o9 Solutions, Inc.

The global technology company, a designer and manufacturer of semiconductors and software, was grappling with a highly manual and disconnected approach to managing their forecasts. The company relied heavily on spreadsheets, the Adexa system, and emails, which made the process inefficient and prone to errors. The supply side, which included inventory planning, order planning, and scheduling, was also entirely manual, necessitating extensive coordination due to the outsourced fabless model. The company's demand planning processes were not agile enough and lacked high accuracy levels. Furthermore, wafer inventory was challenging to manage and often too high due to the large dependency on manual planning. The order commit accuracy was poor, as the overall planning did not consider constraints and business rules.

The company, a global leader in engineered joining technologies, was facing significant challenges in its supply chain management. With over 10,000 customers in approximately 100 countries, the company was grappling with frequent capacity constraints and core planning problems. The lack of forecast visibility was a major issue, with the forecast inaccuracy being high and lagging a few months behind as a standard. This led to a strong need for demand signal improvement to ensure correct capacity adjustments. Additionally, the company was experiencing material and capacity constraints due to a lack of visibility into the capacity load for the 3-5 month horizon. The company was also unable to conduct real-time scenario planning and could not evaluate the financial impact of scenarios. Furthermore, they lacked the ability to evaluate the supply chain supportability of the different scenarios.

The multinational industrial manufacturer, with operations in over 170 countries, was grappling with significant supply chain shifts. The company's product portfolio was becoming increasingly complex, and its customer base was rapidly expanding. The planning processes for mold capacity, blade manufacturing, transportation, and installation at customer sites were disjointed, leading to cost and inventory issues. The Wind Turbine division, in particular, lacked visibility of constraints and costs from mold capacity planning to installation at customer sites. The planning teams were unable to collaborate across multiple functions due to fragmented business processes and supporting systems. The legacy processes and tools resulted in time-consuming planning and reporting efforts by planners, based on snapshots of data, leaving little time for intelligent planning and decision-making.

One of the world's largest beer companies, with over 400 different beer brands, faced significant challenges in its end-to-end planning process. The company was using SAP/APO, which was not providing the desired level of accuracy in forecasting. The use of lagging indicators in the forecasting process resulted in low forecast accuracy. Additionally, the company was unable to run fast and intelligent demand and supply scenarios, leading to suboptimal decision-making. All key scenarios were developed in spreadsheets, which was inefficient and error-prone. Furthermore, key planning processes such as demand planning, supply planning, S&OP, and S&OE were all executed in silos, without the ability to connect the dots across different time horizons. This lack of integration and visibility was a major obstacle in the company's planning process.

One of the world's largest publicly traded international oil and gas companies was grappling with highly manual and Excel-driven planning processes. The company was facing significant data challenges and a lack of inventory visibility. The data structures were complex and unorganized, making it difficult for the company to maintain an overview of all data and processes. This led to the absence of a single source of truth. Furthermore, the company's forecast accuracy was low, and it relied heavily on manual, lagging indicators in the forecasting process. This resulted in excessively high inventory levels. The company's focus was more on execution rather than on planning, leading to a lot of day-to-day volume management and firefighting.

One of the world's largest tire and rubber companies, delivering a wide range of tires to customers globally, was facing significant challenges in its operations. The company was struggling with inaccurate forecasting, which was predominantly based on lagging indicators. This lack of precision in forecasting led to a lack of visibility into supply risk and capacity prioritization. Additionally, the company was unable to effectively use drivers of demand to predict future trends. This resulted in frequent excesses and shortages in inventory, leading to potential inventory liabilities and the need for additional price promotions to clear inventory. The company's current Sales and Operations Planning (S&OP) process was inefficient and lacked visibility into supply risk and capacity prioritization based on financials.

The case study revolves around a major global tire producer that was grappling with highly manual, Excel-driven planning processes across various functions and time horizons. This outdated approach resulted in suboptimal decision-making and inaccurate plans. The company's strategic planning for the next 5-7 years was conducted without leveraging key market trends and macroeconomic developments. Furthermore, the company was unable to execute long-range rough-cut capacity plans due to the majority of constraint information existing as spreadsheets or tribal knowledge. The core planning cycles, including strategic, tactical, and operational, were not interconnected, leading to silos and suboptimal decision-making.

One of the world's largest family-owned agricultural companies, with 350 branches, was grappling with inventory issues due to inaccurate forecasting. This resulted in high costs and an aging inventory. The company had to place product orders, such as crop seeds, a year in advance for each season. However, the forecasting was poorly done due to reliance on lagging indicators to predict demand, leading to a large write-off of seasonal inventory. Additionally, the company was unable to control total inventory costs due to poor planning and sub-par scenario modeling. The company also lacked planning knowledge, with key decision-making processes being disconnected due to multiple sources of data, disconnected analyses, and an immature planning system.

The global biosimilar manufacturer was facing a significant challenge in managing its supply chain data. The company primarily relied on its ERP system for Contract Manufacturing Organization (CMO) financial information, but lacked a comprehensive system for generating and maintaining Supply Chain Management (SCM)-related data such as Bill of Materials (BOM) and Bill of Distribution (BOD) information, planning item, inventory visibility, and fixed plans. As the business continued to grow and launch new products, the need for a system to better control product flows and supply chain plans became increasingly apparent. Additionally, the company did not have a supply chain master planning solution, with all SCM-related data being maintained by planners and CMO execution managers in isolated Excel sheets, leading to a lack of alignment. Furthermore, the manual creation of supply plans by planners meant that item level details were often overlooked, and manufacturing lead times, lot size, and yield were managed at the product group level rather than the SKU level, resulting in a lack of precision and hierarchy.

The manufacturer of heating, ventilating, and air conditioning (HVAC) systems and building management systems and controls was facing significant challenges in its supply chain management. The company was struggling with large delays in replanning for any delay in the supply of critical components, which was causing disruptions in production and customer order fulfillment. Additionally, the company had limited visibility into the supply chain disruptions of its Tier 1 and Tier 2 suppliers, which further complicated the situation. The company was also unable to effectively analyze alternative scenarios to mitigate supply disruptions, which was a significant challenge in the face of events like the COVID-19 pandemic that caused widespread supply chain disruptions.

The customer, a large manufacturer of cigars and traditional pipe tobacco, had grown extensively over the years. This growth led to a scattered IT landscape with fifteen different ERPs and a complex business structure involving different channels such as retail, wholesale, and e-commerce. The company faced challenges with its omnichannel complexity, where each go-to-market channel had its own supply chain configuration and complexities. This led to forecast accuracy issues as the company applied a one-size-fits-all stat forecasting model. Additionally, the company lacked end-to-end visibility across all nodes on their supply chain, leading to an unconstrained supply chain operation. The company also struggled with scenario planning, running its operations based on an inaccurate forecast and an unconstrained supply plan without the ability to run business scenarios and translate that into financial consequences.

The company, one of the largest food and beverage companies in the world, was facing significant challenges with its integrated business planning processes. These processes were highly manual and focused on past data, which hindered the company's ability to quickly identify demand risks and opportunities. This resulted in a lack of responsiveness to market changes and an inability to provide optimal solutions. The company was also unable to detect gaps in planning and other risks and opportunities quickly enough due to their focus on sell-in, a lagging indicator. Furthermore, all commercial and supply chain scenarios were run in Excel, based on inaccurate datasets and incomplete information, leading to suboptimal decision-making. Lastly, IBP meetings were run in PowerPoint and Excel, focused on numbers and assumptions rather than on key market decisions.

The company, one of the world's largest steel wire manufacturers with operations in over 20 countries, was facing significant challenges in customer service, capacity and material planning. Their On Time In Full (OTIF) performance was considerably low compared to their competitors, indicating a lack of efficiency in their operations. The company's capacity and material planning processes were entirely manual and lacked accuracy, leading to an excess of inventory, shortages, and plant underutilization. Furthermore, their Raw Material Planning was inaccurate as detailed BOM compositions, lead times, and alternative sources were not considered in the supply model and were done in Excel. The company also lacked effective capacity planning, leading to inaccurate sales allocations. Lastly, their order promising was inaccurate due to a lack of tools for detailed order planning.

The customer, a pioneer in water and housing products, was facing significant challenges in demand planning due to low forecast accuracy and heavy reliance on Excel spreadsheets. The company's demand planners were spending a lot of time manually copying data from sheets and manipulating it to generate demand scenarios. This manual process was not only time-consuming but also limited the company's ability to react quickly to changes in demand. Furthermore, the company's forecasting process was flawed as it predominantly used only lagging indicators, resulting in low forecast accuracy. The planning processes also varied widely across countries, with no single process or overview, leading to inconsistencies and inefficiencies.

The case study revolves around a leading provider of cargo and load handling solutions aiming to become a leader in sustainable and intelligent cargo handling. The company embarked on a global initiative to implement digital transformation throughout their end-to-end supply chain to drive efficiency, focusing on speed, automation, real-time data, and transparency. However, they faced significant challenges in their business scope. Firstly, they lacked proper forecasting capabilities, relying heavily on their order book for decision-making. Secondly, their configure-to-order business model resulted in a sales cycle varying between 3 to 6 months, with the order-to-delivery time between 2 to 4 months. The company aimed to reduce this lead time to increase customer satisfaction. Lastly, the absence of planning tools led to issues with the finance team, who could not comprehensively view the order lifecycle and the associated revenues.

The customer, a multibillion-dollar global manufacturer of skin care, makeup, fragrance, and hair care products, was facing significant challenges in its supply and demand planning activities. These activities were heavily reliant on Excel and manual processes, which depended largely on the personal knowledge of each planner. The company lacked master production planning capabilities focusing on operational and strategic horizons. The process of pulling a consolidated demand picture was difficult and time-consuming due to the frequent need for manual interventions to estimate the impact of promotions, marketing, and new product introductions. The supply planning/scheduling was primarily a sequential planning process where each stage of the manufacturing network was planned one after the other, and material constraints were not integrated. Furthermore, there was a lack of inclusion of promotions and product launches in the demand forecasting process.

The case study revolves around a direct-to-consumer e-commerce retailer that operates numerous micro-fulfillment centers across the US. The retailer had been managing its business using internally developed shared spreadsheets. However, as the company expanded, it became clear that this approach was creating silos and limiting cross-functional collaboration. The retailer recognized that to continue its growth trajectory, it needed a platform that could scale its supply chain planning capabilities. The company faced challenges with immature planning processes, order fulfillment, and long-range business planning. The use of shared spreadsheets across the business led to a lack of collaboration and business silos. The company needed the right assortment of products at the right time and location for their micro-fulfillment centers to meet local consumer demands. Furthermore, the company was running their business planning processes at monthly intervals, which was not sufficient for their growing needs.

The customer, a global leader in the design, marketing, and distribution of premium lifestyle products, was facing several challenges in their supply chain and planning capabilities. They wanted to respond faster to market opportunities and supply disruptions, requiring a scalable capability to postpone decisions on quantity, model, destination, price, and flow. This would reduce inventory risk by providing transparency and flexibility. The company was also struggling with rapid identification of actions between product lead times and market closure, and there was a lack of alignment on planning and buying strategies. Additionally, the volatile nature of the fashion industry presented optimization challenges regarding the timing and quantity of raw material purchases, dye lots, cutting, etc. to ensure customer demand is met. Lastly, the company found it difficult to communicate effectively and work in a low-touch digital environment that focuses on exception management, due to multiple brands, locations, distribution channels, and suppliers.

A world-renowned retailer of musical instruments and equipment, with nearly 300 stores across the U.S. and a top-ranking direct sales website, was struggling with its merchandise planning system. The system was unable to keep up with the brand and channel needs, leading to disconnected planning processes and suboptimal decision-making. The retailer was grappling with managing sales for a mix of new and existing products. The existing processes were focused solely on the retail brick and mortar channel and were built in Excel spreadsheets, which were cumbersome and prone to human error. There was no holistic view of 'Open to Buy' across the enterprise. Furthermore, the company was missing alignment between pre-season planning and in-season forecasts as the processes were disjointed, non-standardized and managed in silos. The company also wanted to plan the growth and penetration of private label business and strengthen partnerships with top vendors.

The customer, an online retailer of office equipment, was facing challenges with their inventory management and delivery services. They were importing and delivering products directly to local distribution centers (DCs), which put each DC at risk of having excess inventory or running out of stock. The company was trying to mitigate these risks by building a replenishment center and delivering products from this center to each DC. However, the accuracy of their received sales plan was low and relied heavily on lagging indicators. Additionally, truck loading planning was done manually, a time-consuming process that often resulted in miscalculations of the required number of trucks. Lastly, the management of the minimum order quantity and complicated ordering conditions, considering the container’s capacity, were managed in Excel, which was not efficient.

The multinational athletic apparel and footwear brand, with a global presence, was facing challenges due to its quickly changing marketplace, long lead times, and fragmented manual tools. These factors were hampering forecast accuracy and fill rates. The company was struggling with a manual, time-intensive demand forecasting process that was unable to keep pace with market trends or shape demand effectively. Additionally, there was a lack of effective and efficient planning of aggregated raw material purchases, resulting in unnecessarily long lead times and less agility to react to changing demand. The process of matching supply and demand was complex and time-consuming, and it didn’t maximize the ability to respond to market volatility or quickly rebalance inventory based on demand location.

The Indian multinational paint company, engaged in manufacturing, selling, and distributing paints, coatings, and home decor products, faced significant challenges in its procurement process. The company had to manually adjust purchase requisitions daily to keep up with the fluctuating demand and supply. This manual intervention led to errors in purchase order (PO) placement concerning quantity and timing, which negatively impacted revenue and inventory levels. Additionally, the company had to synchronize tanker scheduling with the inventory levels at plants, aligning raw material with demand. There were also instances where POs would unexpectedly be cancelled or sought to be amended by suppliers, leading to potential revenue loss or delay.

A leading Canadian retailer, operating across automotive, hardware, sports, and leisure sectors, was grappling with the challenge of accurately predicting consumer demand and efficiently distributing inventory across its network. The retailer's demand forecasting was hampered by the lack of ability to incorporate various external demand drivers such as weather, demographics, pricing, promotions, product assortment, and location. This was particularly problematic for fashion and seasonal merchandise. Additionally, the allocation process was highly manual and relied on backward-looking information, without considering tailored allocations to stores. The stores were also running over capacity without leveraging intelligence to assist in prioritizing the distribution of new and profitable styles.

The customer, a global leader in optical retailing, was facing challenges with their highly manual and Excel-driven Assortment Planning process. Each country followed its own method of working, leading to sub-optimal assortment and missed sales opportunities. The local country teams had little to no authority from the global team, resulting in a lack of alignment in terms of assortment selection. The company also experienced difficulties with planning at both store level assortment capacity and box constraints. The processes for managing the pre-season and in-season demand, as well as managing the correct ‘active’ assortment in the old systems were highly manual and often lacked accuracy and precision. This led to significant challenges related to store replenishments.

The company, a retail and wholesale business offering a variety of merchandise and services, was grappling with outdated and disconnected systems and processes. This led to manual and redundant work, which hindered their ability to serve customers optimally as an omnichannel retailer with localized apparel products. The company had multiple solutions to support the apparel space allocation and assortment planning process. However, these were mostly spreadsheet-based point solutions, lacking simplification, connectivity, and intuitiveness. The company had tried to implement a solution six times with various vendors, but all attempts were unsuccessful. Furthermore, assortments based on consumer demographics and regional variances were not being planned, leading to a misalignment with space allocation and product offerings. The company was also not leveraging any enterprise analytic insights to optimize the space allocation and assortment development processes, relying instead on manual calculations that varied by department and user.

The beauty retailer, marketing 15 brands and 2000 SKUs across various distribution channels, faced a significant shift from physical retail channels to online due to the COVID-19 pandemic. This shift necessitated a future-proof planning tool to support their ambitious growth plans and to gain a deeper understanding of demand drivers. The company was heavily reliant on Excel, which led to latency and siloed processes. They lacked a comprehensive understanding of the main drivers of demand for their five channels. The company was also unable to plan at the desired level of granularity, leading to shortages and delivery delays. Demand planners were spending most of their time crunching Excel spreadsheets, unable to focus on higher-level strategic tasks. Manual interventions were frequently needed, especially for estimating the effect of New Product Introductions.

The case study revolves around a leading American corporation that designs, manufactures, and sells machinery, engines, financial products, and insurance globally. The company was grappling with the challenge of streamlining its end-to-end planning process on a single platform. This included demand signal management, global supply planning, and inventory planning. The company aimed to match demand and supply across the globe, support scheduled order demand, and improve planner productivity. However, planning across a global network was a significant challenge due to the interconnected nature of the company's operations, which included company-owned manufacturing plants, subsidiaries, and third-party manufacturers. Additionally, the company faced difficulties in matching supply and demand due to long lead times and a complex global supply chain. This made the process resource-intensive and required manual effort.

One of the largest global telecommunications companies, active in the Czech Republic, the Netherlands, Poland, and the United States, faced significant challenges in demand and inventory planning for cell towers linked to the 5G rollout, coverage strategy, and the recent merger with Sprint. The company had a multitude of different planning systems for demand and inventory planning. However, due to the merger, demand exploded, and the company was unable to forecast this demand accurately. Demand Planning was complex as the company installs approximately 1,000 new cell towers, each consisting of about 32,000 components. Additionally, the company experienced planning challenges in upgrading their network to 5G cell towers. The transition from 3G and 4G towers to 5G towers required sophisticated phase-in and phase-out planning. The company also operated in silos and lacked visibility on inventory, supplier capacities, install base of cell towers, new demand for 5G towers, etc.

One of India's largest manufacturing and retail branded-fashion apparel companies was grappling with the challenge of end-of-season excess and unsold inventory. This issue was primarily due to the lack of comprehensive visibility into demand, supply, and inventory at multiple levels. The company's pre-season and in-season demand/supply planning was done manually, which not only consumed a significant amount of time but also offered low visibility of factory capacity constraints. Furthermore, the company frequently had to manage inaccurate fabric requirements, leading to either excesses or shortages of material. These challenges were impacting the company's profitability and sustainability, as unnecessary sourcing, expedites, and inventory reduction were becoming increasingly common.

One of India's largest paint manufacturers, with a presence in multiple countries and serving both B2C and B2B business segments, was facing significant challenges in managing its demand and supply planning processes. The company was growing rapidly, and its existing processes, heavily reliant on manual activities and Excel spreadsheets, were unable to support this growth. The company primarily relied on the Annual Operating Plan (AOP) to determine future demand, which meant they were unable to keep up with the latest market trends. There was limited collaboration between sales, marketing, and supply chain teams, leading to inaccuracies in a heavily regional, promo-driven market. The stocking of depots was controlled by basic automation and overridden by sales team-based manual replenishment requests, leading to slow-moving inventory and stockouts. With a limited planning horizon (one month) and a weekly production plan, the procurement teams struggled to estimate the inventory requirements for raw materials, leading to stockouts or excess inventory with teams operating in silos.

One of Canada's largest retailers, with a network of over 400 stores, was facing significant challenges in its supply chain operations. The company was struggling with network flow volatility, which was causing bottlenecks and creating issues with labor and transportation planning. The goal was to improve on-shelf availability by smoothing demand and aligning labor and transportation capacity. The company was also challenged with moving goods efficiently while reacting to merchant requests. They needed to evaluate options such as adjusting demands, adding another shift at the distribution center (DC), accessing the temporary labor pool, or accessing flexible transportation capacity. Furthermore, the company needed to plan daily for the next day, taking into account near-term capacity problems. There were challenges in aligning capacity with demand and blocking flows of excess demand based on revised capacities and merchant priorities.

The American agricultural cooperative, consisting of over 700 growers of cranberries and grapefruit, was facing significant challenges in its commercial and financial planning processes. The existing systems were highly siloed, leading to disconnected top-down and bottom-up plans. The process of closing gaps was manual and time-consuming, resulting in prolonged planning cycles. The financial planning process was highly manual, prone to errors, labor-intensive, and lacked visibility into the underlying assumptions. There was also a lack of granularity in incorporating future factors impacting the financial forecast into the planning, particularly the lack of multi-currency scenario planning. Furthermore, the lack of visibility and collaboration between business functions led to planning in silos and significant delays in completing the annual operating plan.

The American clothing and home decor retailer, specializing in casual clothing, luggage, and home furnishings, was grappling with highly manual and Excel-driven planning processes across functions and time horizons. This led to suboptimal decisions, inventory, and service level challenges. The key planning processes, including demand planning and replenishment planning, were executed in silos, without the ability to connect the dots. The company lacked a statistical demand forecast, and planners created forecasts based only on sell-out at an item level. They spent a significant amount of time disaggregating the forecast to a size level, leaving little time for actual analysis. Furthermore, the company faced challenges in accurately performing replenishment planning due to a high level of required manual interventions and processes not supported by analytics.

One of the world's largest book publishers, with over 24,000 employees operating in 70 countries, faced significant challenges in consolidating multiple, disparate data sources onto a single platform. The company aimed to automate dynamic and agile data analytics to increase efficiency, effectiveness, and transparency in the Sales and Operations Planning (S&OP) process. However, they had limited ability to quickly understand key trends in their product categories and update demand planning. The long lead times and complex supply chain made supply and demand matching a resource-intensive, time-consuming effort. This led to the inability to review detailed pegging information that connected original demand to final supply. Additionally, the company spent significant manual effort and time preparing for S&OP reviews across product, demand, and supply, having many different source systems.

The customer, a global leader in innovative casual footwear, faced significant challenges in connecting top-down and bottom-up planning, managing inventory while maintaining high margins, and delivering personalized products quickly across various distribution channels. The company struggled with holistic planning, finding it difficult to link top-down planning to sales and inventory planning within a monthly cycle. Additionally, the company faced issues with forecast accuracy due to high demand volatility, short product life cycles, and a variety of global marketplaces. The long lead times and complex supply chain made supply/demand matching a resource-intensive, manual effort.

The company, a major player in the upholstered furniture industry with a sales volume of $1.7bn, primarily in North America, was grappling with the challenge of integrating its S&OP activities. These activities included forecasting, capacity planning, inventory and replenishment planning, and procurement functions. The company was using multiple tools, primarily Excel, for creating their forecast, leading to inaccurate demand forecasts and a lack of demand visibility and collaboration. Furthermore, the company's style level forecasting was hampered by complex processes and lack of data visibility due to the use of legacy systems. This limited them to top-down forecasting. Another challenge was the inclusion of the Distribution Centers' (DCs) capacity to ensure optimized inventory levels for Build-to-Stock.

The customer, a global leader in the design, marketing, and distribution of premium lifestyle products, was facing several challenges. They wanted to respond faster to market opportunities and supply disruptions by transforming their end-to-end planning capabilities. They required a scalable solution that would allow them to postpone decisions on quantity, model, destination, price, and flow, thereby reducing inventory risk by providing transparency and flexibility. The company was also struggling with rapid identification of actions between product lead times and market closure, and there was a lack of alignment on planning and buying strategies. Furthermore, due to the volatile nature of the fashion industry, they were constantly facing optimization challenges regarding the timing and quantity of raw material purchases, dye lots, cutting, etc. to ensure customer demand is met. Lastly, with multiple brands, locations, distribution channels, and suppliers, effective communication and efficient work in a low-touch digital environment focusing on exception management was difficult.

The customer, a leading global supplier to the automotive and industrial sectors, was facing significant challenges in managing the quality and accuracy of OEM forecasts. The company was unable to leverage external market drivers to predict demand and was heavily reliant on Excel. This resulted in high variability in the quality and accuracy of forecasts. Additionally, there was a lack of demand alignment across OEM forecasts, which were stored in multiple Excel sheets. The company also had access to external market data from providers such as IHS, but was unsuccessful in leveraging this data to improve demand planning. Furthermore, capacity checks were done manually in Excel, which resulted in the lack of a layer of intelligence on top of the execution system (SAP APO).

The marine electronics company, a specialist in providing navigation, marine instruments, and fish finding equipment to both the recreational and commercial marine sectors, was facing significant challenges in its supply chain management. The company was unable to integrate commercial, supply, and demand planning due to many siloed processes, leading to lost sales and excess inventory. The lack of end-to-end (E2E) visibility and the inability to respond to changing market dynamics further exacerbated the situation. The company was unable to create true E2E visibility across the supply chain due to a variety of disconnected planning systems operating in a siloed environment. Additionally, the company frequently experienced both inventory excess and shortages, with only 10% of the SKUs having healthy inventory across the network. The forecast accuracy for demand, especially in connection to new product introduction, was rather low.

The case study revolves around a leading producer of rolled aluminium and a global leader in beverage can recycling, which also serves customers in automotive, consumer electronics, construction, foil and packaging. The company has a complex, multi-stage production process that includes both internal and external operations. The challenge was to align these operations to maximize performance and streamline production. The company was also looking to reduce its carbon emissions. The planning processes were previously carried out via Excel, which was not efficient enough. The flow of information between the company and its operational partners was also crucial for driving performance improvements.

The multi-brand beauty retailer, operating over 600 stores across the Americas, was facing significant challenges in its planning process. The company was unable to collaboratively plan between central and local teams and conduct real-time scenario planning. The existing Merchandise Financial Planning (MFP) ecosystem was a combination of a legacy planning tool, data exports, and disparate Excel spreadsheets, leading to inefficiency throughout the planning process. The company's margin planning, a critical link to their global financial performance, was also problematic as they could not plan and review margin components and impacts. Furthermore, the marketing, sales, finance, and supply chain functions were operating in relative silos, each having their own assumptions and versions of the truth.

The company, a global leader in direct selling of beauty, household, and personal care products, was grappling with significant challenges in its supply chain management. The primary issue was the inability to sense demand and supply disruptions in a timely manner, which hindered their response to these disruptions. This was further complicated by a lack of coordination between the commercial, financial, and supply chain functions, leading to disjointed operations and decision-making. The company also lacked the capability to conduct real-time scenario planning, which prevented them from evaluating the financial impact of various scenarios and assessing the supply chain supportability of different scenarios. The marketing, sales, finance, and supply chain functions were operating in silos, each with their own assumptions and versions of the truth.

The multinational company, specializing in sensor manufacturing for fabrication and process automation, was facing significant challenges in its supply chain management. The company lacked end-to-end (E2E) visibility across its supply chain due to a multitude of disconnected planning systems operating in silos. This lack of visibility led to suboptimal decision-making, often based on opinions rather than data-driven facts. Additionally, the planning teams were spending a significant amount of time on manual number crunching activities such as data validation, collection, and manipulation. The company was also unable to conduct real-time scenario planning and evaluate the financial impact of different scenarios. The lack of ability to assess the supply chain supportability of various scenarios further compounded the problem.

A multinational coffee roaster and retailer, with a network of over 30,000 coffee houses worldwide, was facing significant challenges in its operations. The company's baristas were spending around six hours a day on administrative tasks such as ordering, inventory management, and forecasting, which was detracting from their ability to focus on customer service. Additionally, the company was grappling with a significant food waste problem due to inaccurate forecasting. This issue was complex, as each store stocked between 500 and 5,000 SKUs, and demand volatility was influenced by factors such as weather, assortment, pricing, and local events. The company had invested in data science teams and developed proprietary algorithms to predict the impact of weather on demand and store traffic, but these were not being utilized to their full potential.

The customer, an American multinational renewable energy company with operations in over 170 countries, was grappling with significant supply chain shifts. The company was dealing with an increasing number of complex configurations in its product portfolio and a rapidly expanding customer base. The planning processes for mold capacity planning, blade manufacturing, blade transportation, and blade installation at customer sites were disconnected, leading to cost and inventory issues. The company also lacked visibility of constraints and costs from mold capacity planning to installation at customer sites. Furthermore, due to fragmented business processes and supporting systems, the planning teams were unable to collaborate across multiple functions. The legacy processes and tools resulted in time-consuming planning and reporting efforts by planners, based on snapshots of data. The planning workforce spent the majority of their time number crunching rather than intelligent planning and decision-making.

The American multinational technology company, specializing in Internet-related services and products, was facing significant challenges in managing its global data centers. The company lacked an end-to-end material requirements planning system for capacity build-out, leading to issues with on-time delivery and inventory misalignment. The company's planning process for servers and networking equipment was highly complex and unworkable, causing disruptions in their data center delivery. Additionally, the company was unable to plan for the correct technology/supplier allocation mix, leading to artificial shortages. The company's manual processes were not scalable and were impacting predictability, cost coverages, and the ability to support the exponential growth of their business.