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.