Competing in an environment of constant change is always a challenge. But when businesses have no idea what will happen next, or when, the sheer scale and risk of operational upheaval escalates – leaving little to no room for error and delay. 

Uncertainty may be natural, yet it’s no reason to do nothing until something happens. Dramatic shifts are happening too quickly to reserve strategic planning as a traditionally annual exercise. From minor modifications to profound events, businesses must continuously anticipate risks, disruptions, and opportunities and evaluate how every function, role, interaction, and action can mitigate them, turn them into opportunities for growth and ultimately help to build the resilient enterprise

Fortunately, businesses are beginning to get the message as they consider the advantages of what-if scenario planning to foresee the possibilities ahead and get comfortable with a level of unpredictability that paralyzes actionable decision-making. 

The True Meaning of What-If Scenario Planning 

What-if scenario planning recognizes three realities of running sales and operations planning (S&OP): market conditions have already changed, will continue to change, and must always change. By asking “what if” with a disciplined framework of data-driven trending, analysis, and simulation, decision-makers can evaluate strategic and operational alternatives that can challenge the status quo and offer better ways to get business done. 

 But predicting change is just the beginning. The actual value of what-if scenario planning is the ability to prepare for short-, mid-, and long-term shifts, such as: 

  1. Could entering an adjacent market bring losses that outweigh potential gains? 
  1. What should the company do if emerging challenges require unprecedented operational changes?  
  1. Will changing social awareness, expectations, and values call for new business models, product redesign, or more personalized services?  

Technologies that automate and accelerate the entire exercise help ensure forecasting and decision-making are flexible, nuanced, and responsive enough to allow the company to move forward rapidly. More importantly, they open the door to a continuous cycle of integrated business planning (IBP). 

A Prime Entry Point for Intelligent, Integrated Business Planning 

Digitalizing what-if scenario planning empowers businesses to leverage predictive insights to act strategically, confidently, and courageously. Such an edge can foster a shift in strategy, embolden forward-thinking innovation, or even fulfill unmet customer needs. 

When built on a mature, cost-based optimization engine matched with a digital representation of the supply chain , integrated business planning operationalizes what-if intelligence to find the best balance between supply and demand, as well as risk and opportunity. For example, sales organizations can take into account product shelf life and opportunities to sell inventory before the expiration date. Simultaneously, supply chains identify alternative forms of raw materials to define bills of materials that fulfill business requirements better with less risk. Even supply can be pegged geographically to strategically respond to demand across sales channels and within the distribution network. 

Technology-enabled what-if scenario planning can provide on-demand forecasts so organizations can optimize their strategies daily, weekly, or monthly. Demand can be prioritized flexibly and aligned with strategic corporate objectives. Supply chain resilience can be evaluated efficiently through real-time simulations and increased visibility throughout the extended enterprise, including external suppliers and distributors. Financial performance can also be assessed and compared against predefined budgets from multiple perspectives at the same time. 

From Chosen Scenarios to Live Competitive Advantages 

Recent experience with the dynamics and turbulence of uncertainty should compel businesses to rethink how S&OP strategies are created and modified. By tapping into the possibilities of every conceivable scenario, functional and executive decision-makers can discover more resilient, long-term capabilities and bring them to life to better withstand future external shocks.  

This is the beauty of what-if scenario planning, whether in times of crisis or calm. With a fresh look at how they operate and respond to change proactively, companies can emerge from any challenge with greater agility, stability, and ingenuity. 

Check out this great blog post on what makes IBP a proven, powerful planning process. 

What is integrated business planning (IBP)? Is it identical to sales and operations Planning (S&OP), an extension of it, or something altogether separate? A GPS for your business? That sounds more like a slogan than a practical definition.  

Call it what you want, IBP is probably best thought of as mature S&OP. It has overcome an early identity crisis and proved itself as a powerful enterprise planning tool, helping companies meld financial, sales, production, procurement and marketing information into a single plan, grounded in reality. 

What Does IBP offer? 

Integrated business planning is a new level of visualizing, evaluating and optimizing your supply chain. It distills complex, cumbersome and disconnected tasks into a single integrated process that streamlines and unites planning activities to produce better business decisions. Combining data from across the enterprise, IBP creates one planning framework by removing organizational and technology barriers and synchronizing plans across strategic and tactical time horizons. 

So What’s the Problem? 

How can something so potentially valuable be so difficult to achieve? Is there really a problem? Based on recent work with clients, Lora Cecere, founder and CEO of Supply Chain Insights, produced research that clearly demonstrates the decline in perceived S&OP effectiveness. 

why integrated business planning initiatives fail

Given that IBP is an advanced, well-developed state of S&OP, it follows that companies struggling with S&OP may consider IBP a bridge too far. At least temporarily. Unfortunately, some will simply stop trying. Even those organizations that have successful S&OP efforts under way are challenged by the stamina needed to continue the journey to fully-fledged IBP.

Why Integrated Business Planning Initiatives Fail

There are myriad reasons why integrated business planning initiatives fail, but we’ll focus on those we hear most often. 

1) No Executive Engagement 

How many times have you read that? It seems trite now, but one of the fundamental reasons why integrated business planning initiatives fail through lack of executive leadership is because: 

  • IBP done right is going to require hard decisions about risk mitigation and margins, for example, that leadership can’t delegate and can’t dodge; 
     
  • When it gets difficult — and it will — you’ll need to rely on the power of the hierarchy to move forward.
2) Poor or Non-Existent Data Governance 

CIO Magazine says data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used.  

IBP will expose data silos and data turf wars, as well as multiple definitions for seemingly straightforward concepts like “forecast” and “demand.” Be prepared to work hard developing a single source of truth. Turning a world of data into a data-driven world depends on many things, including firm definitions and roles.

3) No Clear Goals 

Lora Cecere reminds us that IBP is actually the third step of maturity in an S&OP implementation. Each stage has a different goal. Over 95% of companies she consults with jump into planning without answering these three questions: 

  1. What is the goal of your S&OP process? 
  1. How do you make decisions? 
  1. What do you measure? 

Each phase builds on what comes before. Usually, after failing, companies find that they must first be able to execute “what if” analyses to determine a feasible plan before attempting to match demand with supply.  And that mastering the process of matching demand with supply is a prerequisite to successful IBP. 

4) No Cross-Functional Collaboration, Lack of Focus on Data Model and Analytical Plan 

These might seem like strange topics to combine. The whole point of IBP is aligning the organization in a forward trajectory, and that’s hard to do without the involvement of all the key stakeholders.  

Inability to translate among different functional views of information can be alienating, especially in the early stages when buy-in might be fragile. Sales inputs revenues by account, Operations may want to see demand in units by product, and Finance is interested in margin. Ideally, the same information should be tailored to stakeholders in a manner they understand.  

5) IBP Gets Positioned as an IT Project 

IBP can’t be viewed as a module that your ERP provider can license to you or a new report that IT could write for you if they had time. It must be positioned and even revered as radical, transformative, permanent, and worth the effort.

6) The Role of the Budget is Undefined 

Many industry experts believe the key change management issue for IBP is wrangling the role of the budget.  Some companies treat the budget as sacrosanct. As a result, they constrain the outcome of IBP to ensure that budget goals are met.  

Others realize that the quest to maximize enterprise opportunity while mitigating risks can mean that IBP outcomes drive budget updates. 

7) Poor Setting of Expectations 

Saving the most obvious for last, perhaps. Consider the following as you sharpen your diplomacy skills, preferably at the outset of your IBP project, not the middle:

  1. The entire contemplation is far-reaching and complex. Don’t strive for “one number” anything. Some companies spend months deriving a single “guiding” number that is so massaged and scrubbed that the only thing you can be sure of is its inaccuracy. Others paralyze themselves trying to evaluate hundreds of metrics. Pick your big 12 and start. Monitor them and learn from them. 
  1. Most IBP projects eventually uncover incentives at cross purposes. It could be Sales and quota credit versus Operations and customer satisfaction. Persevere! (And remember what we said about executive engagement.)  
  1. You are on a journey. There is no IBP finish line. But there are exciting milestones offering better business outcomes. 
Outcomes Worth the Effort


Understanding why integrated business planning initiatives fail is the first step towards success. The reality of IBP is that the effort spans multiple departments, people, timeframes and often continents. The path is not always pretty. But the rewards are dramatic: processes that balance resources and information with corporate objectives to increase revenues, reduce costs and boost profitability. Imagine understanding strategic trade-offs years in advance while making the day-to-day decisions based on the real-time pulse of your business. Top floor and shop floor in sync. But there are pitfalls. We hope the guidance above helps you avoid some of them. 

The power of Integrated Business Planning (IBP) comes in helping companies align financial, sales, production, procurement and marketing information into a single plan, grounded in modern-day reality. 

No one should be surprised that consultants, pundits, marketers and academics have found ways to argue about the definition and ultimate business value of Integrated Business Planning (IBP). Indeed, it would be surprising if they agreed. (Can you imagine adding economists to this already potent mix?) 

IBP is a series of coordinated processes and supporting technologies to translate desired business outcomes into financial and operational resource requirements. The outcomes, for example, can be expressed in terms of balanced achievement across the following: 

  • Revenue and demand 
  • Service levels 
  • Inventory levels 
  • Profits and margins 
  • Cash flow 

Early challenges to IBP included a lack of an accepted academic definition, the absence of a governing body and a bias toward supply chain applications. But research suggests the most widespread and stinging criticism of IBP was that it was just mature Sales & Operations Planning (S&OP) by any other name. These critics sensed a sinister “rebranding” ploy designed to get companies to purchase technology and services they already own but perhaps have not fully implemented.  

Gartner appeared to lend credence to the idea that IBP is just “Black Belt S&OP” in a research note titled Introducing the Five-Stage Sales and Operations Planning Maturity Model for Supply Chain Leaders

As for rebuttals, an Industry Week column penned by Dean Sorenson argued that complex manufacturing environments can benefit from IBP, which he framed as Enterprise Performance Management tools (excellent for long-term goal-setting and full financial picture, weak on supply chain details) coupled with S&OP (excellent tactical solution, but often lacks the ability to do scenario planning on a full set of financial statements that are explicitly reconciled to operational plans). 

Rather than jump into the fray with more arguments and more acronyms, consider this: the most recent of the sources cited above is seven years old! With the passing of time has come more complicated and interdependent global operating environments and higher expectations for IBP; the latter driven by skilled practitioners demanding more capabilities from technology vendors.  

The Future of IBP is Now

It’s time to move beyond the ‘emperor’s new clothes’ argument. Down here on the ground where real people are trying to run real businesses, there’s little time for semantic subtleties. Logility EVP Mac McGary acknowledges the similarity with S&OP, but quickly moves beyond labels and exhorts companies to step up to what it takes to build a resilient enterprise in 2021.  

“IBP will remind many of you of Sales & Operations Planning. But there are new expectations in this realm, and they are focused on creating speed, trust and resiliency. Specifically, your organization must embrace the value of a granular view of the extended enterprise. Can you analyze trends at the SKU level and drill into constraints on the shop floor? If so, then pronouncements from the War Room will be ‘virtually vetted,’ and not the result of panic or guesswork. Challenge yourself to think beyond the traditional Available-to-Promise metric and ask if an action meets the criterion of Profitable-to-Promise. You need to examine revenue, cost and margin impacts of every scenario under consideration. Some of these decisions will be automated, others will require exception-based intervention.” 

You can learn more from Mr. McGary and his roadmap for designing and building the resilient enterprise, including the importance of an IBP process, here.

Logility defines IBP as multi-horizon strategic business planning that combines volumetric and financial data into a single, highly visual, comprehensive planning platform that delivers greater global visibility, more powerful multi-scenario analysis over multiple planning horizons, tighter collaborative workflow, and a wider spectrum of alerts. 

Unfortunately, most companies still use multiple disconnected planning processes managed by different people using different data sets and assumptions. In these fragmented planning environments, strategic planning is based on outdated data and often unrealistic supply chain capabilities, and tactical planning is a constant firefight with no regard for strategic goals and objectives. 

The Very Real Power of Integrated Business Planning

What is needed is a single, integrated planning solution that unites short-, medium-, and long-term planning under one system. Adopting a unified planning and decision support system that covers strategic and tactical horizons empowers near-term S&OP and long-range business planning in one integrated solution. That’s the power of integrated business planning, and according to industry research comparing the performance of companies that follow an IBP approach versus those that don’t, IBP users are: 

  • Better able to align supply and demand over the entire horizon 
  • More effective at collaborative planning and building real trust between groups 
  • Able to reserve capacity at key suppliers earlier and more efficiently 
  • Faster to react to unexpected disruptions in the supply chain 
  • More likely to use alert-driven response and adjustments 
  • Better at handling promotional demand 
  • More likely to use “what-if” analyses to evaluate and prepare for contingencies 
  • Better at using KPIs that reveal the truth about enterprise performance across all planning horizons 

So get started on your journey to realizing the power of integrated business planning. Markets and competitors aren’t waiting for you. Don’t end up like the proverbial “two bald guys arguing over a comb.” There are real insights and lasting business benefits to be had from managing strategic, tactical and operational planning with one platform.  

Take a look at this short video to see how Logility’s IBP solution supports accelerated planning and decision-making across the enterprise. 
https://www.logility.com/explainer-video/responding-with-speed-and-reducing-latency/ 

Logility is working with a leader in the distribution and logistics space that provides delivery to auto dealers for the dealers’ parts and services business. 

This specialization allows them to be more efficient and knowledgeable about their customers and the specific parts they are delivering. Because these parts cannot be sold and services cannot be scheduled and performed until the parts are delivered, it is exceptionally important that they deliver on time and in full. 

The problem this organization faced was knowing which routes were responsible for delayed deliveries and what was causing the delays. The company felt their routes and stopping points caused inefficiency, but they also wanted to forecast the impact of extraneous factors such as weather, road construction, and traffic patterns. With this information, they could adjust schedules to proactively service their customers, adjust delivery times, and gain greater efficiencies from their fleet. In addition, they wanted to incorporate the inventories from their parts suppliers and their geographic locations so they could minimize empty return-trip trucks and duplicate runs, as well as minimize the inventory that they had to keep on hand in their distribution centers while knowing which suppliers had what parts in stock. 

Logistical Challenges 

With 10 distribution centers, each with over 100 routes, and each route with over 15 stops, this posed a significant logistics problem. Add to that over 2,500 parts from over 200 manufacturers and this became a significant supply chain opportunity. In addition, each truck itself – and there are over 1,000 of them – is a constant big data generator, capturing location, efficiency, speed, right turns, left turns, stop times and the length of each stop. How do you coordinate all this data against the business issue of on-time deliveries with the greatest efficiency? 

The Logility Difference 

Using Logility, they first aggregated their enterprise inventory and logistics data with two external data feeds. Then they created a series of operating metrics to be used by managers as well as KPIs for reporting to executives. An important feature of these reports was predictive metrics. These metrics, using Logility’s predictive modeling capabilities, identify routes with risk factors and suggest the likelihood of one or multiple delays in the near future (weeks). Lastly, they distribute these efficiency reports and interactive dashboards using the Logility® Digital Supply Chain Platform. An important feature of this approach was deploying mobile versions of each report for traveling executives. 

Today, everyone from the CEO down to the route dispatcher at each distribution center knows where any issues are and what needs to be done to correct them. It also allows our customer to be much more proactive in dealing with both the delivery points and their suppliers. As orders and inventory stocks can be readily shared, increasing parts production efficiency at the manufacturer and delayed delivery points can be more readily notified so they can more effectively reschedule their work loads. First-year savings from reduced inventory and greater fleet efficiencies are estimated at over $3 million

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The science and practice of predictive analytics is well established and rapidly gaining ground in the public and private sectors. It’s not magic anymore because we now have advanced analytics systems that harness and organize massive amounts of disparate data and model that data in ways that allow humans to be proactive and make informed decisions. Take a moment to read our extremely popular post on selecting the right descriptive, predictive, and prescriptive analytics here. To review: 

Type of analytics What does it do? 
Descriptive Analytics  Answers the question, “What happened?” Uses data aggregation and data mining techniques. 
Predictive  Analytics  Answers the question, “What could happen?” Uses statistical models and forecasting techniques to understand the future. 
Prescriptive  Analytics  Answers the question, “What should we do?” Uses optimization and simulation algorithms to suggest the best course of action. 

The Science of Predictive Analytics 

Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. 

Predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. These models capture relationships amongst many factors to allow assessment of risk or potential associated with a set of conditions, guiding decision-making with better accuracy and significant cost savings. 

Who Knew Supply Chain Optimization and Crime Fighting Were Related? 

Let’s examine two popular applications: supply chain optimization and crime fighting. How can predictive analytics effectively address these seemingly unrelated topics? Because at a macro level the issues are identical. Consider this abbreviated chronology of our quest to make better, faster, data-driven decisions regardless of the setting and the objectives: 

  • We had no data. We used unstructured observations and gut feel 
  • We got some data, but it was incomplete and resided in silos 
  • We got more (and more comprehensive) data, eliminated silos, filled in the gaps, but lacked modeling tools. This was the era of data-rich but information-poor. 

Today, predictive analytics tools allow us to compare possible outcomes of events using scenario analysis and foresee challenges and potential disruptions before they happen.  

Supply Chain Optimization – Use Case for a Domestic Brewery 

Our supply chain optimization use case comes from a top-10 domestic brewery that used Logility to gain better insight into production. Before Logility, this brewery had plenty of data, but was unable to make sense of it and “make it tell us something useful about the future”. Sound familiar? 

The data needed to be more easily translated into actionable information for managers and executives. The company had a variety of tools in-house, but the fragmented technical environment was too difficult to manage for quick scalability. They needed a powerful, analytics-driven solution to integrate and transform the data from their disparate systems, along with a front end for visual analytics, designed for the specific challenges of the beverage industry. 

A key point of differentiation for Logility was the ability to link multiple data sources to a single supply chain planning platform with reporting and analytics capabilities built into the functionality. Logility’s rapid integration framework enables a one-time setup of the platform, followed by easy report creation and access to analytics by business users. An early win included creating a daily shipments and depletions report for the CFO. Using a mobile-ready interface, the CFO can quickly scan variances each morning and immediately drill down to SKU and account-level data to see what’s driving exceptions. 

Based on these early victories, the brewery believes that the early detection of production efficiencies will yield $550,000 to $800,000 savings within 18 months. In addition, the company points to a two full-time staffing equivalent reduction (about $300,000 annually), and the value of faster decision-making by business managers. 

The Crime Fighting Analogy 

Now let’s consider the case of crime fighting and a diagnostic technique called Risk Terrain Management (RTM). The premise of RTM is that location matters. However, it’s no secret that location matters. The question is: how do you utilize data that you currently have to assess spatial risks and prevent undesired outcomes? RTM helps in this process. With a diagnosis of how the environment correlates with certain behaviors or outcomes, you can make very accurate forecasts. 

The reason RTM is used by practitioners across many disciplines, not just law enforcement, is because it was originally developed to solve a problem faced by many: how to leverage data and insights from various sources, using readily accessible methods. RTM gained fame as a crime prevention tool, but today it’s being used in urban planning, injury prevention, public health, traffic safety, pollution, and stopping maritime piracy. (Note that this is at its core the same problem the brewery faced, only the vocabulary and the objectives are different.) 

In the context of crime prevention, the RTM process begins by selecting and weighting factors that are geographically related to crime incidents. Then a final model is produced that basically ‘paints a picture’ of places where criminal behavior is statistically most likely to occur. 

With knowledge of spatial risk factors, intervention activities can be designed to suppress crime in the short term and mitigate the risk factors at these areas so they are less attractive to criminals for the long term. For instance, in one National Institute of Justice (NIJ) study, a 42% (statistically significant) reduction in robberies was achieved by focusing on environmental features of high-risk places, not merely the people located there. With RTM, you can prioritize risk factors and prescribe actions to mitigate these factors, even within the confines of limited resources. 

As you can see, predictive analytics and the underlying tools that support the discipline can be applied in many settings. People like to solve problems, but they need the right information. As business leaders we need to make sure they have it and then set them free. 

Interested to know where your ROI from a supply chain analytics platform will come from? 

Read this great blog:  

How can manufacturers manage disruption and improve productivity? By using advanced analytics for manufacturing, to understand the valuable information concealed within the data they already have!

Most manufacturers have already made the easiest and most obvious changes to improve their operations, leveraging traditional methods to boost their own productivity and simultaneously create a more dependable supply chain.

But the pressure to do even more with less is relentless, especially in a volatile business climate such as the one the global community is currently experiencing with the pandemic. Therefore, manufacturers must continually look for new ways to improve the productivity and profitability of their operations. Advanced analytics for manufacturing is a good place to start.

Optimizing Your Existing Assets

We’ve used this space to say this before. Here it is again: there’s an asset that many manufacturers have not yet optimized – their own data.

Some of you may want to argue that point but note that we’re calling for the optimization of deriving sound business decisions from data. It’s a high bar. You may have called a few meetings, written a few emails, maybe even scribbled on a whiteboard and snapped a photo. That’s not good enough.

Thanks to the power of the cloud and advanced analytics, manufacturers can put data to work, gathering information from multiple data sources and taking advantage of machine learning models and visualization platforms to uncover new ways to streamline processes from sourcing to sales.

Advanced analytics for manufacturing and production not only help manufacturers solve stubborn problems, they may reveal some they never knew about such as weaknesses in the extended supply chain or unprofitable production lines. Specifically, practitioners can use intuitive displays to drill into safety metrics, product quality, on-time delivery, cost efficiency, and much more. Here are some common advanced analytics use cases for manufacturers.

Quality Control

Quality control is key to the customer experience and to your bottom line. Over time, ineffective quality control processes will affect customer satisfaction, buying behaviors, and ultimately lead to lost market share. And the costs don’t stop there.

Poor quality control leads to more customer support costs, warranty issues and repairs, and less efficient manufacturing. Good predictive analytics, however, can provide insight into potential quality issues and trends before they become truly critical issues. You can use advanced analytics for manufacturing to understand defects by item, work center, work order, shift and reason code. Separate trends from one-off outliers.

Life Cycle Maintenance

For manufacturers with major investments in infrastructure and equipment, the ability to manage that capital outlay is critical. By analyzing metrics and data related to the life cycle maintenance of equipment, companies can predict both timelines for probable maintenance events and upcoming capital expenditure requirements, allowing them to streamline their maintenance costs and avoid critical downtime.

In short, knowing when a part is going to break reduces downtime and waste. By analyzing factors that drive the wear and tear of your devices and machinery, you gain transparency on the real lifetime of your products. According to McKinsey & Company’s 2017 report ‘Manufacturing: Analytics Unleashes Productivity and Profitability’, what is often termed predictive maintenance typically reduces machine downtime by 30% to 50% and increases machine life by 20% to 40%.

Supply Chain Optimization

We’ve saved supply chain optimization for last. This use case is especially relevant as we watch global supply chains cope with the damage caused by the COVID-19 pandemic. Start by simply mapping your supply chain to at least the second level and preferably the third. Your overall goal is to identify weaknesses and build resilience. As a practical matter, you will learn to anticipate the right time to produce orders or plan shipments to maximize on-time delivery and resolve storage issues. Analyzing the duration of individual processes and the interdependencies among them provides information about the impact of disruptions and potential options for avoiding those disruptions.

Bear in mind that supply chain excellence requires strong supplier relationships and a constant exchange of information with your suppliers to ensure materials and parts are where they need to be when you need them. Logility fosters collaboration – even outside your company – on a common platform to increase pipeline efficiency.

Managing Your Data

Let’s wrap up with a reminder that data-driven insights require holistic data management. In other words, you need to bring it all together to tease out the deep meaning and enjoy the benefits. The term of art is data warehousing. Because Logility provides data integration across departments and systems, essentially everything your business collects — big data, small data, on premise, off premise — you have the ability to develop business insights that follow your entire business process, not just pieces of it.

Data warehousing builds a common repository that allows you to work with your information with a myriad of information access and visualization tools. No matter how your team chooses to work with your enterprise data, data warehousing ensures everyone has the same set of metrics, rules and assumptions to help them meet common goals.

If you haven’t already, make advanced analytics for manufacturing a priority at your company. Start, or finish what you started. What better way to use the downtime none of us expected to have in 2020?

Want to learn more about tackling common supply chain challenges in the manufacturing industry. Check out our ebook ‘Top Supply Chain Challenges for Manufacturing Companies’.

Detective Joe Friday had a way with words. Even if you’ve never seen a Dragnet re-run in your life, or you’ve never heard of Joe Friday, his catchphrase, “Just the facts, ma’am,” is a good motto for running your business. The foundation of supply chain analytics is the desire to let objective, relevant information drive action – in other words, to empower and enlighten employees about data and to make decisions after they’ve looked carefully at “just the facts,” with the help of well-designed supply chain dashboards.

As humans we have an innate ability to use our neocortex (the part of the brain where abstract reasoning, imagination and mathematics are processed) to validate what our limbic brain (where emotions are processed) is “feeling.” Think about it – when we have a “gut feeling” about how business is going, the first thing we do is try to validate that feeling with data. The problem is we can’t take in and effectively analyze the terabytes of raw information now readily available; we’re just not built that way.

We need systems to filter out the extraneous bits, and, just as importantly, we need systems to present the most relevant information in ways we can easily assimilate. To do that well, a digital supply chain platform must place a premium on dashboard design.

It’s true that aesthetics and user experience seem like soft targets at first, especially if you consider yourself a numbers person. But in the case of a solid digital supply chain platform which offers a very broad spectrum of analytics capabilities, like Logility, the focus on the effective design of supply chain dashboards is all about letting form follow function. Here are four reasons why good design is essential for supply chain dashboards.

1. Well-designed supply chain dashboards make information consumption effortless

When a user glances at a well-designed supply chain dashboard, that user should get an immediate overview of how the company is doing relative to their responsibilities. Everything he or she needs to make the decisions you depend upon him or her to make should be right there, ideally on one easy-to-view screen. Not only should the dashboard be carefully designed for maximum comprehension, but so should each element on the dashboard. KPIs should leap out visually, as should any serious anomalies. That just won’t happen if you’re relying on poor design.

2. Well-designed supply chain dashboards lead viewers through consistent processes

As it turns out, we tend to take in visual representations of information in predictable ways. Most of us, for example, look first at the upper-right hand portion of a screen before glancing at other sections. Quality design can exploit these tendencies and, through thoughtful layout, lead an analytics dashboard user through a consistent process for consuming and acting upon a given set of data. The flip side, of course, is that poor design can prevent this kind of consistency from taking root.

3. Well-designed supply chain dashboards turns data into actionable information

We can all agree that data is dumb. In the raw, a large collection of data can lead knowledge workers astray, or in many cases, virtually paralyze them. That’s a terrible waste of your company’s greatest asset – human intelligence. A well-designed solution can take at least the preliminary layer of analysis off a worker’s plate. Think of something as simple as the judicious use of icons. Putting them in line with raw numbers to show the direction of trends can make scanning a set of metrics and choosing a course of action much simpler.

4. Well-designed supply chain dashboards make ROI possible

A poorly designed tool isn’t an asset, it’s a cost sink. It doesn’t matter if the tool is a $3.00 utility knife from the big-box DIY store or an enterprise-level software solution. If no one trusts the results, or if it’s hard to use, no one is going to turn to it when it’s time to get things done. We’re back to trusting our “gut feel” which seems to ignore the last 40,000 years of neocortex evolution. It’s impossible to reach ROI without validating the gut feeling. Quality design is only one step in the process of optimizing your data assets; it is the beginning of a long road to ensure that employees trust and use your company’s analytics.

So, there you have it – four ways that good design impacts information presentation and a little insight into what that means for the knowledge workers in your organization.

Causal forecasting shines a light on, and isolates, actual demand signals from market “chatter,” thus improving forecast quality. In our increasingly interconnected supply chains, potentially valuable information is hidden in plain sight, obscured by the chaotic flood of structured and unstructured data a business generates and consumes. Causal forecasting techniques can uncover complex patterns that are often missed, helping supply chain professionals focus on the truth and ignore market noise and irrelevant activity.

Now and then we hear from planners who claim immunity to the consistent clamor from today’s always-on global markets. A typical rebuttal might be something like, “Causal forecasting sounds great, but it’s not applicable to us. My company’s planning function isn’t disrupted by irrelevant inputs because we base replenishment on shipments, some seasonal adjustments and a dash of gut feel, nothing more.”

This approach is not sustainable. The race is on to deploy technologies such as machine learning and AI that are needed to convert massive, complex and disparate sets of downstream market data into insights that can improve execution across the enterprise. You can be proactive and use causal forecasting to leverage data you already own, model additional data sources that could help explain demand variability… or do nothing.

What Exactly Is Causal Forecasting?
First, what it’s not is a replacement for demand forecasting. In fact, it enhances basic demand forecasting solutions by leveraging machine learning and advanced analytics to provide more insight to improve forecast quality and demand response. Causal forecasting resides between mid- and long-range planning (typically the realm of time-series planning methods) and extremely short-term Demand Sensing technology (“What should I ship today?”).

Logility’s Causal Forecasting solution is an enterprise application for merging all relevant demand signals into a single source of truth and performing predictive and visual analytics that improve forecast quality and allow a business to take impactful action ahead of the demand curve. As such, causal forecasting is much more than basing inventory positions and replenishment schedules on shipment data. Rather, it is about leaning into complexities and understanding cause and effect, not playing it safe at the expense of margin.

Users of causal forecasting quickly learn that it’s more than forecasting trends and seasonality; it’s about identifying and measuring market signals, then using those signals to shape future demand. Imagine understanding market dynamics well enough to move beyond marginally effective reactions to actually influencing outcomes!

The Benefits Offered by Logility’s Causal Forecasting Solution
In short, forecast reliability and consensus planning will improve as a result of clearly understanding how market forces correlate with changes in true demand. Causal forecasting provides visibility and collaboration tools to create direct line-of-sight to real demand from customers as it happens, using this as vital intelligence to be fed back up through the supply chain.

Let’s apply it to a real scenario. You discover that during a given period your company’s sales into the retail channel are significantly higher than sales reported using POS data. Ultimately, the problem is a combination of an overly optimistic forecast and the wrong product mix. Inventory increased as overall customer service levels fell, and margins are set to erode as product is put on clearance.

To avoid this happening, a deeper understanding of causal factors is needed. For example, a planner using Logility’s Causal Forecasting solution to explore variability and causal relationships among stores, products, territories, inventory, raw material price fluctuations, pricing, promotions, competitive activity and consumer sentiment would discover that several brand extension launches were hindered by competitive activity, negative reviews on social media, raw material shortages and shipping delays. The planner could then immediately model adjustments and commit an improved forecast back to the master demand planning system.

Some Things to Bear in Mind
You may have determined already that causal forecasting works best in certain environments and does present some challenges. Industries such as Fast-Moving Consumer Goods (FMCG), Retail, Pharmaceuticals, High Tech and Chemical align well because they tend to share the same characteristics: inventory-driven, consumer-centric, price sensitive, with a reliance on global supply chains, and highly influenced by external factors like commodity prices. And they often share similar business and operational goals: cycle-time reduction, improved fill rates, improved customer service, better cash flow, elimination of over/under stocks, and waste reduction.

As for challenges, before you can have consistent access you need a data storage management strategy to ensure all relevant data can be gathered, housed, structured and blended appropriately. From there, Causal Forecasting allows users to explore multiple best-fit causal models and discover patterns of “explained variance” in the data. Powerful visualization tools allow users to understand the impact of what-if simulations. The final step is integration back to your master demand planning systems.

In summary, causal forecasting helps you aggregate the data you have with the data you need to improve the accuracy of your forecasts.

When asking the question, “How does machine learning improve supply chain performance?” it’s important to bear in mind that the proper goal of machine learning is not abdication of human responsibility for decision-making. Rather, it’s improving our individual and collective ability to make faster, better decisions by leveraging increased speed, accuracy and absence of bias. Our context here is supply chain planning and execution, but there is no reason to limit the scope of machine learning. 

The User Experience

When it comes to designing and creating technology solutions for supply chain analytics, this is not a throw-away idea buried in a long-forgotten PowerPoint presentation. Logility’s point of view is clear: we prefer to start with the user experience. We identify the optimal way for an expert to interact with data to improve decisions. We then work backwards to find the appropriate solutions that enhance the user experience so that the expert has more data at hand for making better decisions. The expert user interacts with data, reports, and forecasts. The variance from forecast identifies business processes that decision makers will want to understand and take management action on. 

Therefore, always start with a user’s needs and work backwards. 

The Influence of New and Emerging Technologies

A quick examination of some recent examples of the influence of what are still considered emerging technologies — such as machine learning and artificial intelligence (AI) — demonstrates that the human element always was, and in fact always must be, paramount. The early hype focused on harnessing the advantages that machines have over humans. Math, speed, brute force, impartiality: this was exhilarating stuff, but it was never about a diminished role for humans. 

In 2016, Lowe’s introduced LoweBot, an autonomous retail service robot in Lowe’s stores throughout the San Francisco Bay area. For the customer, LoweBot can find products in multiple languages and effectively navigate the store. As LoweBot helps customers with simple questions, it enables employees to spend more time offering their expertise and specialty knowledge to customers. Furthermore, LoweBot is able to assist with inventory monitoring in real time, which helps employees detect patterns that might guide future business decisions. Employees are receiving a double benefit: more time to focus on the strategic versus the mundane and real-time inputs to improve inventory management. What a fascinating example of how machine learning improves supply chain performance.

Or consider Walmart’s use of satellite photography and drones for scanning its warehouses. These technologies offer speed, accuracy, and attention to detail. They dutifully report what they find but offer no commentary. 

In 2015, IBM acquired The Weather Company. According to IBM’s website, businesses can now make better decisions by using weather data. “Our solutions connect newscasters, airline pilots, energy traders, insurance executives, state agency employees, retail management and more to the weather intel they need, on any device. We aggregate the deepest, richest data sets — both business and consumer — to deliver personal, reliable and actionable weather information, analytics and insight.” 

The Machine Learning Evolution

Using weather data as context, we’re living through a progression that looks something like this: 

  1. Everyone has known forever that weather impacts the sales of many goods and services. Absent ML technology, humans did the best they could to forecast demand and plan inventory. Mom’s lemonade-stand wisdom held sway: “Make one pitcher at a time while it’s raining.”
  2. Next was the advent of the descriptive power of big data, enabled by giving powerful computers access to vast troves of digital data on the Internet. Call it the era of correlations. As one Walmart executive put it, “We don’t know why berries sell better when the temperature is below 80 degrees. We just go with it. We stock more berries and sell more berries.”
  3. The next frontier: efficient machine learning implementations that enable machines to learn, refine and steadily improve the utility offered to decision-makers. 

The Next Frontier

What does that next frontier look like in practical terms? To start with, it means closing the loop by benchmarking results, providing feedback and refining plans, again and again. Ultimately, machine learning becomes a closed system when the data flows are highly automated, modeling is continuously updated based on the latest information, and the output of the data processing is an automated input to a business decision — like a forecasting decision or an inventory position decision.  

And it means that the benefits of automation are always balanced against the understanding that positive business results come from human actions. Machine learning has much to offer the modern-day supply chain, but we pesky humans continue to play the most important role. In simple terms, that ‘most important role’ is the cycle of observation followed by critical thinking followed by action.  

Interested in finding out more about machine learning as part of an overall supply chain planning strategy?  Take a look at our eBook The Role of Machine Learning in Supply Chain Planning

Among other aspects of a modern-day business, forecasting has become more complex, with many firms striving to incorporate product, pricing, discounts, channel, and other available data to improve accuracy. This increase in forecasting demand complexity and the associated massive increase in data volume requires a machine learning (ML) forecasting solution. In a nutshell, machine learning is a type of supervised or unsupervised artificial intelligence where software can learn without being explicitly programmed.

Simply stated, traditional forecasting methods cannot scale to handle the complex and data-heavy SKU-level analyses that today’s businesses demand. Because we work with clients every day who are faced with this challenge, we have developed purpose-built ML software for demand forecasting. Machine learning forecasting is very fast, allowing a company to generate hundreds of thousands of SKU-level forecasts in minutes. And with dashboard and report management services, you can get your results into action quickly using a system that has been designed for this type of enterprise-scale forecasting business case.

Are You Too Late to the Party?

If you’re worried that you’ve waited too long to invest in machine learning, don’t panic. Yes, it’s true that ML adoption is in full swing. For more than a decade, companies have used the power of machine learning to improve supply chain planning efficiencies and develop optimized supply chains. Automatic model switching to improve forecast accuracy is just one of many examples of the early use of ML to continually tune the digital supply chain and optimally leverage physical supply chain network performance.

According to research published by Supply Chain Insights, “CEOs expect supply chain leaders to prepare for digital business and want to know how they intend to develop capabilities and use advanced technologies like artificial intelligence to create a flexible, agile and responsive digital supply chain.”

In short, you aren’t early, but it’s never too late. The ML market has grown rapidly, but there’s more growth and more innovation ahead. You should be part of it. Start with doing some homework and learn what’s worked for other businesses like yours. This step will help you develop an ROI model to sell the initiative internally.

#1. Understand where machine learning can have the most impact

To estimate the return on investment from leveraging machine learning, you’ll need to explore several areas of opportunity:

  • Forecasting: Forecast accuracy is a top challenge for many companies and a quick-win application of ML could be the automated adoption of “best-fit” algorithms across your portfolio.
  • Supply Chain Optimization: Another high-value opportunity of ML is continually analyzing the state of your digital supply chain and automatically tuning planning parameters to meet customer requirements while maximizing company objectives.
  • Multi-Echelon Inventory Optimization (MEIO): Using the latest demand and supply information, machine learning can enable continuous improvement in your company’s ability to meet a desired customer service level with the lowest inventory investment.
  • Automation: Automate manual data efforts and processes, leaving more time for analysts to work on value-adding activities. Improving supply chain team efficiency is key in today’s tight labor market.
  • Risk mitigation: Detect problems earlier and gain a sustainable competitive advantage by proactively addressing potential disruptions.
  • New product/service development: Leverage new data sources to analyze phrases and market sentiment to develop more successful new products and services that provide stronger sales and higher profits.
  • Stay competitive: Your competition is likely investing in machine learning.

#2. Build the team; build experience

The right culture, organizational structure and skill sets are essential to realizing the benefits of machine learning. Look for:

  • Executive support – this plays an important role in building both the organizational power and vision for ML capabilities. Engage executives with business benefits, not the merits of “yet another IT project”.
  • Machine learning subject matter experts with a diverse set of skills including communication and influence to build support for ML across an organization.
  • Supply chain and business analysts who understand business needs, can assess the impact of changes, can determine the appropriate response and can communicate recommendations clearly.
  • Data scientists who can gather, analyze and interpret complex data used in business decision-making.
  • Artificial intelligence specialists to work on systems that gather information and formulate recommendations that automatically act.

#3. Choose the right digital supply chain platform

Does your supply chain platform support your plans to harness machine learning? Here are a few capabilities that your supply chain planning platform needs:

  • Process automation to free up resources to focus on strategic activities
  • Advanced visualization to support all your analysis requirements
  • Support for all levels of analytic maturity from descriptive through to cognitive
  • In-memory processing for robust analysis and fast response times
  • Cloud-based for fast deployment, security, reliability and scalability
  • Configurable user interface for cross-functional analysis requirements
  • Data warehousing for consistent, harmonized and managed data across your entire supply chain.

Now, stop waiting and get started. Do a little research, build your ROI case and your team, implement the right platform and course correct as needed. A year from now, you should look back on 2020 as the year you made great progress implementing machine learning to boost performance across your supply chain.

Learn more about the value of harnessing big data to create a digital supply chain transformation supported by advanced technologies such as machine learning –
https://logilityinc.staging.wpengine.com/blog/conquering-the-explosion-of-supply-chain-planning-data/