SCI Outside-In Report v3.0

THE OUTSIDE-IN PLANNING HANDBOOK The Practitioners’ Guidebook for Building Outside-In Supply Chain Planning Processes

NOVEMBER 2023

Lora Cecere Founder of Supply Chain Insights LLC

THE OUTSIDE-IN PLANNING HANDBOOK | 2023

DISCLOSURE Your trust is essential. To build confidence in the industry, we are open and transparent about financial relationships and our research processes. This independent research, written by Lora Cecere, the founder of Supply Chain Insights, explores the possibilities of outside-in planning based on three years of testing. Please share this research freely within your company and across the industry. All we ask for in return is attribution when you use the materials. All we ask for in return is attribution when you use the materials. We publish under the Creative Commons License Attribution, and you will find our citation policy here.

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This report is based on three years of testing of outside-in planning concepts. The pilot work mentioned in the report is based upon pilot work with o9 Solutions and OMP. Overview

In January 2021, we initiated work to define outside-in planning processes with an Advisory Group, nicknamed Project Zebra. The group was composed equally of business and technology leaders who brainstormed on the future state of planning through monthly meetings throughout 2021-2022. Advisory Board Members for Project Zebra: Douglas Kent, ASCM ; Jason Robke, Boeing ; Rebecca Vohl, BSH ; Dave Winstone, Dow Chemical ; Daniel Corsten, IE Business School ; Peter Schram, Independent Consultant ; Chris Tyas, Retired Nestle ; Fred Baumann, o9 Solutions ; Lukasz Zieba, o9 Solutions ; Tanguy Caillet, o9 Solutions ; Steven Daugherty, Samsung ; Olivier Redon, Schneider Electric ; Lora Cecere, Supply Chain Insights ; Phillipe Lambotte, Tonal ; Bob Masching, Trident Seafood ; Arnd Huchzermeier, WHU ; and Stephanie Thomas, University of Arkansas ; The insights from the work with the Project Zebra advisory group were used to build a virtual three-day seminar. The open class, advertised on LinkedIn, filled up to capacity in twenty-four hours with forty-eight business leaders. The goal was to gain additional insights while validating prior work. Methodology

During the summer of 2023, a second advisory group started using the name Project Spark. The members included: Advisory Board Members Project Spark: Vipul Desai, Dow ; Tracy Johnson, Dow ; Dave Winstone, Dow ; Thomas van Woensel, Eindhoven University of Technology ; Danny Kim, General Mills ; Kirsten Olson, General Mills ; Pascal Van Hentenryck, Georgia Institute of Technology ; Tomaz Vrabec, Land O’Lakes ; Margo Cohen, Nestle ; Tim Blake, OMP ; Lennert Smeets, OMP ; Jo Vernelen, OMP ; Aditya Karnik, OMP ; and Bob Herzog, Retired P&G ; The groups tested five use cases to understand the value proposition. 225 business users and technologists completed the training in the past three years. An ongoing steering group continues to guide the direction of the work: Steering Group Members: Michel Huber, BSH ; Dave Winstone, Dow ; Nicole Miera, LKQ ; Margo Cohen, Nestle ; Olivier Redon, Schneider Electric ; Mathew Smith, Western Digital ; and Arnd Huchzermeier, WHU . Figure A depicts the groups' view of an outside-in supply chain planning process.

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Figure A. Overview of an Outside-in Approach Using the SCOR Methodology

Balanced Scorecard

Return on Invested Capital

Inventory Turns

Customer Service

Growth

Margin

Safety

Architect / Strategy Alignment

Plan

Channel Signals

Supply Signals

Sell

Fulfill

Transform

Source

Circular Economy

Enable

Logistics Signals

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TABLE OF CONTENTS

Overview 3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . .7 UnderstandingtheMission. . . . . . . . . . . . . . . . . . . . . . . .9 Inside-OutProcessModeling . . . . . . . . . . . . . . . . . . . . . . .11 A Closer Look at Demand . . . . . . . . . . . . . . . . . . . . . 13 Supply Lead Time and Planning Horizons . . . . . . . . . . . . . . . 15 Understanding Supply Chain Flows . . . . . . . . . . . . . . . . . . 16 Defining An Outside-in Planning Process 18 Bi-Directional Orchestration .. .. .. .. .. .. .. .. .. .. .20 RethinkingInventory.. .. .. .. .. .. .. .. .. .. .. . 21 Unlearning 25 The So What? Who Cares? 29 GettingStarted.. .. .. .. .. .. .. .. .. .. .. .. .. ..30 Driving Change in the Technology Market . . . . . . . . . . . . . . . . . . 31 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 APPENDIX Starting with Data 34 Calculating the Bullwhip Effect . . . . . . . . . . . . . . . . . . . . . . 35 Definitions 36 Background.. .. .. .. .. .. .. .. .. .. .. .. .. .. . 39 About Supply Chain Insights 39 About Lora Cecere .. .. .. .. .. .. .. .. .. .. .. .. .. .39

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Executive Summary

Three assumptions defined the global supply chain: rational government policy, logistics availability, and low variability. Connectivity, sensor advancement, ocean transport scale, and advanced mathematics/optimization advances made the global

turns, and Return on Invested Capital (ROIC). Companies want to grow while improving margins, customer service, inventory turns, and asset utilization 2 . The answer is not easy. The transition requires a shift in focus to understand how improvements in performance drive value.

supply chain possible. During the last decade, while technology advancements increased, the base assumptions are no longer valid. Markets and disruptions increased. Economic cycles increased becoming more regional. The answer? Many might point to technology, but ironically, during this period, while technology advancements to drive insights accelerated, the adoption was low.

______________________________ Definition: Enterprise resilience is the ability to drive consistency in the results of growth, operating margin, inventory turns, quality, and customer service despite market shifts and disruption. ______________________________

The shift from inside-out to outside-in planning is a step change, not an evolution, requiring a redefinition of planning architectures and thinking. The concepts also challenge consortia and consulting thinking. The change takes time and courage while building organizational capabilities. To make the transition, companies need to be clear on the mission (definition of

supply chain excellence), measurement (measurement systems drive behavior), and modeling (modeling changes as companies attempt to be outside-in). Today’s focus is on building better engines without questioning whether current models improve outcomes. So, you might ask, why tackle this journey? Based on three years of testing, we find that the redefinition of supply chain

As business leaders sort through this new reality, driving resilience while improving performance is difficult. They must also recognize that achieving economy of scale in supply chain execution is an opportunity. In our research, regional supply chains outperform global peer groups, and no company gained economy of scale in performance through M&A 1 . The analysis is based on the metrics of growth, operating margin, inventory

1 Supply Chains to Admire 2022 – Supply Chain Insights 2 Lessons Learned From the Supply Chains to Admire Analysis, https://www.forbes.com/sites/loracecere/2022/12/06/lessons-learned-from-supply- chains-to-admire/?sh=7551703951c6, December 6, 2021

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planning to be an outside-in market-driven process improves Forecast Value Added (FVA) by 10%, removes bias, improves

Then, and only then, will business leaders realize that with the movement from regional to global planning, that the problem

the time to know by 150%, and decreases the bullwhip by at least 50%. In the research, we find that the deployment of an outside-in model always beats an inside-out model, reducing process and data latency while improving the organization’s ability to make decisions at the speed of business. As more executive boards drive ESG (Environmental, Social, and Corporate Governmental Policy) improvements, reducing supply chain waste is essential. Making decisions at the speed of markets is core to accomplishing these goals. The shift will take time. Within the industry, technology leaders are pushing solutions to improve the “engines” within existing planning taxonomies without asking, “What is the

changed, but the technology platform did not accommodate the shift. As a result, just updating planning engines is not sufficient. Improvements require rethinking the approach. We must continually redefine the modeling and align metrics to outcomes to drive the industry forward. In the process, the important questions are: • Is the team solving the right problem? • Does the approach help the organization make better decisions? • Do the decisions improve value? • What defines a good plan?

______________________________ A taxonomy is a series of models powered by engines to improve decision-making. In today’s framework, examples of supply chain planning models are Distribution Requirements Planning (DRP), Forecasting, Inventory Management, Production Scheduling, and Transportation Planning (TMS). The consumption and alignment of the outputs, along with the workflow, of multiple models defines the taxonomy. ______________________________

In this report, we start by sharing insights on the current state and follow by sharing an

outside-in approach for supply chain planning and making a case for change. To improve the understanding of the work, we share a glossary of terms in the appendix. We feel that aligning definitions is essential.

problem we need to solve?” . Understanding the inadequacies of the inside-out approach and current taxonomies, becomes clearer as business leaders struggle with results.

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Understanding the Mission

Supply chain excellence is easier to say than to explain. The Supply Chain Insights Supply Chains to Admire™ methodology identifies companies within industry peer groups that drove

by focusing on cross-functional process development and organizational alignment. They tend to be more innovative and data-driven.

higher levels of improvement, better financial performance, and superior value in public markets during a ten-year period. The analysis tracks year-over-year progress on the metrics: year-over-year growth, operating margin, inventory turns, and Return on Invested Capital

Large organizations focused on functional metrics throw the supply chain out of balance. From our research, it is clear that focusing on an efficient organization (lowest cost) degrades the overall corporate performance. Only 20-25% of volume can be managed by efficient measurement tethered to cost reduction objectives. The remaining flows

______________________________ The focus on functional performance, and optimizing one supply chain link at a time is flawed thinking. This traditional view creates “winning functions” and “losing teams.” ______________________________

(ROIC). The study focuses on moving the supply chain excellence from a cost-based focus to margin-driven performance. The selection of the metrics is based on work completed with Arizona State University in 2013. This combination of metrics as shown in Figure 1, improves market capitalization and price to book values. While the individual company performance varies by year, over the ten years of the analysis, the win rate remains constant at 4%. The path to excellence for supply chain leaders takes four to five years, and the most critical factor is leadership. Winning is not magic. Leaders drive higher levels of improvement

must be aligned with market data to improve customer service. Supply chain teams find it easier to drive improvement than to sustain performance. Progress requires patience and building capabilities to manage the supply chain as a complex nonlinear system based on a multi-year roadmap. Let’s take Ecolab as an example. In Figure 2, we show the progress of Ecolab against the chemical industry peer group for 2013-2022. Supply Chain Excellence is a combination of factors. In this orbit chart, Ecolab averages a 14% margin against the chemical peer group of an 11% margin and 5.35 inventory turns against the industry

Figure 1. Performance Factors

Growth

Profitability

Cycle

Complexity

Year-over-Year Growth

Operating Margin

Inventory Turns

Return on Invested Capital

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turns of 4.92. Plotting metrics against each other allows business leaders to see their pattern against their peer group. Ecolab outperformed the industry in both metrics until 2020 but slipped and underperformed in margin during 2020-2022 Performance is complex to sustain through management changes. In the case of

______________________________ “The complexity of the supply chain arises from the number of echelons in the chain and the number of facilities in each echelon. Given the inherent complexity of the typical supply chain, selecting appropriate performance measures for supply chain analysis is particularly critical since the system of interest is generally large and complex.”

Companies following traditional supply chain practices focused on transactional data do not outperform. The use of functional metrics and close coupling of the supply chain to the financial budget is a barrier to driving balance sheet improvement. A worst-case scenario is defining the supply chain as another function

within a rigid set of silos. The best supply chains are focused on using market data and improving customer outcomes. Getting clear on the mission and the definition of supply chain excellence is the first step in building outside-in processes. The next step is re- evaluating the modeling.

Benita Beamon, Measuring Supply Chain Performance, International Journal of Operations and

Ecolab, a new executive team in 2013 drove improvement but could not support it with management turnover and the variability of the pandemic. There are many barriers to beating the peer group in

Production Management, VOL 19, 1999. ______________________________

improvement, performance, and value. Smaller, innovative, newer companies focusing on customer value tend to win the Supply Chains to Admire award.

Figure 2. Orbit Chart: Ecolab Progress Against Peer Group Operating Margin vs. Inventory Turns (2013 - 2022)

Best Scenario

7.00

Fundamental Average Scores Ecolab = 9 Industry = 8

2019

6.50

ECL 0.14, 5.35

6.00

2016

2014

2017

2013 2016

2020

2013

5.50

Chemical 0.11 4.92

2014

2021

5.00

2015

2017

2020

2022

2019

2018

2022

4.50

2018

2021

4.00

0.07

0.08

0.09

0.10

0.11

0.12

0.13

0.14

0.15

0.16

0.17

Operating Margin

Fundamental Average Scores Ecolab = 9 Industry = 8 Ecolab

Ecolab

Chemical Industry

Average (operating margin, inventory turns)

◆ Average (Operating Margin, Inventory Turns) Source: Supply Chain Insights LLC, Corporate Annual Reports 2013-2022 from YCharts

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Inside-Out Process Modeling

In 1982, Keith Oliver, a British Logistician, defined the term supply chain as planning, implementing, and controlling the supply chain operations to satisfy customer requirements as efficiently as possible 3 .

assumed that tight integration of supply chain planning (APS) to ERP improved decision-making. ERP is a ledger of historic transactions which has less importance in the building of outside-in processes.

______________________________ TYPES OF LATENCY

The goal was interoperability across source, make, and deliver. Planning evolved from this definition. Achieving this goal is not possible with today’s supply chain planning processes. The reason? The current definition of planning improves functional optimization but does not drive bi-directional orchestration across source, make and deliver. As a result, the system never considers trade-offs. As a result, cost reductions do not translate to margin. And, while trade-offs are considered in network design technologies, in practice, this modeling is deployed in one-off ad-hoc analyses, not as a systemic planning process. Network design

In traditional deployments, organizations are aligned within a function but fail to make better decisions cross-functionally. This focus on functional optimization is a barrier to improving resilience 4 . The transactional systems in ERP are more far- reaching, providing end-to-end capabilities for order-to-cash and procure-to-pay capabilities, but the optimization within planning systems does not similarly span cross-functional flows. Instead, the planning technologies, with different data models, are linearly threaded through an ERP backbone, increasing waste,

Demand Latency: The time from purchase in the channel to visibility at the order- level. Market Latency: The time for a shift in a market driver to appear in the order signal. Data Latency: The time to collect, clean, synchronize data. Process Latency: The time it takes for an organization to make a decision. Latency, or inaction, is a major barrier to driving agility, and driving improvement in business results. ______________________________

the bullwhip effect, and decision latency. (Our research finds that over 95% of global manufacturers with over $1B in annual revenue implemented ERP during the past two decades 5 .) Tight integration to enterprise data drives insular thinking and adds latency—a risk during disruption. Moving from an inside- out to an outside-in approach improves the time to decide while improving outcomes. The synchronization of supply chain planning to market drivers improves customer service, removes bias, and reduces the time to make a decision. In the testing, we found that inside-out processes increase latency. As a result, the decisions are out of step with market shifts. The greater the variability, the more significant the gap. Market, demand, and process latency make bad decisions on

solutions are usually operated by a separate team operating as a service to help the supply chain and not as a part of holistic planning processes. While organizations bandy about terms like End-to-End Supply Chain Management, the current data models for planning are not end-to-end. To illustrate that companies, have different data models, consider that Distribution Planning (DRP) has nothing in common with Transportation Planning (TMS), and direct procurement does not interoperate with manufacturing applications. As a result, the concept of end-to-end planning is a misnomer. This traditional approach is inside-out. Demand is consumed not managed, and the serial movement of data through supply amplifies the bullwhip effect. The system’s design also

Keith Oliver - Wikipedia, November 23, 2022

4 Supply Chain Organizations Struggle For Alignment, Lora Cecere, Supply Chain Shaman Blog, Organizational Alignment: Overlooked, but So Important. – Supply Chain Shaman, January 2, 2023 5 Supply Chain Insights Report, Embracing the Art of the Possible, Embracing the Art of the Possible – Supply Chain Insights, January 2022

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stale data. In Table 1, we show the reduction in latency with the implementation of outside-in planning. This test was with a durable goods manufacturer.

Data and process latency are problems with a more significant impact during periods of high variability. During the pandemic,

Table 1. Reduction in Supply Chain Latency is Possible with Outside-in Planning Processes

supply chain planners turned off traditional optimization and utilized spreadsheets to make over 90% of decisions. Companies used descriptive analytics and spreadsheet outputs to override conventional planning systems 6 .

The numerous disruptions and fluctuations in demand during the pandemic increased latency. The planning, based on order patterns, was out of step with the market. In an outside-in process, the time to know improves by 85%.

Market Latency

Demand Latency

Process Latency

Inside-out

3-6 months 2-12 weeks 2-6 weeks

Outside-in

1-2 weeks

1-2 Days

1-2 weeks

6 Supply Chain Insights, https://supplychaininsights.com/pandemic-report-temp/, February 2021

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A Closer Look at Demand

Traditional planning processes use customer order and shipment patterns as a proxy for demand. The belief is that future demand can be based on mining the patterns of history. But what happens when history is no longer a good predictor of future demand? Or does the market shift faster

than an organization can make decisions? In these cases, the organization quickly learns that the order falls short as a demand predictor. In Table 2, we share the issues of order- based forecasting.

Table 2. The Fallacy of Order-Based Forecasting

Assumption

Reality

Insight

1. Today’s order patterns can be predicted by historical demand. 2. Collaboration across business teams can improve the forecast.

History is not a good predictor of current demand. Most organizations introduce political bias based on functional incentives and decrease the Forecast Value Added (FVA). The longer the tail of the supply chain, the larger the issue with demand latency.

The pandemic period distorted demand, and current levels of migration further distrupt the patterns. Collaborative forecasting increases process latency and time to action. In process-based industries like beverages, chemicals, consumer electronics, and non-durable goods, the elongation of the tail of the supply chain made demand cycles longer than supply cycles over the last decade. Yet few companies measure and manage demand latency and the bullwhip effect. Monitoring the shifts in flow based on market data improves sensing and time for action.

3. The order pattern represents current demand.

4. Demand is best managed as time-phased transactional data to be consumed by supply. 5. Products are forecastable by conventional planning models.

The average supply chain has multiple supply chains best represented by flow.

With the increase in product complexity and regional assortment, most companies greatly decreased the forecastability.

Today, only 40-50% of products are forecastable by conventional planning models/techniques.

Forecast Value Added (FVA) is a valuable technique to analyze value from the forecasting process.. A positive FVA indicates an improvement over the naïve forecast (prior period sales), whereas a negative FVA means that the methods and technologies are making the forecast worse, not better. In eight

An additional error in the forecast has a multiplier effect on inventory and costs. Of the 154 students in the class, only one was measuring FVA at the start of the course. In the shift from inside-out to outside- in processes, there is a shift from a focus on error to FVA. The reasons are many. Degradation of implementations, turnover of employees, dirty data, wrong models, political bias, and market shifts. Overall, forecastability is worse in most companies than in the days before the pandemic, and few companies are measuring these shifts and testing their

______________________________ Forecast Value Added (FVA) analysis measures the improvement of the output of the forecasting process against the naive forecast. The naive forecast definition varies by demand stream. _____________________________

out of ten companies that we worked with in the 2023 training, we found a negative Forecast Value Added (FVA) result. Most companies that measure FVA soon realize that despite all the money spent on advanced technologies and processes, the methods are making things worse.

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systems to ensure better outputs. In the class, we measured forecastability using the Coefficient of Variation (COV) and segmented demand by stream. The results in Figure 3 are typical for the average manufacturer in the class. Only 54% of

items are forecastable using item/location forecasting based on order history. Fewer items became forecastable throughout the pandemic using history as a proxy for demand.

Figure 3. Forecastability from a Class Participant

25% of Items High Fill Rate

15% of Items Moderate Fill Rate

29% of Items High Fill Rate

31% of Items Lowest Case Fill Rate

Forecastability (Coefficient of Variability)

In an outside-in process, forecastability is tested. The model flags items not forecastable by the model and recommends changes to the model to improve forecastability.

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Supply Lead Time and Planning Horizons

In an outside-in planning process, the elements of strategic, tactical, operational, and execution planning flow change. Tactical processes focus on decisions

Companies do not have one supply chain. This report explains that organizations typically have four-to-seven supply chains.

Based on our research, only 20-30% of the flow is high-volume and predictable. A common mistake is treating all flows the same and not recognizing the characteristics of different supply chains. Today’s architectures do not enable flow visibility, and most organizations do not differentiate strategies based on demand and supply variability. The third issue is governance. Global teams typically make tactical planning decisions, while operational and execution planning are usually regional planning processes. Governance, the definition of who should make decisions, and what defines a sound decision process is essential. Leaders clearly define supply chain excellence for the company, the global organization’s

______________________________ BARRIERS TO EFFECTIVE S&OP PROCESSES Lack of clear operating strategy. To drive the best results, balance the “S” with the “OP.” Clearly identify demand and supply flows. Design of flow based on demand and supply characteristics. Alignment of processes: appropriate use of time horizons. Lack of clear governance: definition by role. Alignment of measurement systems. Failure to recognize latency as a barrier to driving improvement. Measurement of error without driving improvement in Forecast Value Added (FVA) analysis. ______________________________

made outside of supply lead time, while operational planning aligns the organization’s decision-making within supply lead time. Sales and Operations Planning (S&OP) should be a tactical process focusing on improving decisions outside of supply lead time. However, this is not always the case. Many companies mistakenly focus their S&OP efforts on short-term discussions, which is a mistake. During the pandemic, companies became more reactive, focusing on short-term decision- making. A common mistake is not defining lead times for each supply chain within the business and aligning S&OP to these cycles. In Figure 4, we share definitions.

roles, and the regional and divisional teams. Companies with the most robust tactical and strategic planning tend to get the most significant improvements in margin, asset utilization, and inventory turns. At the same time, successful operational and executional processes are essential to improve customer service.

In the horizontal cross-functional process of S&OP, the organization should align based on corporate strategy. The lack of a clear operational strategy is an issue for 80% of companies 7 .

Figure 4. Definition of Planning Time Horizon(s)

Week 2

Month 3

Month 18

Year 5

Granularity: Yearly, Quarterly, or Monthly Forecast Horizon: Depends on the Goal Review Frequency: Depends on the Process Goals

Strategic

Granularity: Quarterly, Monthly, or Weekly Depending on the business Forecast Horizon: End of the Freeze Duration through the Period Required for Asset Planning Focus: Planning for Constraints and Aggregate Buying Process Frequency: Monthly

Tactical

Granularity: Weekly or Daily Forecast Horizon: Within the Freeze Duration Process Cadence: Weekly or Daily

Operational

Granularity: Daily or Hourly Forecast Horizon: With the order duration Process Cadence: Daily or hourly

Executional

Granularity: Daily or Hourly Forecast Horizon: Within a transaction Process Cadence: Daily or hourly

Transactional

7 What Does Good Look Like For S&OP? – Supply Chain Shaman and Strategy. Strategy. Wherefore Art Thou Strategy? – Supply Chain Shaman

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Understanding Supply Chain Flows

Traditional supply chain planning systems use time-phased data as an output to drive alignment. In an outside-in model, the construct is to sense and manage supply chain flows. Making this shift requires overcoming many change management issues. The first is the recognition of multiple

of change to redefine the models from inside-out to outside- in. Focusing on transactional processes results in most planners not seeing the forest for the trees. In addition, the visualization helps organizations to understand better how fad- based processes like Demand- Driven Material Requirements Planning (DDMRP) or flowcasting apply to specific streams but not others. The key is to align the streams with policy and rules to improve outcomes. This is not possible with existing approaches

Table 3. Definition of Supply Chain Flows

Flow Flow (Volume) Forecastability Order Cycle Efficient High Value High (COV .03 or less) Regular Order Cycle Responsive High Low Short and Rapidly Changing Cycles Agile Low Low (COV of .05 or greater) Variable New Product Launch Varies Low Variable Seasonal Medium Low Variable

supply chains; the second is providing role-based views by function and user change. The outside-in process moves from a planner-centric organization to a new environment where the business user is the primary user. Planning self-service enables business leaders to collaborate on supply chain decisions across functions. In traditional definitions of supply chain planning, it is difficult to see demand and supply flows and detect market shifts in individual supply chains. In outside-in processes, the flows align with rules and policies based on market sensing (demand and supply). As shown in Table 3, companies typically have five to seven distinctly different flows. A helpful technique to drive understanding is to map each stream to understand the rocks, barriers and flows within the organization. This activity is beneficial to building a guiding coalition for change. In Figure 5, for example, we show the map of the river of demand for a large food manufacturing company for a new product launch. The mapping of flow changes the paradigm, laying the seeds

and planning taxonomies. In Table 4, note the number of players that struggle to make decisions on the demand stream without synchronized role- based views. Today, there are no applications in the market to enable team collaboration on demand visibility to support the new product launch processes. The second issue is that today, each team operates in isolation using their own data. Each data set has its problems, making integration difficult—the data granularity differs, and the integration is laden with latency and master data issues. For example, the latency of IRI data (a syndicated data provider), in Figure 4, is three weeks. The order latency is four to five weeks. While the company has daily point-of-sale data (twenty- four-hour latency), the data sits in the sales teams and is not used by marketing, finance, or supply chain teams. The lack of standard views for demand collaboration is an opportunity.

Table 4. Data Latency Example for Demand Data

Data

Team

Latency

Sales account point of sale, warehouse withdrawal, and replenishment data Syndicated data. This data comes in many forms, such as IRI, Nielsen, etc.

Sales. Data is managed independently by each sales team. The average food company has 40 account teams. Marketing. Syndicated data has a two-to-three-week latency from the channel. Supply Chain. Order data is a two-week to four-month view of channel demand.

Three days to 2 weeks

A two-to-three-week historical view.

Order data. Retail orders.

Two weeks to four months.

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______________________________ MOVING TO FLOW:

Outside-in processes aim to drive cross- functional collaboration on demand and supply decisions and move the organization to business-led decision-making. While many well-intended data scientists attempt to improve outcomes through better math, the answer lies in better mapping and managing demand flows and rethinking taxonomies. The redefinition needs to precede planning engine redefinition. Today’s taxonomies reinforce functional optimization and do not enable cross-functional collaboration and decision-making. The first step in moving to an outside- in process is to type demand flows and understand the barriers and enablers for each stream. With advances in Web 2.0 technologies and schema on read,

companies can drive substantial improvement through NoSQL to create a real-time perpetual inventory signal to align Available to Promise (ATP) and allocation strategies at the speed of business. In addition, an ontological semantic engine coupled with a knowledge graph can automate rules and policies to monitor planning streams and connect optimization more effectively. However, embracing these changes requires rethinking architectures. Today, there is no way to effectively connect optimization outputs to rules and policies.

•

Identify demand streams.

•

Map supply streams and understand constraints.

•

Align rules and policies based on flow. Define a feasible plan.

•

Design bi-directional orchestration levers.

•

Minimize the obstacles-rocks, eddies, and barriers. Design supply flows based on market drivers, variability, and modulating demand flow.

•

Build collaboration processes for demand.

•

Identify the constraints for make, source and deliver. Build a planning master data layer to align planning engines to market data. ______________________________

•

Figure 5. As-Is Flow for the River of Demand for New Product Launch for a Food Company

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Defining An Outside-in Planning Process

The goal of an outside-in planning process is simple. The objective is to use market data—both demand and supply — to

opportunity, increases risk, and exacerbates organizational alignment issues. While demand-driven senses and translates channel data to improve the demand

decrease the time to sense and improve the supply chain response—the expected results. The design of outside-in planning processes stretches from market to market (channel to the supplier), driving bi-directionally to improve organizational decision-making. This starkly contrasts inside-out processes that linearly thread through a transactional backbone to consume a time-phased demand forecast. The outside-in journey starts with training and organizational alignment. Alignment builds trust. The organization needs trust to outperform against their peers.

signal, a market-driven value chain drives bi-directional orchestration across source, make, and deliver, focusing on enhancing a balanced scorecard. The shift from a marketing-driven to a market-driven process is a challenging but critical goal for traditional retail and consumer goods companies. In our research, when we ask companies to rate the importance of alignment between organizational functions and the current level of alignment, we find that organizational alignment issues have grown three-fold over the last decade. Issues to alignment gaps between

______________________________ What Focus Drives Supply Chain Excellence? 1. Sales-driven: Alignment to a sales-driven forecast. 2. Marketing-driven: Organizational execution of marketing plans. 3. Budget-driven. The budget defines plans as a constraint. 4. Demand-driven: Ingestion of channel data to improve the demand signal. 5. Market-driven: Plans driven by market signals bidirectionally ______________________________

After defining supply chain excellence, the organization needs to clearly determine each function’s role. A sales-driven supply chain is starkly different than a marketing-driven organization. Similarly, introducing the budget as a constraint minimizes

commercial and operations teams are legacy. During the pandemic, alignment issues between the supply chain group and finance, manufacturing and procurement, transportation, and customer service became more acute.

Figure 6. Organizational Alignment Gaps

79+30+ +91+450+ +64+180+ +76+30+ +67+240+ +64+240+ +73+ 30+ +58+240+ +52+ 10+ +73+420+ +219+ Logistics and Supply Chain Planning Supply Chain Planning & Manufacturing Logistics & Procurement Sales & Supply Chain Planning Finance and Supply Chain Planning Finance and Manufacturing Finance and Procurement Customer Service and Distribution Procurement & Manufacturing Supply Chain Planning & Customer Service 79% 73% 30% 91% 45% 42% 21% 64% 76% 73% 58% 52% 30% 67% 64% 24% 24% 21% 24% 33% 18% Greatest Gaps Between Users & Vendors

Corporate Social Responsibility & Manufacturing

Source: Supply Chain Insights LLC, Analytics Digital Transformation Study Q9. In your opinion, how important is it for each of the following pairs of teams to be aligned within your supply chain? Q10. How aligned do you believe that these same pairs of teams actually are with your company?

19 THE OUTSIDE-IN PLANNING HANDBOOK | 2023

______________________________ “83% of manufacturers report that deployed supply chain technologies have not met expectations.” Source PWC Report, 2023 _____________________________

What can we learn from the research? Companies with teams from planning, sourcing, manufacturing, and transportation reporting to a central leader tend to be the most balanced, with the smallest gaps. The organizations with the closest alignment, as shown in Figure 6, have improved employee retention and driven growth faster while outperforming peers on operating margins. An example of a company that drove improvement in

planning redefines demand concepts. The flows are sensed, typed, and profiled using the demand analyzer for demand collaboration and supply optimization. The flows cross over the commercial sales and marketing teams and finance to enable collaboration on the demand cycle. Demand collaboration enables the team to determine the following: • Impact of market drivers. Visibility of market and demand latency. • Baseline demand for products, categories, and brands. • Effectiveness of demand shaping. Percentage of demand shifting. • The bullwhip impact of the portfolio shifts. • New product launch acceptance. This monthly analysis starts the planning cycle and precedes S&OP.

organizational alignment is Clorox 8 . The Company worked with Georgia Tech to train commercial and operations teams to understand supply chain constraints and flows. The leadership team cited this program as a critical factor in outperforming their peer group in job retention, margin, and growth for 2013- 2021 (pre-pandemic). For the period, as shown in Figure 7, Clorox outperformed the household products peer group with margins of 18% compared to the industry average of 16%. Clorox also had inventory turns of 7.33 versus the industry average of 4.78. After defining the roles and focus, the journey for outside-in

Operating Margin vs. Inventory Turns (2013 - 2021) Figure 7. Clorox Supply Chain Performance

Best Scenario

2015

8.50

2014

2020

2013

7.50

CLX 0.18, 7.33

2017

2016

2019

2018

6.50

2015

2014

2013 Household Non-Durable 0.16, 4.78

5.50

2021

2020

2019

2016

2018

4.50

2021

2017

3.50

0.15

0.16

0.17

0.18

0.19

0.20

Operating Margin

Fundamental Average Score Clorox = 8 Industry = 6 Clorox

Clorox Household Non-Durable Industry

Average (Annual Revenue, Return on Invested Capital (ROIC)

Household Non-Durable Industry

◆ Average (Annual Revenue, Return on Invested Capital (ROIC)) Source: Supply Chain Insights LLC, Corporate Annual Reports 2013-2022 from YCharts

THE OUTSIDE-IN PLANNING HANDBOOK | 2023

Bi-Directional Orchestration

In the outside-in process, demand is a flow. The streams are sensed, typed, and constantly aligned and orchestrated to improve supply flows in the demand analyzer. Flow typing enables the assignment

As the organization matures and refines the demand process, the goal is to ask, “ What is the Role of the Forecast? ” Then, based on a definition, the group aligns demand and supply flows. As shown in the six- stage maturity model in Table

of rules and policies while recognizing the characteristics of multiple supply chains. The flow is bi-directional across the organization as

Figure 8. Forecasting Planning Models in the As-Is State

5, the forecast’s role shifts as companies move from maturity stages 2-3 to 4-6 in the design of demand flows. The shift is from measuring

INPUT

ENGINE

PLAN

companies orchestrate demand and supply strategies based on market data. As shown in Figure 8, in traditional demand planning, the inputs are shipments or orders, and the output is a hierarchy of time-phased data. While companies have invested in improving the engines, few have challenged the model. (Model types include attribute-based modeling, probabilistic modeling, attach-rate forecasting, and flow-based modeling through the evolution of the graph.)

error to establishing discipline in forecasting by measuring and tracking Forecast-Value Added (FVA), bias, and bullwhip. In essence, companies implement demand planning solutions focusing on error and bias and never question if they are improving the demand forecast output to improve the signal.

Table 5. Changing Maturity of the Role of the Forecast in Supply Chain Planning

STAGE 1

STAGE 2

STAGE 3

STAGE 4

STAGE 5

STAGE 6

Set Inventory Targets for Safety Stock

Drive Planned Orders for S&OP

Determine the Form and Function of Inventory Targets

Demand Sensing and Translation of Market Signals

Drive Commodity Buying Decisions in Logistics and Procurement Sourcing

Bi-Directional Orchestration Baseline Demand: Analysis of Shifting/Shaping

Balance of Risk and Opportunity for the Organization

Design of Network. Balance and Risk and Opportunity for the Organization.

Cross-functional Demand Collaboration: What-if for Demand across Commercial and Operational Teams

21 THE OUTSIDE-IN PLANNING HANDBOOK | 2023

Rethinking Inventory

The third step is to define the role of inventory in the value chain. Inventory is the most critical buffer to absorb demand and supply volatility. The design of inventory buffers is essential, and the traditional focus solely on finished goods safety stock management is insufficient. Implementing inventory management in outside-in planning requires a redefinition and clarity. The group must answer, “ What is the role of inventory? ” Along with the answer to the question, “ What makes a good inventory plan? ” This is important because of the changing nature of inventory in the supply chain. In the 1980s, when supply chain planning was first defined, safety stock was a higher percentage of total inventory, averaging 40-50% for each organization. Today, safety stock is 10-15% of total inventory. Inventory optimization increased in importance with the impact of the pandemic. (Based on demand and supply variation, the form and function of inventory assess where and how to store inventory and design buffers. For example, as variability increases, companies need to shift from holding finished goods to storing raw materials and semi-finished goods.) And, while inventories should be based on both demand and supply variability, in qualitative interviews today with supply chain leaders, we only find a focus on demand variability. Only 10% of companies include supply variability in the safety stock calculations. As shown in Table 6, on average, companies hold 28 more days

of inventory across industries in 2022 than at the beginning of the recession in 2007. The reason? There are five: 1. Longer Shipping Lanes. The impact of longer in-transit shipments and shipping variability increased inventory. 2. Product Proliferation. The addition of products to portfolios increased manufacturing cycle stock. 3. Bullwhip Effect on Raw Materials. Establishing inventory targets only through MRP introduces the bullwhip effect in raw material management. 4. Increase in Demand and Supply Variability. Over the last decade, as supply and demand variability increased, traditional approaches focusing only on safety stock management based on demand variability were insufficient. 5. Focus. The focus on functional metrics impedes the ability to manage inventory. Effective inventory management requires organizational alignment. Only 5% of companies interviewed can answer, “What makes a good inventory plan?” Ironically, inventory is also the most significant buffer and excellent source of waste, or MUDA, in the supply chain, creating organizational tension. (The Japanese “Muda” word ( 無駄 ) translates as uselessness or futility. In Lean management, Muda represents the changes or actions that do not cause a value-increasing effect on the product or drive improvement

Table 6. Increase in Inventory Across Industry Segments Over Periods from 2004-2022

Industries

Years

% Difference (2020-22 vs. 2004-06)

2004-2006 2007-2008 2009-2013 2014-2019 2020-2022

Medical Device

110

113

131

143

163

Beverage

115

119

138

191

164

Pharmaceuticals

155

144

170

195

197

Beauty

108

116

125

124

Automotive Parts

Household Products

Aerospace & Defense

103

123

Chemicals

Automotive

Food

Semiconductor

THE OUTSIDE-IN PLANNING HANDBOOK | 2023

that a customer will be happy to pay for.) Mature organizations understand the differences between inventory as a buffer and quickly resolve inventory waste issues, while immature organizations implement technologies betting on better

material inventories are more volatile due to the bullwhip effect. For example, in paper manufacturing, note the amplification of the signal. No planning technology today measures and uses the bullwhip impact in calculating raw materials inventories.

answers from optimization. While the traditional planning solutions focus on managing safety stock, they miss the focus on a holistic inventory strategy. As a result, total inventory is not managed in existing systems in most organizations. Today, network design technologies, in concert with supply chain planning, help business leaders set targets for the form and function of inventory, as defined in Table 7. In outside-in processes, inventory targets for the form

We share a calculation of the bullwhip impact in the Appendix. In process flow evolution, the advisory group brainstormed the future state. Note in the current state of functional silos, planners work in isolation, often using spreadsheets. Currently, the organization does not see flow or manage variability. In visualizing the outside-in state, demand, and supply are managed as flows through a collaborative platform. In this future, the business leaders become the targeted users focusing

Table 7. Definition of Form and Function of Inventory

Form

Function

Supplier owned inventory: raw materials

In-transit Inventories: Inventory that is on trucks, barges, and containers. The longer the trade- lanes and the slower the mode, the larger the requirements for in-transit inventory. Cycle Stock: In the planning of production, finished good production is cycled to ensure that the production lines are fully utilized. The average rotation between products on production lines in consumer packaged goods is three weeks Safety stock: Inventory requirements to buffer demand and supply volatility Seasonal Inventories: Inventories required to support seasonal builds. Promoted Items: Inventories to support the promotional lift to support a promotion.

Company owned inventory: raw materials

Work in process (WIP) inventory Finished goods at the company warehouse Finished goods in the channel

and function of inventory happen with each monthly process cycle in the design step before S&OP. While most companies focus on finished goods inventories, raw

on the relationship of outcomes to a balanced scorecard and collaborative platforms to agree on demand shaping/shifting strategies and go-to-market plans.

Figure 9. Raw Material Inventory Comparison to the Value of Shipments for the Paper Industry

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