Supply Chain Planning in the Digital Age

Stephen DeAngelis

November 19, 2019

In 2017, Gartner introduced a Supply Chain Planning Maturity Model with five planning stages. In the lowest stage (Stage 1), supply chain planning is inside-out, fragmented, local, unconstrained, and focused on revenue. In Stage 2, supply chain planning remains inside-out but achieves functional scale, local optimization, and focuses on reducing costs. Stage 3 straddles the line between inside-out and outside-in planning. According to Gartner, this halfway point maturity stage concentrates on horizontal integration and linked optimization. Stage 4 is fully ensconced in outside-in planning, with extended planning views resulting in profit-oriented optimization. The highest stage of maturity (Stage 5), provides companies with supply chain planning that incorporates a network view, is multi-enterprise in its scope, and sees planning and execution converging into a single process. Jeff Bodenstab, Marketing Vice President at ToolsGroup, asserts, “This model has withstood the test of time in depicting what highly evolved planning looks like. The difference is that until recently, stage 5 maturity was so unattainable that one Gartner analyst called those that reach it ‘unicorns’. The vision hasn’t changed dramatically, but the ability to get there has.”[1]

The goals of a good planning process

Charlie Marge, a supply chain optimization expert at Chainalytics, suggests there are five key characteristics of a good planning process.[2] They are:

1. It is consistent and repeatable. “If you automate your process or parts of your process, then that effort will help formalize your process because it will require you to think through and define the steps carefully.”

2. The data driving the process is accurate. “It’s impossible to make a good plan if the underlying data driving the process is shaky.”

3. It provides a clear view of the current situation. “The picture of where things stand in the supply chain is key to understanding and evaluating what to make, what to order, what to move, etc. Does your planning process create views that allow the planner and others to easily see what’s going on?”

4. Recommendations for future actions are achievable. “If you have capacity constraints in your network — whether they are production-related, warehousing-related, or transportation-related — then any plan that doesn’t take them into account is not realistic.”

5. Planners can understand the impact of their changes. “If planners want to make changes to the plan, can they see the effect?”

What Marge describes sounds a lot like what Alexa Cheater (@Alexa_Cheater), Product Marketing Manager at Kinaxis, calls concurrent planning. She explains, “Concurrent planning is genuinely fast, connected and end-to-end. It bridges the gap between planning and execution by interweaving every aspect of your business — from strategic to tactical. Inputs from finance, sales and marketing help drive long-term plans. Data from customers, suppliers and partners feed into short-term plans and detailed execution. If one person makes a change, everyone else instantly understands the impact on themselves, their team and the organization as a whole.”[3] Concurrent planning can’t be achieved if companies rely on siloed data or siloed processes. Concurrent planning also requires the outside-in demand signals found in the highest levels of Gartner’s maturity model.

Beyond demand-driven planning

According to Bodenstab, “At lower stages of maturity, supply chain planning sought to answer ‘What and how much are we going to supply or replenish?’ As supply chains matured, they shifted to a ‘demand-driven supply chain’, where an ‘outside in’ view started with the demand signal. Decisions were driven by customer wants and needs.” He believes companies need to go beyond demand-driven planning. He explains, “At the highest maturity levels, the focus shifts again — from demand-driven to a ‘service-driven supply chain’. The demand forecast is only an intermediate step towards the end goal of delivering service efficiently to the end customer. In other words, a demand-driven supply chain matches supply to the demand forecast, but a service-driven supply chain focuses on achieving a target service level by hedging against demand variability and supply uncertainty via optimal inventories.”

Asena Yosun Denizeri, Senior Director of Retail Solutions at Solvoyo, appears to agree with Bodenstab. She writes, “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.”

What both Bodenstab and Denizeri are describing, is what I call an Intelligent Supply Chain leveraging cognitive technologies. Denizeri explains, “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; and outbound distribution across all channels.” Cognitive solutions, like the Enterra Supply Chain Intelligence System™ (ESCIS), can augment human decision-making in ambiguous, changing, and anomalous situations — and companies are finding such solutions very useful. The ESCIS integrates the Enterra Enhanced Forecast Signal (EEFS) and Enterra Probabilistic Targets™ (EPT). EPT assists Consumer-Packaged Goods companies in situations where supply chain planning, due to short lead times, is highly dependent on the quality of the Demand Forecast. Enterra Probabilistic Targets leverage advanced high dimensional mathematical techniques on historic information about inventory levels, service and customer orders to create probability envelopes around the Demand Forecast. This probability envelope reflects the likelihood of demand reaching a specific level in terms of actual orders. Typically inventory targets are set up in terms of Days Forward Coverage (or Days on Hand), this change in demand propagates into the actual Target for inventory which in turn is a key driver for Supply Chain Planning.

Concluding thoughts

Although Bodenstab believes technology now allows companies to achieve Stage 5 of Gartner’s Supply Chain Planning Maturity Model, Denizeri and Cheater note few have achieved that level of maturity. Denizeri writes, “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.”

Cheater adds, “While many companies claim to solve the sequential planning issues of the past, few have. They have brought parts of the planning process into the cloud, added algorithms and machine learning capabilities, or simplified reporting procedures. But most still rely on parts of the same vicious, siloed cycle that’s been in use forever: wait for someone ahead of you to generate a plan, create your plan using their results, try to reconcile these plans with one another and eventually recognize that everyone’s plans have become meaningless in the time it’s taken to reach a consensus.” It’s time to implement an intelligent supply chain planning system.

Footnotes
[1] Jeff Bodenstab, “‘Digital Transformation’ and Supply Chain Planning,” ToolsGroup Blog, 15 May 2018.
[2] Charlie Marge, “Key Characteristics of a Good Supply Chain Planning Process,” Chainalytics, 20 September 2019.
[3] Alexa Cheater, “What is concurrent planning?Kinaxis Blog, 14 October 2019.
[4] Asena Yosun Denizeri, “How Does a Digital Transformation Apply to Supply Chain Planning?Logistics Viewpoints, 30 July 2019.