Strategic Management Solutions - Assignment Example

 Hewlett Packard is a mammoth corporation in terms of reach and revenue. It is much admired for its extraordinary open culture, technically savvy personnel and a work place of less formal setting (New York Times 2010, Davenport 2010). Both its mode of operations and organisation structure are fully decentralised. The effective handle is attributed to the fact it is a knowledge intensive firm. Its strength in operations research and management sciences is not just a core competency alongside an accepting culture; HP is the embodiment of cost analysis measurement and flow algorithms. This competency is described as the ability to systematically and fairly measure what is commonly thought as hidden costs difficult to quantify (INFORMS2010). Q. In the case study, five scenarios are presented (pages 60-61) but there is no detailed analysis of how the scenarios were identified. Explain how management science could have been used to identify a range of scenarios in this case. In the article “Accelerating the profitability of HP Supply chain,” the challenge to maintain corporate performance compels that a deeper analysis of simple product movement from end to end be drawn. First off, the industry pace dictates short term product value, whereas older products lose value quickly. Second, the HP operation is described by New York Times as an expansive business offering involving tens of thousands of products in million product configurations. Having the greater chunk of market shares in PCs, printers, and servers in over 170 countries across six continents is held by HP, continuous product entry occurs in the pipeline. To ensure effective service levels, HP has to contend with severe market diverse. Certain regions have less concentrated demands but high product variety. In which case, the inventory driven costs are considerably fixed values that can be controlled, if not brought to minimum, relative to its volume production. More importantly, these values can be measured and forecast (Davenport 2010, INFORMS2010, Ward et al 2010). The aggressive to improve on existing production efficiency by reengineering the pipeline was adopted in the fall of 2000 and a scenario selection was put up. The science behind the formulation of scenarios was pioneered by L. R. Ford. The idea is a particular scenario makes up network flows or a chain of related activities. In a set of scenarios, network flows could be analysed by comparison. The analysis assumes all variables remain unchanged except for a single factor in the supply chain configuration, which is altered deliberately to identify profit variation. Or, the cost structure determined in a particular scenario specifies the level of efficiency. In figure 1, five scenarios are represented. Each scenario would result in a different cost structure such as labour input and time lapse. Scenario 1 the supply chain moves from the factory to a single regional hub for localization then to regional DCs. Scenario 2 is without the single regional hub and simplifies the process by and distribution in the regional DCs. Scenario 3 situates localisation at the manufacturing point and moves strait into regional DCs. Scenario 5 is a purely centralized operation s wherein all processes from manufacturing to distribution take place. Every new configuration of the pipeline, dramatically redefines a range of costs. It must be noted however that this form of flexibility can be executed in companies that are efficiently decentralised. Q. Explain the concept of ‘efficient frontier’ as it relates to this case-study (see figure 3, page 62). An efficient frontier is based on the assumption ‘ceteris paribus’ (all other variables being held unchanged). Discuss the variables that are assumed to remain unchanged in developing the efficient frontier in the case-study. The concept of efficient frontier is a geometrical application which defines that every new collation of points on the same plain would result differently using the same solution. In the economic arena, the concept of portfolio selection by Harry Markowitz was published in 1952. Markowitz defined that for every point on the efficient frontier, there is an expected risk and return corresponding to that point. It is for sure that one portfolio can be constructed from all available investments. This algorithm is a scientific expression of the power of diversification. A theory applied in real life situations or operations scenarios, with problems consisting of multiple criteria optimised all at the same time. This is especially true when classical techniques do not apply as solutions where criterion conflict each other. The variability of points combines as an efficient frontier when the right selection is made. The combined portfolio generates the largest possible returns with the least amount of risk (Benoist & Popovici 2001, Gavanelli & Milano 2005, Guinan 2009). In the context of HP, the objective to meet an improved execution, faster delivery, less overhead plus improved customer satisfaction and market share was the objective. In product manufacture, there are inventory driven costs and there are costs which are said to be variability driven. In managing the HP portfolio, the variables assumed to remain unchanged are embedded in the expansive processes of forecasting, required testing, inventory, supplier management, end-of-life and warranty support, shipment and production planning. A few of these cost factors which are inventory driven include scrap costs, material devaluation, write-offs and fire-sale discounts. These values are proportionate to production volumes, can be brought to a minimum and are easily calculated or forecast. All the more fixed costs are a clear figure to tackle. The more complex variable within the demand and supply, are defined by HP as ‘costs resulting from physical mismatches of demand and available supply.’ These include expediting, lost sales including costs of shortage and material price premiums, inventory holding and excess which results in component devaluation, price protection or product discounting (Ward et al 2010). In strictness, an efficient frontier works away the principles of cost recovery because it is a discipline on calculated thrift before the point of sale. It oppose the idea of recovering expenditure, it insists that expenditure should not occur as a matter of precision. Competence of this kind is crucial in a time of globalisation, where manufactures contend markets that are extremely diverse. In diverse markets, manufacturers face up to high levels of demand volatility which in itself reduces the forecast accuracy (Davenport 2010, Gavanelli & Milano 2005, Ward et al 2010). Studying the five different scenarios, the chart indicates that the service level increases after a certain point in time of continuous production, given the particular scenario. By combining distribution centres with the worldwide hub would alter the cost structure to optimum level. The article states that this efficient frontier using scenario 4 freed up cash on inventories by 30% and reduced the overall cost by 5% (Billington et al 2004). To achieve ‘ceteris paribus’ the volume of production should be set on a steady portfolio used in all five scenarios, as the established variables unchanged. What would affect the cost structure are the cost factors attributed to the movement or combination of processes, as described in the scenario outline. On a later year, HP published a framework to assess cost impacts of product variety, after the new solution software on efficient frontiers was released (Ward et al 2010). Table 1 Framework for cost impact of product variety Cost type Nature of relationship Cost categories Variable complexity costs Volume-driven • Material costs: volume discounts • Variability-driven costs are (a) excess costs: financing, storage, depreciation, obsolescence, fire sales and (b) shortage costs: material price premiums, expediting, lost sales because of Shortages. Fixed complexity costs Variety-driven Resource costs: R&D, testing, product management, etc. External cash outlays: tooling, costs to contract manufacturer Indirect impacts of variety: manufacturing switching costs, warranty program expenses, quality impacts, return costs. Q. To support the dissemination of proven technology, discussed on page 63, Knowledge Management System (KMS) could be developed. What IT based systems facilities would be needed in this case and what benefits would accrue these facilities. While optimising value on a supply chain was achieved, the element of uncertainty rests in demand and supply, where opportunity loss is affected by the service levels or standards that are well within an end to end process time. To zero in a precise profit value, new methods of examining the supply chain are necessitated. An earlier mention is that HP contends with severe market diverse, therefore must devise a method to scientifically determine the supply chain costs that result demand variability (INFORMS 2010). In emphasis of its competence in the management of diversity, HP embarked to devise a solution that quickly computes nested sequences of solutions corresponding to points along the efficient frontier. Figure 2 illustrates the concept of an efficient frontier with reference to optimal portfolios. HP labs built on the theory and invented a tool to optimise profit, called the Revenue Coverage Optimization (RCO) Tool. The capability of this software solution extends beyond the hurdle values placed by ROI expectations upon product launch. RCO maintains every product and requisition under constant profit margin assessment. The variable costs originating a particular supply chain is factored in to determine the most profitable manner to supply a product. The calculation of the order revenue coverage against the portfolio size is determined by way of efficient frontiers. This leeway systematically decides the value of a low revenue product on a high revenue order (McVean 2009, Ward et al 2010, INFORMS 2010). The solution Revenue Coverage Optimization tool is an application to manage product variety after the phase of introduction. An analysis of the order history to rank is archived aside the evaluation of an efficient frontier in terms of portfolio size and coverage. Focus on critical product is made possible through this application (Ward et al 2010). And still, the overarching ability of HP to advance Knowledge Management Systems has prompted the PPO knowledge management group on further work on three new initiatives. The first is knowledge on HP competitor information and components group. Second is a Web-based interface to primary and secondary research sires with the objective to create information. An international marketing intelligence system is the third task. These projects are jointly undertaken with the PGIS and other PPO groups internal to HP, such as the Product Marketing and Change Management divisions. HP aims at facilitating the process of structuring and disseminating knowledge with full use of information technology. INVENTORY VARIABILITY IMPACT COST E[shortage] = δ (ƒμ (k) — (k )[1— Fμ(k )]) Lost sales Expediting Material price premiums E[on hand] = δ (ƒμ (k) — k Fμ(k )) Inventory holding component devaluation Excess and obsolence Price protection δ= standard deviation k = critical fractile based on service level target Fμ =unit Normal distribution function ƒμ =unit Normal density function Q. Suppose that there are ten retail outlets in Germany, with the following expected demands (units): Bremen 533, Freiburg 50, Hannover 495, Hamburg 1593, Karlsruhe 261, Koln 928, Munich 1185, Osnabruck 151, Stuttgart 552, and Ulm 103. Select two other cities as your distribution centres and arbitrarily allocate them capacities summing to the total demand over all ten retail centres. Find the allocation of distribution centres to retail centres, which minimises an approximate cost function based on the distance matrix in the EXCEL file ‘German road distances’ this file has been emailed to you. Use the output of your model to find the best allocation of capacities between the two distribution centres and summarise the model findings. Discuss how your model could be extended to find the best possible choice of distribution centres and summaries the model findings The cities selected for DCs are Magdeburg and Wiesbaden. Magdeburg is to tackle north region demand, while Wiesbaden tackles the south region demand. The demarcation city is Koln, which can fall either geographical zone. Demand bundle represents the land route assigned and categorised in different scenarios as decision variables. Scenarios A –D run along the north zone operations and should be matched with one of the south zone options in Scenarios E –I, depending on where it is more profitable to assign the city of Koeln. Again, the assumption is ‘ceteris paribus’ where other variables such as type of vehicle, travel speed, terrain, labour contributions, and cost of portfolio –are presumed unchanged. Table 2 is a summary of the scenario assessment, details of which are found in the appendices. In some scenarios, the demand bundle is constant to some land routes that are not the same. Fy=Hy. Ay=Cy. By=Dy. Ey=Gy. Ey=Iy. Gy=Iy. X = Total distance travelled given the particular land route in the scenario X > 925.51 X < 3000.5 Y= Demand bundle corresponding the land route assigned in the scenario y> 2151 y < 3700 With the assumption of ‘ceteris paribus’ it is argued that the or that profit Figure five illustrates that an efficient frontier is the combination of scenarios E & A where the combined demand bundle is 5851 units in a stretch of 2536.7 kilometres. Scenario A has an efficiency score of 0.334 and scenario E has an efficiency score of 0.523. The combinations places the city of Koeln under the distribution of the southern regions, and taken as one the efficiency level is 0.434. Table 2 Scenario Assessment Road Distance Min-Max (in kilometres) Demand Scenario A Route is Magdeburg–Hamburg– Bremen–Hannover–Osnabruck–Magdeburg 851.5 925.5 2772.0 0.307 0.334 Scenario B Route is the same as scenario A with an extended leg from Osnabruck–Koln–Magdeburg 1161.2 1262.2 3700.0 0.314 0.341 Scenario C Routes consists individual back to back trips Magdeburg–Hamburg– Magdeburg / Magdeburg–Hannover– Magdeburg / Magdeburg–Bremen–Magdeburg / Magdeburg–Osnabruck– Magdeburg 1831.6 1990.9 2772.0 0.661 0.718 Scenario D Route is the same as scenario C with an additional back to back trip from Magdeburg–Koln–Magdeburg 2631.0 2859.8 3700.0 0.711 0.773 Scenario E supplements Scenario A Route is Wiesbaden–Koln–Wiesbaden–Karlsruhe–Stuttgart–Ulm–Freiburg–Munich–Wiesbaden 1482.3 1611.1 3079.0 0.481 0.523 Scenario F supplements Scenario B Route is exactly the same as Scenario E except that it takes out the leg Wiesbaden–Koln–Wiesbaden 1164.3 1265.5 2151.0 0.541 0.588 Scenario G supplements Scenario A but moves clockwise on the map unlike scenario E. Route is Wiesbaden–Koln–Wiesbaden–Karlsruhe–Freiburg–Munich–Ulm–Stuttgart–Wiesbaden 1641.1 1783.8 3079.0 0.533 0.579 Scenario H supplements Scenario B Route is exactly the same as Scenario G except that it takes out the leg Wiesbaden–Koln–Wiesbaden 1323.1 1438.1 2151.0 0.615 0.669 Scenario I Routes consists individual back to back trips Wiesbaden –Koln–Wiesbaden/ Wiesbaden–Karlsruhe–Wiesbaden/ Wiesbaden–Stuttgart–Wiesbaden/ Wiesbaden–Ulm–Wiesbaden/ Wiesbaden –Munich–Wiesbaden / Wiesbaden –Freiburg–Wiesbaden 2760.4 3000.5 3079 0.897 0.975 Q. On page 65, the benefit of increased speed in modelling is discussed. What other benefits may arise from adopting a 'visual interactive modelling approach' in this case? The visual interactive approach is quite beneficial in the analysis of projects with spatial considerations or a network of activities corresponding a geographical location. The work is completely simplified using drag and drop capabilities of the software, where calculations automatically reflect. The visual interactive approach is handy when there are difficulties describing behaviour or volatility of factors affecting the customer satisfaction in term is delivery or portfolio. Data flow systems are easier to understand and have the advantage using this sort of capability. The tool is rapid and a transition diagram is very easy to understand, interpret and at the same time it represents interactive behaviour between the different divisions of manufacturing firms. Q. The authors promote the advantages of the power chain software package, but many other software operations are available to support inventory management. Discuss the criteria that should be adopted to assist the choice between competing inventory software packages. In the broad arena of software packages, the advantage of one over the other is always at the users end. If the application can be learned and used with ease, there is more chance that the application is effectively put in use. That is because the perception of its usefulness and length of use will depend on the maturity of the user in availing the application. Tool adoption or fit to particularities of the inventory line up is a feature manufacturer's look for. Inventory software packages is a necessity in inventory management. But a software is just tools that require people in the background to see an organisation through transitions of usage of applicability, operations coordination and training if needed. In the case of HP, the advantage of an application built and crafted by the in house team is the first advantage, placing the built design at optimal level. References Benoist J. & Popovici N 2001, Contractibility of the efficient frontier of three dimensional simply shaded sets, Journal of Optimization Theory and Applications, Vol. 111, pp. 81–116. Billington C., Callioni G., Crane B., Ruark J., Rapp J., White T. and Willems S 2004, Accelerating the profitability of HP supply chain, Interfaces Vol. 34, No. 1, pp. 59-72. Davenport, T 2010, Innovation in Action, If only HP knew what HP knew, Austin USA, pp.20-25. Gavanelli M. & Milano, M 2005, Cost-Based Filtering for Determining the Pareto Frontier, Miur Prin, pp. 1-7. Guinan, J 2009, Investopedia explains Efficient Frontier INFORMS 2010, HP and Strategic Management Solutions, Founded by Chris Fry '94, Team Up to Win Top Prize in the Practice of OR. McVean J 2005, An alternate approach to efficient frontier balancing portfolio risk and return with efficient frontier, Monte Carlo. Ward J., Zhang B., Jain S., Fry C., Olavson T., Mishal H., Amaral J., Beyer D., Brecht A., Cargille B., Chadinha R., Chou K., DeNyse G., Feng Q., Padovani C., Raj S., Sunderbruch K., Tarjan R., Venkatraman K., Woods J. &Zhou Z 2010, HP transforms product portfolio management with operations research, Hanover USA. Appendices Appendices Read More