WHY DEMAND MANAGEMENT MATTERS MORE THAN EVER

Demand management concepts are now 20 to 25 years old. The first use of the term demand management surfaced in the commercial sector in the late 1980s or early 1990s. Previously, the focus was on a more siloed approach to demand forecasting and planning that was manual, using very simple statistical techniques like moving averaging and simple exponential smoothing, and then, Excel, and a whole lot of gut-feeling judgment. Sound familiar? In the mid-1990s, demand planning and supply planning were lumped together, which gave birth to supply chain management concepts of demand planning and integrated supply chain planning.

Most supply chain professionals are quickly realizing that their supply chain planning solutions have not driven down costs, and have not reduced inventories or speed to market. Companies globally across all industry verticals have actually moved backward over the course of the last 10 years when it comes to growth, operating margin, and inventory turns. In some cases, they have improved days payable, but this has pushed costs and working capital responsibility backward in the supply chain, moving the costs to the suppliers. To make matters worse, Excel is still the most widely used demand forecasting and planning technology in the face of significant improvements in data collection, storage, processing, analytics, and scalability.

According to a 2014 Industry Week report (see Figure 1.2), moving averaging has now become the preferred statistical model of choice for forecasting demand, digressing from Holt-Winters Three Parameter Exponential Smoothing based on studies conducted by the Institute of Business Forecasting in the late 1990s. Furthermore, with all the advancements in analytics and technology, there has been minimal investment in the analytic skills of demand planners. To make matters worse, downstream data—with all the improvements with data collection, minimal latency in delivery, and increased coverage across channels—is still being used in pockets across sales and marketing, rather than the entire supply chain, for demand forecasting and planning.

Companies are quickly learning that in order to move forward, they need to admit their bad practices of the past. They must be willing to risk failure in order to move forward. Leaders must confront a number of mistakes made in the design of their demand management processes over the course of the last decade. The mistakes are many, but all can be corrected with changes to the process, use of downstream data, and most all, the inclusion of analytics. Here are a number of good intentions with poor execution that have caused companies to make key mistakes in demand management.

CURRENT CHALLENGES AND OPPORTUNITIES

Globalization and changes in customer expectations continue to increase market volatility, adding complexity that makes it difficult to balance supply and demand across multiple product lines, business units, and geographic regions on a daily and weekly basis. The sheer size of the corporate footprint due to the diversity of many multinational corporations, compounded by the multiple channels they sell through, are driving much of this complexity. At the same time, market pressures are increasing the range of product offerings. This is evident by the growing number of SKUs that most manufacturers have to manage. Add shorter product life cycles, longer lead times, declining customer loyalty, and rising expectations for immediate product availability, and it quickly becomes clear that any improvements in demand visibility and responsiveness would pay huge dividends.

According to recent industry surveys conducted by several analyst firms, there are five key trends occurring that impact the supply chain, and particularly demand management:

1. Continued demand volatility and expanding product portfolios challenge supply chain leaders across all industries to elevate demand management performance.
2. Persistent cost pressures require supply chain leaders to better align supply with demand for improved performance.
3. New product launch importance drives supply chain leaders to seek stronger alignment between new product development and supply chain planning and execution capabilities for increased launch success.
4. End-to-end partner communication and collaborative execution by all partners in the supply chain from retailers through raw material providers must constantly collaborate on what events are occurring, the data behind those events, and how they can execute as a unified group to respond to the challenges as they unfold. Trading partners are now acting in a concerted manner based on transparent information to resolve issues as they happen. Solving a problem by pushing costs to another supply chain partner is an antiquated proposition as companies realize that cost shifting is not a sustainable, nor a competitive solution.
5. Big data is becoming mandatory. Big data has been the big IT story in recent years. Combining the data of multiple supply chain partners, turning that data into information, and being able to react and execute accordingly requires a lot of information. Big data solutions combined with complex event processing (CEP) solutions are being used more than ever to digest the enormous magnitude of available data and turn it into executable actions. Leveraging these tools with supply chain visibility solutions will quickly become a “must have” rather than a “nice to have” as companies utilizing these tools set the bar for the new normal in supply chain performance.

Companies continue to face an uncertain economy, with volatile demand, expanding product portfolios, and increasing cost pressures. At the same time, they need to aggressively focus on growth and revenue acceleration, increased customer responsiveness, and reduced inventory for improved cash flow. Together, these challenges cause leading organizations to invest in demand-driven transformation to improve demand orchestration, and supply and product capability. Supply chain technology is helping to transform the way companies do business with consumers/customers and each other. If there is one thing these five trends have in common, it is that having constant feedback and control over supply chain functions is key to doing business in today's ever-changing environment. For this reason, these trends are likely to continue for the next several years and beyond.

The primary obstacles responsible for impeding companies from achieving their supply chain goals has not changed much over the past decade. If anything, they have intensified, making it more challenging for those companies that continue to allow their corporate culture (bad habits) to guide their judgment. Most executives continue to focus on three core business priorities: (1) leveraging the supply chain organization to drive business growth, (2) driving business process improvements, and (3) improving customer service. These three primary goals and objectives have not changed very much over the last decade.

It is not surprising that these supply centric strategies still focus primarily on cost reduction to support corporate supply chain initiatives. When asked, “What are the top three obstacles to achieving your organization's supply chain goals and objectives?” the number-one response from supply chain executives is forecast accuracy and demand volatility, followed by the inability to synchronize the supply chain end-to-end, and lack of cross-functional collaboration (planning), consecutively.

Twenty years ago, it made sense to use inventory buffers up and down the business hierarchy when comparatively short supply chains existed, and forecasts based on the previous month adjusted for seasonality were sufficient. Now, with longer supply chains stretching around the world, along with ever-increasing demand variability, companies are realizing that they can no longer use inventory buffers to protect against demand variability. In fact, according to companies we have worked with around the world, demand volatility has not decreased. If anything, demand has gotten more volatile over the past one to two years globally, according to executives in the United States, Latin America, Europe, and China.

Subsequently, among the primary influences causing demand volatility are new product launches, the state of the economy, increased globalization, and SKU (Stock Keeping Units) proliferation on shelf at retailers. As companies have become more global and the size of their product portfolios has increased through acquisitions and industry consolidations there has been an enormous increase in SKU proliferation on shelf. In addition, due to ever-changing consumer demands, new product launches have increased significantly.

As data collection, storage, processing, and analytical power have steadily evolved, systems costs have declined. It is hard to determine if supply and inventory management adoption has kept pace with the growing market complexity. Or, if companies in fact created that complexity by enabling massive global supply chains, and endless product configurations and customer choices to appease the never-ending demands of consumers. Why didn't product life cycle management evolve to maintain continuity across the product portfolio to reduce the increasing complexity of their product offerings?

Across most companies, there is a similar ongoing tension between process, people, analytics, data requirements, and technology needs. The ability to collect, cleanse, and share data across the organization originally required a significant investment and justification within already strained IT budgets, often without an immediate ROI (return on investment). Such investments created the foundation for companies that were the early adopters of advanced data management capabilities. As data collection and analytic tools, applications, and solutions have become more affordable and powerful, they've become easier for companies to justify. For many companies, data management capabilities have advanced so quickly that the challenge now is how to report and make practical use of it all. Furthermore, data storage costs have declined significantly over the past decade. Sales transactional data is being collected at increasingly granular levels across markets, channels, key accounts, brands, and product configurations. Faster in-memory processing is making it possible to run what-if simulations in minutes that previously had to be left to run overnight.

Of course, the output and recommendations of any planning and decision-support system like demand management are only as good as the integrity of the underlying data. The road to achieving the benefits from improved forecast accuracy starts where most business process improvement projects start, with the data. It begins by addressing data quality, and then by breaking down the functional siloes and making the data readily accessible to everyone in the demand management process that feeds into the sales & operations planning (S&OP) and integrated business planning (IBP) processes. According to a 2014 report by Industry Week, both data quality and data availability have improved for companies reporting wider adoption of demand-driven forecasting and planning methods. That implies that better and more widely accessible data are a prerequisite, or that poor quality data and a lack of sharing are most likely the barriers to adoption.²

However, according to the same 2014 Industry Week report, companies have not progressed much in the way of using true demand data to drive their demand-driven forecasting and planning processes. In Figure 1.3, the majority of those companies that participated in the study are still using customer orders and/or customer shipments as their primary data to forecast and plan their demand response. What is even more disturbing is that with all the enhancements in data collection, cleansing, and technology improvements, POS and syndicated scanner data (e.g., Nielsen and Information Resources Inc.—IRI) are the least used data for demand forecasting and planning. According to a book written in 2003 by Oliver Wight, Demand Management Best Practices: Process, Principles and Collaboration, POS data is closest to true demand. So, after over two decades companies are still using customer orders and shipments to sense demand signals and shape future demand to create the most accurate demand response, when in fact they are really sensing supply replenishment signals and shaping the future supply plan, or supply response. Consequently, it has been proven that customer shipments are the most volatile data stream due to inventory replenishment policies and other related trade incentives, creating what is referred to as the bullwhip effect.

When it comes to preferred technology, it is no surprise that Microsoft Excel remains the most widely used forecasting tool in 2014 (see Figure 1.2), with over 77 percent of the participating companies, according to the 2014 Industry Week report. As limited as its capabilities are, spreadsheet applications are ubiquitous on every desktop and laptop computer, as well as most mobile devices. The challenge with spreadsheet analysis, given the SKU-proliferation and data deluge, is that it is simply not powerful or scalable enough to get the job done. Over the last decade, a wide variety of other, non–spreadsheet-based forecasting and planning tools, applications, and enterprise solutions have become available that can scale to the ever-increasing sources of information referred to as big data. Consequently, spreadsheets do not have the depth and breadth of analytical methods available for demand-driven forecasting and planning. The good news is that those companies that participated in the study said that they plan to adopt in the next 12 months a number of these more advanced analytical methods, ranging in complexity from simple moving averaging to time series, regression, and other related analytic methods. The really puzzling fact is that simple moving averaging was the number-one analytical method in this study, which indicates that companies have actually regressed, since only a decade ago almost every survey indicated that Holt-Winters Three Parameter Exponential Smoothing was the number-one mathematical method.

The goal for demand planners shouldn't be to use the latest or most complex tools for their own sake, but to identify the analytical method that best fits for a given product line by providing the necessary intelligence on a timely basis. Of course, when it comes to building consensus plans, analytical outputs are only the beginning. Domain knowledge is also necessary to incorporate the correct business inputs that influence demand outside the precursor that fuels the demand forecasting engines with the understanding that such inputs do not take the form of wishful thinking. No matter how sophisticated the underlying statistical algorithms, people often feel that they haven't participated in the forecast process unless they've tinkered with the numbers. Almost half (47 percent) of manufacturers report that five or fewer people are involved in forecast development, review, and approval (Figure 1.4). There tends to be a wider network of perceived responsibility for more people to participate in the demand forecasting and planning process at larger companies, especially those with $1 billion or more in annual revenues.³

In addition, translating unconstrained demand forecasts into the appropriate demand response requires not only sensing demand signals, but measuring those KPIs (Key Performance Indicators) that influence the demand signal, and shaping future demand to drive revenue and profit, not just to reduce costs to create the most efficient supply response. Just as collaboration with external value chain partners is necessary to optimize the demand response on the supply side, demand shaping practices using key business drivers, or influence factors such as price, in-store merchandising, sales promotions, and more, require internal collaboration to optimize and coordinate activity on the demand side. More than three out of four manufacturers indicate their demand planning and other planning activities are at least somewhat integrated with fulfillment, manufacturing, procurement, and supply chain functions.⁴

PRIMARY OBSTACLES TO ACHIEVING DEMAND MANAGEMENT PLANNING GOALS

With all the enhancements in demand management over the past decade companies are still faced with challenges impeding the advancement of demand-driven planning. Many organizations are struggling with how to analyze and make practical use of the mass of data being collected and stored. Others are perplexed as to how to synchronize and share external information with internal data across their technology architectures. Nevertheless, they are all looking for enterprise-wide solutions (technology) that provide actionable insights to make better decisions that improve corporate performance through improved intelligence.

Before we can address all the demand management challenges of people, process, analytics, and technology we need to address the many bad practices that must be overcome before companies can truly embrace the benefits of demand-driven planning. The reality is that the obstacles to impeding companies from achieving their goals translate into mistakes and perceptions that hinder the progress of demand management. Those mistakes are still being made today after two decades of data collection, storage, processing, analytics, and technology improvements. In many cases, as we mentioned earlier, the conceptual design is sound, but in practice those designs are flawed due to corporate culture and other related political bias.

In fact, the size of the company or its location doesn't matter. I have sat across the table from several supply chain management teams over the past 10 years at companies that span from less than $1 billion in annual sales to over $109 billion in annual sales. Talk about stirring emotions with the words demand forecasting, or demand-driven planning, not to mention being digital-driven. Although the atmosphere in those rooms could be defined by despair, disillusionment, and most of all skepticism, it was far from hopeless. It seemed like déjà vu when I worked for a large CPG company back the early 1990s—no analytics, no technology other than Excel, and 100 percent gut-feeling judgment injected into the process by multiple departments, including sales, marketing, finance, and operations planning—all in an attempt to create a one-number consensus forecast (technically a supply plan). The story goes on with no real attention to accountability and little or no attention to the product mix, as the focus was always a top-down forecast (plan). The supply chain leaders didn't just say they wanted to improve their demand management process, but said that they had no choice due to the fact they were sitting on anywhere from $100 million to over $600 million in finished goods inventory, WHIP, and raw materials. Over $75 million to $400 million was in finished goods inventory alone. Talk about being supply centric in their approach to demand management. I guess using buffer inventory to protect against demand volatility doesn't work after all.

Subsequently, demand forecasting and planning is still the key focus area for most companies. For most, it is the biggest challenge that they will face in their supply chain journey. Companies want to improve demand forecasting and planning, but have focused mainly on the process with little or no attention to improving data quality, people, analytics, and technology. As a result, their skepticism has become prevalent among their supply chain leaders, as many have conceded that they can never be successful in improving demand forecast accuracy. As indicated in survey after survey demand forecasting and planning is important to supply chain leaders, but also an area with the largest gap in user satisfaction.

Based on my personal experiences visiting companies, I have found that demand management is the most misunderstood supply chain planning process with little if any knowledge of how to apply analytics to downstream data (POS/syndicated scanner data). Also, well-intentioned consultants have given bad advice, particularly, that a one number forecast process is the key to success. As discussed earlier the one number forecast only encourages well-intended personal bias, and is used to set sales targets, financial plans, and other factors that are not directly related to an accurate demand response. What drives excellence in demand management is the ability to incorporate sophisticated data-driven analytics into the process using large-scale enabling technology solutions to create the most accurate unconstrained consumer demand response. Once that unconstrained demand response is adjusted for sales, marketing, finance, and/or operational constraints, it becomes a sales plan, marketing plan, financial plan, and/or a supply plan.

WHY DO COMPANIES CONTINUE TO DISMISS THE VALUE OF DEMAND MANAGEMENT?

Today, we live in a polarized world that divides family members, friends, and business colleagues. It affects everything we do, from the way we communicate with one another to how we handle business challenges. I have seen long-time business colleagues have passionate discussions defending their supply chain position regarding what adds more value—demand or supply. As a result, we now work in what I refer to as the polarized supply chain, where you are either a believer in supply or demand. Sound familiar? We get caught up in what we are comfortable with, or what we believe is the “holy grail” to fixing the inefficiencies in the supply chain. The pendulum seems to swing back and forth from decade to decade, focusing on supply or demand “processes and technologies” with little emphasis on people (skills and changing behaviors), and virtually no attention to predictive analytics.

We are at a pivotal point in the three-decades-old supply chain journey. Are we going to continue to address the symptoms, or finally take action to fix the root cause of our supply chain challenges?

Everyone seems to be high on supply these days as they continue to smoke inventory crack. They justify their addiction on the fact that forecasts will always be wrong. Well, I have news for our supply-driven friends. Why is it that when we underforecast, companies experience significant backorders, and when we overforecast, companies sit on millions of dollars of finished goods inventories? What's more, why are our supply-centric colleagues suddenly abandoning their traditional use of buffer inventory (safety stock) to protect against demand volatility?

By the way, I agree completely that a 1 to 3 percent increase in forecast accuracy may have very little impact if any on safety stock or possibly finished goods inventories, particularly if your forecast accuracy is already above 85 percent on average across the entire product hierarchy. At that point, each additional 1 percent of accuracy (or reduction in error) requires exponential investment in time and resources with minimal effect on buffer inventories. Of course, that is if your forecast accuracy at the lower mix levels (i.e., product/SKU or SKU/demand point) is above 70 percent. We have found that over 90 percent of companies still focus on measuring forecast accuracy at the highest aggregate level of the product hierarchy with little if any attention to the lower level mix accuracy. In fact, the average forecast accuracy across all industries is between 50 and 65 percent on average at the aggregate level, and between 25 and 35 percent or lower at the lower mix levels. How do we know? These numbers have been validated not through analyst surveys but by working directly with over 100 companies during the past 10 years. So, there is a lot of room for improvement regarding demand forecast accuracy—--much more than 1 to 3 percent.

A classic case of poor demand forecasting is when a company ships into a channel, or retailer more products than the retailer can sell, while simultaneously incurring backorders. So, you would think, how could that happen? Well it's all about the product mix. Not only did the manufacturer ship in more products than the retailer sold, they shipped in the wrong mix of products. So, now the retailer is sitting on excess inventory that is not selling. So, what does the manufacturer do? The manufacturer discounts the products in inventory at the retailer by running sales promotions and other related consumer incentives in an attempt to reduce the inventory by pulling it through the channel and retailers' stores. This has a negative impact on profit margins and market share.

In addition, the forecasting methods being deployed, mainly moving averaging and nonseasonal exponential smoothing models, are only accurate one to three periods into the future. As a result, the upper/lower forecast ranges (confidence limits) that are a key input to safety stock calculations tend to be cone shaped (exponentially get larger as you forecast beyond one to three periods). This is why the impact in many cases actually increases safety stock volumes, rather than lowering them. This is not the case when using more advanced statistical methods like ARIMA, ARIMAX, and dynamic regression (predictive analytics) as they are more accurate further into the future, so the upper/lower forecast ranges tend to be tighter (more consistent to the forecast, not cone shaped) as you go further out into the future. These models actually help lower safety stocks not only through more accurate forecasts, but by reducing the upper/lower ranges of the forecast, which is a key input to safety stock calculations. Which demand forecast in Figure 1.5 would you choose to drive your safety stock calculations?

The increase in the use of Excel by demand planners over the past decade and the broader use of moving averaging are the result of companies' investment in ERP solutions, which have restricted model selection. They only support simple statistical forecasting methods (mainly exponential smoothing). In fact, the one area of the supply chain that has received the least attention and investment in people, analytics, and technology over the past decade is demand forecasting and planning. If you noticed, I left out process. Process alone cannot improve your forecast accuracy. It requires investment in people skills and behavior, horizontal processes, predictive analytics, and scalable technology putting equal emphasis on all four areas.

Using proof-of-values (POVs use a subset of the customer's data in the software vendor's technology to prove the value—whether it improved forecast accuracy) conducted with multiple companies over the last 10 years, SAS Institute Inc. has shown improvements in forecast accuracy of anywhere between 10 and 30 percent (and in some cases up to 50 percent) on average up/down company's product hierarchies by just deploying “holistic” modeling driven by predictive analytics, not by sales targets, financial plans, and/or judgmental overrides. In fact, it has also been proven using historical demand data (uncleansed—not segmenting demand history into baseline and promoted volumes) combined with predictive analytics that there is no reason to cleanse demand history (shipments/sales orders). Cleansing demand history by segmenting it into baseline and promoted volumes, and for any other reason other than historical realignment or true entry errors, is actually a bad practice. In fact, this type of cleansing makes the forecast less accurate. This cleansing process creates two separate data streams: (1) baseline, which tends to be a moving average, and (2) promoted, which is supposed to reflect promotional spikes and outlier corrections, but is actually a combination of seasonality and promotional volume. Furthermore, the promoted volumes are given to sales and marketing to adjust manually. Finally, the demand planner manually tries to piece these two data sets back together. The result, 1 + 1 now equals 5. In 95 percent of the POVs conducted, there were improvements in demand forecast accuracy in the range of 10 to 25 percent, significantly lowering buffer inventories (safety stock) and finished goods inventory. Furthermore, they found that applying multi-echelon inventory optimization, auto leveling, and other advanced analytics for supply planning along with improved forecast accuracy creates a synergy effect of another 15 to 30 percent reduction in finished goods inventories, thus reducing costs and freeing up working capital.

Another reason why improved forecast accuracy doesn't have a strong impact on inventory safety stock is because no one is actually forecasting demand, but rather supply. Those companies that are moving toward becoming demand-driven and ultimately digital-driven are engaging sales and marketing, and linking downstream data to upstream data using a process called multi-tiered causal analysis (MTCA), or what can be referred to as consumption-based modeling. Many have seen as much as a 25 basis point (or 50 percent) improvement in forecast accuracy for shipments on average across their product portfolios. They have also statistically proven that there is a direct correlation between downstream data and upstream data. So, why do we continue to say that improved demand forecast accuracy has no direct impact on supply?

We need to stop relying solely on either demand or supply as a quick fix to our supply chain challenges. Companies need to take a holistic approach to solving the root cause, which focuses on people skills and behavior, horizontal processes, predictive analysts, and scalable technology that is driven by structured and unstructured data addressing both supply and demand. I call it the holistic supply chain. Successfully implementing an agile demand-driven supply chain will require a holistic view of demand and supply. We can no longer make it and hope consumers will buy it. Companies need to focus across the entire supply chain starting with downstream demand to create a more accurate demand response to fulfilling that demand with the most efficient supply response. It includes a new definition of supply chain management (SCM), which includes the commercial side (sales/marketing) of the business, which is responsible for demand generation. Finally, in order to achieve this new approach to supply and demand, companies need to invest in training people in predictive analytics, implementing horizontal processes with performance metrics, applying and relying on predictive analytics to make better decisions, and implementing scalable technology to allow them to gain the most insights from big data. You cannot get it right by just focusing on demand or supply alone. However, we need to start with demand working with sales and marketing to support demand generation with the intentions of creating the most accurate demand response. Then, work horizontally across the supply chain to meet that demand response with the most efficient supply response.

SUMMARY

As many corporate leaders recognize, investments in people and capabilities are essential for a business to thrive and keep growing. While companies are planning to increase investments in demand management capabilities over the next several years, many are relying on past or ongoing investments to keep moving forward. The top three investment priorities are general demand forecasting and planning, new data management technology, and tools that will help manage new product introductions. When it comes to demand forecast accuracy and maximizing profitability, anything will be better than continuing to use Excel spreadsheets and falling back on simple statistical methods like moving averaging. Excel is simply not scalable, particularly given that SKU proliferation has been on the rise for the past decade. Also, Excel doesn't have the depth and breadth of predicative analytics to support a demand-driven planning process on a large scale.

In order to support an enterprise demand management process, it is critical to have predicative models and a user-friendly, point-and-click user interface. The solution must be highly scalable, allowing the user to sense demand signals and shape future demand up and down the business hierarchy. The rise of demand management capabilities continues to be a broad trend toward improving performance by working smarter without increasing operating costs. Manufacturing leaders readily recognize that the ability to develop more accurate demand forecasts by sensing and shaping consumer demand could have a multimillion-dollar impact on their revenues and cost structures. So, what is impeding them from adopting demand-driven forecasting and planning?

The purpose of this book is to provide a framework and guide for practitioners with a proven structured approach that takes into account people, process, analytics, and technology to transition to the next generation demand management process. These four areas are the key catalysts to move from the current state to the future state along with strong horizontal performance metrics to measure progress. Although adoption requires changes in people behaviors that include new skills, and an integrated process that includes both descriptive and predictive analytics, scalable technology is required that provides scalability to not only gain adoption but also sustainability. It also requires changes in the corporate culture led by a champion who has the authority and leadership to not only drive adoption but also create a new corporate culture that stresses accountability with a focus on customer excellence in order to maintain sustainability.

The primary goal is to provide readers with a strategic roadmap to transform their current demand management process to the next generation demand management process that is not only adopted but also sustainable, becoming part of the corporate culture.

KEY LEARNINGS

• Number one obstacle to achieving supply chain goals is demand forecast accuracy, followed by:
  • Inability to synchronize end-to-end supply chain
  • Lack of cross-functional collaboration
• Increasing demand volatility is creating a sense of urgency within companies to improve their demand management capabilities.
• Companies can no longer use inventory buffer stock to protect against demand volatility.
• Persistent cost pressures are requiring supply chain leaders to better align supply with demand for improved performance.
• 77 percent of demand planners are still using Excel spreadsheets.
• The most widely used statistical method is moving averaging, followed by exponential smoothing.
• Multiple departments and users (47%) are touching the demand forecast each cycle.
• Too many touch points add political bias, resulting in error.
• Focus is on aggregate level forecasts, rather than the lower product mix.
• The one-number consensus forecast philosophy is ineffective because of the corporate culture and lack of accountability.
• The process is politically charged due to different intentions, purposes, and goals.
• Companies are still struggling to know what to do with big data.

FURTHER READING

1. Chase, Charles W. Jr. “Cleanse Your Historical Shipment Data? Why?” Journal of Business Forecasting 34, no. 3 (2015), pp. 29–33.
2. Chase, Charles W. Jr. “Using Downstream Data to Improve Forecast Accuracy.” Journal of Business Forecasting 34 (2015), pp. 21–29.
3. Crum, Colleen, and George E. Palmatier, “Demand Management Best Practices: Process, Principles and Collaboration,” Integrated Business Management Series, co-authored with APICS (2003), pp. 1–206.