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.