Polymers International, a global player in the plastics and rubber industry, was facing significant challenges in tracking the movement of its approximately 200 monthly shipments. The lack of efficient tracking mechanisms led to difficulties in keeping customers informed about shipment statuses, resulting in confusion and subpar customer service. The operations team had to visit multiple carrier websites to gather updates on shipment locations, a process that was not only time-consuming and prone to errors but also diverted the team from focusing on more critical tasks. The inaccuracies in tracking updates led to a surge in customer queries regarding shipment statuses. The team also struggled with accessing timely information on changes in departure and arrival timings, making it challenging to provide customers with accurate delivery dates. Furthermore, the company was unable to leverage crucial insights such as visibility on the carriers offering the best service, trade routes ensuring cost-effectiveness and speedy deliveries, thereby compromising their ability to identify cost-saving opportunities and make informed decisions.

BBQGuys, a leading provider of grilling and outdoor living products, had developed an internal drop ship program to connect vendors with customers as eCommerce became increasingly prevalent. However, this in-house effort led to a need for repetitive, manual documentation and vendor onboarding processes. The lack of communication, compliance insights, and readily available documentation resulted in high maintenance costs for their drop ship program. The executive team, along with representatives from seven internal departments, identified the need for an automated drop ship program that would reduce manhours, offer flexible integrations with vendors, and increase documentation visibility.

Boscov’s, the largest family-owned department store chain in the Northeastern United States, was seeking to expand its online presence beyond its 50 physical department stores. After several years of working with a previous drop ship provider, the executive team decided to find a more scalable solution that would provide a comprehensive eCommerce solution and a platform that would allow them to expand. However, they found themselves stuck in a lengthy and confusing contract negotiation with another organization, losing valuable time. With a strict deadline in place, Boscov’s needed a brand-new drop ship program in under eight weeks.

Nanit, a company that revolutionized the parenting world with its innovative products, faced a significant challenge as its growth began to skyrocket. The company's existing eCommerce solution provider was unable to keep up with the rapidly evolving operations. As a result, Nanit initiated a massive internal changeover to NetSuite. However, as the integrations increased and processes became more complex, Nanit realized the need for a more efficient solution. The company decided to switch to Logicbroker while the NetSuite integration was still underway. The challenge was to develop a 'hybrid' eCommerce solution that would primarily operate outside any one system while onboarding was still taking place. The goal was to ensure a simple, one-connection-point system that could scale with Nanit's continuous growth.

Orangetheory Fitness, a boutique fitness studio franchise, identified a significant challenge within their business model. The franchisees were spending a considerable amount of time purchasing miscellaneous goods such as cleaning supplies and water bottles, which detracted from their ability to build relationships with members. To address this, co-founder and CEO Dave Long proposed the creation of a B2B Marketplace that would allow franchisees to directly purchase approved equipment and supplies. This initiative evolved into a co-branding opportunity with suppliers like PATH, an eco-friendly bottled water company. However, the challenge was to find the right technology partners to support this new B2B marketplace, Supplies Central, and to connect suppliers to it.

Shipwire, a full-service third-party logistics provider (3PL), was seeking a solution to help their clients comply with retailers’ required EDI and drop ship specifications. They were looking to expand their service offerings and provide their clients with full-service eCommerce enablement. The challenge was to find a way to connect their clients to retail and marketplace channels quickly and efficiently without compromising the quality of service. They were also looking for a solution that offered flexible communication protocols to ensure their clients could comply with retail channel mandates and maximize efficiency. The challenge also included finding a way for their clients to connect to limitless channels without the need for IT department involvement or written code.

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 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 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 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 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 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 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.

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.

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 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 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 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.

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.