STARTING THE SUPPLY CHAIN JOURNEY

Still today, many companies focus solely on a supply-centric philosophy, selling into the market channel to obtain operational excellence by matching demand to supply, rather than supply to demand. Because demand was fairly stable with only a few competitors, it made sense to strive for operational excellence that flushed out all the inefficiencies associated with supply—reducing inventories, shortening supplier lead times, and implementing more agile manufacturing capabilities like lean management. Also, because demand was fairly stable, simple statistical baseline forecasts focusing only on trend and seasonality provided a fairly accurate forecast. Top-down judgmental overrides could then be used to modify the forecasts to fit demand to supply. Figure 2.1 outlines this very supply-centric approach to supply chain management referred to as being a supply-driven company.

However, as companies became more global, demand volatility increased, and lead times became longer as companies were unable to gain additional efficiency practicing lean management. They found themselves unable to react to economic downturns, which created high carrying costs as inventory escalated due to lower demand for their products. It required weeks of product discounting to sell those inventories through their market channels, reducing profit margins and resulting in lost revenues. Due to the sheer volume of data, supply chains tend to be managed at higher aggregate levels. This means that the forecasted demand and the resulting inventory is viewed in a planning mode at a much higher level versus focusing on the lower level interior product mix. It is not humanly possible to manage any other way. Nor is Excel scalable enough to manage 18,000 SKUs across multiple markets, channels, customers, and ship-to-locations. This type of planning puts a huge emphasis on replenishment activities to overcome out-of-balance demand and inventory positions. In fact, if one looks at most replenishment solutions, they focus almost entirely on three things: (1) monitor potential out of stocks and fill rates, (2) alert to any actions that need to be taken, and, (3) react, react, react! Rapid replenishment is based on the idea that there are and will always be out of balance inventories. The supply chain should be outwardly focused on customer demand, not ever aggregated demand at each step in the supply chain. Indeed, the latency of demand and the aggregation simply amplifies the supply problem that replenishment cannot fix.

INTRODUCING SALES & OPERATIONS PLANNING (S&OP) INTO THE SUPPLY CHAIN JOURNEY

During the second decade of the supply chain journey, sales & operations planning (S&OP) was introduced to help balance demand and supply. S&OP was introduced by Oliver Wight in 1987 and became the go-to process of the 1990s for synchronizing demand and supply. Since then, companies have gained over 25 years of S&OP knowledge and experience. S&OP was designed as a horizontal process to not only synchronize demand and supply but also build efficiency and operations excellence into the supply chain. (See Figure 2.2.) So, why is it becoming so popular again? Haven't we synchronized demand and supply by now?

SALES & OPERATIONS PLANNING CONNECTION

Sales & operations planning (S&OP) has been adopted worldwide by many companies to help them synchronize demand and supply since the late 1980s. Oliver Wight the founders of S&OP continue to find that the quantity, quality, and sustainability of business performance improvements depend on how the process is used. In fact, those companies that have adopted S&OP as the primary process to manage their business tend to get the most significant and wide-ranging results. However, the majority of companies have not seen sustainable results as their businesses have matured along with markets and consumer preferences. As a result, they have not experienced the true benefits of their S&OP efforts.

Outstanding S&OP processes do not appear overnight. S&OP is a journey in itself and requires persistent support and continuous improvements. Obstacles include limited technology enablers, lack of governance, rigid functional silos, lack of shared performance measures, and ingrained company cultures. The overarching purpose of the S&OP process is to set a realistic and profitable overall direction for the company. S&OP brings together all major operational departments (e.g., sales, marketing, operations planning, manufacturing, and finance) to decide how best to manage company resources to profitably satisfy consumer/customer demand and pursue strategic initiatives, which may include new markets, new product introductions, acquisitions, and other related strategic activities. While the objective of S&OP is to create a realistic demand plan that can be executed, today companies view sales and operations planning as a means to execute corporate strategy. A successful S&OP process aligns the company strategically to execute tactically.

Dividing the spectrum of S&OP sophistication into three proficiency profiles helps companies find their level of maturity and chart a path to success. No matter where an organization starts, this journey is well worth the effort. According to research conducted by several organizations, a successful S&OP initiative can:

• Improve revenue by 2 to 5 percent.
• Reduce inventory by 7 to 15 percent.
• Improve new product launch commercialization by 20 percent.

Often, the biggest obstacles to S&OP excellence stem from complexity. For example, it may be too difficult to gather data, there may be too many markets, channels, and SKUs, the process may be too hard to govern, and key data too difficult to analyze and report. Ultimately, the process may become too hard to execute. Leading supply chain teams have discovered that true S&OP success flows from practical thinking, guided by three principles: (1) make it easy to implement, (2) make it easy to execute, and (3) make it easy to sustain.

However, the biggest contributor to S&OP failing is directly related to the focus on supply planning and inventory costs. Most companies forget that the S actually stands for sales and marketing. Sales and marketing are not measured as much on costs as they are on market share, revenue, and profitability. In other words, the performance metrics are vertically aligned, rather than horizontally aligned. Not to mention conflicting, creating an antagonistic environment. Furthermore, the S&OP meetings themselves are focused almost exclusively on inventory costs, customer service levels, and production scheduling, all of which are operations planning centric, not customer focused. The only real common metric that sales and marketing share with operations planning is customer service levels.

When discussing S&OP with executives, the biggest challenge they face today is getting the commercial side of the business (sales and marketing) to participate in the process. The main reason for the lackluster commitment by sales and marketing is the fact that there are no real benefits for them. Sales and marketing are focused on downstream demand (POS/syndicated scanner data) not upstream supply (sales orders and shipments).

S&OP is more than getting product managers in a room to agree on a demand plan, which is generally a supply plan based on shipments or sales orders, and it's more than using good modeling and simulation software (both of which are a must). Proper S&OP planning requires sales, marketing, and operations planning working collaboratively toward a realistic and trustworthy demand plan created from an unconstrained consumer demand forecast. S&OP requires both horizontal integration (bringing together demand and supply planning) and vertical integration (bringing together finance and operations functions). So, why is it that the supply chain equation looks like Figure 2.3?

By the nature of the equation, they are only doing OP. Most companies that are supply-driven are doing OP, not S&OP. Also, if the commercial side (sales/marketing) of the business is not attending your S&OP meetings, then chances are you are only doing OP. The S stands for sales and marketing, not just sales. So, if you are doing real S&OP, your supply chain equation should be as depicted in Figure 2.4.

In the past, the OP in S&OP created an unbalanced equation where organizations unknowingly biased their supply chains by focusing on the supply side of the business, flushing out inefficiencies in operations (e.g., focusing on reducing inventory costs by improving inventory replenishment capabilities) with little emphasis on understanding how to integrate market opportunities and customer needs. So, if you are not doing SM&OP/F (sales, marketing & operations planning divided by finance), then chances are you are a supply-driven company.

It has been found that companies that have successfully implemented an SM&OP/F process understand it is a journey over several years. Those companies have combined people, process, analytics, and technology to support and enable the change management activities to evolve their business, market, and products. They also make sales and marketing accountable for sensing demand signals and shaping future consumer demand to create a more accurate demand response. This is a radical change in the process. In addition, finance's role changes with a focus on assessing the implications of sales and marketing activities, such as whether those sales promotions actually make a profit. So, instead of providing another input into the consensus forecasting process, which essentially says, “Hold to the annual plan and roll last month's miss into next month (also, known as hold-n- roll),” finance needs to provide financial analysis and assessments of those sales and marketing programs to assure they are actually growing revenue and profit. On the other hand, finance should also be assessing the operations side of the business to assure they provide the most efficient supply response to meet demand. The financial plan is just that, a plan, or guide, not the current consumer demand forecast or supply plan.

This new SM&OP/F process outlined in Figure 2.5 defines the roles and responsibilities of this new radical approach to S&OP, as well as the goals of synchronizing the S&OP process to fit supply to demand, not demand to supply.

The missing component in the traditional approach to S&OP is a demand-driven planning process. This requires one single unconstrained consumer demand forecast used for the entire value chain to create sales plans, marketing plans, financial plans, and operational plans over the appropriate time horizon for all value chain members. This concept is known as the demand-driven value network. Becoming demand-driven helps organizations manage the complexities of today's supply chain, offering a viable alternative to traditional S&OP. The key is to create demand-driven value networks (DDVNs), defined as “a business environment holistically designed to maximize value across the set of processes and technologies that senses, shapes and orchestrates demand based on a reduced latency signal across multiple internal supply chain networks along with external trading partners.”

In 2003, Colleen Crum and George Palmatier, for consulting firm Oliver Wight, the accepted founders of S&OP, wrote a book titled Demand Management Best Practices. In the book, Crum and Palmatier discussed the importance of sales and marketing input in the demand management process.¹ In fact, they said that sales and marketing should own the demand management process, not operations planning where traditional supply-driven companies position demand planners. The book also stressed that without sales and marketing participation in the demand planning process, it is impossible to synchronize supply and demand, as marketing plays a key role in the S&OP process. Finally, true demand is point-of-sale (POS) data, not shipments or sales orders, which are traditionally the data that most companies forecast. Shipments and sales orders are technically replenishment, or supply, not true demand for many companies.

Learning how to better predict the future, rather than letting past forecasting decisions or results constrain future benefits, is well worth the investment in becoming demand-driven. It requires horizontally aligning planning and execution activities based on a single consumer demand forecast in order to drive a profitable balance between marketing investment strategies and operational effectiveness. It's not only about costs, but revenue growth and profitability. A demand-driven planning process takes the actual sales forecast from the retailer POS data and/or sales channel syndicated scanner data to create a consumer demand forecast, which is used to create a customer orders plan (or shipment plan) instead of educated guesses. It then nets these orders against inventory and creates an operational plan (i.e., distribution, logistics, production schedules, and raw material plans) to meet that demand with the most efficient cost-effective supply plan. The process is bidirectional and matches supply to demand in a multi-echelon, multiparty value network while considering capacity and material constrains.

The next step in the supply chain journey requires rethinking demand holistically—the source of demand signals, and the integration of the demand signal into horizontal processes. Figure 2.6 outlines the demand-driven supply chain.

TRANSITIONING TO A DEMAND-DRIVEN SUPPLY CHAIN

Over the past decade, companies have been transitioning to demand-driven supply chain networks focusing on generating a more accurate demand response. It is all about aligning supply to demand with an outside-in focus to obtain customer excellence. Companies are now sensing demand signals and using those signals to shape and translate future demand to improve supply chain processes.

This journey starts with outside-in thinking and focuses on identifying market signals and translating them into the drivers of demand. Demand-driven forecasts focus on accurately predicting what consumers/customers will buy. This is in sharp contrast with the traditional demand management processes that determine what companies will manufacture or ship. The input signals are from the market, channel, brand, product group, product, SKU, and customer. There are many possible inputs, including seasonality, sales promotions, marketing events, pricing, as well as competitive pressures.

The demand-driven planning process relies on data, domain knowledge, and advanced statistical techniques to sense (measure) the key business indicators (KPI's) that influence the demand signal, then using those KPIs to shape future consumer demand and react to changes in the marketplace. The demand-driven planning process provides an unconstrained view or best estimate of market demand based on corporate specific historical sales demand (POS/syndicated scanner data) and translates the demand response into a supply (shipment) plan. The KPIs (demand drivers) used to predict demand (POS/syndicated scanner data) normally include retail price, sales promotions, advertising, merchandising, competitive activities, weather, and other related market factors. Figure 2.7 outlines the key components of the demand-driven planning process.

Demand-driven planning is the set of business processes, people, analytics, and technologies that enable companies to analyze, choose, and execute against the precise mix of customer, product, channel, and geographic segments that achieves their customer-facing business objectives. Demand-driven planning utilizes data from market and channel sources to sense, shape, and translate demand requirements into an actionable demand response that is supported by an efficient supply response. The core focus areas in the demand-driven planning process are:

• Sensing demand signals: Sense true consumer demand (POS/syndicated scanner data) to understand market shifts in demand for their products by measuring the impact of key performance indicators (KPIs) that influence consumer demand using predictive analytics.
• Shaping future demand: Using what-if scenario analysis to shape future demand by varying the future values of price, sales promotions, marketing events, and other related factors that influence demand.
• Demand shifting: During the S&OP process, collaborate across sales, marketing, and operations planning to influence short-term demand by negotiating where necessary to shift future demand based on short-term supply capacity constraints, providing more time for supply to build capacity to meet short-term marketing tactics.
• Cross-functional collaboration: Traditionally, companies have adopted techniques of collaboration to increase dialogue between supply chain members in order to create more accurate short- and long-term plans. Those supply members are sales, marketing, finance, and operations planning, but there could be others. This is known as internal cross-functional collaboration. More and more companies are attempting to collaborate with their retail channel partners/customers like Wal-Mart, Publix, Walgreens, and others. This is known as external collaboration.
• Forecast value added (FVA): Implement FVA with the intent to reduce touch points in the demand forecasting process, thus increasing forecast accuracy and efficiency (reduced cycle time) by eliminating those touch points that are not adding value. The FVA process measures each touch point in the demand forecasting process before and after someone manually adjusts the forecast. If they are not adding value, then eliminate that touch point, or discount it through weighting, minimizing the bias in the forecast, thus reducing forecast error.
• Multi-tiered causal analysis (MTCA): MTCA is a process that links downstream and upstream data (POS/syndicated scanner data to shipments/sales orders data) using a series of quantitative methods to measure the impact of sales and marketing strategies on consumer demand (demand sensing). Then, using the demand model coefficients, MTCA executes various what-if scenarios to shape and predict future demand. Finally, it links demand (POS/syndicated scanner data) to supply (sales orders or shipments) using data, analytics, and domain knowledge. This requires not only data, analytics, and domain knowledge, but also large-scale technology that can handle hundreds of thousands of data series by market, channel, brand, product group, product, SKU, and trading partners. We will discuss MTCA in later chapters.

Several research studies (e.g., Gartner, IDC, Industry Week, Supply Chain Insights, and others) conducted over the past decade indicate that anywhere from a 2 to 10 percent improvement in demand forecast accuracy delivers on average between a 5 and 7 percent improvement in revenue and profit growth. Improved forecast accuracy, when combined with software that translates the demand forecast into demand-driven events, will decrease inventory and operating cost, increase service and sales, improve cash flow and return on investment (ROI), and increase pretax profitability. In the last decade, a host of seasoned and practiced supply chain professionals have presented and published articles and research highlighting the returns for companies that use accurate demand forecasts to drive their demand-driven supply chains with a focus on customer excellence. According to those companies, implementing demand-driven supply chain strategies allows companies to:

• Support periods of increasing sales with less finished goods inventories.
• Improve demand forecast accuracy for products that are low-volume while keeping pace with customer demand for those products that have high volume.
• Achieve higher ROI, profits, and overall lower inventory costs and working capital.

This has led companies to move from just inventory management to invest in inventory optimization technologies. What is inventory optimization? Having the right amount of inventory, in just the right places, to meet customer service and revenue goals, while minimizing costs and working capital.

The focus should be on correct inventory levels of all products and not just on replenishment. This proactive shift moves the inventory focus to demand-driven synchronized replenishment against the consumer demand forecast with the ability to translate the demand response and meet that response with the most efficient supply response using supply shaping (what-if-scenario analysis) exercises instead of having to rely on outdated days of supply rules.

Constantly changing fulfillment demands have moved inventory optimization (IO) to the forefront as a dynamic process to be evaluated continuously, rather than the traditional once-a-year review and monthly adjustments approach. The demand-driven supply chain is outwardly focused on customer demand, not only aggregated demand at each step in the supply chain. Indeed, the latency of demand and the aggregation simply amplifies the supply problem that replenishment cannot fix. Shifting to an inventory-based system focused on the customer takes away the reactionary replenishment problem and drives efficiency. This shift in practice comes about because of multi-echelon inventory optimization and replenishment. In fact, the shift from replenishment to inventory corrects the operational excellence so that it is outwardly focused on customer excellence. Inventory optimization provides the ERP system with unique, optimized order up-to-level, order level, and safety stocks for each and every product/location combination and links them throughout the multi-echelon supply chain (if needed) to coordinate the inventory. No more rules-of-thumb and stocks can be individually manipulated. This is why you can keep the same service levels with 10–15–20 percent less stock. You are not relying on the inefficiencies of ERP rules-of-thumb. With the right people, process, analytics, and technologies companies have been able to reduce total inventory levels on average by 15 to 30 percent, consequently freeing cash that can be used for more productive purposes.²

The journey to become demand-driven requires rethinking demand holistically by understanding the source of demand signals, and the integration of the demand signal into horizontal processes. Demand-driven planning is the use of forecasting technologies along with demand sensing, shaping, and translation techniques to improve supply chain processes. This journey starts with outside-in thinking and focuses on identifying the market signals and translating them into the drivers of consumer demand. Demand-driven plans focus on accurately predicting what consumers/customers will buy, not on what companies think consumers/customers want. This is in sharp contrast with the traditional supply-driven processes that determine what companies will manufacture or ship. The input signals are from the market. There are many possible inputs, including seasonal responses, sales promotions, marketing events, pricing, economic factors, as well as competitive pressures.

Companies have limited ability to reduce supply chain costs using supply-driven levers due to increasingly high demand volatility. Focusing on the demand-driven levers will address the root cause and yield significant benefits to both supply and demand. It has been proven that those companies that have implemented demand-driven supply chain strategies have experienced decreasing inventories simultaneously with increasing sales. With improved demand forecasting and planning, those companies that adopted demand-driven planning manufactured fewer of the products that were low selling and kept pace with consumer/customer demand for what was selling. That led to better gross margin ROI, higher profits, and lower inventory costs, waste, and working capital.

THE DIGITALIZATION OF THE SUPPLY CHAIN

Over the past 10 years consumers have been gaining power and control over the purchasing process. Unprecedented amounts of information and new digital technologies have enabled more control. As a result, there is a major shift underway with the help of technology and advanced analytics that are playing a new and larger role in helping marketers to influence the consumer's purchasing decisions. As consumers increasingly turn to technology to help them make decisions as a result the Internet of Things (IoT), marketers are able to directly engage consumers to influence their purchase decision process.

The next stage in the supply chain journey will be driven by four converging trends:

1. A shift from active engagement to automated engagement where technology takes over tasks from information gathering to actual execution.
2. An expanding Internet of Things, which embeds sensors almost anywhere to generate smart data on consumer preferences and trigger actions and offers by marketers.
3. Improved predictive or anticipatory analytics technology that can accurately anticipate what consumers want or need before they even know it, based not just on past behavior but on real-time information and availability of alternatives that could alter consumer choices.
4. The availability of faster and more powerful software that crunches petabytes of data, filters it through super-sophisticated models, and helps marketers gain previously unheard of efficiencies to make highly targeted offers.³

Technology is helping both marketers and customers take the next evolutionary step in the supply chain journey. Instead of merely empowering customers, technology is making decisions and taking action for them. Customers themselves are initiating this shift. Most are happy to have technology help with presenting and making choices and customizing experiences, as long as they are good choices and desired experiences. In fact, they increasingly demand and expect it. These new trends are leading to the digitalization of the supply chain, or as Figure 2.8 illustrates, the digital-driven supply chain.

Analytics technology will be doing more and more of the work for companies by automating activities around research or making actual purchases. It's not merely about predicting what consumers want. It's more like anticipating, which includes a more sophisticated ability to adapt marketing offers and messages to alternatives based on data from hundreds of possible sources. By anticipating something, companies gain a greater chance of influencing the outcome. Consumers' smart phones and other devices can deliver recommendations and offers of where to go, how to get there, and what to buy based on what they are about to do, not just what they've done in the past. Anticipation is about the near-term future, or even a specific time. Prediction is more about things that will happen further in the future. By anticipating something, companies gain a greater chance of influencing consumer purchase outcome.

Demand signal management, (also known as demand signal repositories—DSR) will require more investment in analytics with a strong SM&OP/F (IBP—integrated business planning) process enabled by scalable technology to synchronize demand and supply, visualizing the entire process using what is known as demand signal analytics. (Demand signal analytics will be discussed in more detail in Chapter 3.) Companies that are truly digital-driven understand how to translate market opportunities into the factors that influence demand, use those influence factors to shape future demand, and utilize demand synchronization to grow their market share and maximize revenue and profit. They also tend to shape those demand projections through collaborative fulfillment responsiveness, which is focused on visibility and control throughout the supply chain network to coordinate planning activities to actual demand.

Companies will begin to integrate structured and unstructured data into the supply chain management process (e.g., social media, Twitter, RFID, and others). The Internet of Things (also called Internet of Everything or Network of Everything) is the network of physical objects or things embedded with electronics, software, sensors, and connectivity to enable objects to exchange information (data) with the production, operator, and/or other connected devices based on the infrastructure of ITU's (International Telecommunication Union) Global Standards Initiative.

The IoT allows objects to be sensed and controlled remotely across existing network infrastructures, creating opportunities for more direct integration between the physical world and computer-based systems. This will result in improved efficiency, accuracy and economic benefit across complex supply networks. Each thing will be uniquely identifiable through its embedded computing system and will be able to interoperate within the existing Internet infrastructure. Experts estimate that the IoT will consist of almost 50 billion objects by 2020.

As a result of IoT, the capacity for effective commerce will rely on how well companies integrate, including scaling up or down to match new channels and markets while keeping a keen eye on the audience of one. To address these challenges, companies are turning to the omni-channel, which delivers a positive experience across all avenues of interaction, both digital and traditional channels. The omni-channel offers both assisted service and self-service options for retailers, but also for manufacturers as they complement their traditional channels of distribution with an online presence. As companies enter into automated engagement with their brand-loyal consumers, the omni-channel will play a key role. It will also require demand-signal repositories to collect, process, enrich, and harmonize large amounts of data and information from multichannels. Advanced predictive analytics, visualization, and exploration technology will be required to gain insights to make better business decisions in order to anticipate consumer preferences, what they want to buy and when they are prepared to buy.

Finally, event stream processing (ESP), which is a set of technologies designed to assist the support and structure of event-driven information systems (EDIS), will include event visualization, event databases, event-driven middleware, and event processing languages, or complex event processing (CEP). In practice, the terms ESP and CEP are often used interchangeably. ESP deals with the task of processing streams of event data with the goal of identifying the meaningful patterns within those streams, employing techniques such as detection of relationships between multiple events, event correlation, event hierarchies, and other aspects such as causality, membership, and timing. ESP will enable many different applications such as algorithmic trading in financial services, RFID event processing applications, fraud detection, process monitoring, and location-based services in telecommunications. All these new technologies will slowly be integrated into the supply chain network to create an autonomic learning, or self-healing (self-correcting) network system requiring only monitoring and tweaking based on exceptions.

According to a 2015 Consumer Goods Technology (CGT) report, consumer packaged goods companies are nearly unanimous (Figure 2.9—91.4 percent) in predicting the most impact and benefit from advanced analytics and digitalization will come in the area of forecasting and planning, which includes both customer as well as supply chain forecasting and planning.⁴ This is an area that has already benefited from analytics to date, so it's reasonable to assume that greater advancements will drive greater impact. Consumer centricity and automated engagement is a dominant theme in consumer products, and the goal of many analytics programs is to get better insights about consumers. So it's not surprising that two-thirds see deeper consumer insights as an absolute business imperative. One underlying assumption for many is likely the ability to factor new data streams into the analysis, such as social media, loyalty data, location, and weather data to achieve not just nuance but reduced guesswork and latency in decisive action.

Finally, product innovation makes a surprisingly low showing at 40 percent. Given the significant working capital bets that are placed on new product development, and the high level of risk, the opportunity to apply analytics to richer sources of data such as social media (Twitter, for example) would seem to be an obvious lever for gaining insights into the appetite for new products, thereby improving confidence.

Very few, if any, companies have risen to the level of being digital-driven. Some are making great strides. When companies achieve digital-driven maturity, not only is there better synchronization but also greater agility to match supply to demand. Digital-driven supply chains will allow companies to better balance (synchronize) growth and efficiency, cost and customer service, and demand fluctuations. The bottom-line, digital-driven processes are designed from the consumer back, based on sensing, shaping, and responding to consumer demand, optimizing supply to demand, not demand to supply.

LEVERAGING NEW SCALABLE TECHNOLOGY

Managing demand volatility is no longer just about predicting the future. It is now essential for companies to see and correct imbalances while there's time to deal with them in a cost-effective manner. Companies now need quick and comprehensive simulation that seamlessly links downstream aggregated and detailed consumer plans to upstream supply plans. They also need the ability to combine data from disparate data sources and from all trading partners for instant decision making. Unfortunately, this is not possible with traditional demand forecasting and planning processes, methods, and most of all, technology.

The answer is enterprise technology solutions, not tools, which can manage with an expanded value network, global sourcing for both components and products, as well as customer demands for increasing variety and speed. To this end, organizations today must have strong, effective software solutions for demand forecasting, sales and marketing planning, capacity planning, and operations planning. The technology must also work across all trading partners to create a single demand plan. Demand-driven technology solutions should include a directional link to the financial P&L that focuses on anticipated results. In addition to the demand revenue, a demand-driven solution should include the cost of demand shaping and the respective cost impact based on increased volume and profit. The combination of directional P&L and what-if scenarios helps an organization not only to understand the plan but also to optimize the plan around profitability, not just costs, while weighing it against other criteria such as customer service levels.

BENEFITS

The potential payoffs for more timely and accurate demand forecasts are higher revenues and lower costs. That translates into higher profitability and higher market share. It's as straightforward as basic Business Management 101 class. According to a 2014 Industry Week research study, when manufacturers were asked exactly how much of a sales boost they believed they could get from better demand forecasting and planning, the results were insightful. Almost a third (31 percent) anticipated a sales increase between 3 and 5 percent. That's $3 million to $5 million for a company with $100 million in annual sales (and the majority of the respondents reported much higher sales levels). What's even more interesting is that over one-third of companies (35 percent) reported that better forecasting would increase sales by 6 percent or more. And 1 out of 10 manufacturers reported a potential revenue gain of 11 percent or more.⁵

According to those companies that responded, eliminating lost sales due to stockouts and backorders for both existing and new products would account for much of the their sales gain. Subsequently, those same companies anticipated similar multimillion-dollar cost reductions from more accurate demand forecasts. Figure 2.10 outlines the potential benefits companies will experience from more accurate demand forecasts. The top three in terms of potential impact are:

1. Inventory reductions
2. Better customer service
3. Increased sales

Some of the specific ways more accurate demand forecasting and planning can improve financial performance include:

• Reduced safety stocks, lower carrying costs, and working capital requirements improve overall supply chain efficiencies.
• Reduced inventory ultimately reduces asset requirements through redesigned distribution networks with fewer warehouses.
• Lower freight and logistics costs improve profit margins.
• Enhanced relationships drive superior customer service and responsiveness, which lead to repeat business and growth.

Companies will also see general overhead reductions, higher productivity, reduced overtime, and lower changeover costs as additional benefits from more accurate demand forecasts.

SUMMARY

Investments in people, process, analytics, and technology are essential for a business to thrive and keep growing. Only a third of manufacturers according to the Industry Week 2014 research study are planning to increase investments in demand-driven planning capabilities over the next 12 to 24 months, and many are relying on past or ongoing investments to keep moving forward. The top three investment priorities are general forecasting and planning, new data management technology, and tools that will help manage new product introductions. When it comes to forecast accuracy and maximizing profitability, anything will be better than continuing to fall back on spreadsheets. Excel is still the most widely used technology across all industries. However, Excel is 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 forecasting and planning process on a large scale. In order to support large-scale demand-driven forecasting and planning, it is critical to have predicative analytics and a user-friendly, point-and-click user interface. This also requires an investment in new analytical skills and talent. The solution must be highly scalable, allowing the user to sense demand signals and shape future demand up and down the business hierarchy for hundreds of thousands of SKUs. Without continuous investment in people, process, analytics, and technology, companies will never gain adoption nor maintain sustainability.

Company leaders are beginning to recognize that the ability to develop more accurate demand plans requires improvements in demand forecast accuracy. The only way to address demand volatility is with more advanced predictive analytics that can sense demand signals by measuring the impact of KPIs and the market dynamics that influence the demand signal, and then use those KPIs to shape future consumer demand to create the most accurate demand response. The result has a multimillion-dollar impact on revenue and profit by reducing costs and maximizing their marketing investment, thus improving overall profit margin and market share.

KEY LEARNINGS

• Companies have been on a supply chain journey for the past 25 years.
• Many companies started the supply chain journey focused solely on a supply-centric philosophy focused on operational excellence.
  • This first stage in the supply chain journey is referred to as being supply-driven.
• The objective of S&OP is to create a realistic plan that can be executed.
  • A successful S&OP process requires getting demand right first.
  • You cannot synchronize demand and supply without sales and marketing input.
  • S&OP requires sales and marketing input and accountability for developing the unconstrained consumer demand forecast.
  • True demand is POS/syndicated scanner data, not sales orders or shipments.
• The new formula for S&OP is SM&OP/Finance:
  • Sales and marketing's role changes, becoming accountable for the unconstrained consumer demand forecast.
  • Finance's role changes to financially assessing and holding accountable sales and marketing for program effectiveness at generating revenue and profitable growth, not providing another input into the consensus demand plan.
• Companies are moving toward obtaining customer excellence by transitioning to a demand-driven supply chain.
  • This requires a focus on six key activities:
    • Demand sensing
    • Demand shaping
    • Demand shifting
    • Forecast value added (FVA)
    • Cross-function collaborative planning
    • Consumption-based modeling using multi-tiered causal analysis (MTCA) process
• Demand-driven planning is the set of business processes, people, analytics, and technologies that enable companies to analyze, choose, and execute against customer-facing business objectives.
• The next stage in the supply chain journey is becoming digital-driven. Four key trends are influencing companies to digitalize their supply chains:
  • A shift from active engagement to automated engagement where technology takes over tasks from information gathering to actual execution.
  • An expanding Internet of Things, which embeds sensors almost anywhere to generate smart data on consumer preferences and trigger actions and offers by marketers.
  • Improved predictive or anticipatory analytics technology that can accurately anticipate what consumers want or need before they even know it based not just on past behavior but on real-time information and availability of alternatives that could alter consumer choices.
  • The availability of faster and more powerful software that crunches petabytes of data, filters it through super-sophisticated models, and helps marketers gain previously unheard of efficiencies and make highly targeted offers.⁶
• Analytics technology will be doing more and more of the work for companies by automating activities around research or making actual purchases. It's not merely about predicting what consumers want. It's more like anticipating, which includes a more sophisticated ability to adapt marketing offers and messages to alternatives based on data from hundreds of possible sources.
• As companies enter into automated engagement with their brand loyal consumers the omni-channel will play a key role. Advanced predictive analytics, visualization, and exploration technology will be required to gain insights to make better business decisions in order to anticipate consumer preferences, what they want to buy and when they are ready to buy.
• Event stream processing (ESP), which is a set of technologies designed to assist and support the structure of event-driven information systems (EDIS), will include event visualization, event databases, event-driven middleware, and event processing languages, or complex event processing (CEP).
• New technologies will slowly be integrated into the supply chain network to create an autonomic learning, or self-healing (self-correcting) system requiring only monitoring and tweaking based on exceptions.
• Integrated enterprise technology solutions, not tools, are required to support quick and comprehensive simulation that seamlessly links downstream aggregated and detailed consumer plans to upstream supply plans.
  • They need the capability to combine data from disparate data sources and from all trading partners for instant decision making.
• The potential payoff for more timely and accurate consumer demand forecasts is higher revenues and lower costs. That translates into several key benefits:
  • Reduced safety stocks, lower carrying costs and working capital requirements.
  • Reduced inventory ultimately reduces asset requirements through redesigned distribution networks with fewer warehouses.
  • Lower freight and logistics costs improve profit margins.
• Enhanced relationships drive superior customer service and responsiveness, which lead to repeat business and growth.