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How Does a Digital Transformation Apply to Supply Chain Planning?

Supply Chain Planning | By •

What is Digital Transformation?

A digital transformation requires that companies transform their decision-making processes with technology to enhance customer experience and employee productivity and ultimately improve business performance.  Technologies such as artificial intelligence, machine learning, Internet of Things, and virtual reality can aid in this transformation.

Digital transformation implies shifting the way organizations interact with their customers and the way they make business decisions. In the Supply Chain Planning context, these include decisions about which products to keep in stock, where to keep them, when to replenish them, how to improve service levels for customers, how to liquidate excess stock in the most profitable way, how to respond to changes in customer demand in the most agile way, etc. These shifts include real-time tracking and analysis of customer and product data, decision-making based on predictive and prescriptive models, using new technologies such as machine learning, and automation of daily operational decisions. As many organizations have experienced already, legacy systems cannot keep up with their needs.

As Jeff Bezos, the CEO of Amazon, has said: “Day 2 companies make high-quality decisions, but they make high-quality decisions slowly. To keep the energy and dynamism of Day 1, you have to somehow make high-quality, high-velocity decisions. Easy for start-ups and very challenging for large organizations…. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course-correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.”

3 Steps Organizations Need to Take to Make Digital Transformation Happen

1. Pushing for Speed, Mobility and Data Democratization

Speed in decision-making and execution is critical, particularly for companies that understand the importance of getting closer to their customers. This is emerging as the top differentiator for businesses as they re-organize to support agile decision making and digital transformations. However, we often see business intelligence as a separate solution built on top of a data warehouse and used mostly for reporting, and dashboards, data visualization and extracting data for further offline analysis. These all slow down an organization’s ability and agility to respond to customer needs. In the digitally-transformed world, weekly decisions are turning into daily or hourly decisions. Reports generated by data downloads from separate databases and pasted manually to spreadsheets do not suffice anymore.

Digital Transformation

Ted Colbert, the CIO of Boeing, hammered this point home. “You have to figure out how to really democratize the data-analytics capability, which means you have to have a platform through which people can easily access data. That helps people to believe in it and to deliver solutions that don’t require an expensive data scientist. When people begin to believe in the data, it’s a game changer: They begin to change their behaviors, based on a new understanding of all the richness trapped beneath the surface of our systems and processes..”

Data analytics cannot stay within the realm of the few data scientists anymore, information needs to be accessible real-time, presented in an actionable way, and accessible by all levels in the organization.

Ibrahim Gokcen, chief digital officer, A.P. Moller – Maersk has made the same point, “Data has to flow across the organization seamlessly. Now that our data is democratized, thousands of people can access it for their daily work. We see a lot of energy.”

2. Bringing Agility into Supply Chain Management

As companies explore their own paths to agility and digital transformation, existing legacy systems often come up as the main limitation. Agility calls for encouraging teams to come together and take accountability for end to end processes related to products or services offered by the business. However, we often see that legacy systems for analysis and decision making are separate and disconnected from operational execution systems. This goes for Supply Chain Management and the surrounding IT infrastructure as well. Even today, in most of the businesses, Supply Chain Planning and Management systems have been configured to serve very specific roles and/or functions organizations. In many cases, different planning and execution decisions are being managed in different tools and require manual interventions and data feeds. Collaboration with outside vendors is still very manual and disconnected. Information flow, for the most part, is managed across multiple platforms by sending documents and spreadsheets back and forth. Digital transformation requires integrated planning capabilities with concurrent analytical models combining forecasting, inventory planning, purchase order management and logistics decisions as well as real-time online collaboration with vendors, minimizing need for manual data input, automating data flow as much as possible.

Digital Transformation

3. Migrating to Cloud-Based SaaS Solutions in a Test-and-Learn Manner

The business environment is changing fast, new data sources are being utilized, and new technologies are being introduced continuously. Organizations need flexible, scalable, and configurable solutions that can be updated seamlessly and accessed anytime and anywhere. This is where cloud-based SaaS solutions are a big help. However, there is still the question of where to start and how to transform all of the processes to a new digital way of working. Of course, all of that doesn’t happen overnight. The bigger the organization, the older the legacy systems, the harder it is to leave behind the old ways of working and adopt new technology and processes. Changing over to cloud-based solutions should not replicate the existing silos, but rather focus on an end-to-end process and start small and build from a Test & Learn approach. Since culture plays a big role in the success of digital transformation efforts,  it is best to initiate the change in a segment of the business. Doing so increases the chance of success, provides the organization with proven benefits, and creates a success story  to support additional initiatives. Looking for opportunities to automate decisions in the end-to-end planning and execution cycle of one product category would be an example of such a measured approach. In the case of CPG companies, this could mean looking for ways to link the interrelated decisions along the supply chain. For instance,  sudden changes such as  peaks or drops in demand automatically can update future forecasts and trigger changes in supply plans and update production plans or purchase orders . An integrated workflow would link and automate data analysis and decision-making for:

  • Managing supply and demand
  • Sourcing raw materials and parts
  • Manufacturing and assembly
  • Warehousing and inventory management
  • Order entry and vendor management
  • Inbound transportation management
  • Outbound Distribution across all channels

A recent Forbes article features a success story by a global CPG company. This company, with their partner Solvoyo, initiated a Test & Learn process. In this case, Test and Learn has proven to be more effective than doing a pilot. This Test and Learn process lowered costs, led to a faster delivery of the solution, which resulted in a more immediate improvement in the business’s end-to-end planning process. The solution is now proven and can be scaled to other categories and geographies.

In the case of retailers, digital transformation of the daily/weekly decisions such as Markdown Management (in fashion), Purchase Order Management and Store Replenishment are the most common starting points. With automated decision-making, a multi-national fashion retailer can manage markdowns in 30 countries with local pricing decisions.  All this can be done with a small central-planning team with limited resources. Retailers can open new stores, and replenishing them, without growing their allocation teams. Similarly, a fast growing e-commerce company can automate inventory and purchase orders with predictive analytics while reducing inventory costs per SKU. All of these transformations can happen within months without having to spend millions. So why wait?

Author of a Digital Transformation article

Asena Yosun Denizeri is the Senior Director of Retail Solutions at Solvoyo. Ms. Denizeri has more than 20 years of experience in implementing Planning, Pricing and Optimization solutions in global companies in U.S. and Europe. She has led cross-functional teams in large-scale projects touching different points in Retail lifecycle, including Category Planning, Assortment Localization, Demand Planning, Size Optimization, Promotion Planning, Allocation and Replenishment, Markdown Optimization and Supply Chain Management. Following her consulting tenure with Silicon-valley based software companies, she brought her Advanced Analytics and Business Process Engineering experience to Apparel Retail where she worked at Gap Inc. and Cache where she led Merchandise Planning and Distribution teams to adopt new capabilities in Forecasting, Assortment Localization and Price Optimization to improve sales and profitability. Ms. Denizeri holds a B.S. and Master of Engineering degree in Operations Research and Industrial Engineering from Cornell University. She is one of the contributing authors in Oxford Handbook of Price Management and has been a guest lecturer in San Francisco State and Columbia University business school on multiple occasions.

Be Careful of the Term “Fail Fast” in Digital Transformations

Learning & Leadership | By •

The phrase “fail fast” is increasingly finding its way into conversations about digital transformations.  The implication seems to be that fail-fast is a necessary component of a digital work culture. From an IT perspective, it makes sense. However, it really doesn’t align with how supply chain and plant operations need to evolve. To me, there is a looseness of interpretation in the use of “fail fast” that is either self-serving or a miss-the-mark attempt at communicating a point of what it means to have a digital-first industrial work culture.

Fail Fast in a Work Culture Context

Various fail-fast philosophies have been around for a while and can be traced back to software and Internet startup roots in the early 2000s. The idea was particularly suited to entrepreneurs who needed to try various iterations (solutions, economic models, customer engagement, etc.) in the market to find what resonated with customers and prospects. It was meant to counter “keep-the-ship-steady” approaches to building a business. It made its way into IT agile software tenets to distinguish that modern development method from established waterfall processes.

Industrial Work Culture fail%20early%20fail%20fast%20fail%20often.JPGLike a lot of business phrases, fail fast has taken on a larger context to describe an operating philosophy that can, seemingly, be applied almost anywhere. As it moved beyond the scope of agile software methodology, it lost its distinction, where the “fast” part of it had been about applying a feedback loop to reduce the communication time from test to development.

In current parlance outside of IT, people using the phrase seem to imply that you quickly try things, see if they work, and then learn from them. From what I can tell, that line of thinking also implies that failure is just a natural and inevitable part of the process, which is a far cry from its use in agile software coding development.

As industrial companies ramp up digital transformation activities, I’m seeing this broad-brush definition find its way into the increasingly frequent interactions of IT and technology providers and personnel. I get it, operations need to think about new ways of doing things; they can’t be afraid to change and it’s hard to do so.

However, pushing this line of thinking to operational personnel is a not only counterproductive, it ultimately clouds meaningful dialogue. Failure at any pace is anathema to how they have been trained to think or operate, and rightly so. The consequences of risk won’t allow it when the fast failure could lead to a lost life or millions in compromised revenue. It’s not acceptable. Effective control is mission critical.

Citizen Data Scientist
Mike Guilfoyle, Dir. of Research and a Key Member of ARC’s Digitization Team

A Digital Transformation Work Culture Experiments Quickly

Yet, organizations know they need to rethink deeply entrenched approaches to how they operate and increasingly understand the consequences of not doing so. Those that are having success are working on instilling a digital-first industrial work culture. They aren’t concentrating on failing fast. Instead, these cultures are building into the work norm the idea of experimenting quickly, where failure might occur but isn’t necessary to achieve success. A digital-first work culture understands:

  • When it comes to digital transformation, the notion of experimentation can map very accurately (and positively) to an organization’s work culture in a way that fail fast can’t. These types of companies have first assessed their organizations to understand where digital experimentation is possible and likely has the greatest chance to succeed.
  • Collaboration across diverse skills sets is common, beneficial, and enables an organization to explore more possibilities. A method is in place to enable the organization to combine traditionally siloed skills to support exploratory processes. This method is often how new competencies are identified and grown (versus hire-only). As digital initiatives are planned, people aren’t viewed simply within the context of their individual roles and skills. Instead, they are viewed in terms of how any of their skills support new ways of deriving business value.  Those organizations aren’t afraid to change long-standing roles and responsibilities when it is beneficial to do so.
  • Iteration for improvement is an accepted norm, and it’s different from failure. Testing, deployment, and success are not defined by standard, existing measures. Experimentation might work in practice but fail initially in production and numerous reapplications or additional scale might be necessary to pave a path to ROI. Speed isn’t the fuel for the engine. Risk and expectations, particularly for executives, are managed using that mindset.

Words are just words, of course, but work cultures are built on concepts that gain gravitas and weight based on how they are presented and communicated throughout organizations. Fail fast should give way to experiment quickly. It’s the hallmark of a digital-first work culture, which is necessary to digital transformation.

Achieving Energy Savings through a Digital Transformation: The Vopak Case Study

Logistics Service Providers | By •

Royal Vopak is a leading independent tank storage company founded over 400 years ago. It specializes in bulk storage of liquids including oil, chemicals, vegetable oils, biofuels, and gas. The Rotterdam based company had revenues of 1.3 billion Euros at the end of their last fiscal year and operates 66 global terminals, mostly near ports. They are a public company and thus publish an annual report. The annual report states that they are “making substantial investments to deliver the full benefits of the digital transformation.” These include investments in Big Data, dashboarding analytics, optimization technologies, new sensors at their terminals, mobile technologies, and smart robots and drones.

Their terminal in Savannah Georgia serves as one of their test bed sites for developing and implementing new digital technologies. Savannah is where Vopak piloted a project with Atomiton called the digital terminal to reduce their energy usage and help meet their sustainability goals. A week and a half ago, at ARC’s Driving Digital Transformation forum in Orlando, Diana Salguero – the IT Director for the Americas at Vopak – and Jane Ren – the CEO at Atomiton – spoke about this project.

Digital Transformation
Vopak’s Savannah Terminal Undergoes a Digital Transformation

Ms. Salguero said “technology leadership is a strategic pillar” for Vopak. And technology leadership requires innovation. Based on business challenges, proof of concepts (POC) are reviewed and approved. Some of these PoCs move to a pilot, and if successful Vopak then scales the technology across the remaining terminals that have a need for the solution and are capable of using it.

The company has enabled a group of bright people across operations and IT to work together with startups or leading innovative technology companies through a variety of proof of concepts and pilots. These vendors can include suppliers that would have historically been considered too young or too small for this conservative 400-year-old firm to work with. These vendors help Vopak understand what might be possible. The innovation team has enough funds to work on number of proof of concepts. In deciding which POCs to tackle, they create user stories to share with the business to gauge the interest and potential value of a project. If a POC is deemed successful, they create videos to share across the business. The goal of these videos is to create excitement, and to get other terminals with similar problems to pony up the money for a pilot.

A Digital Transformation at the Savannah Terminal

In this case, the challenge involved getting visibility of electricity usage in real time. Peak time energy costs are higher, but these hours were also when Vopak’s operational activities were at its peak. The goal of the project was to reduce peak energy usage and energy costs, and shift activities like heating (and some cooling) to non-peak hours while reducing energy usage. To do this, they needed real-time energy consumption across different machines and activities. The equipment included legacy meters that were not digitally enabled, but the company did not want to spend millions of dollars to swap them out.

This is where the Atomiton solution came in. The Atomiton platform allows for communication with sensors, actuators, devices and machines and then provides a programming language to help make this data intelligible and usable. Then the software uses analytical predictions to recommend optimal scheduling to reduce peak energy.

From a material flow perspective, multiple trucks come to the terminal each day. As customers scheduled their daily or weekly activities for product movement, the planner at Vopak now had enhanced visibility through the Atomiton platform. The planner was able to schedule the daily activities so that more work could be done outside peak electricity usage.

At the terminals, some products in the tanks required heating to the right temperature to make them viscous enough to travel through the pipes. The pipelines and the pumping stations are also big users of electricity. In the proof of concept, the goal was for the planners to see the energy usage, and then intelligently shift operations off peak hours to lower electricity costs. Historically, because there was not visibility to both heating operations and the slot booking schedule, the tanks were heated on an ongoing basis to keep them in the required temperature range.

The project started as a proof of concept at just one substation at the terminal. This POC did not generate the savings Vopak was looking for. “But a proof of concept never fails,” Ms. Salguero said, “you just learn from it.” The issue with this POC was that it was limited to one area of the terminal, which limited the ability of planners to shift heating operations off peak hours. This was because shifting energy usage for this specific substation was not productive unless all the substations were also added to the platform.

Machine Learning and Optimization Enable a Digital Transformation at the Savannah Terminal

Nevertheless, the company decided to move forward with the pilot. They believed they had learned enough to predict that if the visibility extended across the entire terminal, there would be significant savings. During the pilot stage they also employed machine learning to discover just how long it takes for a tank with a particular product stored in it to cool. The Atomiton platform’s machine learning also factors in external conditions like ambient temperature and humidity. The machine learning feedback loop is still in place.  The platform continues to improve its ability to predict how fast materials will cool.

One of the goals of this project was to develop of an application that helps with the optimization of the operational activities.  The problem was complex enough – material/tank cooling properties, a week-long slot booking schedule, the weather forecast, and a set of linked tank heating, pipeline movement, and pumping activities – that manual planning was not going to lead to optimal savings.

They were right to proceed! The pilot achieved a 25% reduction in peak energy, 20% in overall energy savings, and a 5% increase in labor productivity. Vopak will soon finish the evaluation and decide whether to scale this across multiple terminals. While Ms. Salguero did not definitively say they would proceed further as this decision will be made by the Global Operational Department, the project was so successful it certainly seems likely. For Vopak, the phrase ‘digital transformation’ is not empty rhetoric.

Digital Transformation Workforce Evolution

Labor | By •

Currently, many companies view digital transformation as being technology-driven. Machine learning, cloud architecture, microservices, augmented reality, and industrial IoT platforms are just some of the technologies driving endless discussion among end users as well as fierce competition among solution providers. Often missing from these conversations is a focus on the human element of digital transformation.  Where do people fit in? What changes will occur in the digital transformation workforce?

Digital Transformation Workforce Impact

The changes that digital transformation will have in the workforce are likely to be the most far-reaching and sustained effects.  Not only will digital transformation change the number of people needed to do work, it will rewrite how that work gets done. As such, those planning or going through digital transformation quickly realize that managing the human element successfully can be the most difficult aspect of the journey. That makes sense, as machines don’t “push back” when it comes to change, but people often do.

Organizations need to apply as much, if not more, energy to managing workforce and culture change as they do technology improvement. This focus needs to start in the initial discovery phase. As the organization outlines short- and long-term objectives, it must ask human-centered questions, such as:
•    What roles and resources are needed to begin digital transformation and how do those shift over time?
•    What skills are high-value in an increasingly digital and data-driven organization?
•    How will knowledge management change?
•    Will the expectations for leadership evolve? If so, how?

Digital Transformation Workforce Maturity Model

The digital transformation maturity model ensures those workforce questions stay front-and-center. ARC classifies this workforce maturity in five stages: Discover & Inform, Pilot & Improve, Identify & Transition, Blend & Extend, and Transformed.

Digital Transformation
ARC Advisory Group Digital Transformation Maturity Model

ARC clients that wish to view the full strategy report containing this maturity model as well as explanations of each stage can do so via the ARC Main Client Portal or at ARC Office 365 Client Portal.

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