Planning for short life cycle products

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What's in this article:

• What is the product life cycle?
• Short Life Cycles Products are complex to forecast

• Optimal Demand Forecasting Techniques of Short Life Cycle Products

1. Scenario 1: Product Replaces a Previous Version
2. Scenario 2: Product is Brand-New Launch

• Inventory Management and Supply Chain Strategies
• Aligning Supply Chain Methodologies with Product Life Cycle
• How to effectively deploy these supply chain strategies in different PLC phases?

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Due to client demand and intense competition, continuous new product creation is crucial for companies. Such advancements have shortened product life cycles. Therefore supply chain planning should closely monitor product sales to mitigate economic risk. However, firms that make items with a short life cycle find it challenging to forecast and change production plans for such products. Clothing, laptops, and mobile phones, to name a few, are examples of products with extremely short life cycles.

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What is the product life cycle?

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The life cycle of the product cycle is the series of phases that a product undergoes during its lifetime, beginning with the introduction and concluding with the decline. A product life cycle (PLC) is typically divided into four stages: Introduction, Growth, Maturity, and Decline. Business owners and marketers utilize the product life cycle (PLC) to make crucial decisions and formulate plans about advertising budgets, product prices, and product packaging.

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There is enough discussion and understanding amongst the business leaders about aligning marketing strategies to the different phases of product life cycles. But there needs to be more discussion about a solid supply chain strategy that aligns and changes with the stages of a PLC.

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It is usually seen that supply chain leaders follow a standard approach without significant changes from introduction to decline. Choosing an appropriate Supply Chain Strategy (SCS) aligned with the product's life cycle can bring substantial efficiencies, reduce waste and uncertainties, and, as a result, can positively impact the bottom line.

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Short life cycles products are complex to forecast

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Suppose that a new iPhone is launched, and you are responsible for predicting its sales. In the initial week, 50,000 phones are sold at your store. If the company wants to forecast the demand for the next week purely based on historical demand, it will be 50,000 (+/-10%) units. Unfortunately, such a forecast would be optimistic because tech enthusiasts would have purchased the phone in the first week. There would be fewer buyers for sales during the second week.

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Hence, unlike matured products, which have years of demand history (for example, Cheerios or Tylenol), forecasting for short shelf life products, in the same manner, can be suicidal for the companies. It can either lead to a loss of sales due to stockouts or excess inventory due to low take-off. Such excess inventory has to be liquidated at a substantial discount, resulting in a loss for the company.

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Optimal demand forecasting techniques for short life cycle products

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So, forecasting demand for a short life cycle product is complicated since you cannot rely on historical data. How shall the planners then confront this problem?

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Here are some recommendations.

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Scenario 1: Product replaces a previous version

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If you release a new product version annually, planners must ensure that (product life-cycle) the master data is accurate. They can then map links from one product version to another (e.g., "ABC in 2021" to "XYZ in 2022"). Then the planners can use the cleaned historical demand using the usual forecasting models to establish a forecast baseline.

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If there is a potential for cannibalization between old and new versions, supply planners can keep track of any remaining old stocks while converting forecasts to a supply plan. The planning tool/software/team is responsible for allocating the forecast to old/new versions based on remaining stocks or applying a split factor.

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Scenario 2: Product is brand-new launch

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Some new items have no historical precedents, although they are comparable to existing products. You can then forecast the demand for new products using a historical analogy. That is, predicting the demand for new products based on demand patterns seen for other similar products. For instance, to forecast the debut of a new variety of breakfast cereals, you can use the past sales pattern of similar types of cereals.

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If there is not enough historical demand, try the following:

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1. Forecast based on sales and marketing's judgment. Such judgment can be arrived at by looking at the distribution channel length and breadth. However, such a forecast should be assumed to have high variability and should be re-checked every week (or even daily) to ensure a corrective action to any cognitive biases.
2. Forecast demand at a higher hierarchical level: planners can estimate demand at a high degree of aggregation (such as per category or brand) and then break it down back to the SKU level.
3. To further improve your forecast, apply collaborative forecasting (discuss with your clients, suppliers, and front-line sales employees) or use modern demand forecasting solutions, like Ikigai's aiCasting, which is a great solution when forecasting with limited historical data.

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Inventory management and supply chain strategies

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Instead of focusing only on forecasts, supply chain planners should consider optimizing inventory and supply chain strategies (SCS) according to the phase of the product's shelf life. Remember that a forecast helps in purchasing/producing the right things. In summary, stocking the shelves with the appropriate amount of stuff matters, not receiving the most accurate forecast projection.

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The supply chain team (demand planners, inventory managers, supply planners, and production planners) should also collaborate with the production side to minimize the minimum batch size, enhance flexibility, and reduce end-to-end lead times. This will allow the company to respond quickly to fluctuating demand which is very common with fast-paced products with a short life cycle.

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Aligning supply chain methodologies with product life cycle

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Organization plans must be developed with the understanding of the Product Life Cycle phases and its proper alignment with Supply Chain Management strategies in mind. We currently consider four SCM strategy methods Lean, Flexible, Responsive, and Agile. These methods are a development of years of manufacturing strategy thinking and innovations. As a result, it carries great potential for bringing a competitive advantage to the company when executed properly.

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Let us look at each strategy and how it can be applied effectively.

1. Agile Supply Chain: Agility is the capacity of a company to meet the needs and desires of new clientele. Agile Supply Chain can quickly introduce new products to serve new markets. Speed is paramount here, and the supply chain costs can be higher.
2. Responsive Supply Chain is the ability to adjust to market demands while focusing on the time and cost of taking the product to the market. Here the supply chain can still react to demand, but the costs are also considered an essential factor.
3. Flexible Supply Chain system focuses on adaptability and maximizing output. The resources are utilized to reduce costs and increase production, making the whole supply chain more productive.
4. Lean Supply Chain: An LSC focuses on maximizing production and reducing costs by eliminating waste. The costs are the primary focus, while the output can be secondary.

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Each of these techniques must correspond with features of demand and supply of the product phase. In addition, whether the product is innovative or functional should also be considered.

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How to effectively deploy these supply chain strategies in different PLC phases?

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Here we look at the relationship between PLC and SCS under actual business scenarios.

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Introduction Phase: This phase signifies the product's introduction to the market. The product is supposed to be innovative, with substantial profit margins and limited to no direct competition. In this scenario, however, there is significant demand uncertainty because how consumers would respond to new products is unknown. Uncertainties around the forecast lead to difficulties in committing volumes to suppliers, the amount of inventory to be kept as finished goods, and deciding capacities for production. Here, Agile Supply Chain is the more relevant SCS in this scenario since it focuses on the speed of reaching the market (even at a higher cost). This enables the company to capture maximum share, and as the margins in the introduction phase are high, the higher supply chain costs can be absorbed.

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Growth Phase: In this stage, additional products of similar nature are introduced to the market, and consumers begin to have options. Competitors create cheaper copies of the product, lowering the profit margin. Nonetheless, supply uncertainty begins to diminish as new suppliers emerge. The chain focuses on responsiveness, which entails attending to client needs more quickly. In contrast, the business must cut manufacturing and distribution expenses. This phase enables new avenues to competitive advantage.

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During the Growth phase, organizations with agile or responsive supply chain strategies can compete for a limited time. However, a company that introduces a product must choose whether to modify its approach to responsiveness or keep the product's agility and production. This does not signify the end of the PLC product line, as the corporation may grant production licenses to other companies in different regions. Some companies continue to manufacture similar products.

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Maturity Phase: At this point, the product is well established on the market and begins the shift from innovative to practical. Due to organizations' understanding of demand, demand uncertainty is reduced. However, supply uncertainty increases due to producers' rivalry for scarce resources.

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Customers, who do not accept failures in products at the mature stage and do not pay more for these products, are a further factor affecting business margins. As competition and supply uncertainty are high and demand is fragmented among participants, the most effective response is to employ a flexible supply chain strategy.

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In the Maturity stage, profit tends to settle down or decline. Businesses must determine whether to develop a Flexible Supply Chain Strategy to maintain their position in this segment, return to earlier phases and introduce new products, or remain in the responsive part (growth).

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Decline Phase: When products reach this stage, the business understands the market, its suppliers, and its competitors. Extremely low levels of demand and supply uncertainty are observed. Some companies exit the market.

In this situation, businesses must adopt a Lean Supply Chain Strategy. They must eliminate waste in the supply chain's nodes to ensure product sales' viability.

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Ikigai is an operational BI platform that uses augmented actions to navigate toward company objectives. As the only commercially available product based on cutting-edge MIT research on AI and machine learning, Ikigai helps supply chain teams improve the speed and accuracy of their decisions in the VUCA world, thereby increasing the return on investment (ROI) of the business.

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Ikigai combines data analytics, visualizations, and automation with its proprietary technologies, such as aiMatch and aiCast time series forecasting, and translates them into more precise decision-making data. Let's connect for a quick chat.

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