The author describes the unique problems that occur when forecasting spare parts, and how to best deal with them. He also discusses SKU proliferation as well as the legal obligations that accompany forecasting spare parts. The author explains in detail how to use it and why. To make the process robust, we need executive support, sales team engagement, leverage, and accountability. The best way to leverage the input of Sales is to focus on the goals they are pursuing, and making transparent their input as well as ensuring they are adding value.
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It is mandatory to procure user consent prior to running these cookies on your website. Ask Dr. Log In Register. Remember Me. Lost your password? A salesperson receives a call from a customer who wants to purchase 20, extra units of a popular product. The customer would also like a price reduction because of the incremental and rather large additional order. The salesperson assures the customer that the company will gladly support the business and begins internal collaboration. The salesperson calls the demand planner to inquire if there is ample stock availability to support the request, and to enter the opportunity into the forecast.
They also contact the sales manager and marketing department to discuss the price incentive. The supply chain organization needs to understand the end-to-end impact of this large immediate request to see if or when it can be supported.
The business also needs to understand the financial impact of the demand at a reduced selling price. In IBP, scenarios such as this can be used to simulate how users change data and analyze the affects of those changes on other parts of the end-to-end and overall plan.
Users can create new scenarios to manage each of these what-if simulations and even share them with other planning colleagues or management. Referring back to the business example above, our demand planner would be able to use IBP to create a new scenario and test this business opportunity. After building the scenario, the planner would enter the promotional opportunity into the forecast and share it with the supply planner to run MRP and understand the supply and inventory implications.
This multi-dimensional analysis capability allows decision makers to examine a business plan from multiple angles. Product mix what-ifs include evaluations of different product mix strategies including assortment and SKU portfolio optimization. Product mix analyses evaluate different go-to-market strategies to identify the optimal product mix at the customer, regional, or national levels, often extending the scenarios to new product introduction and SKU sun-setting decisions.
Users try to create realistic plans that maximize the financial impact of their strategies while meeting other business objectives e. This type of solution takes into account cross-elasticities between related SKUs to find the optimal combination for a given customer, store type, and region. Supply-side what-ifs include evaluation of different supply planning strategies, including manufacturing, inventory, procurement, and logistics.
Users alter constraints e. The key requirement for capturing this upside is the ability to conduct these multi-dimensional what-if analyses on a forward-looking basis. Executives need the ability to evaluate the effect various strategies, policies, and tactics have on business plans. Decision makers can make changes to tactical and strategic plans that optimize the balance between financial performance, customer service, and risk.
Integrated Business Planning provides the optimal outlook for an enterprise. We offer a suite of supply chain planning, network optimization, order allocation, and general planning solutions that are purpose-built for business users rather than data scientists.
Just click the button below, and grab a time slot that works for your schedule. Get in Touch. Demand Shaping and What-if Analysis Demand shaping includes analyses of pricing, promotions, and specific customer deals. A best practice analysis sequence would include: Start by entering the what-if analysis in the system.
Typical data would consist of the expected demand uplift, pricing, and any costs associated; example, executing a trade promotion campaign. A similar example would be a special deal for a customer, which typically includes a certain volume and pricing. This generally is done by editing a demand plan or through a scenario wizard that allows users to enter only the necessary variables. Once the scenario is entered, users re-create the plan considering all the constraints in the system.
The re-optimized plan establishes the impact on overall financial performance, product, and campaign profitability. Typically, there will be deeper questions as to why the impact is what it is. Root-cause analyses help determine the key drivers of performance. For example, capacity availability may limit the ability to fulfill additional demand or force the use of overtime or build-ahead, resulting in higher cost and lower campaign profitability.
Additionally, users may find their policies such as working capital or inventory could potentially drive higher out of stock situations. If the scenarios are complex, users may allow the system to select from multiple campaigns full or partial based on total profit impact; therefore, reducing workload and leading to the best answer quickly.
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