I wrote an article for the Journal of Business Forecasting a few years ago about dangerous habits that lead to software abandonment.  Since then I have reflected on the behaviors I’ve witnessed over my career and considered the stories others have told me about their journeys in change management. From this, I have developed some insights on why many find it hard to attempt digital transformations (or supply chain, planning or any other transformation) and succeed.

Transformations are challenging and are often loaded with what feels like more struggles than wins. When it’s done well, business processes benefit and our teams flourish. Unfortunately, most of us struggle or flat out fail in our transformation initiatives. So, here are some observations on why transformations are so challenging and suggestions on how to improve our chances of success. We will concentrate on new tools or software but the observations apply to most kinds of transformation.

Digital transformation is a series of large changes on a mass scale

First, intentionally doing a digital transformation by design is a change. It is not a minor change; it’s a series of large changes on a mass scale. Most people welcomes changes in others but are less enthusiastic when change is required of themselves.  In fact, they are often resistant to change and complain even about minor changes. My father has been known to say “I hate change” about something as trivial as upgrading his cell phone. Why then do we wonder why digital transformations are so hard, especially when we know so many do not like change?

While not all changes will be met with resistance, many—if not most—will. Just because we see value and in the proposed changes, we can’t assume that everyone else has the same understanding or expectation. One thing I have noticed in transformations that struggle or fail is a lack of properly set expectations and a failure to educate users.

Success Comes When Teams Know What We’re Doing & Why

We should educate users on why we need to change, not just on the mechanics of the change. Project education isn’t just a line item or a project statement. Rather than being a separate component, education should be a routine part that starts at the beginning of the project. Success comes when our teams understand what we are doing, why we are doing it and, most importantly, how it will benefit them.

This kind of education isn’t communicated within a formal classroom structure, but in the everyday conversations you have with users of the final solution. If your conversations during the transformation are limited to a core group of ‘superusers’ that excludes all end users, you may already be set up to fail.

Assign a Change Champion

A common theme of failed transformation projects is the lack of a change agent or a change champion. The change champion should not be a consultant or leader from another team or from a separate part of the organization. While it’s important to have the right implementation partner, they are not the change champions either.

Instead, change champions are team members who facilitate the transformation. They should be respected individuals from among the users who will embrace the change, share excitement for the new software and be able to demonstrate the new solution. Change champions listen to users and communicate requirements or bridge the gaps for the team. They should have the ability to convince other employees to use the new tools.

Change champions listen to users and communicate requirements for the team

It’s very difficult to have a successful transformation journey without a change agent or change champion. If you’re struggling with launching a transformation project, you may be missing this critical team member.

One organization I worked with had attempted to implement new planning software on three or four different occasions but ultimately failed at various points in the journey. They had a lead for the project and yet struggled. It wasn’t that this person wasn’t influential or a good leader, however, in this case, there was one Planner with equal clout that was incredibly resistant to any software change and prevented the launch of the new tool.

On the next attempt to implement advanced planning software, we knew we had to do some things differently to succeed. We added a new change champion, expanded the project strategy to include a business intelligence solution, and included process change to supplement the planning software. While the same user put up a roadblock and refused to use the new tool, we were still able to successfully launch by creating a strategy that supported the business and allowed the organization to launch the tool while working around the resistant user.

Build Process Maps

Another cause of transformation failure is the lack of understanding of the current state compared to the desired future state. Especially in an implementation that involves new software, part of the change involves moving to a new tool with enhanced capabilities.

When we fail to understand the differences between our current process flow and our to-be process flow, it is incredibly difficult to create a transformation roadmap that will lead to the desired to-be state. One of the most basic elements is to have process maps defined for both the current state and the desired future state.

A basic element is a process map for the desired future state

Process maps provide clear visibility into what must be implemented immediately and what can wait for a future enhancement. Digital transformation success is dependent on having a clearly defined future state process. Without it, the tool isn’t likely to be configured to support the desired future state. The journey may be made of one giant leap or many mid-size changes and mini launches.

Don’t Allow New Tools To Be Used For Old Ways of Working

Finally, I think most of us understand the importance of not using the design of the current tool for the new tool. However, the reality is that many teams fall into this trap. This is probably my biggest pet peeve when joining new teams who are at the end of a digital transformation or have just completed one.

This always creates issues, including some that will felt for many years to come. In such cases, the new tool is being forced to behave in ways it wasn’t designed to work. This results in a tool that is clumsy, slow and ultimately doesn’t support the desired to-be process or future growth. Despite good intentions, this can really derail long-term business success.

Encourage users to describe the behaviors they want to see

Often this comes about as users want to be sure to get their favorite functionality in the new tool so it works just like the tool they currently use. Be wary of having to implement functionality just because it exists a certain way in the current tool.

Instead, encourage users to describe the behaviors they want to see and how they’d like to view those results. Then, compare that need to how the new tool is designed to work and implement accordingly.  In my opinion, combining this strategy with process maps improves chances for a successful transformation.

In Summary

I haven’t yet found the key to secrets to make transformation projects easy or guarantee success. However, I have observed some common themes in transformations that struggle or fail outright. The biggest opportunity common to all of them was the need for a change champion with a clear vision of the destination and who can generate excitement for the project.

Having a change champion doesn’t lessen the importance of educating users—ideally, the change champion also leads user education. Change champions are also the experts that need to understand and be able to communicate process maps for the current state and to-be states. The best change champions also make a conscious effort to avoid imposing old tool designs and processes onto new tools, identify strategies to transition between current and future states, and become the enabler for change.  Failing at any or all of these can result in user resistance, inefficient workflows, hindered growth and even stalled or failed projects.

This article first appeared in the summer 2024 issue of the Journal of Business Forecasting. To get the Journal delivered to your door every quarter, become an IBF member. Member benefits include discounted entry to all IBF training events and conferences, access to the entire IBF knowledge library, and exclusive members workshops. 

It is a great time for business forecasting and planning and those who do it! We have technology and data analytics tools that could not have been imagined just 30 years ago. We have able to shift from manual, time-consuming data collection and analysis to enabling better business decisions quicker and easier, adding more value to our businesses.

It has been a time of great expansion of data, and of great advancement in forecasting tools to exploit it. We can better understand the purchasing behavior of customers and consumers. And we have better forecasting methods and models to generate demand forecast and revenue projections.

Demand Forecasters and Planners are now business partners in decision making across the entire enterprise – and that should be our goal.

Your Role Is Only Getting More Important

Operational and financial plans can are only as good as the underlying demand forecasts and plans. This places Demand Planners in uniquely important position, and one with great responsibility when it comes to the success of the company. Welcome to the 2020’s where Demand forecasting and demand planning are foundational. Welcome to a time of multi-dimensional business thinking. Having seen planning transition from a traditional approach of reverse engineering from the top line to the bottom-line performance, it’s like shifting from 2-dimensional chess to 3-dimensional chess.

Welcome to the 2020’s where Demand forecasting and demand planning are foundational

Shift Away From Short-Term Forecasting

What can we do to leverage the full range of capabilities available to us? Currently, a limiting factor for many is the continued use of spreadsheets. Spreadsheets are still widely used by Demand Planners in companies of all sizes. Spreadsheets require significant time to import data, maintain it, perform modeling, and export data for use in other software. Integrated software solutions within the company should be standard, freeing up time for Demand Planners to create insights of greater value to the company. Innumerable cost-benefit studies support this. Time is money.

It is important that time be dedicated to identifying and analyzing the drivers of demand

A Demand Planner’s time should shift from away purely performing short-term forecasting, for which Machine Learning (ML), and Artificial Intelligence (AI) can be readily deployed. If technology can assist in short term forecasting, what should we do with this extra time? It is important that time and effort be dedicated to identifying and analyzing the drivers of demand (and the forces that affecting these drivers), both now and in the future. This places the Demand Planner in a position to understand and anticipate bigger picture forces impacting the enterprise, and to inform and assist the strategic discussion and decision-making. That is enormously valuable.

Make Collaboration Your Superpower

Given all functions throughout the company plan and depend on demand forecasts and plans, development of relationships across functions is a major value-added. Become familiar with the terminology used by the functional areas participating in and using information from your demand forecasting and planning processes.

Help them to use the information you provide more effectively for their unique roles. Determine how you may be able to reshape information that you provide in a better format or segmentation structure. Ask them to share information with you from their functional area that may add to the effectiveness of the work in demand forecasting and planning, such as industry publications, research reports, market research, and other inside and outside information that they use.

Ask them about their views on events, situations, competitors, and other developments that impact their area of the business. Ask them how they might expect this to affect the company in the future. Ask them how it might affect their function in the future. Ask them to suggest conferences that they attend that they believe would add to the quality of the work that Demand Planners are doing. Ask and listen to inside and outside authoritative sources of information. Be genuinely interested in others and their functions.

Use Technology to Elevate Your Role to Business Partner

Implement technology solutions that enable better operational forecasts more quickly, while serving the company with insights and longer term forecasts that will drive strategic initiatives. Think of yourself as a trusted advisor for the company, aiming to deliver on enterprise goals and objectives and interested in its business success – and well qualified to guide the business in the right direction. This reframing of your role can enhance your value to the company and contribute to its future success and sustainability. Look constantly for ways to improve your functional area and the company as a whole.

Think of yourself as a trusted advisor for the company

As ML and AI develop in the coming years, they will likely be one of our best enablers and sources of rapid research and information for the work of demand forecasting and planning for future company success. The pace of change is rapid and unlikely to slow down in the future. Acquiring tools and applications that enable us to work more effectively and efficiently is essential. We should not fear technological developments but embrace them as they will be fundamental to our success, both as individuals and companies.

 There have been amazing changes for Demand Forecasters and Demand Planners in the past 25-30 years. And more change will come even faster in the next 5-10 years. It will be a great time and opportunity for business forecasting and business planning and for those who do it!

To get up to speed with the fundamentals of S&OP and IBP, join IBF for our 2- or 3-day Boot Camp in Miami, from Feb 6-8. You’ll receive training in best practices from leading experts, designed to make these processes a reality in your organization. Super Early Bird Pricing is open now. Details and registration.

Are you a Starbucks customer? If so, the coffee chain is analyzing your purchases to create a personalized experience for you – and to get you to spend more money. To do this, they have created what they call the digital flywheel program which analyses 900 million weekly transactions, taking into account customer purchases, store locations, meteorological data, inventory data, and more. The coffee giant is leveraging this approach to predict and drive sales.

I don’t like the term AI, but they are using next level stuff here to micro target you with personalized offers based on your preferences and to get you to engage more closely with the brand. Starbucks has cracked the code here when it comes to integrating data analytics and planning.

They are successful not only because of the data and technology they have; they’re successful because of their people and their processes. I talked to Brian Nagy, Senior Demand Planning Manager at Starbucks, who is driving next level planning at the coffee chain and is at the forefront of their analytics and planning efforts. We talked about AI, demand sensing, demand shaping, and how they overcome the same planning challenges we all face. Here are the highlights of that conversation.

How COVID has caused fundamental shifts in consumer behavior

“One of the things we are looking at is the impact of COVID in terms of demographic change. We’ve seen massive population shifts in the last 3 three years – there’s been a mass movement of people leaving places like New York and moving to Florida and people from California moving to Texas. We’re trying to get ahead of that and make sure that our footprint’s there and getting ahead of our competitors. Having that information about demographic shifts is hugely powerful.”

On scenario planning for strategic planning/budgeting

We prioritize end-to-end capabilities and being able to assess all those outcomes, what we would call either strategic planning or budgeting. Companies tend to do this manually once a year, looking holistically at their business figuring out what their strategic direction looks like. We’re doing it with a lot of directional input and projecting trends forward. Those trends may or may not make sense but we need to know what is actually driving those plans. So being able to integrate some of this information like pricing scenarios and internal and external data to look at risks and opportunities is important.

“If you have a revenue or margin target you can input that and theoretically find the different paths of getting there within your mix, pricing, and customer base, and with regional approaches and different promotional strategies. It’s that capability to really dial in on how to optimize the business and get everyone understanding the risks and opportunities. That’s what S&OP and IBP is all about so it’s really just about getting a tool to get us there.

On the planning tools of the future

“What comes to my mind is an 80s stereo with a thousand different knobs. We want most of this to be AI and machine learning facilitated and with the capability to play with those dials and levers, whether it’s demographic data, different pricing alternatives, external data. We want to look at different things that can impact your business whether that be marketing strategies, promotional strategies, what you bring to the table from an innovation standpoint and being able to run those scenarios seamlessly and quickly.

“Obviously, this all starts with the demand plan but then it needs to go the whole way through the stream of the supply chain so we can think about things like warehousing strategies, ocean freight availability and costs etc. to the point where you have really robust contingencies in place.

“We’ve seen ocean freights just go through the roof with COVID – in a case like that what kind of scenarios have you thought about as a business? We need these things in place to steer a different direction if required. Planning tools should help facilitate those types of things just by asking “What if we go this route? What if we produce domestically versus importing?” That’s really the value of integrating a true end-to-end type capability; being able to assess the entire way through and make decisions as a group.”

On Starbucks overcoming the continuous challenges of planning

“I have been a manager for eight years now and the same principles always seem to work as far as having really robust exception management, having the right tools to understand the business, and getting the right data. 

“Sometimes you gotta be scrappy and pull it together but being clever and thinking of different ways to problem solve is important. Getting away from shipment data is something that’s been necessary because shipment history for most businesses over the last three years has been relatively worthless as an input. So just trying to come up with creative ways, looking more at POS data so we can get real-time signals of where customers are going where they’re headed.

“But largely it’s the same principles of forecasting that have always worked: the right exceptions, having the right training, and getting the team up to speed and just having Demand Planners know what to do and when they need to do it. Then the rest kind of takes care of itself.

 “The only other thing I’d say is being more vocal in the business and calling these trends out as we’re seeing them. I’ve seen demand planning emerge in a lot of different business settings since COVID. It’s been an unfortunate event for the world obviously but I think we’ve seen a lot of benefit on the demand planning side as more business leaders have recognized the value of it and are more willing to listen to what we have to say.”

Click to order your copy now.

Post-COVID, brand royalty has dissolved, meaning that companies must work harder to retain them and that requires personalized experiences. If you’re not talking directly to the consumer the way they want to be interacted with and on the right platforms, you may lose them. Underlying this is predictive analytics for demand sensing and demand shaping which on the one hand helps us understand behaviours and on the other allows us to micro target customers in ways that get them in store and maximize spend.

 It’s not just about coffee or retail either, demand sensing and shaping applies to all industries. For further information on this, there is a chapter about Starbucks’ demand sending and shaping approach in my book Predictive Analytics For Business Forecasting & Planning.

Recent upheavals in global supply chains and changes in consumer behavior have taxed even the most robust processes used by elite planners on top tier software. Supply chain is the new buzzword, discussed in boardrooms and in mainstream media.

For sure, it’s a great time to be in planning. We know demand planning is the backbone of our supply chain and S&OP is the nervous system that makes it function.

But many times, we fail to give it the necessary attention or perform preventative maintenance to keep our demand planning and S&OP in top condition. Knowing it’s importance and value to the organization, now is a great time to reevaluate the planning tools we use, processes we have including S&OP, and consider upgrading our own skills.

Planning Tools are Good, but…

In this post, I’d like to offer some considerations when looking at the systems, processes and people that manage our planning processes the next time you need to drive improvements.

If we aren’t maximizing the value of our current software, then what makes us think a new one will produce better results?

Too often, we think we need a new tool or the latest software. Maybe that’s true, but my experience suggests that most of us aren’t taking advantage of the functionality offered in our current tools. If we aren’t maximizing the value of our current software, then what makes us think a new one will produce better results?

Most demand planning software use the same sets of algorithms and have comparable functionality

Most demand planning software available on the market not only use the same sets of algorithms but often have comparable functionality. Comparable functionality might include the ability to allow the software to recommend a best pick model, forecast simulations, manual forecast overrides, and outlier detection.

Most of them typically use some sort of internal sources of data like order bookings or shipments and all use varying time increments for forecasting and measuring results.

However, they can differ greatly in types of data available to build models (internal, external, structured, and unstructured data), user ability to interact with the data, segmentation, metrics and even how results can be shared.

Replacing existing software with something new is expensive. The cost to replace software includes not just the initial investment of a onetime implementation fee, but also ongoing subscription fees and costs relating to ongoing training, system enhancements and maintenance.

Before making that investment, make sure users understand how to use the existing tool

Before making that investment, take the time to evaluate the current tools and make sure users understand how to use the existing tool and see if there’s additional functionality your tool offers for the business to use. You own this tool, why not first explore maximizing the value of the existing software?

Examine Your Processes Before Spending Money On New Bells & Whistles

Next, before rushing to replace that tool, evaluate your processes. Complicated or highly manual processes will undermine the efforts of even the most sophisticated software.

Replacing one software for another while continuing to use the existing complicated or inefficient processes will, at best, let us get the same (or perceived better) results faster. That may be worthwhile in the immediate future, but it won’t be long before we’re back in the same situation looking for something “new” to fix our problems.

Another way to look at it: my new software gave the appearance of fixing the issue since I got the results, I thought I wanted, faster, but it failed to address the underlying issues creating the initial problem.

Instead, we should be just as critical of our internal processes, corporate behaviors, and policies as we are to the tools currently used to drive the business. Process issues not only increase the time in our demand planning and S&OP cycles – they also impact the speed of our software and general confusion in the business.

Understanding our process and the relationships it has with our tools is a critical component in understanding if we need a new tool or perhaps, we should re-implement or change how we use the current one.

Check Your Team’s Skills Relative To Industry Standards

One last thing to consider are the skills and knowledge of our planners. We have a team of skilled forecasters but where do their skills lie? For example, they could be skilled in how our company has always done business but not really understand industry best practices or the interconnected relationships with the software.

Our forecasters could be new to forecasting or maybe even veterans loaded with industry experience, but not really understand how our organization runs its business. Just like with processes and tools, skills of our people are equally important in determining the success or failure of our demand planning and S&OP processes.

Understanding the skills of the people currently running our process, and comparing them to the skills we believe we need in those roles, can prove enlightening.

No matter where individuals on your team fall (forecast newbies to veteran planners), having your team network and collaborate with industry peers, participate in conferences and other events, and seek ongoing professional education creates better understanding and provides opportunity to improve their processes. Encourage planners to seek opportunities to learn the new skills needed, perhaps consider being certified as a CPF (Certified Professional Forecaster) with IBF!

Reinvigorate the Tool, Tweak the Process, or Upskill Your Talent?

Demand planning and S&OP are critical in the value they provide to the success of the business. As we function in this highly variable, global market, it’s imperative that we keep up with the latest software and technology available.

However, it’s equally important to keep driving process improvements and keep the knowledge and skills of our planners up to date. Improving demand planning and S&OP processes and increasing their agility is necessary to keep up with the rapidly evolving global market. Ask yourselves if it’s time to reinvigorate the tool, tweak the process, or upskill your talent.

Misty will be speaking at IBF’s Global S&OP & IBP Best Practices Conference in Chicago from June 15-17. You’ll learn the ingredients of effective planning, whether you’re just getting started or are finetuning an existing process. Early Bird Pricing now open – more details here.

When it comes to the term “S&OP”, there is some uncertainty around how it all started. Some say it started with Oliver Wight in the early 1980’s, others say it was Richard Ling and Walter  Goddard in their book Orchestrating Success: Improve Control of the Business with Sales & Operations Planning. I’m not here to debate that because for me, it all started in 2007.

I remember the first S&OP presentation that I saw. It was as basic as you can imagine with Excel graphs copied and pasted into PowerPoint presentations. I am sure some of you are thinking to yourselves “we still do that”. You’re not alone. S&OP is a journey, not a destination and with every journey, it takes time.

Over the next few years, improvements were slow but gradual. We stayed with Excel and PowerPoint for quite some time. Different metrics came and went and unfortunately, so did some Demand Planners. The most important thing stayed constant, though – support from leadership. When you have buy-in from leadership and they truly understand the value behind S&OP, then resources such as personnel and systems start falling into place.

Remember, change is good. Change means stopping doing what’s not working, keeping doing what is working, and always making improvements.

With the support of leadership, we continued to upgrade our forecasting tools, we integrated our systems with both our promotion planning tool and a short term demand sensing tool. Things were starting to click, but we still needed improvement on the presentation side.

In 2019, Microsoft invited a few members from my company, mostly IT people, to their corporate offices in New York City. The point of the meeting was to introduce some of their tools that we weren’t taking advantage of: Yammer, Teams, and Power BI. Fortunately for me, someone couldn’t attend, so the invitation was passed along. So on a cold rainy day in the Fall, I took a short train ride with a coworker, spent the day in the city, and was immediately enamored with Power BI.

If you’re not familiar with Power BI, some say it’s closer to Excel than Microsoft Access but I like to think it’s the best of both worlds. Not only can it handle large amounts of data, like Access, using it’s query editor, but it’s a great tool that has interactive data visualization options that can help tell your S&OP story.

Power Bi was introduced into our S&OP process shortly after the trip.

When this happened, it brought energy back into the process. You could tell there was more excitement than there was in recent years. We were still showing similar data, but we were showing it using our shiny new toy.

We weren’t done yet.

We needed to keep up the momentum so it was time to partner with our friends from IT. We wanted to make the shift from static data to interactive data. We wanted our S&OP meetings to be able to answer questions on the fly. “What was case fill last year?”, “What caused that drop in forecast accuracy last month?”, “How does our inventory this quarter compare to inventory last quarter?”. The objective was to be able to answer these questions at any time.

Luckily, we had the right support. So, by partnering with IT, we were able to directly connect Power BI with data coming from our transactional system and just like that, magic. We had an interactive S&OP presentation. Don’t get me wrong; this took a lot of time and energy.

We now had one Power BI document that could be filtered on a specific Planner’s brands. Not only that, but we could filter our visualizations to show different time periods and we could begin to answer the questions that we previously had to follow up on.

One of the biggest improvements was time. There was no longer the need to make a dozen different PowerPoint presentations with Planners doing the same repetitive work. Instead they could use time more wisely; looking into forecast accuracy misses, explaining gaps to other forecasts, and laying out assumptions. We were working smarter, not harder.

And that is currently where we stand.

Over the last 15 years, I like to think we came a long way. It wouldn’t have been possible without the support and dedication from those involved.

The journey isn’t over. We are further along today than we were yesterday so let’s start thinking about tomorrow.

How To Present Forecasts Properly

Spreadsheets Are Obsolete In The Age of Big Data —What Is Replacing Them?

The Intersection Of Forecasting, Machine Learning & Business Intelligence

Keeping up with available technology and being able to respond to the rapid changes in the market make it necessary for organizations to adopt “new” planning software including enterprise resource planning (ERP) tools.

Hours of work are put into getting approvals, securing financing, building excitement, and getting support of the organization. Once launched, it’s easy to think we’ve succeeded since we’ve passed the hurdles of approval, selection and launch. Much emotional energy has been spent discussing the frustration and failures of the old software while customizing and launching the new. Users are excited for the new software and the perceived promise it will fix all our issues or at least certain issues.

Practically speaking though, users are excited until they get through the initial honeymoon phase and now realize they must use the new tools and make changes to their daily workflow, habits and schedules.

When processes and tools have been in place for years or even decades, change can be especially challenging. As a result, the new software we’ve just launched could be in danger of being abandoned with unintentional self-sabotaging behaviors. The following details common post-launch behaviors that, if unchecked will limit the ROI of any investment in technology.

Behavior 1: Treating The Software Like A Black Box

Launching new software is both hard work and exciting. Because of this excitement, any time users discuss the results, reports and metrics produced by the new software, it gets both the praise and the blame. Conversations will typically start with “new software says…” as if the new software was to blame for the poor result or is the sole reason for success. The new software may have an issue in calculation, formula or connection, but with post launch support, these are usually quickly resolved and fixed. Unfortunately, this language continues long after any bugs and errors in the software have been corrected.

When used indiscriminately, especially for negative results, our customers distrust the new program and start longing for “the good ole days”, forgetting the issues they had before. We must give our internal stakeholders a reality check—if the software is operating correctly, it’s only providing the results of the data supplied based on the assumptions loaded, and it isn’t a mysterious black box. In other words, if you don’t like the results, there is either a data issue, or an assumption has changed and is no longer applicable.

These assumptions are more than just the forecast (although the forecast or demand plan is important) but may include other factors such as lead times, cycle time, capacity, variability, yields, bills of material, minimum order quantities or even item attributes such as case pack, cost and many others.

New software usually provides expanded capabilities and visibility to the organization. It has better algorithms, calculated assumptions like demand or supply variability and may even have additional fields not available in the primary system of record. The improvements provided by the new software typically provide solutions to the weaknesses of the old planning tools. Forgetting the new features and failing to remember or understand how they work negatively affects perception of both the new software and reputations of planner’s abilities and knowledge.

Rather than constantly saying the “new software says,” try changing terminology to “per data,” or “based on our current plan.” Both of those recognize the power within the organization to control the results rather than the new software seeming to operate independently as a black box.

Behavior 2: Training Just Once On The New Software

Users typically receive extensive training as part of their pre-launch activities. However, that is a lot of information crammed into a very short period, including how to do regular daily activities in the new system and how to use the exciting new features. Unfortunately, there’s so much data even the most tech savvy users will struggle to remember it all, let alone use it in the months post launch.

Post launch, users are busy trying to apply the new tools to their daily jobs and keep the business running. If continued post-launch support and training is offered, it’s either limited to a few super users or isn’t fully taken advantage of by the team. Failing to offer post-launch training and refreshers is a missed opportunity to help users incorporate the new tools into their daily activities and inhibits long term success.

Many a software platform has failed or been abandoned not because users aren’t willing or because it isn’t working, but because it isn’t being used to its full potential. In general, without continued support, users may find it easier to fall into old habits because it’s familiar and using new tools is time-consuming and, initially, difficult. User training including refreshers and enhancements post launch is critical to long term success and organization adoption. Continued training provides the best opportunity to maximize user abilities, process improvement and future enhancements to the new software to meet the evolving needs of the business.

Behavior 3: Failure To Celebrate Wins—Even The Little Ones

An important part of any new system launch is understanding how success is defined. Defining what success will look like pre-launch using goals and desired target results can provide the tools necessary to help change negative perceptions by reminding the organization of where we’ve been, and the improvements made. The desired targets should include a broad spectrum of goals surrounding implementation, such as hitting launch timelines, user acceptance or adoption rate, and business goals, such as KPI improvements pre vs post launch or new metrics available as a result of the new software.

While big celebrations are typically done for big milestones, it’s just as important to celebrate and provide recognition for the small everyday improvements that may not be as easy to measure such as time saved or improved process visibility. Little win celebrations in the months after go-live are big opportunities to continue to sell the new software to casual users.

Behavior 4: Measuring Success In A Vacuum & Ignoring The Complete Picture

Measuring successes—both big and small—is necessary. However, be wary of attributing too much importance to any one measure, especially any measure in isolation. For example, it would be easy to say we have more on hand inventory than last year and therefore the new software isn’t working. However, if holding more inventory meant taking advantage of opportunities with customers and making more sales, fewer stock outs, and improved service levels, then the statement more inventory equates to a failure by the new system is misleading. Yes, inventory is greater than last year, but increased inventory was necessary to meet the other goals set by the organization.

When we make big changes, it takes time to see the long-term impacts on the business. Setting appropriate expectations for what will happen post-launch helps manage expectations. Unfortunately, there will be times members of the organization will get hyper focused on a single metric. “Single metric mindedness” may be normal but making the effort to keep the focus on the broader scope and agreed upon measures of success is necessary to prevent abandonment of the new software.

Conclusion

Hyper focusing on a single measure to define success of new software, especially by those in leadership roles, is dangerous. Instead, refocus efforts by regularly reporting and celebrating results—even the little wins. This maintains focus on how much positive change was brought about by the new software as well as providing a realistic and comprehensive view of performance.

Adding consistent, frequent user support and training post launch gives the organization the best opportunity to not only take advantage of the new software but to identify any future improvements that may be needed due to the natural changes in business.

We must be especially mindful of how we speak about the new software as this positively or negatively shapes user perception. Positive speech has the potential to reap enormous rewards not only regarding longer-term adoption of the software, but also confidence in user expertise and the organization’s planning processes.

Misty will be sharing further insight into forecasting and planning at IBF’s Business Forecasting, Planning & S&OP Conference in Orlando, held from October 19-22 at the Wyndham Orlando Resort. The biggest and best event of it’s kind, it’s your opportunity to learn best practices in S&OP, demand planning and forecasting, and network and socialize in a fantastic setting. See here for details.

As demand planners/supply chain planners, we now have a range of advanced supply chain planning solutions available to us. The options are wide-ranging, but one thing is becoming increasingly clear – the value of demand planning/S&OP in the Cloud. Here I share my implementation journey at De La Rue, the world’s largest printer of banknotes, and listed on the London Stock Exchange.

New Tools Mean New Opportunities For Connected Supply Chains

Anaplan has made big strides with their UI and core engine with a renewed focus on supply chain; SAP is solidifying their position in the S&OP space with their IBP solution capitalising on their immense footprint and legacy solutions, connecting with their existing, more traditional core ERP; and Microsoft Dynamics seems to grow exponentially in both connectivity and functionality with seamless integration across business areas starting with CRM.

Even Tableau, at its core a data visualization tool (and a very good one at that) is adding more and more functionality to their inbuilt logical functions suite to run more complicated computations. In short, we have a lot of sophisticated solutions to choose from.

Throw in some SQL skills (which is increasingly common for a supply chain analyst) and you can have a custom, self-service solution built in no time!

S&OP In The Cloud Is The Future Now

I was recently involved in designing and developing custom supply planning solutions using Cloud software. As a supply chain professional, I can’t stress enough the importance of tools that facilitate a medium-long term vision for managing a global demand collection cycle, data analysis, and RCCP planning for a number of global manufacturing sites (covering a big chunk of the S&OP cycle). Thanks to the Cloud, this was managed through a single, central location, without ever relying on IT.

 While there were challenges, there is a simple way to implement Industry 4.0 cloud solutions to run not only a fully connected supply chain but to facilitate full Integrated Business Planning (IBP).

By sharing lessons learned from my own implementation, I am hoping to give you a blueprint for selecting connected planning  software.

What Does Your Planning Software Need To Do?

You have two overarching goals when it comes to software solutions:

1. To establish a centralized approach to run a global supply chain that will drive major short and long-term decision making for the company.

2. To establish a straightforward way of designing and implementing a fully connected global planning system, including advanced manufacturing and scheduling.

For me, the first takeaway is clear. Make no mistake, this is the way forward! Going forward, traditional supply chains will fundamentally change. Supply chain/demand planning will expand its responsibilities, and will empower decision making across an organization. Some organizations have embraced this already, some are trying, and many are yet to consider it.

Most medium and large enterprises are not in the position to unlock the full potential of their existing data.

Now, let’s focus on the second point, the systems that should empower this vision of the modern supply chain. This is easier said than done. Without one of the best of breed solutions, most medium and large enterprises are not in the position to unlock the full potential of their existing data. Nor will they benefit from a fully connected environment.

The Problems Of Trying To Make Legacy Systems Work

Many companies are still plagued my multiple legacy systems that do not “talk” to each other and require extensive capital and resources just to keep them operational. This is where we are at in my current company. We are at the stage where some progress is being made but a strong foundation is lacking.

And this is no surprise. Experienced systems consultants have had to scramble and acquire new Cloud skills and brilliant graduates are grasping the new technologies and solutions well but lack the functional knowledge. Given that stakeholders will often rely on the experts (that’s us), this means there’s a gap between the functional knowledge and technical expertise that needs to be closed as soon as possible. Generally, supply chain leaders and consultancy firms are waking up to this emerging need for a hybrid mix of skills.

The S&OP Implementation Vision

Below is our S&OP Process vision. Take a few minutes to digest it, because it informs every stage of the design, implementation, and adoption process.

“To provide the required functionality that will enable the wider business planning functions to Plan, Execute and Deliver in a Fast, Connected and Sustainable way against our customer expectations, while providing the required Visibility for the business as whole”.

This is the first starting point for any systems implementation – a clear, strong vision that identifies goals and the purpose. This is your first priority, not a document with detailed requirements or organizational design. Those can wait for a bit.

The rationale behind the keywords in our S&OP Process vision is:

Plan: Be able to collect, analyse and run scenario planning against finite and infinite capacity before committing to the best operational and financial outcome for a given opportunity.

Execute: Be able to run advanced scenario planning for manufacturing scheduling, making the best use of available plant resources.

Fast: Enable quick decision making centrally through connecting commercial demand and RCCP, cutting down the noise and letting manufacturing focus on execution.

Connected: Support a multi-site environment managed through a central location with data flowing back and forth continuously.

Sustainable: Implement the vision while sticking to the budget and further leveraging systems for lean business execution.

Visibility: Enable easy to build, fast and powerful reporting functionality leveraging all available data produced by the above, providing “live” visibility of all levels of hierarchy in the business.

Fully understanding this vision and respecting it as much as possible during the design, implementation and adoption phases is critical to having the right solution in place and selling it internally.

Don’t make the mistake of looking at systems implementation as only an IT implementation!

In many cases, a major change in systems causes subsequent changes in processes, but you still need the right approach for the system’s design. Having in mind the longer-term game of how that will affect processes is crucial for people change management. Don’t make the mistake of looking at systems implementation as only an IT implementation! It’s not. It’s about both systems and people!

The more connected the system’s architecture, the more synergy this will drive down the line for process alignment and adoption, which is key. In other words, it’s not only about “systems driving the right business behavior” or “business process design driving systems implementation”; both are important but on their own neither facilitates an optimal long-term approach.

It’s about making sure the wider vision for systems implementation considers the desired state of business processes and requirements while remaining as connected as possible in order to encourage adoption.

Challenges In Systems Implementation

You should expect some bumps along the way. Some of these are:

– Software being developed for only some areas to save time, not taking the full picture into account in the design phase, making it significantly harder to build and implement any changes down the line.

– ERP not being equipped with functionality for all business areas yet, i.e, advanced scheduling solutions, forcing sometimes complicated links between these niche applications and the ERP which adds cost and complexity.

– Connectivity issues between the core ERP and 3rd parties that are often prone to user error and require manual interventions.

– Continued use of Excel based reports and exports which are updated offline only.

– Disconnected operating sites using their own in-house built tools (still a widespread practice).Lack of scenario planning functionality meaning it is often done in Excel which is prone to errors and time consuming.

– Lack of functional expertise which causes stakeholders to misinterpret key S&OP/demand planning concepts.

Conclusion

What is your experience with supply chain planning systems based on cloud solutions and what do you think the areas of improvement are? Maybe you are currently on this journey and have some key elements to share, at least conceptually. What are they? Or maybe you’re a supply chain consultant dealing with design and implementations – how do you guide your clients to achieve a more connected business environment?

I hope this article has provided some food for thought. I am happy to discuss S&OP systems implementation with both experienced practitioners and people just starting to build their roadmap. Contact me at cristian.circiumaru@gmail.com, connect on LinkedIn or comment below. 

You’re not going to get advanced modeling like machine learning in Excel. Excel can’t handle large data sets either, making it clunky and problematic. And when you start feeding it multiple SKU’s or a whole lot of different variables, running all the different simulations and computations can weigh even the best machine down.   

This is where open source software comes in for analysts who want a little more to work with. Open Source Software is a type of software where the source code is publicly accessible or open and grants users the right to change, modify or share it.

To help handle and extract insight from Big Data, people have turned to open source platforms like Hadoop and Apache Spark. For a lot of people in the data science world, they used software like SAS at college and learned to code in languages like R and Python. All of these, as well as some others not mentioned, do an excellent job on the platforms they have set out. While some of us might be afraid of coding and learning these languages, they are all relatively user-friendly and many elements are simpler than an Excel macro.

What is Hadoop? Hadoop is used frequently with big volumes of previously unmanageable data. It is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. Hadoop is used by companies with Big Data like Airbnb, Uber, and Netflix.

What is Apache Spark? Apache Spark is another platform used to manage data and actually can work with Hadoop. It is an open-source engine developed specifically for handling large-scale data processing and analytics. Spark offers the ability to access data in a variety of sources, including Hadoop Distributed File System (HDFS), OpenStack Swift, Amazon and Cassandra.

What is Python? Originating as an open source scripting language, Python usage has grown over time. It is an interactive and interpreted high-level object-oriented programming language. It is easy to learn and understand. It is largely used as an open-source scripting language that supports many libraries used for data analysis (pandas), scientific computation (NumPy, SciPy), and machine learning (scikit-learn). Python is used by many of the larger tech giants such as Google, Quora and Reddit, etc.

What is R? R is a free open-source platform. As it is open-source, it is highly extensible and there are quick releases of the software with the latest techniques. R is strong in visualizations and graphics and offers multiple different functions. It is not hard to learn to code in R and once you learn the fundamentals of the logic, the possibilities are endless. You can find multiple information sources for R over the web. Companies that use R include Facebook, Google and Microsoft.

How Is Open Source Software Different To Specialized Demand Planning Software?

These are what we referred to as open source software which makes them unique compared to a demand planning package that you purchase and may install. In general, open source is any program whose source code is made available for use or modification as users or other developers see fit.  Additionally, they are available for free with a user community made up of fellow practitioners creating packages and codes anyone can use.

With the open source, someone may have already tried to solve your problem and has developed the model you need.

Open Source Is Highly Flexible

For Big Data and advanced analytics professionals, the flexibility of the open source code and minimal/ no cost are what makes platforms like these so attractive. But what makes them even more worthwhile is that with the open source, someone may have already tried to solve your problem and has developed the model you need. Basic neural networks, decision trees, logistic regression, and even time-series models have been developed, tested, and are available for copying and pasting. Users do not find themselves limited to the methods and configurations of an off the shelf package that is part of a legacy system, but rather can design and develop what they need.

Open source also gives you the capability to code and create something new.

Open source also gives you the capability to code and create something new. Open source tools give developers the ability to tinker with them, thereby increasing the chances of rapid improvements or experimentation that could expand the usage or features of tools.

Open Source Communities Help Solve Your Problems

People who work with open source machine learning tools also find they have thriving online communities at their disposal that allow them to tap into collective thinking when they run into unexpected difficulties. R and Python are both open-source programming languages with a large community. New libraries and tools are added regularly. Those forums currently have hundreds of answers to common problems, and as machine learning tools become even more popular, the knowledge base will expand even more.

Should I Use Open Source Software Then?

All of this does not come without risk or problems though. While many new college kids may cut their teeth on data analytics tools, there are not many people experienced enough to code or create models. While coding is not as scary as it sounds, it still requires time, effort, experience, and working through many potential bugs. Given the need for specific skills and the time and effort required to leverage open source software, investing in specialized demand planning software may be more advisable.

Open source platforms do come with limitations. A good planning system can do a lot more than just model, which justifies its cost. Besides being most likely more user friendly, most software packages offer the advantages of stability, easier deployment, better support, and governance. Advancements in these software packages mean the models today are more advanced than they were, and many even offer interfaces that integrate with open source platforms like R. This provides the various features of advanced planning systems while providing modeling extension capabilities with R and Python.

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Eric will be speaking at IBF’s Predictive Business Analytics, Forecasting & Planning Conference in New Orleans from April 28-20, 2020. Learn more about the tools discussed in this article and how to leverage them as a competitive advantage. Includes special data science workshop.

Thanks to Big Data, computational leaps, and the increased availability of analytics tools, a new age of data analysis has emerged, and in the process has revolutionized the planning field. With the explosion of data and the increasing desire to leverage it as a competitive tool, companies are moving from looking in the rear-view mirror to what is in front of them – and even charting their own paths.

In their book, Competing on Analytics, Thomas Davenport and Jeanne Harris describe the competitive advantage to degrees of information, or what they call intelligence. The authors divide these into two quadrants: those that are descriptive, or what I would call traditional or reactive, and those that are predictive, or what I would call revolutionary and proactive. Building on this we can further look at the progression from pure descriptive to past predictive to prescriptive and even what some call cognitive. As we continue along, the graph allows us to see what benefits we  each analytics type provide see (figure 1).

Figure 1:

As you up the X axis and along the Y axis, your competitive advantage increases. But wherever your processes land on the chart, all of these process and outputs are intended to support decision making. Depending on the stage of the workflow and the requirement of data analysis, there are five main kinds of analytics – descriptive, diagnostic, predictive, prescriptive and cognitive.  The five types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are complementary, and in some cases additive i.e, you cannot employ the more sophisticated analytics without using the more fundamental analytics first.

Success lies in reconciling all of these approaches within the same strategic framework. It is important to understand that all levels of analytics provide value whether it is descriptive or predictive, and all are used in different applications. That said, those that are truly leveraging analytics for competitive advantage right now are using predictive analytics, and it is this type of analytics that is driving the revolution happening today in demand planning.

Descriptive Analytics

This is simplest stage of analytics and for this reason most organizations today use some type of descriptive analytics. The easiest way to define it is the process of gathering and interpreting data to describe what has occurred.  For the most part, most reports that a business generates are descriptive and attempt to summarize historic data or try to explain why one event in the past differed from another. In addition to reports, some queries and classification processes can fall into the category of descriptive analytics. We can use advanced machine learning algorithms at this level for more complex data mining and clustering which helps us prepare data for other types of analysis.

Descriptive analytics takes the raw data and, through data aggregation or data mining, provides valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, more mature demand planning functions do not content themselves with descriptive analytics only and prefer to combine it with other types of data analytics.

Diagnostic Analytics

At this stage you can begin to answer some of those why questions. Historical data can begin to be measured against other data to answer the question of why something happened in the past.  This is the process of gathering and interpreting different data sets to identify anomalies, detect patters, and determine relationships. Some approaches that uses diagnostic analytics include alerts, drill-down, data discovery, data mining and correlations. This can include some traditional forecasting techniques that uses ratios, likelihoods and the distribution of outcomes for the analysis. Supervised machine learning training algorithms for classification and regression also fall in this type of analytics.

Most Business Intelligence stops short of this stage and is stuck in just reporting KPI’s or historical data. Companies that employ seasoned demand planners go for diagnostic analytics as it gives in-depth insights into a problem and more information to support business decisions. At the same time, however, diagnostic analytics means we are reactive, and even when used in tandem with forecasting, we can only predict what existing trends may continue.

Predictive Analytics

Predictive analytics, broadly speaking, is a category of business intelligence that uses descriptive and predictive variables from the past to analyze and identify the likelihood of an unknown future outcome. It brings together a number of data mining methodologies, forecasting methods, predictive models and analytical techniques to analyze current data, assess risk and opportunities, and capture relationships and make predictions about the future. At this stage you are no longer just asking what happened, but why it happened, and what could happen in the future.

By successfully applying many traditional forecasting techniques to more advanced machine learning predictive algorithms, businesses can effectively interpret Big Data to gain huge competitive advantages. Unfortunately, most companies are still only scratching the surface of the capabilities of predictive analytics and operate solely in the green shaded area of Figure 1, stuck between “what happened” and “what could happen”. Such are the limitations of traditional business forecasting. They miss the bigger picture of predictive analytics being a new, better way to understand business. They haven’t realized that predictive analytics allows you to understand demand drivers and then use that knowledge to proactively respond to the market.

Prescriptive Analytics

Prescriptive analytics is the next step in the progression of analytics where we take:

• The data we gathered in the descriptive stage that told us what happened,
• Combine it with the diagnostic analytics that told us why it happened,
• Combine those with the predictive analytics that told us when it may occur again.

The result is prescriptive analytics that will highlight what you can now make happen. Prescriptive analytics is a combination of data, mathematical models, and various business rules to infer actions to influence future desired outcomes. Some refer to this as demand shaping but it can also include simulation, probability maximization and optimization.

Prescriptive analytics are comparatively complex in nature and many companies are not yet using them in day-to-day business activities. Admittedly, to consistently operate at this level of maturity, this requires new people, process and technology, and an analytics driven culture for the entire organization.  That said, if implemented properly it can have a major impact on business growth and be a competitive game changer. Larger scale organizations like Amazon, Target and McDonald’s are already using prescriptive analytics in their demand planning to optimize customer experience and maximize sales.

Cognitive Analytics

Wouldn’t it be nice if we could take all of the analytics and data and the software learns by itself without us telling it what to do; welcome to cognitive analytics.  Cognitive analytics brings together a number of intelligent technologies to accomplish this, including semantics, artificial intelligence algorithms and a number of learning techniques such as deep learning and machine learning. Applying such techniques, a cognitive application can get smarter and self-heal and become more effective over time by learning from its interactions with data and with humans. With this we may even begin to blur the boundary between the physical and the virtual worlds and automate processes and processing to bring new capabilities to demand planning.

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Eric will be speaking at IBF’s Predictive Business Analytics, Forecasting & Planning Conference in New Orleans from April 28-20, 2020. Learn more about the methods discussed in this article and how to leverage them as a competitive advantage. Includes special data science workshop.

The special issue of the Journal of Business Forecasting, Technology in Forecasting & Planning, is now available for download. Download it here.

This special issue is designed to help you make the right investment in forecasting and planning software. It includes articles on how to define your planning software requirements and how to avoid common post-implementation pitfalls, brought to you by experienced planning leaders that have taken the journey you may be embarking on now. It also features excellent commentary on whether Artificial Intelligence should be a decision maker or decision supporter in your organization, and valuable insight into how blockchain will improve demand planning in the years to come.

Underlying all this, we have a revealing interview with Doug Laney, the creator of Infonomics, which reveals the importance of developing a data-centric culture to properly leverage new technology. I am also pleased to say we interviewed Sir Ralf Speth, the CEO of Jaguar Land Rover, who discusses JLR’s journey in Big Data and the IoT. See the full list of articles here.

IBF spoke with Jaguar Land Rover CEO, Sir Ralf Speth, about Big Data and the IoT.

Articles in this special issue:

• Planning Software – The Features You Need, What It Costs & Estimated ROI
• Blockchain – Its Role in Demand and Supply Planning
• Interview With CEO of Jaguar Land Rover, Sir Ralf Speth
• 4 Dangerous Habits That Can Lead to Abandonment of Forecasting & Planning Software
• Shimano North America’s S&OP Technology Journey
• Artificial Intelligence – Decision Maker or Supporter?
• A Primer on Probabilistic Demand Planning
• The Next Generation of Demand Management Technology
• Interview With Doug Laney, Best-Selling Author & Creator of Infonomics
• And more

Become An IBF Member & Get Full Access To The Journal

When you become an IBF member, you get the Journal of Business Forecasting delivered to your door quarterly, as well as up to $100 off all events, access to exclusive members only tutorials and workshops, access to the entire IBF knowledge library, and more. Get your membership.