Ideally, the consumer-goods supply chain should function as a real-time information loop, in which consumer demand is captured at each Point of Sale (POS), projected accurately into the near term, and made visible instantly to wholesalers, product manufacturers, and upstream materials suppliers. All value-chain partners share a cohesive business plan based on the same live data stream. They also adhere to policies designed to get product to consumer most efficiently, at the greatest net profit to the value chain as a whole.
Unfortunately, this picture is not quite a reality yet, though advances in data science, cloud computing, and communications technology are bringing us ever closer.
The Bullwhip Effect
For now, consumer-product supply chain partners must contend with forecast errors and communication inefficiencies that accumulate into the so-called Bullwhip Effect. This happens when forecast inaccuracy (usually from poorly gathered or poorly synthesized demand signals) increases in amplitude the further upstream you get from the point of sale (or the further you get from the whip handle to the tip). For this reason, orders to suppliers, and those suppliers’ orders to their suppliers, become increasingly erratic and hard to predict, even when consumer demand is fairly steady.
Most of the problem boils down to two things:
- Inaccurate forecasting
- Uncoordinated supply chain planning
Regardless of exactly why these shortcomings exist, the results are almost invariably the same: higher Cost of Goods Sold (COGS), and lower profit margins for the supply chain as a whole. Even if downstream partners can effectively pass those costs onto consumers (for a while), the supply chain as a whole becomes a less competitive (and less sustainable) product-to-market operation.
Let’s draw a distinction here between those who survive, and those who thrive.
- Those who survive (for a while) do so as autonomous supply chain actors. They respond to each other’s order and shipment variability with costly inventory policies and reactionary emergency measures. Retailers increase inventories to cover uncertainty about replenishment. Suppliers do the same to buffer order variability from retailers. Manufacturers expedite shipments, or make last-minute shifts in production schedules to accommodate the unexpected. Despite these and other costly workarounds, the Bullwhip Effect and its ensuing order-replenishment mayhem cut into on-time delivery, fill rates (a slightly broader measure), and product availability at the stores.
- Organizations who thrive have learned that they are part of an integrated value chain, or partners in a grand synergy that comes from collaboration in both process, and technology.
Collaborative Planning, Forecasting, and Replenishment (CPFR)
In the 1990s, big retailers (beginning with Walmart) and their consumer goods partners identified unmet potential in forecast accuracy, supply chain coordination, and general supply chain visibility. Their awareness gave rise to Collaborative Planning, Forecasting, and Replenishment, an end-to-end supply chain scheme designed to close the loop in the consumer-goods-to-retail supply chain. Over the years, makers and sellers have together made astonishing advances in communication, logistic optimization, cost reduction, and product availability. Their work is ongoing.
The challenge now is that despite these strides, consumer habits, expectations, and control of commerce have mushroomed beyond the capabilities of low-to-mid tier consumer goods providers. Only the smartest will survive and thrive.
Today’s consumers, with their bent for unlimited choice in shopping, purchasing, and consumption, have sparked a competitive explosion in product variation, points of sale, delivery options, demand signal tracking, and integrated sales and delivery schemes. Meanwhile, global sourcing, regulatory variation, and supply risk have complicated the upstream picture, making it harder than ever in some cases to meet consumer demand at a profit. Indeed, product makers, distributors, and retailers sacrifice margins, working capital, customer satisfaction, and more because of inventory stock-outs and overages that stem from bad forecasts and poor visibility.
The CPFR Process
Every supply chain and associated collaborative planning model is different. That said, researchers at NC State’s Poole College of Management laid out a four stage general guide:
- Strategy and planning
- Demand & supply management
Each stage is described in detail below.
Strategy and planning
Establish the ground rules for the collaborative relationship. Determine product mix and placement, and develop event plans for the period.
- Define collaboration arrangement
- Set business goals and define the scope of the relationship
- Assign roles, responsibilities, checkpoints, and escalation procedures
- Develop a joint business plan
- Identify significant events that affect supply and demand: promotions, inventory policy changes, store openings/closings, product introductions
Demand & Supply Management
Project POS demand and order and shipment requirements over the planning horizon.
- Use consumption data to project sales forecast
- Project demand at points of sale
- Apply sales forecast, inventory policies, etc. to forecast and plan orders
- Future product order and delivery requirements based upon the sales forecast
- Account inventory positions, transit lead times, shipment quantities, and other factors taken into account
Place orders, prepare and deliver shipments, receive and stock product on retail shelves, record sales transactions and make payments, also called the order to cash cycle.
- Order generation
- Order forecasts transition into firm demand
- Order fulfillment
- Producing, shipping, delivering and stocking the products
The Analysis stage includes the following:
- Exception management:
- Actively monitor pre-defined “out-of-bounds” conditions
- Assess performance
- Calculate key metrics to evaluate the achievement of business goals, uncover trends, or develop alternative strategies
- Performance assessment and collaboration: Specific measures vary from one situation to the next, but generally fall into two categories:
- Operational measures: Fill rates, service levels, forecast accuracy, lead times, inventory turns, etc.
- Financial measures: Costs, item and category profitability, etc.
Technology to the rescue
You’ve adopted collaborative supply chain processes, now you’re ready for the gear. Advances in computing capacity, e-commerce, and data science have redefined consumer choice, and are redefining supply chain management. Nowadays, a product’s value is very much a function of when, where, and how consumers can obtain it. As such, collaborative supply chains must have the ability to transition from periodic, disparate, and isolated forecasting activities to a single, real-time enterprise forecasting process to predict and satisfy demand at a profit. This requires new levels of visibility, such as:
- The effects of changes to data and assumptions in real time
- Rolling, automatically updating forecasts
- What-if scenario testing to understand the implications of alternative decisions and competing courses of action
Collaborative, cloud-based forecasting applications for demand planning and supply planning are turning this vision into reality. These technologies are based on a few fundamental concepts: balancing analytics and insight, real-time updates of data and assumptions, and openness and transparency.
As such, the technology key to a successful CPFR implementation is a centralized platform to gather information, analyze data, and share insights with all involved parties. Software that enables integrated supply and demand planning is the centerpiece. With the help of advanced-analytic technology, your supply chain can turn data, and insight, into implementable strategies that will raise revenue, reduce cost, and improve profitability.
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