Unit Six Case The Hawley Lighting Company manufactures four types of lamps at its factory including table lamps, floor lamps, ceiling lamps, and pendant lamps. Table 1 presents the average material costs for each of the products. Each product is made in one of two production processes by purchasing components, assembling and testing the product, and finally packaging it for shipping. Table lamps and floor lamps go through the assembly and finishing process in Department 1, while ceiling fixtures and pendant lamps go through the process in Department 2. Variable production costs and capacities are shown in Table 2. The capacities are measured in units of product. Note that there are regular and overtime possibilities for each department. Average selling prices for the four products are known, and estimates have been made of the market demand for each product at these prices (see Table 3). Sales levels can also be affected by advertising expenditures. Starting with the demand levels in the table, an increase of up to $10,000 in advertising raises the demand by the percentage shown in the last row. An expenditure of less than $10,000 in advertising will lead to a proportional effect on demand. For example, an increase in advertising of$5,000 for table lamps would raise demand by 6 percent, or 3,600 units. However, there is a budget limit of $18,000 on the total amount to be spent on advertising among all four products. Table 1 Product Material cost Table Floor Ceiling Pendant $66 85 50 80 Table 2 Process Regular Time Overtime Unit Cost Capacity Unit Cost Capacity Department 1 $16 100,000 18 25,000 Department 2 12 90,000 15 24,000 Table $120 Floor $150 Ceiling $100 Pendant $160 60 20 100 35 12% 10% 8% 15% Table 3 Selling price Potential sales (000) Advertising effect ANALYTICS Integrate Analytics Across Your Entire Business by Brian McCarthy OCTOBER 03, 2014 An Accenture survey conducted last year found that only one in five companies said that they were “very satisfied” with the returns they’ve received from analytics to date. One of the reasons analytics is working for the companies in this select group is because they tend to deploy analytics technologies and expertise across the breadth of the enterprise. But the survey also found that only 33% of businesses in the U.S. and Western Europe are aggressively adopting analytics across the entire enterprise. This percentage marks an almost four times increase in the trend of enterprise-wise adoption compared to a survey conducted three years earlier, but the question must still be asked — how can we improve this number? Cross-functional analytics can be a challenge to implement for a variety of reasons including functional silos and a shortage in analytics talent. Yes, these obstacles can seem daunting at first, but our experiences tell us that they are not insurmountable. Following are tips organizations can follow to drive a horizontal focus on analytics and achieve their desired business outcomes, such as customer retention, product availability, or risk mitigation. Identify the right metrics that “move the needle.” First, senior management should decide on the business goal for an analytics initiative and the key performance indicators to track that will put them on the right path toward success. For a high-performing retailer, we found that customer retention, product availability, labor scheduling, product assortment, and employee engagement were all leading indicators to driving growth and profitability for the company. Selecting the right critical metrics is a cornerstone of success as it brings focus and clarity on what matters most to the business. Establish a center of gravity for analytics. Next, create an Analytics Center of Excellence (CoE) that spans the enterprise. A CoE is a team of data scientists, business analysts and domain experts from various business functions — sales, marketing, finance, and R&D, for example — that are brought together to facilitate a cross-pollination of experiences and ideas to find solutions to a variety of business goals. The CoE itself is organized into pods — generally made up of four to six people, with each person offering a different skillset — that are deployed across the business to solve problems that span multiple functions. Develop a robust root cause analysis capability. Once CoE is created, the pod teams should perform root cause analyses to support the performance management process. The retailer example mentioned above used root cause analysis to answer the question around what factors contributed to an unsuccessful marketing promotion. They tested hypotheses by asking questions such as: were results poor because of the marketing message, pricing and bundling, product availability, labor awareness of the promotion or did a competitor have an attention-grabbing marketing campaign happening at the same time? A successful CoE model provides a company with the capability to not only answer these questions with validated cross-functional insight, but also to determine the best decision around what to do next. Make collaborative decisions. Using a CoE affords functional managers the ability to make collaborative and informed decisions. They are not left alone to develop root cause analysis insights in a vacuum. Rather, as a team, the managers and the CoE are able to make decisions and take actions based on the insights garnered together. To accomplish this, it is critical to establish a forum with the cross-functional business leaders to share and visualize the data and interpret the insights for the purpose of decision making. As an example, a consumer products company used a weekly executive management meeting as the forum to discuss the CoE’s insights and make decisions based on the outputs. In this instance, the head of the Analytics CoE was the facilitator of the meeting and focused the executives’ time on the decisions that needed to be made based on the important insights the data identified versus the noise that should be ignored (e.g. to better understand the effectiveness of a new product launch). The combination of data science, advanced visualization, and active decision making — along with an impartial facilitator with deep content expertise — was key to collaborative and effective decision making. It’s important to note that once data-driven decisions are made and actions are set in motion, companies should track their progress against the metrics that were established at the start of their analytics journey. If goals are not being realized, they should repeat the process to understand the root causes of an issue that will help them achieve their business goals. In one instance, a bank’s Analytics CoE delivered such consistently positive results that the company formally branded all analysis coming out of the CoE so the business leaders could be aware of its quality and credibility outright. The branding encouraged business leaders to trust the insights and act on them faster. When a company expands its analytics purview from functional to horizontal, it opens the door to greater opportunities and successes. While removing silos and taking a teaming approach to analytics is part of an internal virtuous cycle, another cycle is also created — the attained results are experienced by the customers and will keep them coming back for more.
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