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Extra Content. Editorial Reviews Praise for Bricks Matter "This book is like no other that has been written about supply chain management. It is a must-read for the supply chain leadership team. Here she teaches us to take a bold step to change our thinking and turn the supply chain around to focus on the consumer. I enjoyed working with Lora to understand what the future transformation of digital marketing to digital business could look like.
This is a guidebook for leaders driving the transformation. I realized that she has a really cool blog handle, and I need to get one, as well. The reality is that those mistakes are still being made today. It is concerning that after three decades of supply chain pioneers those mistakes are still inherent in the demand management process at many companies.
Talk about stirring emotions with the words demand planning, or demand-driven demand management, not to mention being market-driven. Although, the atmosphere in those rooms could be defined by despair, disillusionment, and most of all skepticism it was far from hopeless.
The story goes on with no real attention to accountability and little if no attention to the product mix as the focus is on a top down forecast. Talk about being supply centric in their approach to demand management…wow! As Lora mention in her blog that after two decades of process and technology refinement, excellence in demand management still eludes supply chain teams. This is an understatement. After two decades the demand forecasting and planning process is still the largest gap between satisfaction and performance. Given our research in writing Bricks Matter demand forecasting and planning is the key focus area for most companies over the next two years.
For most it is the biggest challenge that they will face in the supply chain journey. Companies want to improve demand forecasting and planning, but have focused mainly on the process with little or no attention to data quality and analytics. As a result, their skepticism has become prevalent among their supply chain leaders that they can never be successful in improving demand forecasting. Based on my personal experiences visiting companies, we find that demand forecasting and planning is the most misunderstood supply chain planning process with little if any knowledge of how to apply analytics to downstream data.
Also, well-intentioned consultants have given bad advice, particularly, that a one number forecast process is the key to success. In my experience, the one number forecast does not work.
It 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 forecasting and planning is the ability to incorporate sophisticated data driven analytics into the process using large scale enabling technology solutions to create the most accurate unconstrained demand response.
Demand Management concepts are now twenty to twenty-five years old. The first use of the term demand management in the commercial sector was lay claim to in the late eighties or early nineties. Sound familiar. In the mid-nineties demand planning and supply planning were lumped together, which gave birth to supply chain management concepts of demand planning and integrated supply chain planning.
What we have found companies globally across all industry verticals have actually moved backwards over the course of the last ten years when it comes to growth, operating margin and inventory turns. As we mention through the Bricks Matter book in order to move forward, companies need to admit their mistakes of the past. They must be willing to fail in order to move forward. In their supply chain journey to sense demand signals and shape future demand the use of demand information will be critical in driving a more profitable demand response.
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. It is a Myth. Well-intentioned academics and consultants tout the concept of one-number forecasting. Enthusiastic supply chain executives have drunk the Kool-Aid, as they say. But, the reality is it does not reduce latency and it is too simplistic. As a result, the concept adds unintentional and in many cases intentional bias, or forecast error to the demand forecast.
The reason is it is too simplistic, but the reality is that all the participants have different purposes, or intentions. They pause, and say all the above. I ask, all the above are plans, not an unconstrained demand forecast. The people who push this concept really do not understand demand forecasting and planning. A demand forecast is hierarchical around products, time, geographies, channels, and attributes.
It is a complex set of role-based time-phased data. An effective demand forecast has MANY numbers that are tied together in an effective data model for role-based planning and what-if analysis. Even the eventual demand plan is sometimes not reflective of the original demand forecast due to capacity restraints, which results in demand shifting to accommodate supply constraints. In fact, most companies who described demand shaping during our 75 interviews with supply chain managers were actually describing demand shifting, not demand shaping.
A one-number plan is too constraining for the organization. A forecast is a series of time-phased plans carefully architected in a data model of products, calendars, channels and regions. The numbers within the plans have different purposes to different individuals within the organization. So, instead of a one number forecast, the focus needs to be a common set of plans for marketing, sales, finance and operations planning with different plan views based on the agreement on market assumptions and one unconstrained demand response. This requires the use of an advanced forecasting technology solutions and the design of the system to visualize role-based views that can only be found in the more advanced demand forecasting and planning systems.
The entire basis for the concept of consensus forecasting and planning is based on the belief that each organization within the company can add insight value to improve the accuracy of the demand forecast. In concept, if designed properly this is correct.
Like many concepts the reality is a result of the how it is implemented. In this case, the implementation has been flawed. The challenge is that most companies did not hold the groups within the organization accountable for their bias and error. Each group within the company has a natural bias purpose , and corresponding error based on incentives.
Unless the process has structure regarding error reporting, the process of consensus forecasting and planning will distort the demand forecast adding error despite well-intended efforts to improve the forecasting and planning process. We have worked with many companies that have redesigned their collaborative demand planning processes many times. Each time it was to improve the user interface to make data collection easier by sales.
We struggle with why more companies do not apply the principles of Lean to the consensus forecasting and planning process through Forecast-Value Add FVA Analysis. In its simplest form FVA measures the impact of each touch point in the consensus forecasting and planning process before and after the statistical baseline forecast is adjusted by one of the participating organizations i. This requires that all the forecasts be captured each cycle and compared to determine any bias. It is a Reality.
Given all the acquisitions and consolidation that has taken place of the past 20 years, SKU proliferation, as well as companies selling their products across geographic regions, markets, channels, and key accounts customers has made it difficult to touch every product every cycle.
This could lead to millions of forecasts each cycle. It is virtually impossible to touch every product every cycle. Companies forecast at some aggregate level in their product hierarchy with little attention to the lower levels product mix. Then, imagine doing this in an Excel spreadsheet. Well that is reality.
This requires a large scale automatic forecasting system that can do all the heavy lifting using analytics, and can filter on an exception basis those products and locations that need the most attention based on a set of business rules, and error statistics e. Excel is simply not scalable, nor does it have the depth and breadth of analytics. Traditionally, companies focused on forecasting what manufacturing should make, rather than what the market and channel were demanding. This is a supply centric approach to demand forecasting and planning that compensates for the lack of a strong demand management process.
This situation has changed the process focusing on modeling what is being sold in the channel to determine the best demand response.
This difference, while it may sound insignificant, is a major change. It requires an additional step after demand sensing and shaping to translate demand into a more accurate demand response. Forecasting channel demand reduces demand latency and gives the organization a more current demand signal. It also allows the augmentation of the forecast with demand insights signals to improve the quality of the forecast. For most companies, this requires a re-implementation of demand planning methodologies and new enabling technologies.
Recently, while meeting with the supply chain management team of a large appliances manufacturer, I was asked to provide them with a detailed description of the skills required to hire demand planners. This is not uncommon as most demand planners have minimal statistical skills. Their primary role in the demand management process is focused on taking aggregate level forecasts and disaggregating them into ship to location by SKU forecasts.
This requires minimal statistical skills. This is done using Excel spreadsheets, and then, manually entered into a legacy ERP system. Those companies who invested in demand planners with advanced analytical skills combined with new demand forecasting and planning enabling technology based on demand sensing and shaping have significantly improved their forecasting processes. Most traditional demand planning organizations are positioned in the operations planning departments too far upstream to understand how to apply analytics to downstream channel data. In other words, demand forecasting and planning requires analytics and domain knowledge.
The new demand management organization of the future needs to be positioned in marketing for two key factors; 1 to provide statistical support, and 2 gain domain knowledge. As marketing products managers move every years the demand planners will remain as the product domain knowledge experts, as well as the analytics expert over time.
As a result, the companies will begin to models sell through versus sell into the channels of distributions, as demand planners begin to analyze using statistics to measure the effects of those factors that influence customers to buy their products. As a result, inventories will be managed more efficiently in the channels avoiding discounting, sales promotions, and other vehicles required to push products through the channels. This will have a positive impact on profit margins resulting in higher revenues.
Sales and marketing are responsible for demand generation, and ultimately for creating the most accurate demand response.
The primary role is to identify market opportunities, translate those opportunities into demand signals, measure the signals, and use them to shape influence future demand. The consensus should be between sales and marketing with financing assessing the programs to determine if they are profitable. If not, then it is finances role to push back on sales and marketing.
This is a truly a market-driven demand management process. Operations planning should not provide another input into the consensus forecasting process other than to assess the implications from a supply perspective. There is a large CPG manufacturer who does this best by following a structured demand management process that is supported by new demand-driven technology that allows them to measure sales promotions and marketing events mathematically calculating the lift, and then, assesses the lift to determine if it generates profit. If not, the sales promotion is not implemented.
This combination of data, analytics, domain knowledge and financial assessment has significantly improve forecast accuracy as well as performance resulting in higher profit margins and lower finished goods inventory safety stock. Last week, based on the content of the book Bricks Matter, Supply Chain Insights rolled out our first public training class.
For more on this concept, reference the Supply Chain Insights report, Conquering the Supply Chain Effective Frontier We try to help companies make these two concepts actionable through a series of experiential activities. At the end of the class, after the completion of the activities, we had some time to talk as a group. I was asked two very good questions that I thought I would answer here in this blog post.
Get proven guidance to build a market-driven supply chain management Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation. MATTER. Cecere, Lora M., and Charles W. Chase. Bricks Matter: The Role of Supply . market driven than in any other area of supply chain management.
We had just finished the training on supply chain talent see the infographic below , when I was asked these questions:. In fact, we find an inverse correlation between companies that have depended intensively on third-party process consulting assistance and peer group rankings on the Supply Chain Effective Frontier. The best results happen when there has been consistency in leadership that built supply chain potential and focused on improving flexibility and balance.
So, as a group, we brainstormed the answer to these two questions. Similarly, a great planner in the organization has similar skills.
While the role in the organization may be more junior, they have complementary skill sets. As talent issues increase, finding these skills will become more difficult. Let us know what you think of the latest infographic that we have built to help companies start to take this issue more seriously. The students packed the room. I was fascinated by the multicultural diversity of the talent in the room.
Today, the future supply chain leaders of the world are primarily coming from European and North American schools. As a result, the Spanish-speaking regions of the world are migrating to schools in Spain, and the English-speaking regions of the world are attending schools in London. Job shortages in the area of supply chain management were hard for the audience to conceive. I could not help but contrast the grim mood in the room with the upbeat and jubilant mood of the graduating Penn State students at a recent conference that I attended in December.
I leave Spain believing that multinational investments in the European schools could pay big dividends. I think that it is an important source of untapped talent. We started planning a year ago. For those that have not begun planning, the pending events will overtake them. Is this unusual?