Insights / Supply Chain / Article

The 7 Dimensions of Digital Supply Chain Planning

October 01, 2019

Contributor: Sarah Hippold

A systematic approach to help supply chain planning leaders make the right decisions on which digital technologies to deploy in their specific organization.

Many organizations want to digitalize their supply chain planning (SCP), but few supply chain planning leaders know what this really means. Most believe that some form of digital technology will address their needs. But even if a new machine learning (ML) algorithm improves the accuracy of a demand forecast, there will most likely be no rise in overall quality.

Generally this is because the improved forecast is fed into existing capabilities — usually some offline spreadsheets and requirements formulas that can’t process what the ML algorithm produced. This is not digital planning. 

“ While any organization can throw technology at planning, it makes more sense to pause a minute and think about what type of digital technology is appropriate”

Gartner defines digital supply chain planning as the use of digital technologies, such as cloud, big data, robotic process automation (RPA), artificial intelligence (AI) or ML to improve or transform the quality of planning decision making in the supply chain.

“Digital planning is a tremendous opportunity for SCP leaders to address the four key factors that regularly degrade planning decision quality,” says Tim Payne, Vice President Analyst, Gartner. “These are variability, bias, low-quality data and decision models. And while any organization can throw technology at planning, it makes more sense to pause a minute and think about what type of digital technology is appropriate for the organization in its current maturity state."

Learn More: Optimize Supply Chain Planning

Gartner sees digital SCP through the lens of seven key dimensions. The further a company progresses along each of these dimensions, the more digitalized its SCP will be. The gradual improvement will generate more business value and lead to a transformation in the way the company plans its supply chain. 

Gartner illustrates the seven dimensions of digital supply chain planning.

Dimension 1: Horizontal alignment of decisions

Organizations, especially those at a lower level of planning maturity, should focus on this dimension first, as it aims to align planning decisions across the end-to-end supply chain. This may involve ditching spreadsheets for a unified and cloud-based planning system. Organizations that have already mastered this step should look at how to include trading partners and other ecosystem entities.  

Dimension 2: Vertical alignment of decisions

In this dimension the goal is to make sure that all planning decisions are connected to and support the execution of the overall business strategy. The model for this vertical alignment is sales and operations planning (S&OP), which provides a strategic framework for all daily operational decisions.  

Dimension 3: Degree of decision automation

Many organizations want to use digital technologies such as RPA and ML to automate planning decisions and open up capacities for planners to work on more value-adding projects. While this is a perfectly fine reason, the true value of those technologies is that they reduce — or completely remove — human decision-making bias. 

“It is possible, for example, to automate demand planning to the point that 90% of the process is handled without human involvement,” Payne says. “And even when humans make decisions, advanced analytics technology can provide better visibility across the supply chain to help planners make better-informed decisions.”

Dimension 4: Mix of decision type

In planning, decisions are being made on a daily basis. In low-maturity organizations, you see a lot of departmental best-practice decisions and some chaotic decisions. The second kind occurs when the whole supply chain will be affected, but decision makers don’t know exactly how and have no means to align their decisions across the supply chain. An organization can reduce chaotic decisions via technology, with, for example, a planning system of record that provides more visibility and data. With full insights into all cause-and-effect relationships, decisions become much easier and most can eventually be automated. 

Dimension 5: Latency of decision data

All good decision making requires data. The dilemma for many supply chain planning leaders is that they have to rely on out-of-date data to predict the future, which tends to degrade the quality of the planning decisions.

“In a lot of cases, the initial data from the execution stage is already several hours old when the planning analytics run on it to make a decision for the future. This means, that by the time the data is used, execution can already be in a completely different situation,” Payne says. “Technology can reduce data latency significantly, so planners have a better grasp on what reality looks like.”

Dimension 6: Granularity of decision data

The more digital the planning is, the more granular the decision data will be. For example, planners today often receive aggregate demand data only at a monthly or weekly level Technology will allow them to use the data as soon as an order is placed or an event occurs that impacts the order. The same applies for the supply side, where there will be Internet of Things (IoT) sensor data available to respond to changes almost in real time. 

Read more: How to Set Up S&OE in Supply Chain Planning

Dimension 7: Degree of bimodal planning

The final dimension relates to the type of planning that an organization can perform. Mode 1 describes how a company does planning now, its best practices. Technology can help with the continuous improvement of Mode 1 by, for example, optimizing planning algorithms.

Mode 2 is about innovation and step change improvements. Most organizations currently have little to no Mode 2 in planning. However, technology can significantly empower Mode 1 and help a strong Mode 2 capability emerge. Think of utilizing social sentiment data to improve forecast accuracy or detect new correlations and causalities from disparate dataset. If a pilot from this mode demonstrates adequate ROI, it can be transitioned to Mode 1 and become the latest best practice.


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