 20 years ago. The internet, globalization and changing customer demand have transformed it from being a traditional, high-volume and personal business to one where it is now possible for a customer to buy a single sheet of paper online. Naturally, these changes have resulted in market consolidation. Mohawk’s SVP of supply chain John Angleson harbored no illusions about the scale of the challenge: “The relationship between the manufacturer and distributor in the paper business has typically been characterized by various forms of manipulation, both positive and negative. We had to move all the players out of their comfort zones, into a data-driven, collaborative culture.”

Moleskine

→ Moleskine had to manage supply chain planning across increasing brand complexity in a mix of make-to-stock and make-to-order environments. They needed a demand forecasting tool that would increase service levels and reduce working capital.

Multi-National Coffee Bar Company

→ One of Europe's fastest growing coffee shop brands offers airports and railway stations, hospitals and universities, convenience stores and more the opportunity to profit from the increasing demand for high-quality coffee on the go. The company provides partners with self-manned coffee machines and regular replenishment of coffee and supplies.

Multi-national Food Company

→ Multi-national food company needed a reliable forecasting tool that would consistently predict the actual lift to baseline demand from trade promotions and media events. The challenge was to understand the correlations among the large numbers of variables with complex interactions.

NHS

→ A collection of manually intensive databases and spreadsheets grew increasingly unfit to support decisions like determining the optimal blood inventory mix for a specific hospital, or performing ‘what-if’ calculations to prepare for different crisis scenarios. It was a sub-optimal process that could not respond to the future needs of its customers.

O2

→ Since 2013, O2’s inventory had been growing by 60 percent annually, driven by constant technological advances, new device manufacturers and a movement into new product markets. Crucially, the relationship O2’s customers have with their phone has changed - it’s become the remote control of their lives. This means their expectations in the level of service they receive has also changed as well. In this era of rapid evolution, O2 recognized the need to evolve the way it does business and the systems that support it.

Piaggio

→ Piaggio was faced with an abundance of backorders and was dealing with a high level of dead stock resulting from an inability to sense regular versus specialized demand. And despite experiencing service levels that had dipped as low as 50 percent, Piaggio was relying far too much on expediting shipments via air freight.

Pilkington Automotive (NSG Group)

→ Pilkington Automotive was faced with unpredictable demand, a growing number of SKUs and a diverse range of customers, each expecting very different service levels, pricing and lead-times. This drove them to embark on a journey to build a more competitive supply chain.

Pompea

→ Pompea’s business was becoming increasingly complex and it was challenged to build the right inventory mix to serve its retail channels. The labor-intensive, time-consuming monthly planning process was no longer cutting it. Something needed to change.

Rajapack

→ As a branch of the RAJA Group headquartered in Italy, Rajapack distributes more than 4,000 packaging and office supply products to 70,000 customers throughout the country, guaranteeing delivery in 24 to 48 hours.

Ratioform

→ Ratioform had 90 percent of its inventory items in the long-tail and found it hard to balance its costs with service level goals. Despite having many slow movers, lumpy demand and a seasonal peak around Christmas, the old system could only handle aggregated moving averages. This led to excess inventory levels and considerable manual work for its planners.

Repsol

→ Repsol wanted to start a project to improve product service levels and knew it would not be easy because they wanted a flexible solution that did not interfere with the systems the company was already using.

Shamir Optical

→ Shamir Optical had a decentralized global supply chain which was very difficult to manage. Visibility across the chain was very low. Due to high stock-out cost, the company had to keep very high stock levels in order to ensure sufficient service levels. In addition, more than 70% of its 200K SKU-L’s were slow movers.

SKF

→ Digital transformation is tough for any firm, but very tough for a $9 billion industrial manufacturer. SKF is currently on a journey towards this transformation. A central part of its transformation is changing their integrated planning model from regional to global.

Spar Gran Canaria

→ Based on the Canary Islands, Spar Gran Canaria was facing new direct to POS distribution restrictions from its suppliers trying to contain costs. As a result, it need to change its stock planning process.

Sports Car Co.

→ This sports car company’s management set a challenging target of raising the spare parts service level from 70% to 90% in two years, without increasing inventory levels.

The Absolut Company

→ For five years, the number of SKU’s rose by 19% as the number of core flavors increased from 11 to 18 and the number of limited editions went from 2 to 12, an increase by 600%. In contrast to the highly automated production process, the production planning process was a manual task. Forecasting and production planning was performed by only one planner, with the help of spreadsheets. But as complexity rose, a change was needed, and The Absolut Company started to look at alternatives. Peter Neiderud, Director Supply Chain and QE, understood that this was a major project, necessary in order to move to a new era of Absolut Vodka while honoring traditions and continuing the manufacturing in Åhus, Sweden.

Thule

→ Prior to using SO99+, Thule’s forecasting process was first based on Excel and later on decentralized demand planning. To obtain better forecasts, the process was centralized but this brought too much manual work and a lack of connection between service and inventory.

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